

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






@is_pipeline_test
class 	A_		(	unittest.TestCase    ):
							_UpperCAmelCase   :  Dict										=				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							_UpperCAmelCase   :  Optional[int]										=				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														_UpperCAmelCase   :  Optional[Any]										=				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														_UpperCAmelCase   :  Optional[int]										=				{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}
							@require_torch
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    List[str] 	=			pipeline(
								    task='text-classification'       ,model='hf-internal-testing/tiny-random-distilbert'       ,framework='pt')

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

								__lowerCamelCase    :    int 	=			text_classifier('This is great !'       ,top_k=2)
								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}])

								__lowerCamelCase    :    Dict 	=			text_classifier(['This is great !', 'This is bad']       ,top_k=2)
								self.assertEqual(
								    nested_simplify(SCREAMING_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}],
								    ]       ,)

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

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

								__lowerCamelCase    :    Union[str, Any] 	=			text_classifier('This is great !'       ,return_all_scores=SCREAMING_SNAKE_CASE__)
								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,[[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]])

								__lowerCamelCase    :    Dict 	=			text_classifier(['This is great !', 'Something else']       ,return_all_scores=SCREAMING_SNAKE_CASE__)
								self.assertEqual(
								    nested_simplify(SCREAMING_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}],
								    ]       ,)

								__lowerCamelCase    :    Optional[Any] 	=			text_classifier(['This is great !', 'Something else']       ,return_all_scores=SCREAMING_SNAKE_CASE__)
								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,[
								        {'label': 'LABEL_0', 'score': 0.504},
								        {'label': 'LABEL_0', 'score': 0.504},
								    ]       ,)
							@require_torch
							def    lowerCAmelCase					(		self		:      Dict):
								import torch

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

								__lowerCamelCase    :    Optional[Any] 	=			text_classifier('This is great !')
								self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'LABEL_0', 'score': 0.504}])
							@require_tf
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    List[str] 	=			pipeline(
								    task='text-classification'       ,model='hf-internal-testing/tiny-random-distilbert'       ,framework='tf')

								__lowerCamelCase    :    List[Any] 	=			text_classifier('This is great !')
								self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'LABEL_0', 'score': 0.504}])
							@slow
							@require_torch
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Optional[Any] 	=			pipeline('text-classification')

								__lowerCamelCase    :    Optional[Any] 	=			text_classifier('This is great !')
								self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'POSITIVE', 'score': 1.0}])
								__lowerCamelCase    :    str 	=			text_classifier('This is bad !')
								self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'NEGATIVE', 'score': 1.0}])
								__lowerCamelCase    :    Union[str, Any] 	=			text_classifier('Birds are a type of animal')
								self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'POSITIVE', 'score': 0.988}])
							@slow
							@require_tf
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    str 	=			pipeline('text-classification'       ,framework='tf')

								__lowerCamelCase    :    List[str] 	=			text_classifier('This is great !')
								self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'POSITIVE', 'score': 1.0}])
								__lowerCamelCase    :    int 	=			text_classifier('This is bad !')
								self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'NEGATIVE', 'score': 1.0}])
								__lowerCamelCase    :    Optional[Any] 	=			text_classifier('Birds are a type of animal')
								self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': 'POSITIVE', 'score': 0.988}])
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    Optional[Any] 	=			TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__       ,tokenizer=SCREAMING_SNAKE_CASE__)
								return text_classifier, ["HuggingFace is in", "This is another test"]


							def    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    Dict 	=			text_classifier.model
								# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
								__lowerCamelCase    :    List[Any] 	=			'HuggingFace is in'
								__lowerCamelCase    :    Optional[Any] 	=			text_classifier(SCREAMING_SNAKE_CASE__)

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

								__lowerCamelCase    :    Union[str, Any] 	=			['HuggingFace is in ', 'Paris is in France']
								__lowerCamelCase    :    Union[str, Any] 	=			text_classifier(SCREAMING_SNAKE_CASE__)
								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,[{'label': ANY(SCREAMING_SNAKE_CASE__), 'score': ANY(SCREAMING_SNAKE_CASE__)}, {'label': ANY(SCREAMING_SNAKE_CASE__), 'score': ANY(SCREAMING_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
								__lowerCamelCase    :    List[Any] 	=			text_classifier(SCREAMING_SNAKE_CASE__       ,top_k=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    int 	=			len(model.config.idalabel.values())
								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,[[{'label': ANY(SCREAMING_SNAKE_CASE__), 'score': ANY(SCREAMING_SNAKE_CASE__)}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE__), 'score': ANY(SCREAMING_SNAKE_CASE__)}] * N]       ,)

								__lowerCamelCase    :    Any 	=			{'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
								__lowerCamelCase    :    List[Any] 	=			text_classifier(SCREAMING_SNAKE_CASE__)
								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,{'label': ANY(SCREAMING_SNAKE_CASE__), 'score': ANY(SCREAMING_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.
								__lowerCamelCase    :    List[str] 	=			[['HuggingFace is in ', 'Paris is in France']]
								with self.assertRaises(SCREAMING_SNAKE_CASE__):
									text_classifier(SCREAMING_SNAKE_CASE__)

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







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

								__lowerCamelCase    :    str 	=			tf.convert_to_tensor(
								    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

								__lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
								__lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
								self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
								# compare the actual values for a slice.
								__lowerCamelCase    :    Any 	=			tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]       ,dtype=tf.floataa       ,)
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




