



"""simple docstring"""

import argparse
from collections import defaultdict

import yaml


lowercase__			   =  "docs/source/en/_toctree.yml"





def       __magic_name__    (   _lowerCamelCase				: str					):
							__a		:		str									=       defaultdict(_lowerCamelCase					)
							__a		:		Any									=       []
							__a		:		int									=       []
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}					)
														else:
																					new_doc_list.append(_lowerCamelCase					)

							__a		:		Optional[int]									=       new_doc_list
							__a		:		Union[str, Any]									=       [key for key, value in counts.items() if value > 1]

							__a		:		Dict									=       []
							for duplicate_key in duplicates:
														__a		:		Union[str, Any]									=       list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}					)
														if len(_lowerCamelCase					) > 1:
																					raise ValueError(
																					    F'''{duplicate_key} is present several times in the documentation table of content at '''
																					    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
																					    """others."""					)
														# Only add this once
														new_doc.append({"""local""": duplicate_key, """title""": titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]					)
							__a		:		Optional[int]									=       sorted(_lowerCamelCase  ,       key=lambda _lowerCamelCase					: s["title"].lower()					)

							# "overview" gets special treatment and is always first
							if len(_lowerCamelCase					) > 1:
														raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""					)

							overview_doc.extend(_lowerCamelCase					)

							# Sort
							return overview_doc





def       __magic_name__    (   _lowerCamelCase				: int=False					):
							with open(_lowerCamelCase  ,       encoding="""utf-8"""					) as f:
														__a		:		List[Any]									=       yaml.safe_load(f.read()					)

							# Get to the API doc
							__a		:		str									=       0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							__a		:		Any									=       content[api_idx]["""sections"""]

							# Then to the model doc
							__a		:		Tuple									=       0
							while api_doc[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							__a		:		List[str]									=       api_doc[scheduler_idx]["""sections"""]
							__a		:		Tuple									=       clean_doc_toc(_lowerCamelCase					)

							__a		:		Dict									=       False
							if new_scheduler_doc != scheduler_doc:
														__a		:		Dict									=       True
														if overwrite:
																					__a		:		Optional[Any]									=       new_scheduler_doc

							if diff:
														if overwrite:
																					__a		:		Optional[Any]									=       api_doc
																					with open(_lowerCamelCase  ,       """w"""  ,       encoding="""utf-8"""					) as f:
																												f.write(yaml.dump(_lowerCamelCase  ,       allow_unicode=_lowerCamelCase					)					)
														else:
																					raise ValueError(
																					    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""					)





def       __magic_name__    (   _lowerCamelCase				: int=False					):
							with open(_lowerCamelCase  ,       encoding="""utf-8"""					) as f:
														__a		:		Optional[Any]									=       yaml.safe_load(f.read()					)

							# Get to the API doc
							__a		:		Optional[Any]									=       0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							__a		:		Union[str, Any]									=       content[api_idx]["""sections"""]

							# Then to the model doc
							__a		:		int									=       0
							while api_doc[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							__a		:		Tuple									=       False
							__a		:		Any									=       api_doc[pipeline_idx]["""sections"""]
							__a		:		Dict									=       []

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					__a		:		Any									=       pipeline_doc["""section"""]
																					__a		:		Optional[Any]									=       clean_doc_toc(_lowerCamelCase					)
																					if overwrite:
																												__a		:		List[Any]									=       new_sub_pipeline_doc
														new_pipeline_docs.append(_lowerCamelCase					)

							# sort overall pipeline doc
							__a		:		int									=       clean_doc_toc(_lowerCamelCase					)

							if new_pipeline_docs != pipeline_docs:
														__a		:		List[Any]									=       True
														if overwrite:
																					__a		:		Optional[Any]									=       new_pipeline_docs

							if diff:
														if overwrite:
																					__a		:		Union[str, Any]									=       api_doc
																					with open(_lowerCamelCase  ,       """w"""  ,       encoding="""utf-8"""					) as f:
																												f.write(yaml.dump(_lowerCamelCase  ,       allow_unicode=_lowerCamelCase					)					)
														else:
																					raise ValueError(
																					    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""					)


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	lowercase__			   =  parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)






"""simple docstring"""

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.
lowercase__			   =  {"LayoutLMv2Config", "LayoutLMv3Config"}




@is_pipeline_test
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):
						_lowerCAmelCase							=					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						_lowerCAmelCase							=					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       pipeline(
													    task="""text-classification"""						,     model="""hf-internal-testing/tiny-random-distilbert"""						,     framework="""pt"""							)

													__a		:		Tuple									=       text_classifier("""This is great !"""							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """LABEL_0""", """score""": 0.504}]							)

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

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

													__a		:		List[str]									=       text_classifier("""This is great !"""						,     top_k=1							)
													self.assertEqual(nested_simplify(_lowercase							)						,     [{"""label""": """LABEL_0""", """score""": 0.504}]							)

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

													__a		:		Tuple									=       text_classifier("""This is great !"""						,     return_all_scores=_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]]							)

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

													__a		:		Union[str, Any]									=       text_classifier(["""This is great !""", """Something else"""]						,     return_all_scores=_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [
													        {"""label""": """LABEL_0""", """score""": 0.504},
													        {"""label""": """LABEL_0""", """score""": 0.504},
													    ]						,     )



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													import torch

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

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



						@require_tf
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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

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



						@slow
						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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

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



						@slow
						@require_tf
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       pipeline("""text-classification"""						,     framework="""tf"""							)

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Dict									=       TextClassificationPipeline(model=_lowercase						,     tokenizer=_lowercase							)
													return text_classifier, ["HuggingFace is in", "This is another test"]





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

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

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

													__a		:		Optional[int]									=       ["""HuggingFace is in """, """Paris is in France"""]
													__a		:		Dict									=       text_classifier(_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [{"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}, {"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}]						,     )
													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
													__a		:		Dict									=       text_classifier(_lowercase						,     top_k=_lowercase							)
													__a		:		Dict									=       len(model.config.idalabel.values()							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     [[{"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}] * N, [{"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}] * N]						,     )

													__a		:		Dict									=       {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
													__a		:		Any									=       text_classifier(_lowercase							)
													self.assertEqual(
													    nested_simplify(_lowercase							)						,     {"""label""": ANY(_lowercase							), """score""": ANY(_lowercase							)}						,     )
													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.
													__a		:		Dict									=       [["""HuggingFace is in """, """Paris is in France"""]]
													with self.assertRaises(_lowercase							):
																				text_classifier(_lowercase							)

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

