





'''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 			SCREAMING_SNAKE_CASE__	(					unittest.TestCase						):







					"""simple docstring"""






					def __init__(					self    :	Optional[Any]						,     UpperCAmelCase_    :	str						,     UpperCAmelCase_    :	Optional[int]=13						,     UpperCAmelCase_    :	Optional[int]=3						,     UpperCAmelCase_    :	Any=18						,     UpperCAmelCase_    :	List[Any]=30						,     UpperCAmelCase_    :	Union[str, Any]=400						,     UpperCAmelCase_    :	Optional[Any]=True						,     UpperCAmelCase_    :	Optional[int]=None						,     UpperCAmelCase_    :	Any=True						,     UpperCAmelCase_    :	Dict=[0.5, 0.5, 0.5]						,     UpperCAmelCase_    :	Optional[Any]=[0.5, 0.5, 0.5]						,     ):







							"""simple docstring"""
							__UpperCAmelCase     :				str					   =			size if size is not None else {"height": 18, "width": 18}
							__UpperCAmelCase     :				str					   =			parent
							__UpperCAmelCase     :				int					   =			batch_size
							__UpperCAmelCase     :				Optional[int]					   =			num_channels
							__UpperCAmelCase     :				Tuple					   =			image_size
							__UpperCAmelCase     :				Optional[Any]					   =			min_resolution
							__UpperCAmelCase     :				List[Any]					   =			max_resolution
							__UpperCAmelCase     :				Tuple					   =			do_resize
							__UpperCAmelCase     :				List[Any]					   =			size
							__UpperCAmelCase     :				Tuple					   =			do_normalize
							__UpperCAmelCase     :				Dict					   =			image_mean
							__UpperCAmelCase     :				str					   =			image_std






					def 						lowerCamelCase_					(					self    :	Any						):







							"""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,
							}






@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__	(					A_				,unittest.TestCase						):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE				=     EfficientNetImageProcessor if is_vision_available() else None
					def 						lowerCamelCase_					(					self    :	int						):







							"""simple docstring"""
							__UpperCAmelCase     :				int					   =			EfficientNetImageProcessorTester(self						)
					@property
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						):







							"""simple docstring"""
							return self.image_processor_tester.prepare_image_processor_dict()
					def 						lowerCamelCase_					(					self    :	int						):







							"""simple docstring"""
							__UpperCAmelCase     :				int					   =			self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(_A						,     "image_mean"						)						)
							self.assertTrue(hasattr(_A						,     "image_std"						)						)
							self.assertTrue(hasattr(_A						,     "do_normalize"						)						)
							self.assertTrue(hasattr(_A						,     "do_resize"						)						)
							self.assertTrue(hasattr(_A						,     "size"						)						)
					def 						lowerCamelCase_					(					self    :	Any						):







							"""simple docstring"""
							__UpperCAmelCase     :				int					   =			self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size						,     {"height": 18, "width": 18}						)

							__UpperCAmelCase     :				Union[str, Any]					   =			self.image_processing_class.from_dict(self.image_processor_dict						,     size=42						)
							self.assertEqual(image_processor.size						,     {"height": 42, "width": 42}						)
					def 						lowerCamelCase_					(					self    :	Tuple						):







							"""simple docstring"""
							# Initialize image_processing
							__UpperCAmelCase     :				Optional[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     :				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.size["height"],
							        self.image_processor_tester.size["width"],
							    )						,     )

							# Test batched
							__UpperCAmelCase     :				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.size["height"],
							        self.image_processor_tester.size["width"],
							    )						,     )
					def 						lowerCamelCase_					(					self    :	Optional[int]						):







							"""simple docstring"""
							# Initialize image_processing
							__UpperCAmelCase     :				Any					   =			self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							__UpperCAmelCase     :				int					   =			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     :				Any					   =			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
							__UpperCAmelCase     :				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.size["height"],
							        self.image_processor_tester.size["width"],
							    )						,     )






					def 						lowerCamelCase_					(					self    :	Dict						):







							"""simple docstring"""
							# Initialize image_processing
							__UpperCAmelCase     :				Optional[Any]					   =			self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							__UpperCAmelCase     :				Optional[Any]					   =			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     :				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.size["height"],
							        self.image_processor_tester.size["width"],
							    )						,     )

							# Test batched
							__UpperCAmelCase     :				Tuple					   =			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.size["height"],
							        self.image_processor_tester.size["width"],
							    )						,     )






import copy
from typing import Dict

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


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Tuple           =		"semantic"





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





     def __init__(self      :    Any				,    _A      :    Union[str, Any]=True				,    _A      :    List[Any]=False				,    _A      :    Any=False				,    _A      :    Union[str, Any]=False				,    _A      :    Optional[Any]=False				,    _A      :    str=0.7				,    _A      :    int=True				,    _A      :    List[str]=1_2_0_4_8				,    _A      :    Optional[int]=7_5				,    _A      :    Any=2				,    _A      :    Optional[Any]=1_2_0_5_0				,    _A      :    Tuple=2_5_6				,    _A      :    int = 6_3_0				,    _A      :    int = 6_0				,    **_A      :    int				,    )						->						Union[str, Any]:
         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      =					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      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		"fine_acoustics"





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

         snake_case      =					max_fine_history_length
         snake_case      =					max_fine_input_length
         snake_case      =					n_fine_codebooks







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		"bark"
     UpperCAmelCase__				:							Union[str, Any]           =		True





     def __init__(self      :    Union[str, Any]				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Optional[int]=2_4_0_0_0				,    _A      :    Any=1_0_2_4				,    **_A      :    List[str]				,    )						->						Optional[Any]:
         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(**_A   )
         snake_case      =					BarkCoarseGenerationConfig(**_A   )
         snake_case      =					BarkFineGenerationConfig(**_A   )

         snake_case      =					sample_rate
         snake_case      =					codebook_size





     @classmethod
     def 		UpperCAmelCase(cls      :    List[Any]				,    _A      :    BarkSemanticGenerationConfig				,    _A      :    BarkCoarseGenerationConfig				,    _A      :    BarkFineGenerationConfig				,    **_A      :    Optional[int]				,    )						->						Union[str, Any]:
         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      :    List[str]   )						->						int:
         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

