
"""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 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> str:
							A     = 'ylacombe/bark-small'
							A     = tempfile.mkdtemp()
							A     = 'en_speaker_1'
							A     = 'This is a test string'
							A     = 'speaker_embeddings_path.json'
							A     = 'speaker_embeddings'


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,**A_  :   Any   )  -> List[Any]:
							return AutoTokenizer.from_pretrained(self.checkpoint	,**A_   )


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Dict:
							A     = self.get_tokenizer()

							A     = BarkProcessor(tokenizer=A_   )

							processor.save_pretrained(self.tmpdirname   )
							A     = BarkProcessor.from_pretrained(self.tmpdirname   )

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


						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Dict:
							A     = 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	,)

							A     = self.get_tokenizer(bos_token='(BOS)'	,eos_token='(EOS)'   )

							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> str:
							A     = BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint	,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,)

							A     = 35
							A     = 2
							A     = 8

							A     = {
							    '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
							A     = processor(text=self.input_string	,voice_preset=A_   )

							A     = 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
							A     = os.path.join(self.tmpdirname	,'file.npz'   )
							np.savez(A_	,**A_   )
							A     = processor(text=self.input_string	,voice_preset=A_   )
							A     = 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
							A     = processor(text=self.input_string	,voice_preset=self.voice_preset   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[str]:
							A     = self.get_tokenizer()

							A     = BarkProcessor(tokenizer=A_   )

							A     = processor(text=self.input_string   )

							A     = 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()   )







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a   :							Dict						= logging.get_logger(__name__)

__a   :							Dict						= {
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"gpt_bigcode"
     SCREAMING_SNAKE_CASE     =					["past_key_values"]
     SCREAMING_SNAKE_CASE     =					{
         "hidden_size": "n_embd",
         "max_position_embeddings": "n_positions",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      Dict=50_257			,						UpperCamelCase_							:      int=1_024			,						UpperCamelCase_							:      List[Any]=768			,						UpperCamelCase_							:      str=12			,						UpperCamelCase_							:      Union[str, Any]=12			,						UpperCamelCase_							:      Optional[Any]=None			,						UpperCamelCase_							:      Tuple="gelu_pytorch_tanh"			,						UpperCamelCase_							:      Optional[Any]=0.1			,						UpperCamelCase_							:      List[str]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      int=1e-5			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      Dict=50_256			,						UpperCamelCase_							:      Optional[Any]=50_256			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      List[Any]=True			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						vocab_size
         __A       =						n_positions
         __A       =						n_embd
         __A       =						n_layer
         __A       =						n_head
         __A       =						n_inner
         __A       =						activation_function
         __A       =						resid_pdrop
         __A       =						embd_pdrop
         __A       =						attn_pdrop
         __A       =						layer_norm_epsilon
         __A       =						initializer_range
         __A       =						scale_attn_weights
         __A       =						use_cache
         __A       =						attention_softmax_in_fpaa
         __A       =						scale_attention_softmax_in_fpaa
         __A       =						multi_query

         __A       =						bos_token_id
         __A       =						eos_token_id

         super().__init__(bos_token_id=UpperCamelCase_			,						eos_token_id=UpperCamelCase_			,						**UpperCamelCase_					)




