




'''simple docstring'''

import copy
from typing import Dict

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


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Optional[Any]    =  """semantic"""






						def __init__(   self						:						str					,   __UpperCamelCase						:						Optional[int]=1_0_0_0_0					,   __UpperCamelCase						:						Any=True					,   __UpperCamelCase						:						Any=7_6_8					,   __UpperCamelCase						:						Union[str, Any]=False					,   __UpperCamelCase						:						Union[str, Any]=False					,   __UpperCamelCase						:						List[str]=False					,   __UpperCamelCase						:						Tuple=False					,   __UpperCamelCase						:						Optional[int]=0.7					,   __UpperCamelCase						:						Union[str, Any]=True					,   __UpperCamelCase						:						str=1_0_0_4_8					,   __UpperCamelCase						:						Dict=1_2_9_5_9_5					,   __UpperCamelCase						:						Dict=1_2_9_5_9_9					,   __UpperCamelCase						:						Tuple=1_0_0_0_0					,   __UpperCamelCase						:						Optional[Any]=2_5_6					,   __UpperCamelCase						:						int=49.9					,   **__UpperCamelCase						:						Optional[int]					,   ):



									'''simple docstring'''






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

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







class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					List[str]    =  """coarse_acoustics"""






						def __init__(   self						:						List[Any]					,   __UpperCamelCase						:						Optional[Any]=True					,   __UpperCamelCase						:						str=False					,   __UpperCamelCase						:						Dict=False					,   __UpperCamelCase						:						Optional[Any]=False					,   __UpperCamelCase						:						Dict=False					,   __UpperCamelCase						:						int=0.7					,   __UpperCamelCase						:						List[Any]=True					,   __UpperCamelCase						:						Any=1_2_0_4_8					,   __UpperCamelCase						:						Dict=7_5					,   __UpperCamelCase						:						Any=2					,   __UpperCamelCase						:						List[str]=1_2_0_5_0					,   __UpperCamelCase						:						List[str]=2_5_6					,   __UpperCamelCase						:						int = 6_3_0					,   __UpperCamelCase						:						int = 6_0					,   **__UpperCamelCase						:						List[Any]					,   ):



									'''simple docstring'''






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

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







class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					List[str]    =  """fine_acoustics"""






						def __init__(   self						:						Optional[int]					,   __UpperCamelCase						:						Optional[int]=0.5					,   __UpperCamelCase						:						Union[str, Any]=5_1_2					,   __UpperCamelCase						:						Optional[Any]=1_0_2_4					,   __UpperCamelCase						:						List[Any]=8					,   **__UpperCamelCase						:						Tuple					,   ):



									'''simple docstring'''






									super().__init__(temperature=__UpperCamelCase       )

									snake_case__          =						max_fine_history_length
									snake_case__          =						max_fine_input_length
									snake_case__          =						n_fine_codebooks







class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					Tuple    =  """bark"""
						UpperCAmelCase_		:					Any    =  True






						def __init__(   self						:						Optional[int]					,   __UpperCamelCase						:						Dict = None					,   __UpperCamelCase						:						Dict = None					,   __UpperCamelCase						:						Dict = None					,   __UpperCamelCase						:						str=2_4_0_0_0					,   __UpperCamelCase						:						Optional[int]=1_0_2_4					,   **__UpperCamelCase						:						str					,   ):



									'''simple docstring'''






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

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

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

									snake_case__          =						BarkSemanticGenerationConfig(**__UpperCamelCase       )
									snake_case__          =						BarkCoarseGenerationConfig(**__UpperCamelCase       )
									snake_case__          =						BarkFineGenerationConfig(**__UpperCamelCase       )

									snake_case__          =						sample_rate
									snake_case__          =						codebook_size






						@classmethod
						def       __lowerCAmelCase(   cls						:						Dict					,   __UpperCamelCase						:						BarkSemanticGenerationConfig					,   __UpperCamelCase						:						BarkCoarseGenerationConfig					,   __UpperCamelCase						:						BarkFineGenerationConfig					,   **__UpperCamelCase						:						Dict					,   ):



									'''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()					,   **__UpperCamelCase					,   )







						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						copy.deepcopy(self.__dict__       )

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

									snake_case__          =						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 ViltImageProcessor

class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		def __init__(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=7	,       __SCREAMING_SNAKE_CASE     :					Dict=3	,       __SCREAMING_SNAKE_CASE     :					Any=18	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=30	,       __SCREAMING_SNAKE_CASE     :					int=400	,       __SCREAMING_SNAKE_CASE     :					int=True	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=None	,       __SCREAMING_SNAKE_CASE     :					Any=2	,       __SCREAMING_SNAKE_CASE     :					Any=True	,       __SCREAMING_SNAKE_CASE     :					int=[0.5, 0.5, 0.5]	,       __SCREAMING_SNAKE_CASE     :					List[str]=[0.5, 0.5, 0.5]	,       ):






					"""simple docstring"""
					_snake_case														=   size if size is not None else {"shortest_edge": 30}
					_snake_case														=   parent
					_snake_case														=   batch_size
					_snake_case														=   num_channels
					_snake_case														=   image_size
					_snake_case														=   min_resolution
					_snake_case														=   max_resolution
					_snake_case														=   do_resize
					_snake_case														=   size
					_snake_case														=   size_divisor
					_snake_case														=   do_normalize
					_snake_case														=   image_mean
					_snake_case														=   image_std



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}







		def 					_a					(      self     :					Any	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[Any]=False ):






					"""simple docstring"""
					if not batched:
								_snake_case														=   self.size["shortest_edge"]
								_snake_case														=   image_inputs[0]
								if isinstance(__SCREAMING_SNAKE_CASE	,       Image.Image ):
											_snake_case							,			_snake_case														=   image.size
								else:
											_snake_case							,			_snake_case														=   image.shape[1], image.shape[2]
								_snake_case														=   size / min(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
								if h < w:
											_snake_case							,			_snake_case														=   size, scale * w
								else:
											_snake_case							,			_snake_case														=   scale * h, size

								_snake_case														=   int((1_333 / 800) * size )
								if max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) > max_size:
											_snake_case														=   max_size / max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   newh * scale
											_snake_case														=   neww * scale

								_snake_case							,			_snake_case														=   int(newh + 0.5 ), int(neww + 0.5 )
								_snake_case							,			_snake_case														=   (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								_snake_case														=   []
								for image in image_inputs:
											_snake_case							,			_snake_case														=   self.get_expected_values([image] )
											expected_values.append((expected_height, expected_width) )
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[0] )[0]
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[1] )[1]

					return expected_height, expected_width

@require_torch
@require_vision
class 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   ViltImageProcessor if is_vision_available() else None



		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   ViltImageProcessingTester(self )



		@property
		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					return self.image_processor_tester.prepare_image_processor_dict()



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_mean" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_std" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_normalize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_resize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size_divisor" ) )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 30} )

					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict	,       size=42 )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 42} )



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					pass



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       Image.Image )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       numpify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       np.ndarray )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )







		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       torchify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       torch.Tensor )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )

