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+%global _empty_manifest_terminate_build 0
+Name:		python-amazon-textract-caller
+Version:	0.0.29
+Release:	1
+Summary:	Amazon Textract Caller tools
+License:	Apache License Version 2.0
+URL:		https://github.com/aws-samples/amazon-textract-textractor/tree/master/caller
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/29/a6/a2404130a56e1ac26ddc57fba4c7609957b63d3ad033ab234cb7eb5775ae/amazon-textract-caller-0.0.29.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-boto3
+Requires:	python3-botocore
+Requires:	python3-amazon-textract-response-parser
+Requires:	python3-amazon-textract-response-parser
+Requires:	python3-pytest
+
+%description
+# Textract-Caller
+
+amazon-textract-caller provides a collection of ready to use functions and sample implementations to speed up the evaluation and development for any project using Amazon Textract.
+
+Making it easy to call Amazon Textract regardless of file type and location.
+
+## Install
+
+```bash
+> python -m pip install amazon-textract-caller
+```
+
+## Functions
+
+```python
+from textractcaller import call_textract
+def call_textract(input_document: Union[str, bytes],
+                  features: Optional[List[Textract_Features]] = None,
+                  queries_config: Optional[QueriesConfig] = None,
+                  output_config: Optional[OutputConfig] = None,
+                  kms_key_id: str = "",
+                  job_tag: str = "",
+                  notification_channel: Optional[NotificationChannel] = None,
+                  client_request_token: str = "",
+                  return_job_id: bool = False,
+                  force_async_api: bool = False,
+                  call_mode: Textract_Call_Mode = Textract_Call_Mode.DEFAULT,
+                  boto3_textract_client=None,
+                  job_done_polling_interval=1) -> dict:
+```
+
+Also useful when receiving the JSON response from an asynchronous job (start_document_text_detection or start_document_analysis)
+
+```python
+from textractcaller import get_full_json
+def get_full_json(job_id: str = None,
+                  textract_api: Textract_API = Textract_API.DETECT,
+                  boto3_textract_client=None)->dict:
+```
+
+And when receiving the JSON from the OutputConfig location, this method is useful as well.
+
+```python
+from textractcaller import get_full_json_from_output_config
+def get_full_json_from_output_config(output_config: OutputConfig = None,
+                                     job_id: str = None,
+                                     s3_client = None)->dict:
+```
+
+## Samples
+
+### Calling with file from local filesystem only with detect_text
+
+```python
+textract_json = call_textract(input_document="/folder/local-filesystem-file.png")
+```
+
+### Calling with file from local filesystem only detect_text and using in Textract Response Parser
+
+(needs trp dependency through ```python -m pip install amazon-textract-response-parser```)
+
+```python
+import json
+from trp import Document
+from textractcaller import call_textract
+
+textract_json = call_textract(input_document="/folder/local-filesystem-file.png")
+d = Document(textract_json)
+```
+
+### Calling with Queries for a multi-page document and extract the Answers
+
+sample also uses the amazon-textract-response-parser
+
+```
+python -m pip install amazon-textract-caller amazon-textract-response-parser
+```
+
+```python
+import textractcaller as tc
+import trp.trp2 as t2
+import boto3
+
+textract = boto3.client('textract', region_name="us-east-2")
+q1 = tc.Query(text="What is the employee SSN?", alias="SSN", pages=["1"])
+q2 = tc.Query(text="What is YTD gross pay?", alias="GROSS_PAY", pages=["2"])
+textract_json = tc.call_textract(
+    input_document="s3://amazon-textract-public-content/blogs/2-pager.pdf",
+    queries_config=tc.QueriesConfig(queries=[q1, q2]),
+    features=[tc.Textract_Features.QUERIES],
+    force_async_api=True,
+    boto3_textract_client=textract)
+t_doc: t2.TDocument = t2.TDocumentSchema().load(textract_json)  # type: ignore
+for page in t_doc.pages:
+    query_answers = t_doc.get_query_answers(page=page)
+    for x in query_answers:
+        print(f"{x[1]},{x[2]}")
+```
+
+
+### Calling with file from local filesystem with TABLES features
+
+```python
+from textractcaller import call_textract, Textract_Features
+features = [Textract_Features.TABLES]
+response = call_textract(
+    input_document="/folder/local-filesystem-file.png", features=features)
+```
+
+### Call with images located on S3 but force asynchronous API
+
+```python
+from textractcaller import call_textract
+response = call_textract(input_document="s3://some-bucket/w2-example.png", force_async_api=True)
+```
+
+### Call with OutputConfig, Customer-Managed-Key
+
+```python
+from textractcaller import call_textract
+from textractcaller import OutputConfig, Textract_Features
+output_config = OutputConfig(s3_bucket="somebucket-encrypted", s3_prefix="output/")
+response = call_textract(input_document="s3://someprefix/somefile.png",
+                          force_async_api=True,
+                          output_config=output_config,
+                          kms_key_id="arn:aws:kms:us-east-1:12345678901:key/some-key-id-ref-erence",
+                          return_job_id=False,
+                          job_tag="sometag",
+                          client_request_token="sometoken")
+
+```
+
+### Call with PDF located on S3 and force return of JobId instead of JSON response
+
+```python
+from textractcaller import call_textract
+response = call_textract(input_document="s3://some-bucket/some-document.pdf", return_job_id=True)
+job_id = response['JobId']
+```
+
+
+
+
+%package -n python3-amazon-textract-caller
+Summary:	Amazon Textract Caller tools
+Provides:	python-amazon-textract-caller
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-amazon-textract-caller
+# Textract-Caller
+
+amazon-textract-caller provides a collection of ready to use functions and sample implementations to speed up the evaluation and development for any project using Amazon Textract.
