



'''simple docstring'''







import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


_UpperCamelCase  						=			logging.get_logger(__name__)

class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	A__		=	'''semantic'''




	def __init__(  self		:  Optional[Any]   ,		__A		:  List[str]=1_0000   ,		__A		:  Optional[int]=True   ,		__A		:  Any=768   ,		__A		:  int=False   ,		__A		:  Optional[int]=False   ,		__A		:  Optional[int]=False   ,		__A		:  List[Any]=False   ,		__A		:  Tuple=0.7   ,		__A		:  Any=True   ,		__A		:  int=1_0048   ,		__A		:  Dict=12_9595   ,		__A		:  Any=12_9599   ,		__A		:  Union[str, Any]=1_0000   ,		__A		:  str=256   ,		__A		:  List[str]=4_9.9   ,		**__A		:  Any   ,		)     ->			Any:




								'''simple docstring'''





								super().__init__(
								    temperature=__A   ,		do_sample=__A   ,		eos_token_id=__A   ,		renormalize_logits=__A   ,		max_new_tokens=__A   ,		output_scores=__A   ,		return_dict_in_generate=__A   ,		output_hidden_states=__A   ,		output_attentions=__A   ,		**__A   ,		)

								lowerCAmelCase__  =   text_encoding_offset
								lowerCAmelCase__  =   text_pad_token
								lowerCAmelCase__  =   eos_token_id
								lowerCAmelCase__  =   semantic_infer_token
								lowerCAmelCase__  =   semantic_vocab_size
								lowerCAmelCase__  =   max_input_semantic_length
								lowerCAmelCase__  =   semantic_rate_hz





class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	A__		=	'''coarse_acoustics'''




	def __init__(  self		:  str   ,		__A		:  Optional[Any]=True   ,		__A		:  List[str]=False   ,		__A		:  Dict=False   ,		__A		:  List[Any]=False   ,		__A		:  Tuple=False   ,		__A		:  List[str]=0.7   ,		__A		:  List[str]=True   ,		__A		:  Optional[Any]=1_2048   ,		__A		:  Any=75   ,		__A		:  Optional[int]=2   ,		__A		:  Tuple=1_2050   ,		__A		:  int=256   ,		__A		:  int = 630   ,		__A		:  int = 60   ,		**__A		:  Optional[int]   ,		)     ->			Any:




								'''simple docstring'''





								super().__init__(
								    temperature=__A   ,		do_sample=__A   ,		renormalize_logits=__A   ,		output_scores=__A   ,		return_dict_in_generate=__A   ,		output_hidden_states=__A   ,		output_attentions=__A   ,		**__A   ,		)

								lowerCAmelCase__  =   coarse_semantic_pad_token
								lowerCAmelCase__  =   coarse_rate_hz
								lowerCAmelCase__  =   n_coarse_codebooks
								lowerCAmelCase__  =   coarse_infer_token
								lowerCAmelCase__  =   max_coarse_input_length
								lowerCAmelCase__  =   max_coarse_history
								lowerCAmelCase__  =   sliding_window_len





class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	A__		=	'''fine_acoustics'''




	def __init__(  self		:  Optional[Any]   ,		__A		:  List[Any]=0.5   ,		__A		:  Tuple=512   ,		__A		:  Optional[Any]=1024   ,		__A		:  List[str]=8   ,		**__A		:  int   ,		)     ->			Optional[int]:




								'''simple docstring'''





								super().__init__(temperature=__A					)

								lowerCAmelCase__  =   max_fine_history_length
								lowerCAmelCase__  =   max_fine_input_length
								lowerCAmelCase__  =   n_fine_codebooks





class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	A__		=	'''bark'''
	A__		=	True




	def __init__(  self		:  Union[str, Any]   ,		__A		:  Dict = None   ,		__A		:  Dict = None   ,		__A		:  Dict = None   ,		__A		:  int=2_4000   ,		__A		:  Union[str, Any]=1024   ,		**__A		:  List[str]   ,		)     ->			str:




