


'''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 ):






     def  a_					(			self		)       ->    Dict:
          _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  a_					(			self     ,							**__UpperCamelCase		)       ->    Optional[int]:
          return AutoTokenizer.from_pretrained(self.checkpoint     ,							**__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[int]:
          shutil.rmtree(self.tmpdirname		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.get_tokenizer()

          _a						=		BarkProcessor(tokenizer=__UpperCamelCase		)

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

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






     @slow
     def  a_					(			self		)       ->    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  a_					(			self		)       ->    Union[str, Any]:
          _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(__UpperCamelCase		),
              "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=__UpperCamelCase		)

          _a						=		inputs["history_prompt"]
          for key in voice_preset:
               self.assertListEqual(voice_preset[key].tolist()     ,							processed_voice_preset.get(__UpperCamelCase     ,							np.array([]		)		).tolist()		)

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

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

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






     def  a_					(			self		)       ->    int:
          _a						=		self.get_tokenizer()

          _a						=		BarkProcessor(tokenizer=__UpperCamelCase		)

          _a						=		processor(text=self.input_string		)

          _a						=		tokenizer(
              self.input_string     ,							padding="max_length"     ,							max_length=256     ,							add_special_tokens=__UpperCamelCase     ,							return_attention_mask=__UpperCamelCase     ,							return_token_type_ids=__UpperCamelCase     ,							)

          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


lowercase__				     =		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				     =		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

   lowercase__				     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

