

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_        =      {'LayoutLMv2Config', 'LayoutLMv3Config'}

@is_pipeline_test
class 				a  (	unittest.TestCase				):
						_lowercase    						=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						_lowercase    						=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


						@require_torch
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										_UpperCAmelCase       :		Optional[int] 		=    text_classifier("This is great !"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "LABEL_0", "score": 0.5_04}]	)

										_UpperCAmelCase       :		int 		=    text_classifier("This is great !"						,					top_k=2	)
										self.assertEqual(
										    nested_simplify(A_	)						,					[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]	)

										_UpperCAmelCase       :		List[Any] 		=    text_classifier(["This is great !", "This is bad"]						,					top_k=2	)
										self.assertEqual(
										    nested_simplify(A_	)						,					[
										        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
										        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
										    ]						,					)

										_UpperCAmelCase       :		List[Any] 		=    text_classifier("This is great !"						,					top_k=1	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "LABEL_0", "score": 0.5_04}]	)

										# Legacy behavior
										_UpperCAmelCase       :		int 		=    text_classifier("This is great !"						,					return_all_scores=A_	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "LABEL_0", "score": 0.5_04}]	)

										_UpperCAmelCase       :		Optional[int] 		=    text_classifier("This is great !"						,					return_all_scores=A_	)
										self.assertEqual(
										    nested_simplify(A_	)						,					[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]	)

										_UpperCAmelCase       :		Tuple 		=    text_classifier(["This is great !", "Something else"]						,					return_all_scores=A_	)
										self.assertEqual(
										    nested_simplify(A_	)						,					[
										        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
										        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
										    ]						,					)

										_UpperCAmelCase       :		Optional[int] 		=    text_classifier(["This is great !", "Something else"]						,					return_all_scores=A_	)
										self.assertEqual(
										    nested_simplify(A_	)						,					[
										        {"label": "LABEL_0", "score": 0.5_04},
										        {"label": "LABEL_0", "score": 0.5_04},
										    ]						,					)


						@require_torch
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										import torch

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

										_UpperCAmelCase       :		Any 		=    text_classifier("This is great !"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "LABEL_0", "score": 0.5_04}]	)


						@require_tf
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    pipeline(
										    task="text-classification"						,					model="hf-internal-testing/tiny-random-distilbert"						,					framework="tf"	)

										_UpperCAmelCase       :		Union[str, Any] 		=    text_classifier("This is great !"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "LABEL_0", "score": 0.5_04}]	)


						@slow
						@require_torch
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    pipeline("text-classification"	)

										_UpperCAmelCase       :		int 		=    text_classifier("This is great !"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "POSITIVE", "score": 1.0}]	)
										_UpperCAmelCase       :		Any 		=    text_classifier("This is bad !"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "NEGATIVE", "score": 1.0}]	)
										_UpperCAmelCase       :		str 		=    text_classifier("Birds are a type of animal"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "POSITIVE", "score": 0.9_88}]	)


						@slow
						@require_tf
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    pipeline("text-classification"						,					framework="tf"	)

										_UpperCAmelCase       :		List[Any] 		=    text_classifier("This is great !"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "POSITIVE", "score": 1.0}]	)
										_UpperCAmelCase       :		Union[str, Any] 		=    text_classifier("This is bad !"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "NEGATIVE", "score": 1.0}]	)
										_UpperCAmelCase       :		Optional[Any] 		=    text_classifier("Birds are a type of animal"	)
										self.assertEqual(nested_simplify(A_	)						,					[{"label": "POSITIVE", "score": 0.9_88}]	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    TextClassificationPipeline(model=A_						,					tokenizer=A_	)
										return text_classifier, ["HuggingFace is in", "This is another test"]





						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


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

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

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

										_UpperCAmelCase       :		Union[str, Any] 		=    {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
										_UpperCAmelCase       :		Dict 		=    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.
										_UpperCAmelCase       :		Optional[int] 		=    [["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
										_UpperCAmelCase       :		Tuple 		=    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 ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"timesformer"


						def __init__(       self						,					A_=224						,					A_=16						,					A_=3						,					A_=8						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.0						,					A_=0.0						,					A_=0.02						,					A_=1e-6						,					A_=True						,					A_="divided_space_time"						,					A_=0						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_size
										_UpperCAmelCase       :		Tuple 		=    patch_size
										_UpperCAmelCase       :		Any 		=    num_channels
										_UpperCAmelCase       :		List[Any] 		=    num_frames

										_UpperCAmelCase       :		str 		=    hidden_size
										_UpperCAmelCase       :		Any 		=    num_hidden_layers
										_UpperCAmelCase       :		Dict 		=    num_attention_heads
										_UpperCAmelCase       :		Optional[int] 		=    intermediate_size
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Tuple 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Union[str, Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_eps
										_UpperCAmelCase       :		str 		=    qkv_bias

										_UpperCAmelCase       :		Tuple 		=    attention_type
										_UpperCAmelCase       :		List[Any] 		=    drop_path_rate