+
+Making it easy to call Amazon Textract regardless of file type and location.
+
+## Install
+
+```bash
+> python -m pip install amazon-textract-caller
+```
+
+## Functions
+
+```python
+from textractcaller import call_textract
+def call_textract(input_document: Union[str, bytes],
+                  features: Optional[List[Textract_Features]] = None,
+                  queries_config: Optional[QueriesConfig] = None,
+                  output_config: Optional[OutputConfig] = None,
+                  kms_key_id: str = "",
+                  job_tag: str = "",
+                  notification_channel: Optional[NotificationChannel] = None,
+                  client_request_token: str = "",
+                  return_job_id: bool = False,
+                  force_async_api: bool = False,
+                  call_mode: Textract_Call_Mode = Textract_Call_Mode.DEFAULT,
+                  boto3_textract_client=None,
+                  job_done_polling_interval=1) -> dict:
+```
+
+Also useful when receiving the JSON response from an asynchronous job (start_document_text_detection or start_document_analysis)
+
+```python
+from textractcaller import get_full_json
+def get_full_json(job_id: str = None,
+                  textract_api: Textract_API = Textract_API.DETECT,
+                  boto3_textract_client=None)->dict:
+```
+
+And when receiving the JSON from the OutputConfig location, this method is useful as well.
+
+```python
+from textractcaller import get_full_json_from_output_config
+def get_full_json_from_output_config(output_config: OutputConfig = None,
+                                     job_id: str = None,
+                                     s3_client = None)->dict:
+```
+
+## Samples
+
+### Calling with file from local filesystem only with detect_text
+
+```python
+textract_json = call_textract(input_document="/folder/local-filesystem-file.png")
+```
+
+### Calling with file from local filesystem only detect_text and using in Textract Response Parser
+
+(needs trp dependency through ```python -m pip install amazon-textract-response-parser```)
+
+```python
+import json
+from trp import Document
+from textractcaller import call_textract
+
+textract_json = call_textract(input_document="/folder/local-filesystem-file.png")
+d = Document(textract_json)
+```
+
+### Calling with Queries for a multi-page document and extract the Answers
+
+sample also uses the amazon-textract-response-parser
+
+```
+python -m pip install amazon-textract-caller amazon-textract-response-parser
+```
+
+```python
+import textractcaller as tc
+import trp.trp2 as t2
+import boto3
+
+textract = boto3.client('textract', region_name="us-east-2")
+q1 = tc.Query(text="What is the employee SSN?", alias="SSN", pages=["1"])
+q2 = tc.Query(text="What is YTD gross pay?", alias="GROSS_PAY", pages=["2"])
+textract_json = tc.call_textract(
+    input_document="s3://amazon-textract-public-content/blogs/2-pager.pdf",
+    queries_config=tc.QueriesConfig(queries=[q1, q2]),
+    features=[tc.Textract_Features.QUERIES],
+    force_async_api=True,
+    boto3_textract_client=textract)
+t_doc: t2.TDocument = t2.TDocumentSchema().load(textract_json)  # type: ignore
+for page in t_doc.pages:
+    query_answers = t_doc.get_query_answers(page=page)
+    for x in query_answers:
+        print(f"{x[1]},{x[2]}")
+```
+
+
+### Calling with file from local filesystem with TABLES features
+
+```python
+from textractcaller import call_textract, Textract_Features
+features = [Textract_Features.TABLES]
+response = call_textract(
+    input_document="/folder/local-filesystem-file.png", features=features)
+```
+
+### Call with images located on S3 but force asynchronous API
+
+```python
+from textractcaller import call_textract
+response = call_textract(input_document="s3://some-bucket/w2-example.png", force_async_api=True)
+```
+
+### Call with OutputConfig, Customer-Managed-Key
+
+```python
+from textractcaller import call_textract
+from textractcaller import OutputConfig, Textract_Features
+output_config = OutputConfig(s3_bucket="somebucket-encrypted", s3_prefix="output/")
+response = call_textract(input_document="s3://someprefix/somefile.png",
+                          force_async_api=True,
+                          output_config=output_config,
+                          kms_key_id="arn:aws:kms:us-east-1:12345678901:key/some-key-id-ref-erence",
+                          return_job_id=False,
+                          job_tag="sometag",
+                          client_request_token="sometoken")
+
+```
+
+### Call with PDF located on S3 and force return of JobId instead of JSON response
+
+```python
+from textractcaller import call_textract
+response = call_textract(input_document="s3://some-bucket/some-document.pdf", return_job_id=True)
+job_id = response['JobId']
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for amazon-textract-caller
+Provides:	python3-amazon-textract-caller-doc
+%description help
+# Textract-Caller
+
+amazon-textract-caller provides a collection of ready to use functions and sample implementations to speed up the evaluation and development for any project using Amazon Textract.
