





"""simple docstring"""






import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetModel,
		)




class  __lowercase	:







					'''simple docstring'''




					def __init__(  self		,	_UpperCAmelCase		,	_UpperCAmelCase=13		,	_UpperCAmelCase=7		,	_UpperCAmelCase=True		,	_UpperCAmelCase=True		,	_UpperCAmelCase=False		,	_UpperCAmelCase=True		,	_UpperCAmelCase=99		,	_UpperCAmelCase=64		,	_UpperCAmelCase=5		,	_UpperCAmelCase=4		,	_UpperCAmelCase=64		,	_UpperCAmelCase="gelu"		,	_UpperCAmelCase=0.1		,	_UpperCAmelCase=0.1		,	_UpperCAmelCase=512		,	_UpperCAmelCase=16		,	_UpperCAmelCase=2		,	_UpperCAmelCase=0.0_2		,	_UpperCAmelCase=3		,	_UpperCAmelCase=4		,	_UpperCAmelCase=None		,	):
									__a       :  Optional[int]					=     parent
									__a       :  Optional[Any]					=     batch_size
									__a       :  Optional[int]					=     seq_length
									__a       :  str					=     is_training
									__a       :  List[str]					=     use_input_mask
									__a       :  Union[str, Any]					=     use_token_type_ids
									__a       :  Any					=     use_labels
									__a       :  Any					=     vocab_size
									__a       :  str					=     hidden_size
									__a       :  str					=     num_hidden_layers
									__a       :  Union[str, Any]					=     num_attention_heads
									__a       :  Optional[int]					=     intermediate_size
									__a       :  Tuple					=     hidden_act
									__a       :  int					=     hidden_dropout_prob
									__a       :  List[Any]					=     attention_probs_dropout_prob
									__a       :  str					=     max_position_embeddings
									__a       :  int					=     type_vocab_size
									__a       :  Any					=     type_sequence_label_size
									__a       :  List[str]					=     initializer_range
									__a       :  List[Any]					=     num_labels
									__a       :  Optional[Any]					=     num_choices
									__a       :  Optional[Any]					=     scope







					def        _lowerCamelCase						(  self   ):
									return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Tuple					=     ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size   )

									__a       :  Dict					=     None
									if self.use_input_mask:
													__a       :  List[Any]					=     random_attention_mask([self.batch_size, self.seq_length]   )

									__a       :  Optional[Any]					=     None
									__a       :  Optional[Any]					=     None
									__a       :  List[Any]					=     None
									if self.use_labels:
													__a       :  Optional[int]					=     ids_tensor([self.batch_size]		,	self.type_sequence_label_size   )
													__a       :  Dict					=     ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels   )
													__a       :  Optional[Any]					=     ids_tensor([self.batch_size]		,	self.num_choices   )

									__a       :  int					=     self.get_config()
									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







					def        _lowerCamelCase						(  self   ):
									return MPNetConfig(
									    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		,	initializer_range=self.initializer_range		,	)







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase   ):
									__a       :  List[Any]					=     MPNetModel(config=a__   )
									model.to(a__   )
									model.eval()
									__a       :  Union[str, Any]					=     model(a__		,	a__   )
									__a       :  int					=     model(a__   )
									self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)   )
									self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size)   )







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase   ):
									__a       :  Optional[int]					=     MPNetForQuestionAnswering(config=a__   )
									model.to(a__   )
									model.eval()
									__a       :  str					=     model(
									    a__		,	attention_mask=a__		,	start_positions=a__		,	end_positions=a__		,	)
									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        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase   ):
									__a       :  Optional[int]					=     self.num_labels
									__a       :  Union[str, Any]					=     MPNetForSequenceClassification(a__   )
									model.to(a__   )
									model.eval()
									__a       :  List[str]					=     model(a__		,	attention_mask=a__		,	labels=a__   )
									self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)   )







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase   ):
									__a       :  Union[str, Any]					=     self.num_choices
									__a       :  Tuple					=     MPNetForMultipleChoice(config=a__   )
									model.to(a__   )
									model.eval()
									__a       :  Tuple					=     input_ids.unsqueeze(1   ).expand(-1		,	self.num_choices		,	-1   ).contiguous()
									__a       :  int					=     input_mask.unsqueeze(1   ).expand(-1		,	self.num_choices		,	-1   ).contiguous()
									__a       :  Optional[Any]					=     model(
									    a__		,	attention_mask=a__		,	labels=a__		,	)
									self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_choices)   )







