

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				):




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			='ylacombe/bark-small'
        _lowercase      			=tempfile.mkdtemp()
        _lowercase      			='en_speaker_1'
        _lowercase      			='This is a test string'
        _lowercase      			='speaker_embeddings_path.json'
        _lowercase      			='speaker_embeddings'




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Union[str, Any]:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.get_tokenizer()

        _lowercase      			=BarkProcessor(tokenizer=lowerCAmelCase   )

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

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




  @slow
  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=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				,  )

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

        _lowercase      			=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   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint				,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,  )

        _lowercase      			=35
        _lowercase      			=2
        _lowercase      			=8

        _lowercase      			={
            'semantic_prompt': np.ones(lowerCAmelCase   ),
            'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)   ),
            'fine_prompt': np.ones((nb_codebooks_total, seq_len)   ),
        }

        # test providing already loaded voice_preset
        _lowercase      			=processor(text=self.input_string				,  voice_preset=lowerCAmelCase   )

        _lowercase      			=inputs['history_prompt']
        for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()				,  processed_voice_preset.get(lowerCAmelCase				,  np.array([]   )   ).tolist()   )

        # test loading voice preset from npz file
        _lowercase      			=os.path.join(self.tmpdirname				,  'file.npz'   )
        np.savez(lowerCAmelCase				,  **lowerCAmelCase   )
        _lowercase      			=processor(text=self.input_string				,  voice_preset=lowerCAmelCase   )
        _lowercase      			=inputs['history_prompt']

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

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




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=self.get_tokenizer()

        _lowercase      			=BarkProcessor(tokenizer=lowerCAmelCase   )

        _lowercase      			=processor(text=self.input_string   )

        _lowercase      			=tokenizer(
            self.input_string				,  padding='max_length'				,  max_length=256				,  add_special_tokens=lowerCAmelCase				,  return_attention_mask=lowerCAmelCase				,  return_token_type_ids=lowerCAmelCase				,  )

        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


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """distilbert"""
  _a		     =    {
      """hidden_size""": """dim""",
      """num_attention_heads""": """n_heads""",
      """num_hidden_layers""": """n_layers""",
  }




  def __init__(							self				,  lowerCAmelCase=30_522				,  lowerCAmelCase=512				,  lowerCAmelCase=False				,  lowerCAmelCase=6				,  lowerCAmelCase=12				,  lowerCAmelCase=768				,  lowerCAmelCase=4 * 768				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.1				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.02				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.2				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=vocab_size
        _lowercase      			=max_position_embeddings
        _lowercase      			=sinusoidal_pos_embds
        _lowercase      			=n_layers
        _lowercase      			=n_heads
        _lowercase      			=dim
        _lowercase      			=hidden_dim
        _lowercase      			=dropout
        _lowercase      			=attention_dropout
        _lowercase      			=activation
        _lowercase      			=initializer_range
        _lowercase      			=qa_dropout
        _lowercase      			=seq_classif_dropout
        super().__init__(**lowerCAmelCase				,  pad_token_id=lowerCAmelCase   )





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  @property
  def     A__	(							self   )    ->					Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






        if self.task == "multiple-choice":
              _lowercase      			={0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
              _lowercase      			={0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]   )


