





import copy
from typing import Dict

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


__UpperCAmelCase			:  int										=				logging.get_logger(__name__)







class        __snake_case			(				UpperCAmelCase__     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""semantic"""




       def __init__(    self					:       Optional[Any]				,							A					:       Union[str, Any]=10_000				,							A					:       str=True				,							A					:       Optional[int]=768				,							A					:       List[str]=False				,							A					:       int=False				,							A					:       Dict=False				,							A					:       Optional[Any]=False				,							A					:       Union[str, Any]=0.7				,							A					:       Union[str, Any]=True				,							A					:       List[str]=10_048				,							A					:       Tuple=129_595				,							A					:       int=129_599				,							A					:       Any=10_000				,							A					:       str=256				,							A					:       Union[str, Any]=49.9				,							**A					:       List[Any]				,							):
         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				,							)

         __snake_case:		str					    =		text_encoding_offset
         __snake_case:		str					    =		text_pad_token
         __snake_case:		List[str]					    =		eos_token_id
         __snake_case:		Tuple					    =		semantic_infer_token
         __snake_case:		int					    =		semantic_vocab_size
         __snake_case:		Optional[int]					    =		max_input_semantic_length
         __snake_case:		Dict					    =		semantic_rate_hz







class        __snake_case			(				UpperCAmelCase__     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""coarse_acoustics"""




       def __init__(    self					:       Union[str, Any]				,							A					:       Union[str, Any]=True				,							A					:       List[str]=False				,							A					:       List[Any]=False				,							A					:       Optional[Any]=False				,							A					:       Dict=False				,							A					:       Optional[int]=0.7				,							A					:       Dict=True				,							A					:       int=12_048				,							A					:       Any=75				,							A					:       List[str]=2				,							A					:       Optional[Any]=12_050				,							A					:       Dict=256				,							A					:       int = 630				,							A					:       Optional[int] = 60				,							**A					:       int				,							):
         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				,							)

         __snake_case:		List[str]					    =		coarse_semantic_pad_token
         __snake_case:		Union[str, Any]					    =		coarse_rate_hz
         __snake_case:		Tuple					    =		n_coarse_codebooks
         __snake_case:		Dict					    =		coarse_infer_token
         __snake_case:		Union[str, Any]					    =		max_coarse_input_length
         __snake_case:		int					    =		max_coarse_history
         __snake_case:		Any					    =		sliding_window_len







class        __snake_case			(				UpperCAmelCase__     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""fine_acoustics"""




       def __init__(    self					:       List[str]				,							A					:       Any=0.5				,							A					:       Union[str, Any]=512				,							A					:       Union[str, Any]=1_024				,							A					:       List[Any]=8				,							**A					:       List[str]				,							):
         super().__init__(temperature=A						)

         __snake_case:		Optional[Any]					    =		max_fine_history_length
         __snake_case:		Optional[int]					    =		max_fine_input_length
         __snake_case:		List[str]					    =		n_fine_codebooks







class        __snake_case			(				UpperCAmelCase__     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""bark"""
       lowerCAmelCase__ 						=	True




       def __init__(    self					:       int				,							A					:       str = None				,							A					:       Optional[Any] = None				,							A					:       Tuple = None				,							A					:       int=24_000				,							A					:       Optional[Any]=1_024				,							**A					:       List[Any]				,							):
         if semantic_config is None:
           __snake_case:		List[Any]					    =		{}
           logger.info("""semantic_config is None. initializing the semantic model with default values."""						)

         if coarse_acoustics_config is None:
           __snake_case:		Optional[int]					    =		{}
           logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""						)

         if fine_acoustics_config is None:
           __snake_case:		List[str]					    =		{}
           logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""						)

         __snake_case:		int					    =		BarkSemanticGenerationConfig(**A						)
         __snake_case:		Tuple					    =		BarkCoarseGenerationConfig(**A						)
         __snake_case:		List[Any]					    =		BarkFineGenerationConfig(**A						)

         __snake_case:		Optional[Any]					    =		sample_rate
         __snake_case:		Tuple					    =		codebook_size




       @classmethod
       def 				UpperCAmelCase__					(    cls					:       Any				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Union[str, Any]				,							**A					:       List[str]				,							):
         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 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		int					    =		copy.deepcopy(self.__dict__						)

         __snake_case:		Optional[Any]					    =		self.semantic_config.to_dict()
         __snake_case:		Union[str, Any]					    =		self.coarse_acoustics_config.to_dict()
         __snake_case:		str					    =		self.fine_acoustics_config.to_dict()

         __snake_case:		Any					    =		self.__class__.model_type
         return output








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 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						def __init__(  self  , A  , A=7  , A=3  , A=18  , A=30  , A=4_00  , A=True  , A=None  , A=True  , A=None  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , )  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__		:str 		=    crop_size if crop_size is not None else {'height': 18, 'width': 18}

									UpperCAmelCase__		:Dict 		=    parent
									UpperCAmelCase__		:Optional[Any] 		=    batch_size
									UpperCAmelCase__		:Optional[Any] 		=    num_channels
									UpperCAmelCase__		:str 		=    image_size
									UpperCAmelCase__		:Optional[Any] 		=    min_resolution
									UpperCAmelCase__		:Optional[int] 		=    max_resolution
									UpperCAmelCase__		:Dict 		=    do_resize
									UpperCAmelCase__		:Any 		=    size
									UpperCAmelCase__		:Any 		=    do_center_crop
									UpperCAmelCase__		:Optional[int] 		=    crop_size
									UpperCAmelCase__		:Dict 		=    do_normalize
									UpperCAmelCase__		:List[Any] 		=    image_mean
									UpperCAmelCase__		:Tuple 		=    image_std


						def    A__       (  self	)  ->str:
									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 	UpperCamelCase__       (  UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Tuple												=			DeiTImageProcessor if is_vision_available() else None
						__a     :    Tuple												=			True


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:int 		=    DeiTImageProcessingTester(self	)


						@property
						def    A__       (  self	)  ->List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:Tuple 		=    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    A__       (  self	)  ->Tuple:
									UpperCAmelCase__		:Union[str, Any] 		=    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}	)

									UpperCAmelCase__		:List[Any] 		=    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    A__       (  self	)  ->List[str]:
									pass


						def    A__       (  self	)  ->List[str]:
									# Initialize image_processing
									UpperCAmelCase__		:int 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									UpperCAmelCase__		:Tuple 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , Image.Image	)

									# Test not batched input
									UpperCAmelCase__		:int 		=    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
									UpperCAmelCase__		:List[str] 		=    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    A__       (  self	)  ->str:
									# Initialize image_processing
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		:Dict 		=    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
									UpperCAmelCase__		:List[str] 		=    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
									UpperCAmelCase__		:Optional[Any] 		=    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    A__       (  self	)  ->Tuple:
									# Initialize image_processing
									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		:str 		=    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
									UpperCAmelCase__		:List[str] 		=    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
									UpperCAmelCase__		:Optional[int] 		=    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'],
									    )  , )





