




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_		:				Optional[Any]									=     {"LayoutLMv2Config", "LayoutLMv3Config"}




@is_pipeline_test
class 		__A      (			unittest.TestCase			):
						UpperCamelCase     						=					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						UpperCamelCase     						=					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

						if model_mapping is not None:
												UpperCamelCase     						=					{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
						if tf_model_mapping is not None:
												UpperCamelCase     						=					{
												    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
												}

						@require_torch
						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=pipeline(
										    task="""text-classification"""    , model="""hf-internal-testing/tiny-random-distilbert"""    , framework="""pt""" )

										__magic_name__	:	str								=text_classifier("""This is great !""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """LABEL_0""", """score""": 0.504}] )

										__magic_name__	:	Union[str, Any]								=text_classifier("""This is great !"""    , top_k=2 )
										self.assertEqual(
										    nested_simplify(__snake_case )    , [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}] )

										__magic_name__	:	str								=text_classifier(["""This is great !""", """This is bad"""]    , top_k=2 )
										self.assertEqual(
										    nested_simplify(__snake_case )    , [
										        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
										        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
										    ]    , )

										__magic_name__	:	Optional[Any]								=text_classifier("""This is great !"""    , top_k=1 )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """LABEL_0""", """score""": 0.504}] )

										# Legacy behavior
										__magic_name__	:	Optional[int]								=text_classifier("""This is great !"""    , return_all_scores=__snake_case )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """LABEL_0""", """score""": 0.504}] )

										__magic_name__	:	Union[str, Any]								=text_classifier("""This is great !"""    , return_all_scores=__snake_case )
										self.assertEqual(
										    nested_simplify(__snake_case )    , [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]] )

										__magic_name__	:	Any								=text_classifier(["""This is great !""", """Something else"""]    , return_all_scores=__snake_case )
										self.assertEqual(
										    nested_simplify(__snake_case )    , [
										        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
										        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
										    ]    , )

										__magic_name__	:	List[Any]								=text_classifier(["""This is great !""", """Something else"""]    , return_all_scores=__snake_case )
										self.assertEqual(
										    nested_simplify(__snake_case )    , [
										        {"""label""": """LABEL_0""", """score""": 0.504},
										        {"""label""": """LABEL_0""", """score""": 0.504},
										    ]    , )

						@require_torch
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										import torch

										__magic_name__	:	Union[str, Any]								=pipeline(
										    task="""text-classification"""    , model="""hf-internal-testing/tiny-random-distilbert"""    , framework="""pt"""    , device=torch.device("""cpu""" )    , )

										__magic_name__	:	Optional[int]								=text_classifier("""This is great !""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """LABEL_0""", """score""": 0.504}] )

						@require_tf
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=pipeline(
										    task="""text-classification"""    , model="""hf-internal-testing/tiny-random-distilbert"""    , framework="""tf""" )

										__magic_name__	:	Dict								=text_classifier("""This is great !""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """LABEL_0""", """score""": 0.504}] )

						@slow
						@require_torch
						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=pipeline("""text-classification""" )

										__magic_name__	:	Optional[int]								=text_classifier("""This is great !""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """POSITIVE""", """score""": 1.0}] )
										__magic_name__	:	Tuple								=text_classifier("""This is bad !""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """NEGATIVE""", """score""": 1.0}] )
										__magic_name__	:	Dict								=text_classifier("""Birds are a type of animal""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """POSITIVE""", """score""": 0.988}] )

						@slow
						@require_tf
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=pipeline("""text-classification"""    , framework="""tf""" )

										__magic_name__	:	Optional[Any]								=text_classifier("""This is great !""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """POSITIVE""", """score""": 1.0}] )
										__magic_name__	:	str								=text_classifier("""This is bad !""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """NEGATIVE""", """score""": 1.0}] )
										__magic_name__	:	Dict								=text_classifier("""Birds are a type of animal""" )
										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": """POSITIVE""", """score""": 0.988}] )

						def     A__						(		self   :str    , __snake_case   :Optional[Any]    , __snake_case   :Optional[Any]    , __snake_case   :str ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=TextClassificationPipeline(model=__snake_case    , tokenizer=__snake_case )
										return text_classifier, ["HuggingFace is in", "This is another test"]


						def     A__						(		self   :Tuple    , __snake_case   :Union[str, Any]    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=text_classifier.model
										# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
										__magic_name__	:	str								="""HuggingFace is in"""
										__magic_name__	:	str								=text_classifier(__snake_case )

										self.assertEqual(nested_simplify(__snake_case )    , [{"""label""": ANY(__snake_case ), """score""": ANY(__snake_case )}] )
										self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )

