


'''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    __lowerCAmelCase		(					self		:						str			)  -> str:
			__magic_name__       :    Tuple     	=     '''ylacombe/bark-small'''
			__magic_name__       :    Dict     	=     tempfile.mkdtemp()
			__magic_name__       :    Union[str, Any]     	=     '''en_speaker_1'''
			__magic_name__       :    Optional[Any]     	=     '''This is a test string'''
			__magic_name__       :    Dict     	=     '''speaker_embeddings_path.json'''
			__magic_name__       :    Union[str, Any]     	=     '''speaker_embeddings'''







		def    __lowerCAmelCase		(					self		:						Union[str, Any]  ,     **_A		:						str			)  -> Any:
			return AutoTokenizer.from_pretrained(self.checkpoint  ,     **lowercase_			)







		def    __lowerCAmelCase		(					self		:						Any			)  -> Tuple:
			shutil.rmtree(self.tmpdirname			)







		def    __lowerCAmelCase		(					self		:						Any			)  -> str:
			__magic_name__       :    Dict     	=     self.get_tokenizer()

			__magic_name__       :    Optional[Any]     	=     BarkProcessor(tokenizer=lowercase_			)

			processor.save_pretrained(self.tmpdirname			)
			__magic_name__       :    Any     	=     BarkProcessor.from_pretrained(self.tmpdirname			)

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







		@slow
		def    __lowerCAmelCase		(					self		:						List[Any]			)  -> List[Any]:
			__magic_name__       :    List[Any]     	=     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  ,     )

			__magic_name__       :    List[Any]     	=     self.get_tokenizer(bos_token='(BOS)'  ,     eos_token='(EOS)'			)

			__magic_name__       :    Optional[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    __lowerCAmelCase		(					self		:						int			)  -> List[str]:
			__magic_name__       :    int     	=     BarkProcessor.from_pretrained(
			    pretrained_processor_name_or_path=self.checkpoint  ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,     )

			__magic_name__       :    str     	=     35
			__magic_name__       :    int     	=     2
			__magic_name__       :    Optional[int]     	=     8

			__magic_name__       :    int     	=     {
			    '''semantic_prompt''': np.ones(lowercase_			),
			    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)			),
			    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)			),
			}

			# test providing already loaded voice_preset
			__magic_name__       :    str     	=     processor(text=self.input_string  ,     voice_preset=lowercase_			)

			__magic_name__       :    Optional[Any]     	=     inputs['''history_prompt''']
			for key in voice_preset:
				self.assertListEqual(voice_preset[key].tolist()  ,     processed_voice_preset.get(lowercase_  ,     np.array([]			)			).tolist()			)

			# test loading voice preset from npz file
			__magic_name__       :    List[Any]     	=     os.path.join(self.tmpdirname  ,     'file.npz'			)
			np.savez(lowercase_  ,     **lowercase_			)
			__magic_name__       :    Optional[Any]     	=     processor(text=self.input_string  ,     voice_preset=lowercase_			)
			__magic_name__       :    List[Any]     	=     inputs['''history_prompt''']

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

			# test loading voice preset from the hub
			__magic_name__       :    Union[str, Any]     	=     processor(text=self.input_string  ,     voice_preset=self.voice_preset			)







		def    __lowerCAmelCase		(					self		:						Tuple			)  -> List[Any]:
			__magic_name__       :    Optional[int]     	=     self.get_tokenizer()

			__magic_name__       :    Any     	=     BarkProcessor(tokenizer=lowercase_			)

			__magic_name__       :    Optional[int]     	=     processor(text=self.input_string			)

			__magic_name__       :    List[str]     	=     tokenizer(
			    self.input_string  ,     padding='max_length'  ,     max_length=256  ,     add_special_tokens=lowercase_  ,     return_attention_mask=lowercase_  ,     return_token_type_ids=lowercase_  ,     )

			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__    :  str							    =	{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  Tuple							    =	[
         '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XCLIPModel''',
         '''XCLIPPreTrainedModel''',
         '''XCLIPTextModel''',
         '''XCLIPVisionModel''',
     ]

if TYPE_CHECKING:
     from .configuration_x_clip import (
         XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         XCLIPConfig,
         XCLIPTextConfig,
         XCLIPVisionConfig,
     )
     from .processing_x_clip import XCLIPProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_x_clip import (
              XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              XCLIPModel,
              XCLIPPreTrainedModel,
              XCLIPTextModel,
              XCLIPVisionModel,
          )

else:
     import sys

     lowercase__    :  Tuple							    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





