






"""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.
SCREAMING_SNAKE_CASE						:						int					   =       {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}





@is_pipeline_test
class       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Dict   			=					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	SCREAMING_SNAKE_CASE_:							List[str]   			=					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


	@require_torch
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       pipeline(
					    task='text-classification'  ,					model='hf-internal-testing/tiny-random-distilbert'  ,					framework='pt'						)

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

					A__							 =       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}]						)

					A__							 =       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}],
					    ]  ,					)

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

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

					A__							 =       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}]]						)

					A__							 =       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}],
					    ]  ,					)

					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					import torch

					A__							 =       pipeline(
					    task='text-classification'  ,					model='hf-internal-testing/tiny-random-distilbert'  ,					framework='pt'  ,					device=torch.device('cpu'						)  ,					)

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


	@require_tf
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       pipeline(
					    task='text-classification'  ,					model='hf-internal-testing/tiny-random-distilbert'  ,					framework='tf'						)

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


	@slow
	@require_torch
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       pipeline('text-classification'						)

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


	@slow
	@require_tf
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       pipeline('text-classification'  ,					framework='tf'						)

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       text_classifier.model
					# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
					A__							 =       'HuggingFace is in'
					A__							 =       text_classifier(__a						)

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

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

					A__							 =       {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
					A__							 =       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.
					A__							 =       [['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
					A__							 =       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()						)










"""simple docstring"""






from __future__ import annotations



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = None				):
				A__							 =       word_bank or []
				# create a table
				A__							 =       len(lowerCAmelCase__				) + 1

				A__							 =       []
				for _ in range(lowerCAmelCase__				):
								table.append([]				)
				# seed value
				A__							 =       [[]]  # 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:
																				A__							 =       [
																				    [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'''],
					    )
					)


