

"""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 EfficientNetImageProcessor





class 				lowerCamelCase  (unittest.TestCase     ):

			def __init__(   self			:  str      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Dict=1_3      ,			__UpperCAmelCase			:  Dict=3      ,			__UpperCAmelCase			:  Optional[int]=1_8      ,			__UpperCAmelCase			:  List[Any]=3_0      ,			__UpperCAmelCase			:  List[Any]=4_0_0      ,			__UpperCAmelCase			:  Tuple=True      ,			__UpperCAmelCase			:  List[Any]=None      ,			__UpperCAmelCase			:  List[Any]=True      ,			__UpperCAmelCase			:  List[str]=[0.5, 0.5, 0.5]      ,			__UpperCAmelCase			:  int=[0.5, 0.5, 0.5]      ,			)				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= size if size is not None else {"""height""": 1_8, """width""": 1_8}
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= batch_size
						SCREAMING_SNAKE_CASE__									= num_channels
						SCREAMING_SNAKE_CASE__									= image_size
						SCREAMING_SNAKE_CASE__									= min_resolution
						SCREAMING_SNAKE_CASE__									= max_resolution
						SCREAMING_SNAKE_CASE__									= do_resize
						SCREAMING_SNAKE_CASE__									= size
						SCREAMING_SNAKE_CASE__									= do_normalize
						SCREAMING_SNAKE_CASE__									= image_mean
						SCREAMING_SNAKE_CASE__									= image_std





			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      List[str]:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}





@require_torch
@require_vision
class 				lowerCamelCase  (A__     ,unittest.TestCase     ):
			lowerCamelCase__				:						List[str]   						=							EfficientNetImageProcessor if is_vision_available() else None

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= EfficientNetImageProcessorTester(self    )

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Dict:
						return self.image_processor_tester.prepare_image_processor_dict()

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.image_processing_class(**self.image_processor_dict    )
						self.assertTrue(hasattr(__UpperCAmelCase      ,			"""image_mean"""    )    )
						self.assertTrue(hasattr(__UpperCAmelCase      ,			"""image_std"""    )    )
						self.assertTrue(hasattr(__UpperCAmelCase      ,			"""do_normalize"""    )    )
						self.assertTrue(hasattr(__UpperCAmelCase      ,			"""do_resize"""    )    )
						self.assertTrue(hasattr(__UpperCAmelCase      ,			"""size"""    )    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= self.image_processing_class.from_dict(self.image_processor_dict    )
						self.assertEqual(image_processor.size      ,			{"""height""": 1_8, """width""": 1_8}    )

						SCREAMING_SNAKE_CASE__									= self.image_processing_class.from_dict(self.image_processor_dict      ,			size=4_2    )
						self.assertEqual(image_processor.size      ,			{"""height""": 4_2, """width""": 4_2}    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Any:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__									= self.image_processing_class(**self.image_processor_dict    )
						# create random PIL images
						SCREAMING_SNAKE_CASE__									= prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=__UpperCAmelCase    )
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase      ,			Image.Image    )

						# Test not batched input
						SCREAMING_SNAKE_CASE__									= 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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    )      ,			)

						# Test batched
						SCREAMING_SNAKE_CASE__									= image_processing(__UpperCAmelCase      ,			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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    )      ,			)

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Optional[Any]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__									= self.image_processing_class(**self.image_processor_dict    )
						# create random numpy tensors
						SCREAMING_SNAKE_CASE__									= prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=__UpperCAmelCase      ,			numpify=__UpperCAmelCase    )
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase      ,			np.ndarray    )

						# Test not batched input
						SCREAMING_SNAKE_CASE__									= 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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    )      ,			)

						# Test batched
						SCREAMING_SNAKE_CASE__									= image_processing(__UpperCAmelCase      ,			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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    )      ,			)





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      str:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__									= self.image_processing_class(**self.image_processor_dict    )
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE__									= prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=__UpperCAmelCase      ,			torchify=__UpperCAmelCase    )
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase      ,			torch.Tensor    )

						# Test not batched input
						SCREAMING_SNAKE_CASE__									= 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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    )      ,			)

						# Test batched
						SCREAMING_SNAKE_CASE__									= image_processing(__UpperCAmelCase      ,			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.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    )      ,			)





"""simple docstring"""




import copy
from typing import Dict

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


A_							:     str     =	logging.get_logger(__name__)





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Any   						=							'semantic'

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

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





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'coarse_acoustics'

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

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





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'fine_acoustics'

			def __init__(   self			:  List[Any]      ,			__UpperCAmelCase			:  Union[str, Any]=0.5      ,			__UpperCAmelCase			:  List[Any]=5_1_2      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_2_4      ,			__UpperCAmelCase			:  Optional[Any]=8      ,			**__UpperCAmelCase			:  Tuple      ,			)				->      Optional[Any]:
						super().__init__(temperature=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= max_fine_history_length
						SCREAMING_SNAKE_CASE__									= max_fine_input_length
						SCREAMING_SNAKE_CASE__									= n_fine_codebooks





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Tuple   						=							'bark'
			lowerCamelCase__				:						Dict   						=							True

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  List[Any]=2_4_0_0_0      ,			__UpperCAmelCase			:  Any=1_0_2_4      ,			**__UpperCAmelCase			:  int      ,			)				->      Any:
						if semantic_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

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

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

						SCREAMING_SNAKE_CASE__									= BarkSemanticGenerationConfig(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= BarkCoarseGenerationConfig(**__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= BarkFineGenerationConfig(**__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= sample_rate
						SCREAMING_SNAKE_CASE__									= codebook_size

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  int      ,			__UpperCAmelCase			:  BarkSemanticGenerationConfig      ,			__UpperCAmelCase			:  BarkCoarseGenerationConfig      ,			__UpperCAmelCase			:  BarkFineGenerationConfig      ,			**__UpperCAmelCase			:  Optional[int]      ,			)				->      Tuple:
						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    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= copy.deepcopy(self.__dict__    )

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

						SCREAMING_SNAKE_CASE__									= self.__class__.model_type
						return output


