



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 							_snake_case     (self					):
						lowerCamelCase__			:   Union[str, Any]										=		'''ylacombe/bark-small'''
						lowerCamelCase__			:   Dict										=		tempfile.mkdtemp()
						lowerCamelCase__			:   int										=		'''en_speaker_1'''
						lowerCamelCase__			:   Dict										=		'''This is a test string'''
						lowerCamelCase__			:   int										=		'''speaker_embeddings_path.json'''
						lowerCamelCase__			:   List[Any]										=		'''speaker_embeddings'''





			def 							_snake_case     (self				,     **__magic_name__					):
						return AutoTokenizer.from_pretrained(self.checkpoint				,     **_a					)





			def 							_snake_case     (self					):
						shutil.rmtree(self.tmpdirname					)





			def 							_snake_case     (self					):
						lowerCamelCase__			:   int										=		self.get_tokenizer()

						lowerCamelCase__			:   Optional[Any]										=		BarkProcessor(tokenizer=_a					)

						processor.save_pretrained(self.tmpdirname					)
						lowerCamelCase__			:   Optional[Any]										=		BarkProcessor.from_pretrained(self.tmpdirname					)

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





			@slow
			def 							_snake_case     (self					):
						lowerCamelCase__			:   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				,     )

						lowerCamelCase__			:   int										=		self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""					)

						lowerCamelCase__			:   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					):
						lowerCamelCase__			:   List[Any]										=		BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )

						lowerCamelCase__			:   Optional[int]										=		35
						lowerCamelCase__			:   str										=		2
						lowerCamelCase__			:   Dict										=		8

						lowerCamelCase__			:   Any										=		{
						    '''semantic_prompt''': np.ones(_a					),
						    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)					),
						    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)					),
						}

						# test providing already loaded voice_preset
						lowerCamelCase__			:   int										=		processor(text=self.input_string				,     voice_preset=_a					)

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

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

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

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





			def 							_snake_case     (self					):
						lowerCamelCase__			:   int										=		self.get_tokenizer()

						lowerCamelCase__			:   Any										=		BarkProcessor(tokenizer=_a					)

						lowerCamelCase__			:   int										=		processor(text=self.input_string					)

						lowerCamelCase__			:   Optional[Any]										=		tokenizer(
						    self.input_string				,     padding="""max_length"""				,     max_length=256				,     add_special_tokens=_a				,     return_attention_mask=_a				,     return_token_type_ids=_a				,     )

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]				,     encoded_processor[key].squeeze().tolist()					)






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
			from transformers import TvltImageProcessor

if is_speech_available():
			from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       int:
												_a      :       Dict   =						'''ZinengTang/tvlt-base'''
												_a      :       List[str]   =						tempfile.mkdtemp()


							def 							__lowercase							(     self			,				**_a			)       ->       int:
												return TvltImageProcessor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			,				**_a			)       ->       List[Any]:
												return TvltFeatureExtractor.from_pretrained(self.checkpoint			,				**_a			)


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


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Union[str, Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Optional[int]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)
												processor.save_pretrained(self.tmpdirname			)
												_a      :       Any   =						TvltProcessor.from_pretrained(self.tmpdirname			)

												self.assertIsInstance(processor.feature_extractor			,				_a			)
												self.assertIsInstance(processor.image_processor			,				_a			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       Union[str, Any]   =						np.ones([1_2_0_0_0]			)

												_a      :       Dict   =						feature_extractor(_a			,				return_tensors='''np'''			)
												_a      :       Tuple   =						processor(audio=_a			,				return_tensors='''np'''			)

												for key in audio_dict.keys():
																	self.assertAlmostEqual(audio_dict[key].sum()			,				input_processor[key].sum()			,				delta=1e-2			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Optional[Any]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[Any]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						image_processor(_a			,				return_tensors='''np'''			)
												_a      :       Optional[int]   =						processor(images=_a			,				return_tensors='''np'''			)

												for key in image_dict.keys():
																	self.assertAlmostEqual(image_dict[key].sum()			,				input_processor[key].sum()			,				delta=1e-2			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Any   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[str]   =						np.ones([1_2_0_0_0]			)
												_a      :       Optional[int]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						processor(audio=_a			,				images=_a			)

												self.assertListEqual(list(inputs.keys()			)			,				['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']			)

												# test if it raises when no input is passed
												with pytest.raises(_a			):
																	processor()




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												self.assertListEqual(
												    processor.model_input_names			,				image_processor.model_input_names + feature_extractor.model_input_names			,				msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''			,				)