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase   ):
									__a       :  Union[str, Any]					=     self.num_labels
									__a       :  List[str]					=     MPNetForTokenClassification(config=a__   )
									model.to(a__   )
									model.eval()
									__a       :  Dict					=     model(a__		,	attention_mask=a__		,	labels=a__   )
									self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels)   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Tuple					=     self.prepare_config_and_inputs()
									(__a)       :  int					=     config_and_inputs
									__a       :  str					=     {"""input_ids""": input_ids, """attention_mask""": input_mask}
									return config, inputs_dict






@require_torch
class  __lowercase	(	SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase     =     (
					    (
					        MPNetForMaskedLM,
					        MPNetForMultipleChoice,
					        MPNetForQuestionAnswering,
					        MPNetForSequenceClassification,
					        MPNetForTokenClassification,
					        MPNetModel,
					    )
					    if is_torch_available()
					    else ()
					)
					__lowerCAmelCase     =     (
					    {
					        "feature-extraction": MPNetModel,
					        "fill-mask": MPNetForMaskedLM,
					        "question-answering": MPNetForQuestionAnswering,
					        "text-classification": MPNetForSequenceClassification,
					        "token-classification": MPNetForTokenClassification,
					        "zero-shot": MPNetForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__lowerCAmelCase     =     False
					__lowerCAmelCase     =     True







					def        _lowerCamelCase						(  self   ):
									__a       :  int					=     MPNetModelTester(self   )
									__a       :  Any					=     ConfigTester(self		,	config_class=a__		,	hidden_size=37   )







					def        _lowerCamelCase						(  self   ):
									self.config_tester.run_common_tests()







					def        _lowerCamelCase						(  self   ):
									__a       :  List[Any]					=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*a__   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Any					=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*a__   )







					def        _lowerCamelCase						(  self   ):
									__a       :  List[str]					=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*a__   )







					def        _lowerCamelCase						(  self   ):
									__a       :  str					=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*a__   )







					def        _lowerCamelCase						(  self   ):
									__a       :  int					=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*a__   )






@require_torch
class  __lowercase	(	unittest.TestCase							):







					'''simple docstring'''




					@slow
					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[Any]					=     MPNetModel.from_pretrained('''microsoft/mpnet-base'''   )
									__a       :  str					=     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
									__a       :  Optional[int]					=     model(a__   )[0]
									__a       :  List[Any]					=     torch.Size((1, 11, 768)   )
									self.assertEqual(output.shape		,	a__   )
									__a       :  str					=     torch.tensor(
									    [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]   )
									# compare the actual values for a slice.
									self.assertTrue(torch.allclose(output[:, :3, :3]		,	a__		,	atol=1e-4   )   )
"""simple docstring"""




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 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """ylacombe/bark-small"""
       _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       int  			=     """en_speaker_1"""
       _lowerCAmelCase						:       List[Any]  			=     """This is a test string"""
       _lowerCAmelCase						:       Any  			=     """speaker_embeddings_path.json"""
       _lowerCAmelCase						:       List[Any]  			=     """speaker_embeddings"""






     def     __A  (		self				,     **a__  ):
       return AutoTokenizer.from_pretrained(self.checkpoint				,     **a__  )






     def     __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

       processor.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:       str  			=     BarkProcessor.from_pretrained(self.tmpdirname  )

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






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     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				,     )

       _lowerCAmelCase						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

       _lowerCAmelCase						:       List[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     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     35
       _lowerCAmelCase						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       Dict  			=     {
           """semantic_prompt""": np.ones(a__  ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
       }

       # test providing already loaded voice_preset
       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string				,     voice_preset=a__  )

       _lowerCAmelCase						:       Tuple  			=     inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from npz file
       _lowerCAmelCase						:       List[Any]  			=     os.path.join(self.tmpdirname				,     """file.npz"""  )
       np.savez(a__				,     **a__  )
       _lowerCAmelCase						:       List[Any]  			=     processor(text=self.input_string				,     voice_preset=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     inputs["""history_prompt"""]

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

       # test loading voice preset from the hub
       _lowerCAmelCase						:       str  			=     processor(text=self.input_string				,     voice_preset=self.voice_preset  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor(tokenizer=a__  )

       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer(
           self.input_string				,     padding="""max_length"""				,     max_length=256				,     add_special_tokens=a__				,     return_attention_mask=a__				,     return_token_type_ids=a__				,     )

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




