
'''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 OwlViTImageProcessor






class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def __init__(	self							:       Optional[Any]   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       Union[str, Any]=7   ,     UpperCamelCase__							:       Optional[Any]=3   ,     UpperCamelCase__							:       Union[str, Any]=18   ,     UpperCamelCase__							:       int=30   ,     UpperCamelCase__							:       Union[str, Any]=400   ,     UpperCamelCase__							:       Tuple=True   ,     UpperCamelCase__							:       List[str]=None   ,     UpperCamelCase__							:       Dict=True   ,     UpperCamelCase__							:       Tuple=None   ,     UpperCamelCase__							:       int=True   ,     UpperCamelCase__							:       str=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     UpperCamelCase__							:       Union[str, Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     UpperCamelCase__							:       Optional[Any]=True   ,     )		->       Tuple:






				"""simple docstring"""







				snake_case   :    int			       =		parent
				snake_case   :    str			       =		batch_size
				snake_case   :    int			       =		num_channels
				snake_case   :    Union[str, Any]			       =		image_size
				snake_case   :    str			       =		min_resolution
				snake_case   :    Optional[Any]			       =		max_resolution
				snake_case   :    Tuple			       =		do_resize
				snake_case   :    int			       =		size if size is not None else {'''height''': 18, '''width''': 18}
				snake_case   :    Optional[Any]			       =		do_center_crop
				snake_case   :    Any			       =		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
				snake_case   :    int			       =		do_normalize
				snake_case   :    str			       =		image_mean
				snake_case   :    Tuple			       =		image_std
				snake_case   :    int			       =		do_convert_rgb



		def 	lowerCAmelCase							(	self							:       Union[str, Any]	)		->       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,
				    "do_convert_rgb": self.do_convert_rgb,
				}







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		OwlViTImageProcessor if is_vision_available() else None



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






				"""simple docstring"""







				snake_case   :    int			       =		OwlViTImageProcessingTester(self	)



		@property
		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       Optional[int]:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    int			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_center_crop'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''center_crop'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



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






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.image_processing_class.from_dict(self.image_processor_dict	)
				self.assertEqual(image_processor.size   ,     {'''height''': 18, '''width''': 18}	)
				self.assertEqual(image_processor.crop_size   ,     {'''height''': 18, '''width''': 18}	)

				snake_case   :    Union[str, 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 	lowerCAmelCase							(	self							:       int	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    Tuple			       =		prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase__	)

				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    Tuple			       =		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
				snake_case   :    Optional[int]			       =		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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )



		def 	lowerCAmelCase							(	self							:       str	)		->       str:






				"""simple docstring"""







				snake_case   :    Tuple			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				snake_case   :    Tuple			       =		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
				snake_case   :    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
				snake_case   :    Any			       =		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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )



		def 	lowerCAmelCase							(	self							:       Any	)		->       int:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				snake_case   :    List[Any]			       =		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
				snake_case   :    List[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.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Any			       =		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.crop_size['''height'''],
				        self.image_processor_tester.crop_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

