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):


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							'''ylacombe/bark-small'''
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							tempfile.mkdtemp()
												SCREAMING_SNAKE_CASE							:					Dict           =							'''en_speaker_1'''
												SCREAMING_SNAKE_CASE							:					str           =							'''This is a test string'''
												SCREAMING_SNAKE_CASE							:					int           =							'''speaker_embeddings_path.json'''
												SCREAMING_SNAKE_CASE							:					List[Any]           =							'''speaker_embeddings'''


						def  __A ( self			:       str					,	**UpperCamelCase__			:       int		):





												'''simple docstring'''





												return AutoTokenizer.from_pretrained(self.checkpoint					,	**UpperCamelCase__		)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												shutil.rmtree(self.tmpdirname		)


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					int           =							BarkProcessor(tokenizer=UpperCamelCase__		)

												processor.save_pretrained(self.tmpdirname		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							BarkProcessor.from_pretrained(self.tmpdirname		)

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


						@slow
						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					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					,	)

												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  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint					,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,	)

												SCREAMING_SNAKE_CASE							:					Tuple           =							35
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							2
												SCREAMING_SNAKE_CASE							:					int           =							8

												SCREAMING_SNAKE_CASE							:					List[str]           =							{
												    '''semantic_prompt''': np.ones(UpperCamelCase__		),
												    '''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							:					Tuple           =							processor(text=self.input_string					,	voice_preset=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							inputs['''history_prompt''']
												for key in voice_preset:
																		self.assertListEqual(voice_preset[key].tolist()					,	processed_voice_preset.get(UpperCamelCase__					,	np.array([]		)		).tolist()		)

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

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

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



						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					str           =							BarkProcessor(tokenizer=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					str           =							processor(text=self.input_string		)

												SCREAMING_SNAKE_CASE							:					Any           =							tokenizer(
												    self.input_string					,	padding='''max_length'''					,	max_length=256					,	add_special_tokens=UpperCamelCase__					,	return_attention_mask=UpperCamelCase__					,	return_token_type_ids=UpperCamelCase__					,	)

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






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							hidden_size
												SCREAMING_SNAKE_CASE							:					int           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					List[Any]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Dict           =							type_vocab_size
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							relative_attention
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							max_relative_positions
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pad_token_id
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							position_biased_input

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{0: '''batch''', 1: '''sequence'''}
												if self._config.type_vocab_size > 0:
																		return OrderedDict(
																		    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]		)
												else:
																		return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]		)


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().generate_dummy_inputs(preprocessor=UpperCamelCase__					,	framework=UpperCamelCase__		)
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs





