






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 : Optional[int]						   =       {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}

@is_pipeline_test
class 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					_A						:			Any									=      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
					_A						:			Any									=      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

					if model_mapping is not None:
										_A						:			Optional[int]									=      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
					if tf_model_mapping is not None:
										_A						:			List[Any]									=      {
										    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
										}



					@require_torch
					def       lowerCAmelCase				(  self				:	Tuple							)			->  Union[str, Any]:


												"""simple docstring"""

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

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

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

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

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

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

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

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

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



					@require_torch
					def       lowerCAmelCase				(  self				:	Tuple							)			->  Union[str, Any]:


												"""simple docstring"""

												import torch

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

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



					@require_tf
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     pipeline(
												    task="""text-classification"""		,     model="""hf-internal-testing/tiny-random-distilbert"""		,     framework="""tf"""							)

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



					@slow
					@require_torch
					def       lowerCAmelCase				(  self				:	Any							)			->  str:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     pipeline("""text-classification"""							)

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



					@slow
					@require_tf
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  str:


												"""simple docstring"""

												__lowercase      :							Dict				=     pipeline("""text-classification"""		,     framework="""tf"""							)

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



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Optional[Any]		,     __a				:	Tuple		,     __a				:	Union[str, Any]							)			->  Dict:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	Union[str, Any]		,     __a				:	Optional[int]							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     text_classifier.model
												# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
												__lowercase      :							int				=     """HuggingFace is in"""
												__lowercase      :							Any				=     text_classifier(__a							)

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

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

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

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







from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						list[str] | None = None      ):
							__lowercase      :							Tuple				=     word_bank or []
							# create a table
							__lowercase      :							int				=     len(lowerCAmelCase_      ) + 1

							__lowercase      :							list[list[list[str]]]				=     []
							for _ in range(lowerCAmelCase_      ):
														table.append([]      )
							# seed value
							__lowercase      :							Dict				=     [[]]  # because empty string has empty combination

							# iterate through the indices
							for i in range(lowerCAmelCase_      ):
														# condition
														if table[i] != []:
																					for word in word_bank:
																												# slice condition
																												if target[i : i + len(lowerCAmelCase_      )] == word:
																																			__lowercase      :							list[list[str]]				=     [
																																			    [word, *way] for way in table[i]
																																			]
																																			# adds the word to every combination the current position holds
																																			# now,push that combination to the table[i+len(word)]
																																			table[i + len(lowerCAmelCase_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
							for combination in table[len(lowerCAmelCase_      )]:
														combination.reverse()

							return table[len(lowerCAmelCase_      )]


if __name__ == "__main__":
	print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
	print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
	print(
	    all_construct(
	        '''hexagonosaurus''',
	        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
	    )
	)