




from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		)




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					List[Any] ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					List[str]=13 ,				__lowerCamelCase    :					Union[str, Any]=7 ,				__lowerCamelCase    :					Tuple=True ,				__lowerCamelCase    :					Union[str, Any]=True ,				__lowerCamelCase    :					Any=True ,				__lowerCamelCase    :					int=True ,				__lowerCamelCase    :					List[str]=99 ,				__lowerCamelCase    :					int=32 ,				__lowerCamelCase    :					Optional[int]=2 ,				__lowerCamelCase    :					Optional[Any]=4 ,				__lowerCamelCase    :					Dict=37 ,				__lowerCamelCase    :					str="gelu" ,				__lowerCamelCase    :					Optional[Any]=0.1 ,				__lowerCamelCase    :					Union[str, Any]=0.1 ,				__lowerCamelCase    :					Dict=5_12 ,				__lowerCamelCase    :					Dict=16 ,				__lowerCamelCase    :					Any=2 ,				__lowerCamelCase    :					List[Any]=0.02 ,				__lowerCamelCase    :					str=3 ,				__lowerCamelCase    :					List[str]=4 ,				__lowerCamelCase    :					List[str]=None ,				) ->					Any:
							a   =      parent
							a   =      13
							a   =      7
							a   =      True
							a   =      True
							a   =      True
							a   =      True
							a   =      99
							a   =      32
							a   =      2
							a   =      4
							a   =      37
							a   =      "gelu"
							a   =      0.1
							a   =      0.1
							a   =      5_12
							a   =      16
							a   =      False
							a   =      -1
							a   =      True
							a   =      2
							a   =      0.02
							a   =      3
							a   =      4
							a   =      None





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Dict:
							a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size							)

							a   =      None
							if self.use_input_mask:
										a   =      random_attention_mask([self.batch_size, self.seq_length]							)

							a   =      None
							if self.use_token_type_ids:
										a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size							)

							a   =      None
							a   =      None
							a   =      None
							if self.use_labels:
										a   =      ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)
										a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels							)

							a   =      DebertaConfig(
							    vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				relative_attention=self.relative_attention ,				max_relative_positions=self.max_relative_positions ,				position_biased_input=self.position_biased_input ,				initializer_range=self.initializer_range ,				return_dict=__lowerCamelCase ,				)

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Tuple							) ->					List[str]:
							a   =      TFDebertaModel(config=__lowerCamelCase							)
							a   =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							a   =      [input_ids, input_mask]
							a   =      model(__lowerCamelCase							)

							a   =      model(__lowerCamelCase							)

							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					int							) ->					Optional[int]:
							a   =      TFDebertaForMaskedLM(config=__lowerCamelCase							)
							a   =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							a   =      model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Union[str, Any]							) ->					List[Any]:
							a   =      self.num_labels
							a   =      TFDebertaForSequenceClassification(config=__lowerCamelCase							)
							a   =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							a   =      model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Dict							) ->					Optional[int]:
							a   =      self.num_labels
							a   =      TFDebertaForTokenClassification(config=__lowerCamelCase							)
							a   =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							a   =      model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[int]							) ->					Union[str, Any]:
							a   =      TFDebertaForQuestionAnswering(config=__lowerCamelCase							)
							a   =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							a   =      model(__lowerCamelCase							)
							self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)							)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Dict:
							a   =      self.prepare_config_and_inputs()
							(
							    a  
							)   =      config_and_inputs
							a   =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_tf
class    snake_case__							(snake_case__   ,	snake_case__   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							str										= (
				    (
				        TFDebertaModel,
				        TFDebertaForMaskedLM,
				        TFDebertaForQuestionAnswering,
				        TFDebertaForSequenceClassification,
				        TFDebertaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_	:							Optional[Any]										= (
				    {
				        "feature-extraction": TFDebertaModel,
				        "fill-mask": TFDebertaForMaskedLM,
				        "question-answering": TFDebertaForQuestionAnswering,
				        "text-classification": TFDebertaForSequenceClassification,
				        "token-classification": TFDebertaForTokenClassification,
				        "zero-shot": TFDebertaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE_	:							List[str]										= False
				SCREAMING_SNAKE_CASE_	:							Tuple										= False





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Any:
							a   =      TFDebertaModelTester(self							)
							a   =      ConfigTester(self ,				config_class=__lowerCamelCase ,				hidden_size=37							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[Any]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Tuple:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[Any]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Dict:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[str]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Tuple:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					List[str]:
							a   =      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"							)
							self.assertIsNotNone(__lowerCamelCase							)




@require_tf
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@unittest.skip(reason="Model not available yet"							)
				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Union[str, Any]:
							pass





				@slow
				def 							__UpperCAmelCase							(				self    :					int							) ->					Tuple:
							a   =      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"							)
							a   =      tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
							a   =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
							a   =      model(__lowerCamelCase ,				attention_mask=__lowerCamelCase							)[0]

							a   =      tf.constant(
							    [
							        [
							            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
							            [1.4_484_025, -0.93_483_794, -0.80_593_085],
							            [0.3_122_741, 0.00_316_059, -1.4_131_377],
							        ]
							    ]							)
							tf.debugging.assert_near(output[:, 1:4, 1:4] ,				__lowerCamelCase ,				atol=1e-4							)












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






