"""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			:				List[str]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				"ylacombe/bark-small"
									SCREAMING_SNAKE_CASE__		  =				tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE__		  =				"en_speaker_1"
									SCREAMING_SNAKE_CASE__		  =				"This is a test string"
									SCREAMING_SNAKE_CASE__		  =				"speaker_embeddings_path.json"
									SCREAMING_SNAKE_CASE__		  =				"speaker_embeddings"



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , **_snake_case			:				Dict		)							->       Dict:
									return AutoTokenizer.from_pretrained(self.checkpoint   , **_snake_case		)



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



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.get_tokenizer()

									SCREAMING_SNAKE_CASE__		  =				BarkProcessor(tokenizer=_snake_case		)

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

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



					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				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   , )

									SCREAMING_SNAKE_CASE__		  =				self.get_tokenizer(bos_token="(BOS)"   , eos_token="(EOS)"		)

									SCREAMING_SNAKE_CASE__		  =				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			:				Union[str, Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				BarkProcessor.from_pretrained(
									    pretrained_processor_name_or_path=self.checkpoint   , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path   , )

									SCREAMING_SNAKE_CASE__		  =				35
									SCREAMING_SNAKE_CASE__		  =				2
									SCREAMING_SNAKE_CASE__		  =				8

									SCREAMING_SNAKE_CASE__		  =				{
									    "semantic_prompt": np.ones(_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
									SCREAMING_SNAKE_CASE__		  =				processor(text=self.input_string   , voice_preset=_snake_case		)

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

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

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

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



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				self.get_tokenizer()

									SCREAMING_SNAKE_CASE__		  =				BarkProcessor(tokenizer=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				processor(text=self.input_string		)

									SCREAMING_SNAKE_CASE__		  =				tokenizer(
									    self.input_string   , padding="max_length"   , max_length=256   , add_special_tokens=_snake_case   , return_attention_mask=_snake_case   , return_token_type_ids=_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_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