										__magic_name__	:	Tuple								=["""HuggingFace is in """, """Paris is in France"""]
										__magic_name__	:	Optional[int]								=text_classifier(__snake_case )
										self.assertEqual(
										    nested_simplify(__snake_case )    , [{"""label""": ANY(__snake_case ), """score""": ANY(__snake_case )}, {"""label""": ANY(__snake_case ), """score""": ANY(__snake_case )}]    , )
										self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
										self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values() )

										# Forcing to get all results with `top_k=None`
										# This is NOT the legacy format
										__magic_name__	:	Union[str, Any]								=text_classifier(__snake_case    , top_k=__snake_case )
										__magic_name__	:	int								=len(model.config.idalabel.values() )
										self.assertEqual(
										    nested_simplify(__snake_case )    , [[{"""label""": ANY(__snake_case ), """score""": ANY(__snake_case )}] * N, [{"""label""": ANY(__snake_case ), """score""": ANY(__snake_case )}] * N]    , )

										__magic_name__	:	int								={"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
										__magic_name__	:	str								=text_classifier(__snake_case )
										self.assertEqual(
										    nested_simplify(__snake_case )    , {"""label""": ANY(__snake_case ), """score""": ANY(__snake_case )}    , )
										self.assertTrue(outputs["""label"""] in model.config.idalabel.values() )

										# This might be used a text pair, but tokenizer + pipe interaction
										# makes it hard to understand that it's not using the pair properly
										# https://github.com/huggingface/transformers/issues/17305
										# We disabled this usage instead as it was outputting wrong outputs.
										__magic_name__	:	Union[str, Any]								=[["""HuggingFace is in """, """Paris is in France"""]]
										with self.assertRaises(__snake_case ):
														text_classifier(__snake_case )

										# This used to be valid for doing text pairs
										# We're keeping it working because of backward compatibility
										__magic_name__	:	Optional[Any]								=text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]] )
										self.assertEqual(
										    nested_simplify(__snake_case )    , [{"""label""": ANY(__snake_case ), """score""": ANY(__snake_case )}]    , )
										self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )










from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests




def 		lowerCAmelCase_   (	lowerCamelCase = "isbn/0140328726"   ):
				__magic_name__	:	Optional[Any]								=olid.strip().strip("""/"""   )  # Remove leading/trailing whitespace & slashes
				if new_olid.count("""/"""   ) != 1:
								__magic_name__	:	Union[str, Any]								=F"{olid} is not a valid Open Library olid"
								raise ValueError(lowerCamelCase   )
				return requests.get(F"https://openlibrary.org/{new_olid}.json"   ).json()




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								={
				    """title""": """Title""",
				    """publish_date""": """Publish date""",
				    """authors""": """Authors""",
				    """number_of_pages""": """Number of pages:""",
				    """first_sentence""": """First sentence""",
				    """isbn_10""": """ISBN (10)""",
				    """isbn_13""": """ISBN (13)""",
				}
				__magic_name__	:	List[Any]								={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
				__magic_name__	:	Dict								=[
				    get_openlibrary_data(author["""key"""]   )["""name"""] for author in data["""Authors"""]
				]
				__magic_name__	:	List[str]								=data["""First sentence"""]["""value"""]
				for key, value in data.items():
								if isinstance(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Tuple								=""", """.join(lowerCamelCase   )
				return data


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase_		:				List[Any]									=     input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
														if isbn.lower() in ("", "q", "quit", "exit", "stop"):
																					break

														if len(isbn) not in (10, 13) or not isbn.isdigit():
																					print(F"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
																					continue

														print(F"""\nSearching Open Library for ISBN: {isbn}...\n""")

														try:
																					UpperCAmelCase_		:				str									=     summarize_book(get_openlibrary_data(F"""isbn/{isbn}"""))
																					print("\n".join(F"""{key}: {value}""" for key, value in book_summary.items()))
														except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
																					print(F"""Sorry, there are no results for ISBN: {isbn}.""")




