





"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


A       	=     logging.get_logger(__name__)

A       	=     {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}




class  __lowercase	(	SCREAMING_SNAKE_CASE_							):







					'''simple docstring'''




					__lowerCAmelCase     =     "bert"







					def __init__(  self		,	_UpperCAmelCase=30522		,	_UpperCAmelCase=768		,	_UpperCAmelCase=12		,	_UpperCAmelCase=12		,	_UpperCAmelCase=3072		,	_UpperCAmelCase="gelu"		,	_UpperCAmelCase=0.1		,	_UpperCAmelCase=0.1		,	_UpperCAmelCase=512		,	_UpperCAmelCase=2		,	_UpperCAmelCase=0.0_2		,	_UpperCAmelCase=1e-1_2		,	_UpperCAmelCase=0		,	_UpperCAmelCase="absolute"		,	_UpperCAmelCase=True		,	_UpperCAmelCase=None		,	**_UpperCAmelCase		,	):
									super().__init__(pad_token_id=a__		,	**a__   )

									__a       :  Optional[Any]					=     vocab_size
									__a       :  Tuple					=     hidden_size
									__a       :  Union[str, Any]					=     num_hidden_layers
									__a       :  Union[str, Any]					=     num_attention_heads
									__a       :  str					=     hidden_act
									__a       :  Dict					=     intermediate_size
									__a       :  Union[str, Any]					=     hidden_dropout_prob
									__a       :  Dict					=     attention_probs_dropout_prob
									__a       :  List[Any]					=     max_position_embeddings
									__a       :  List[str]					=     type_vocab_size
									__a       :  int					=     initializer_range
									__a       :  List[str]					=     layer_norm_eps
									__a       :  Union[str, Any]					=     position_embedding_type
									__a       :  str					=     use_cache
									__a       :  Union[str, Any]					=     classifier_dropout






class  __lowercase	(	SCREAMING_SNAKE_CASE_							):







					'''simple docstring'''




					@property
					def        _lowerCamelCase						(  self   ):
									if self.task == "multiple-choice":
													__a       :  Tuple					=     {0: """batch""", 1: """choice""", 2: """sequence"""}
									else:
													__a       :  str					=     {0: """batch""", 1: """sequence"""}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''token_type_ids''', dynamic_axis),
									    ]   )
"""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()  )