+
+Making it easy to call Amazon Textract regardless of file type and location.
+
+## Install
+
+```bash
+> python -m pip install amazon-textract-caller
+```
+
+## Functions
+
+```python
+from textractcaller import call_textract
+def call_textract(input_document: Union[str, bytes],
+                  features: Optional[List[Textract_Features]] = None,
+                  queries_config: Optional[QueriesConfig] = None,
+                  output_config: Optional[OutputConfig] = None,
+                  kms_key_id: str = "",
+                  job_tag: str = "",
+                  notification_channel: Optional[NotificationChannel] = None,
+                  client_request_token: str = "",
+                  return_job_id: bool = False,
+                  force_async_api: bool = False,
+                  call_mode: Textract_Call_Mode = Textract_Call_Mode.DEFAULT,
+                  boto3_textract_client=None,
+                  job_done_polling_interval=1) -> dict:
+```
+
+Also useful when receiving the JSON response from an asynchronous job (start_document_text_detection or start_document_analysis)
+
+```python
+from textractcaller import get_full_json
+def get_full_json(job_id: str = None,
+                  textract_api: Textract_API = Textract_API.DETECT,
+                  boto3_textract_client=None)->dict:
+```
+
+And when receiving the JSON from the OutputConfig location, this method is useful as well.
+
+```python
+from textractcaller import get_full_json_from_output_config
+def get_full_json_from_output_config(output_config: OutputConfig = None,
+                                     job_id: str = None,
+                                     s3_client = None)->dict:
+```
+
+## Samples
+
+### Calling with file from local filesystem only with detect_text
+
+```python
+textract_json = call_textract(input_document="/folder/local-filesystem-file.png")
+```
+
+### Calling with file from local filesystem only detect_text and using in Textract Response Parser
+
+(needs trp dependency through ```python -m pip install amazon-textract-response-parser```)
+
+```python
+import json
+from trp import Document
+from textractcaller import call_textract
+
+textract_json = call_textract(input_document="/folder/local-filesystem-file.png")
+d = Document(textract_json)
+```
+
+### Calling with Queries for a multi-page document and extract the Answers
+
+sample also uses the amazon-textract-response-parser
+
+```
+python -m pip install amazon-textract-caller amazon-textract-response-parser
+```
+
+```python
+import textractcaller as tc
+import trp.trp2 as t2
+import boto3
+
+textract = boto3.client('textract', region_name="us-east-2")
+q1 = tc.Query(text="What is the employee SSN?", alias="SSN", pages=["1"])
+q2 = tc.Query(text="What is YTD gross pay?", alias="GROSS_PAY", pages=["2"])
+textract_json = tc.call_textract(
+    input_document="s3://amazon-textract-public-content/blogs/2-pager.pdf",
+    queries_config=tc.QueriesConfig(queries=[q1, q2]),
+    features=[tc.Textract_Features.QUERIES],
+    force_async_api=True,
+    boto3_textract_client=textract)
+t_doc: t2.TDocument = t2.TDocumentSchema().load(textract_json)  # type: ignore
+for page in t_doc.pages:
+    query_answers = t_doc.get_query_answers(page=page)
+    for x in query_answers:
+        print(f"{x[1]},{x[2]}")
+```
+
+
+### Calling with file from local filesystem with TABLES features
+
+```python
+from textractcaller import call_textract, Textract_Features
+features = [Textract_Features.TABLES]
+response = call_textract(
+    input_document="/folder/local-filesystem-file.png", features=features)
+```
+
+### Call with images located on S3 but force asynchronous API
+
+```python
+from textractcaller import call_textract
+response = call_textract(input_document="s3://some-bucket/w2-example.png", force_async_api=True)
+```
+
+### Call with OutputConfig, Customer-Managed-Key
+
+```python
+from textractcaller import call_textract
+from textractcaller import OutputConfig, Textract_Features
+output_config = OutputConfig(s3_bucket="somebucket-encrypted", s3_prefix="output/")
+response = call_textract(input_document="s3://someprefix/somefile.png",
+                          force_async_api=True,
+                          output_config=output_config,
+                          kms_key_id="arn:aws:kms:us-east-1:12345678901:key/some-key-id-ref-erence",
+                          return_job_id=False,
+                          job_tag="sometag",
+                          client_request_token="sometoken")
+
+```
+
+### Call with PDF located on S3 and force return of JobId instead of JSON response
+
+```python
+from textractcaller import call_textract
+response = call_textract(input_document="s3://some-bucket/some-document.pdf", return_job_id=True)
+job_id = response['JobId']
+```
+
+
+
+
+%prep
+%autosetup -n amazon-textract-caller-0.0.29
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-amazon-textract-caller -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.29-1
+- Package Spec generated