
'''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					):






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	'''ylacombe/bark-small'''
											lowercase_     :					List[str]													=	tempfile.mkdtemp()
											lowercase_     :					List[str]													=	'''en_speaker_1'''
											lowercase_     :					Tuple													=	'''This is a test string'''
											lowercase_     :					List[str]													=	'''speaker_embeddings_path.json'''
											lowercase_     :					str													=	'''speaker_embeddings'''






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											shutil.rmtree(self.tmpdirname		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.get_tokenizer()

											lowercase_     :					List[str]													=	BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE		)

											processor.save_pretrained(self.tmpdirname		)
											lowercase_     :					str													=	BarkProcessor.from_pretrained(self.tmpdirname		)

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






					@slow
					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	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_     :					Optional[int]													=	self.get_tokenizer(bos_token='''(BOS)'''    ,							eos_token='''(EOS)'''		)

											lowercase_     :					Optional[int]													=	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      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint    ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,							)

											lowercase_     :					Optional[int]													=	35
											lowercase_     :					int													=	2
											lowercase_     :					Dict													=	8

											lowercase_     :					Optional[Any]													=	{
											    '''semantic_prompt''': np.ones(__SCREAMING_SNAKE_CASE		),
											    '''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_     :					Tuple													=	processor(text=self.input_string    ,							voice_preset=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	inputs['''history_prompt''']
											for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()    ,							processed_voice_preset.get(__SCREAMING_SNAKE_CASE    ,							np.array([]		)		).tolist()		)

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

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

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.get_tokenizer()

											lowercase_     :					Any													=	BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	processor(text=self.input_string		)

											lowercase_     :					int													=	tokenizer(
											    self.input_string    ,							padding='''max_length'''    ,							max_length=2_56    ,							add_special_tokens=__SCREAMING_SNAKE_CASE    ,							return_attention_mask=__SCREAMING_SNAKE_CASE    ,							return_token_type_ids=__SCREAMING_SNAKE_CASE    ,							)

											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			:  List[Any]   							=	{
    "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			:  int   							=	[
							    "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			:  List[str]   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



