

"""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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



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







					def        __lowerCAmelCase (       self			:				Optional[int]   ,**lowercase_			:				Union[str, Any]		):
						return AutoTokenizer.from_pretrained(self.checkpoint   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						shutil.rmtree(self.tmpdirname		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						List[Any]  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Optional[int]  				=	BarkProcessor(tokenizer=lowercase_		)

						processor.save_pretrained(self.tmpdirname		)
						lowerCAmelCase__	:						List[Any]  				=	BarkProcessor.from_pretrained(self.tmpdirname		)

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







					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						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   ,)

						lowerCAmelCase__	:						str  				=	self.get_tokenizer(bos_token='''(BOS)'''   ,eos_token='''(EOS)'''		)

						lowerCAmelCase__	:						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        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						int  				=	BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint   ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path   ,)

						lowerCAmelCase__	:						List[Any]  				=	3_5
						lowerCAmelCase__	:						Any  				=	2
						lowerCAmelCase__	:						List[str]  				=	8

						lowerCAmelCase__	:						int  				=	{
						    "semantic_prompt": np.ones(lowercase_		),
						    "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__	:						List[str]  				=	processor(text=self.input_string   ,voice_preset=lowercase_		)

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

						# test loading voice preset from npz file
						lowerCAmelCase__	:						Tuple  				=	os.path.join(self.tmpdirname   ,'''file.npz'''		)
						np.savez(lowercase_   ,**lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	processor(text=self.input_string   ,voice_preset=lowercase_		)
						lowerCAmelCase__	:						Dict  				=	inputs["history_prompt"]

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

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







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Tuple  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Optional[int]  				=	BarkProcessor(tokenizer=lowercase_		)

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

						lowerCAmelCase__	:						int  				=	tokenizer(
						    self.input_string   ,padding='''max_length'''   ,max_length=2_5_6   ,add_special_tokens=lowercase_   ,return_attention_mask=lowercase_   ,return_token_type_ids=lowercase_   ,)

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





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}



class   snake_case_       (		__A       ):
   __A	:		List[str]         =				"convbert"






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     str=3_05_22	,						lowercase_				:     Any=7_68	,						lowercase_				:     Tuple=12	,						lowercase_				:     List[str]=12	,						lowercase_				:     Optional[int]=30_72	,						lowercase_				:     Union[str, Any]="gelu"	,						lowercase_				:     str=0.1	,						lowercase_				:     List[str]=0.1	,						lowercase_				:     Optional[Any]=5_12	,						lowercase_				:     Dict=2	,						lowercase_				:     Union[str, Any]=0.02	,						lowercase_				:     Optional[Any]=1E-12	,						lowercase_				:     Optional[int]=1	,						lowercase_				:     List[Any]=0	,						lowercase_				:     Optional[int]=2	,						lowercase_				:     str=7_68	,						lowercase_				:     Dict=2	,						lowercase_				:     Optional[Any]=9	,						lowercase_				:     Union[str, Any]=1	,						lowercase_				:     Any=None	,						**lowercase_				:     Optional[Any]	,						)					->						Dict:
       super().__init__(
           pad_token_id=lowercase_	,						bos_token_id=lowercase_	,						eos_token_id=lowercase_	,						**lowercase_	,						)

       lowercase__ :   List[str]    					=				vocab_size
       lowercase__ :   Union[str, Any]    					=				hidden_size
       lowercase__ :   Any    					=				num_hidden_layers
       lowercase__ :   List[str]    					=				num_attention_heads
       lowercase__ :   Union[str, Any]    					=				intermediate_size
       lowercase__ :   Optional[Any]    					=				hidden_act
       lowercase__ :   int    					=				hidden_dropout_prob
       lowercase__ :   str    					=				attention_probs_dropout_prob
       lowercase__ :   Union[str, Any]    					=				max_position_embeddings
       lowercase__ :   Optional[int]    					=				type_vocab_size
       lowercase__ :   Tuple    					=				initializer_range
       lowercase__ :   List[str]    					=				layer_norm_eps
       lowercase__ :   List[Any]    					=				embedding_size
       lowercase__ :   Optional[Any]    					=				head_ratio
       lowercase__ :   Dict    					=				conv_kernel_size
       lowercase__ :   Tuple    					=				num_groups
       lowercase__ :   Optional[int]    					=				classifier_dropout



class   snake_case_       (		__A       ):






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           lowercase__ :   Tuple    					=				{0: "batch", 1: "choice", 2: "sequence"}
       else:
           lowercase__ :   str    					=				{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]  )