								'''simple docstring'''





								if semantic_config is None:
															lowerCAmelCase__  =   {}
															logger.info("""semantic_config is None. initializing the semantic model with default values."""					)

								if coarse_acoustics_config is None:
															lowerCAmelCase__  =   {}
															logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""					)

								if fine_acoustics_config is None:
															lowerCAmelCase__  =   {}
															logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""					)

								lowerCAmelCase__  =   BarkSemanticGenerationConfig(**__A					)
								lowerCAmelCase__  =   BarkCoarseGenerationConfig(**__A					)
								lowerCAmelCase__  =   BarkFineGenerationConfig(**__A					)

								lowerCAmelCase__  =   sample_rate
								lowerCAmelCase__  =   codebook_size




	@classmethod
	def      lowercase__							(  cls		:  Optional[Any]   ,		__A		:  BarkSemanticGenerationConfig   ,		__A		:  BarkCoarseGenerationConfig   ,		__A		:  BarkFineGenerationConfig   ,		**__A		:  Optional[int]   ,		)     ->			str:




								'''simple docstring'''





								return cls(
								    semantic_config=semantic_config.to_dict()   ,		coarse_acoustics_config=coarse_acoustics_config.to_dict()   ,		fine_acoustics_config=fine_acoustics_config.to_dict()   ,		**__A   ,		)




	def      lowercase__							(  self		:  int					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   copy.deepcopy(self.__dict__					)

								lowerCAmelCase__  =   self.semantic_config.to_dict()
								lowerCAmelCase__  =   self.coarse_acoustics_config.to_dict()
								lowerCAmelCase__  =   self.fine_acoustics_config.to_dict()

								lowerCAmelCase__  =   self.__class__.model_type
								return output










'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DeiTImageProcessor

class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Any   ,		__A		:  Tuple=7   ,		__A		:  Dict=3   ,		__A		:  List[str]=18   ,		__A		:  Optional[int]=30   ,		__A		:  int=400   ,		__A		:  Union[str, Any]=True   ,		__A		:  Tuple=None   ,		__A		:  int=True   ,		__A		:  int=None   ,		__A		:  List[str]=True   ,		__A		:  Union[str, Any]=[0.5, 0.5, 0.5]   ,		__A		:  List[Any]=[0.5, 0.5, 0.5]   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   size if size is not None else {"""height""": 20, """width""": 20}
								lowerCAmelCase__  =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   batch_size
								lowerCAmelCase__  =   num_channels
								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   min_resolution
								lowerCAmelCase__  =   max_resolution
								lowerCAmelCase__  =   do_resize
								lowerCAmelCase__  =   size
								lowerCAmelCase__  =   do_center_crop
								lowerCAmelCase__  =   crop_size
								lowerCAmelCase__  =   do_normalize
								lowerCAmelCase__  =   image_mean
								lowerCAmelCase__  =   image_std




	def      lowercase__							(  self		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}





@require_torch
@require_vision
class      lowerCamelCase__     (     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	DeiTImageProcessor if is_vision_available() else None
	A__		=	True




	def      lowercase__							(  self		:  List[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   DeiTImageProcessingTester(self					)




	@property
	def      lowercase__							(  self		:  Any					)     ->			int:




								'''simple docstring'''





								return self.image_processor_tester.prepare_image_processor_dict()




	def      lowercase__							(  self		:  str					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(__A   ,		"""do_resize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""size"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_normalize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_mean"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_std"""					)					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size   ,		{"""height""": 20, """width""": 20}					)
								self.assertEqual(image_processor.crop_size   ,		{"""height""": 18, """width""": 18}					)

								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict   ,		size=42   ,		crop_size=84					)
								self.assertEqual(image_processor.size   ,		{"""height""": 42, """width""": 42}					)
								self.assertEqual(image_processor.crop_size   ,		{"""height""": 84, """width""": 84}					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			List[str]:




								'''simple docstring'''





								pass




	def      lowercase__							(  self		:  Dict					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		Image.Image					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)




	def      lowercase__							(  self		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		numpify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		np.ndarray					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)




	def      lowercase__							(  self		:  List[str]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		torchify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		torch.Tensor					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)





