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       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       str					,	**UpperCamelCase__			:       int		):





												'''simple docstring'''





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


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												shutil.rmtree(self.tmpdirname		)


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					int           =							BarkProcessor(tokenizer=UpperCamelCase__		)

												processor.save_pretrained(self.tmpdirname		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							BarkProcessor.from_pretrained(self.tmpdirname		)

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


						@slow
						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							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							:					Dict           =							self.get_tokenizer(bos_token='''(BOS)'''					,	eos_token='''(EOS)'''		)

												SCREAMING_SNAKE_CASE							:					Union[str, 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  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint					,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,	)

												SCREAMING_SNAKE_CASE							:					Tuple           =							35
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							2
												SCREAMING_SNAKE_CASE							:					int           =							8

												SCREAMING_SNAKE_CASE							:					List[str]           =							{
												    '''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
												SCREAMING_SNAKE_CASE							:					Tuple           =							processor(text=self.input_string					,	voice_preset=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							os.path.join(self.tmpdirname					,	'''file.npz'''		)
												np.savez(UpperCamelCase__					,	**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							processor(text=self.input_string					,	voice_preset=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							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
												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor(text=self.input_string					,	voice_preset=self.voice_preset		)



						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					str           =							BarkProcessor(tokenizer=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					str           =							processor(text=self.input_string		)

												SCREAMING_SNAKE_CASE							:					Any           =							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()		)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Optional[Any]    		=  ['MaskFormerFeatureExtractor']
					__UpperCamelCase	: List[Any]    		=  ['MaskFormerImageProcessor']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Optional[int]    		=  [
					    'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MaskFormerForInstanceSegmentation',
					    'MaskFormerModel',
					    'MaskFormerPreTrainedModel',
					]
					__UpperCamelCase	: Union[str, Any]    		=  [
					    'MaskFormerSwinBackbone',
					    'MaskFormerSwinModel',
					    'MaskFormerSwinPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
					from .configuration_maskformer_swin import MaskFormerSwinConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_maskformer import MaskFormerFeatureExtractor
										from .image_processing_maskformer import MaskFormerImageProcessor
					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskformer import (
										    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskFormerForInstanceSegmentation,
										    MaskFormerModel,
										    MaskFormerPreTrainedModel,
										)
										from .modeling_maskformer_swin import (
										    MaskFormerSwinBackbone,
										    MaskFormerSwinModel,
										    MaskFormerSwinPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase	: List[str]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure)





