



'''simple docstring'''
import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)






class 							lowercase__	(							unittest.TestCase   ):





	def __init__( self					:				List[str] ,lowerCamelCase__					:				List[str] ,lowerCamelCase__					:				List[str]=13 ,lowerCamelCase__					:				Dict=7 ,lowerCamelCase__					:				Union[str, Any]=True ,lowerCamelCase__					:				Any=True ,lowerCamelCase__					:				List[Any]=True ,lowerCamelCase__					:				Any=True ,lowerCamelCase__					:				Dict=99 ,lowerCamelCase__					:				int=32 ,lowerCamelCase__					:				Tuple=5 ,lowerCamelCase__					:				Dict=4 ,lowerCamelCase__					:				Any=37 ,lowerCamelCase__					:				str="gelu" ,lowerCamelCase__					:				List[Any]=0.1 ,lowerCamelCase__					:				Optional[Any]=0.1 ,lowerCamelCase__					:				Optional[Any]=512 ,lowerCamelCase__					:				Any=16 ,lowerCamelCase__					:				Tuple=2 ,lowerCamelCase__					:				int=0.0_2 ,lowerCamelCase__					:				int=4 ,):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   parent
							_UpperCamelCase					:		Dict 				=   batch_size
							_UpperCamelCase					:		Union[str, Any] 				=   seq_length
							_UpperCamelCase					:		Optional[Any] 				=   is_training
							_UpperCamelCase					:		Optional[int] 				=   use_attention_mask
							_UpperCamelCase					:		Any 				=   use_token_type_ids
							_UpperCamelCase					:		str 				=   use_labels
							_UpperCamelCase					:		Any 				=   vocab_size
							_UpperCamelCase					:		List[Any] 				=   hidden_size
							_UpperCamelCase					:		Dict 				=   num_hidden_layers
							_UpperCamelCase					:		Dict 				=   num_attention_heads
							_UpperCamelCase					:		str 				=   intermediate_size
							_UpperCamelCase					:		int 				=   hidden_act
							_UpperCamelCase					:		Any 				=   hidden_dropout_prob
							_UpperCamelCase					:		Any 				=   attention_probs_dropout_prob
							_UpperCamelCase					:		List[str] 				=   max_position_embeddings
							_UpperCamelCase					:		Optional[int] 				=   type_vocab_size
							_UpperCamelCase					:		str 				=   type_sequence_label_size
							_UpperCamelCase					:		Dict 				=   initializer_range
							_UpperCamelCase					:		List[Any] 				=   num_choices





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )

							_UpperCamelCase					:		Union[str, Any] 				=   None
							if self.use_attention_mask:
													_UpperCamelCase					:		Union[str, Any] 				=   random_attention_mask([self.batch_size, self.seq_length] )

							_UpperCamelCase					:		Any 				=   DistilBertConfig(
							    vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=lowerCamelCase__ ,)

							return config, input_ids, attention_mask





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   self.prepare_config_and_inputs()
							_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		List[Any] 				=   config_and_inputs
							_UpperCamelCase					:		Optional[int] 				=   {'input_ids': input_ids, 'attention_mask': attention_mask}
							return config, inputs_dict






@require_flax
class 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	lowercase__  				=			(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   FlaxDistilBertModelTester(self )





	@slow
	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							for model_class_name in self.all_model_classes:
													_UpperCamelCase					:		Dict 				=   model_class_name.from_pretrained('distilbert-base-uncased' )
													_UpperCamelCase					:		Optional[int] 				=   model(np.ones((1, 1) ) )
													self.assertIsNotNone(lowerCamelCase__ )






@require_flax
class 							lowercase__	(							unittest.TestCase   ):





	@slow
	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )
							_UpperCamelCase					:		List[Any] 				=   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
							_UpperCamelCase					:		Tuple 				=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
							_UpperCamelCase					:		Dict 				=   model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ )[0]
							_UpperCamelCase					:		Any 				=   (1, 11, 768)
							self.assertEqual(output.shape ,lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )

							self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,lowerCamelCase__ ,atol=1E-4 ) )






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		'''ylacombe/bark-small'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE						:     str					=		'''en_speaker_1'''
										__SCREAMING_SNAKE_CASE						:     Any					=		'''This is a test string'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings_path.json'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings'''


				def 		__magic_name__(       self			:List[str]			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										return AutoTokenizer.from_pretrained(self.checkpoint			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			int:
										shutil.rmtree(self.tmpdirname	)


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										processor.save_pretrained(self.tmpdirname	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BarkProcessor.from_pretrained(self.tmpdirname	)

										self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer.get_vocab()	)


				@slow
				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)
										processor.save_pretrained(
										    self.tmpdirname			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			speaker_embeddings_directory=self.speaker_embeddings_directory			,			)

										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		BarkProcessor.from_pretrained(
										    self.tmpdirname			,			self.speaker_embeddings_dict_path			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			)

										self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer_add_kwargs.get_vocab()	)


				def 		__magic_name__(       self			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		35
										__SCREAMING_SNAKE_CASE						:     str					=		2
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		8

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{
										    '''semantic_prompt''': np.ones(lowerCAmelCase__	),
										    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)	),
										    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)	),
										}

										# test providing already loaded voice_preset
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		inputs['''history_prompt''']
										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from npz file
										__SCREAMING_SNAKE_CASE						:     str					=		os.path.join(self.tmpdirname			,			'''file.npz'''	)
										np.savez(lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inputs['''history_prompt''']

										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from the hub
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=self.voice_preset	)



				def 		__magic_name__(       self			:Tuple	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Any					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenizer(
										    self.input_string			,			padding='''max_length'''			,			max_length=256			,			add_special_tokens=lowerCAmelCase__			,			return_attention_mask=lowerCAmelCase__			,			return_token_type_ids=lowerCAmelCase__			,			)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]			,			encoded_processor[key].squeeze().tolist()	)
