






'''simple docstring'''

import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)

class       UpperCAmelCase (						unittest.TestCase  ):




		def __init__(self      :	List[Any]							,			A__      :	Dict							,			A__      :	str=1_3							,			A__      :	List[str]=7							,			A__      :	int=True							,			A__      :	Tuple=True							,			A__      :	str=True							,			A__      :	List[str]=True							,			A__      :	str=9_9							,			A__      :	str=3_2							,			A__      :	Tuple=5							,			A__      :	Dict=4							,			A__      :	Optional[Any]=3_7							,			A__      :	Dict="gelu"							,			A__      :	Optional[int]=0.1							,			A__      :	Any=0.1							,			A__      :	Dict=5_1_2							,			A__      :	Any=1_6							,			A__      :	Tuple=2							,			A__      :	Optional[Any]=0.0_2							,			A__      :	Any=4							,			)					-> str:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = seq_length
						lowercase         = is_training
						lowercase         = use_attention_mask
						lowercase         = use_token_type_ids
						lowercase         = use_labels
						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = type_sequence_label_size
						lowercase         = initializer_range
						lowercase         = num_choices




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> int:
						lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size				)

						lowercase         = None
						if self.use_attention_mask:
										lowercase         = random_attention_mask([self.batch_size, self.seq_length]				)

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

						lowercase         = RobertaConfig(
						    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							,			is_decoder=A__							,			initializer_range=self.initializer_range							,			)

						return config, input_ids, token_type_ids, attention_mask




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Tuple:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = config_and_inputs
						lowercase         = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
						return config, inputs_dict





		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Tuple:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = config_and_inputs

						lowercase         = True
						lowercase         = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
						lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			vocab_size=2				)

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)




@require_flax
class       UpperCAmelCase (						_lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	str 			=		True

		UpperCAmelCase					:	List[str] 			=		(
		    (
		        FlaxRobertaModel,
		        FlaxRobertaForCausalLM,
		        FlaxRobertaForMaskedLM,
		        FlaxRobertaForSequenceClassification,
		        FlaxRobertaForTokenClassification,
		        FlaxRobertaForMultipleChoice,
		        FlaxRobertaForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)




		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[Any]:
						lowercase         = FlaxRobertaModelTester(self				)





		@slow
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Tuple:







						for model_class_name in self.all_model_classes:
										lowercase         = model_class_name.from_pretrained("roberta-base"							,			from_pt=A__				)
										lowercase         = model(np.ones((1, 1)				)				)
										self.assertIsNotNone(A__				)











'''simple docstring'''

import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Optional[Any] 			=		'''MCTCTFeatureExtractor'''
		UpperCAmelCase					:	Tuple 			=		'''AutoTokenizer'''




		def __init__(self      :	int							,			A__      :	Tuple							,			A__      :	Union[str, Any]				)					-> Dict:
						super().__init__(A__							,			A__				)
						lowercase         = self.feature_extractor
						lowercase         = False




		def __call__(self      :	Tuple							,			*A__      :	str							,			**A__      :	Dict				)					-> int:
						# For backward compatibility
						if self._in_target_context_manager:
										return self.current_processor(*A__							,			**A__				)

						if "raw_speech" in kwargs:
										warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."				)
										lowercase         = kwargs.pop("raw_speech"				)
						else:
										lowercase         = kwargs.pop("audio"							,			A__				)
						lowercase         = kwargs.pop("sampling_rate"							,			A__				)
						lowercase         = kwargs.pop("text"							,			A__				)
						if len(A__				) > 0:
										lowercase         = args[0]
										lowercase         = args[1:]

						if audio is None and text is None:
										raise ValueError("You need to specify either an `audio` or `text` input to process."				)

						if audio is not None:
										lowercase         = self.feature_extractor(A__							,			*A__							,			sampling_rate=A__							,			**A__				)
						if text is not None:
										lowercase         = self.tokenizer(A__							,			**A__				)

						if text is None:
										return inputs
						elif audio is None:
										return encodings
						else:
										lowercase         = encodings["input_ids"]
										return inputs




		def 				UpperCAmelCase__   (self      :	Tuple							,			*A__      :	str							,			**A__      :	str				)					-> str:
						return self.tokenizer.batch_decode(*A__							,			**A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			*A__      :	List[Any]							,			**A__      :	List[str]				)					-> Tuple:
						# For backward compatibility
						if self._in_target_context_manager:
										return self.current_processor.pad(*A__							,			**A__				)

						lowercase         = kwargs.pop("input_features"							,			A__				)
						lowercase         = kwargs.pop("labels"							,			A__				)
						if len(A__				) > 0:
										lowercase         = args[0]
										lowercase         = args[1:]

						if input_features is not None:
										lowercase         = self.feature_extractor.pad(A__							,			*A__							,			**A__				)
						if labels is not None:
										lowercase         = self.tokenizer.pad(A__							,			**A__				)

						if labels is None:
										return input_features
						elif input_features is None:
										return labels
						else:
										lowercase         = labels["input_ids"]
										return input_features




		def 				UpperCAmelCase__   (self      :	Tuple							,			*A__      :	Optional[int]							,			**A__      :	Optional[int]				)					-> Tuple:
						return self.tokenizer.decode(*A__							,			**A__				)





		@contextmanager
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Union[str, Any]:
						warnings.warn(
						    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
						    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
						    "your audio inputs, or in a separate call."				)
						lowercase         = True
						lowercase         = self.tokenizer
						yield
						lowercase         = self.feature_extractor
						lowercase         = False




