






"""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.
__lowerCamelCase							=  {'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   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[str]:
											__magic_name__:	Optional[int]							=       pipeline(
											    task="""text-classification"""     ,		model="""hf-internal-testing/tiny-random-distilbert"""     ,		framework="""pt"""					)

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

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

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

											__magic_name__:	str							=       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__:	int							=       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   lowerCamelCase__   (			self  :      str					)			->							Dict:
											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__:	Union[str, Any]							=       text_classifier("""This is great !"""					)
											self.assertEqual(nested_simplify(__snake_case					)     ,		[{"""label""": """LABEL_0""", """score""": 0.504}]					)

					@require_tf
					def   lowerCamelCase__   (			self  :      Any					)			->							Optional[int]:
											__magic_name__:	Any							=       pipeline(
											    task="""text-classification"""     ,		model="""hf-internal-testing/tiny-random-distilbert"""     ,		framework="""tf"""					)

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

					@slow
					@require_torch
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							str:
											__magic_name__:	Tuple							=       pipeline("""text-classification"""					)

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

					@slow
					@require_tf
					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											__magic_name__:	Optional[Any]							=       pipeline("""text-classification"""     ,		framework="""tf"""					)

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

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      Dict     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      Any					)			->							Any:
											__magic_name__:	int							=       TextClassificationPipeline(model=__snake_case     ,		tokenizer=__snake_case					)
											return text_classifier, ["HuggingFace is in", "This is another test"]






					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Dict					)			->							Any:
											__magic_name__:	Tuple							=       text_classifier.model
											# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
											__magic_name__:	str							=       """HuggingFace is in"""
											__magic_name__:	List[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__:	Union[str, Any]							=       ["""HuggingFace is in """, """Paris is in France"""]
											__magic_name__:	List[str]							=       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__:	int							=       text_classifier(__snake_case     ,		top_k=__snake_case					)
											__magic_name__:	Dict							=       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__:	List[str]							=       {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
											__magic_name__:	Optional[Any]							=       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__:	List[str]							=       [["""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__:	int							=       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()					)








import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
