
'''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							)      ->		List[str]:




									'''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__							)      ->		Tuple:




									'''simple docstring'''



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



			def A__			(       self							)      ->		Tuple:




									'''simple docstring'''



									shutil.rmtree(self.tmpdirname							)



			def A__			(       self							)      ->		Union[str, 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							)      ->		Dict:




									'''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							)      ->		Tuple:




									'''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[str]:




									'''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()							)











'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_					:      List[Any]    			=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'VanForImageClassification',
					    'VanModel',
					    'VanPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      List[Any]    			=    _LazyModule(__name__, globals()['__file__'], _import_structure)


