





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


lowerCAmelCase							:       str					 =	logging.get_logger(__name__)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   Any					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   b.T
						__SCREAMING_SNAKE_CASE:				str           =   np.sum(np.square(_A					)       ,			axis=1					)
						__SCREAMING_SNAKE_CASE:				Dict           =   np.sum(np.square(_A					)       ,			axis=0					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   np.matmul(_A       ,			_A					)
						__SCREAMING_SNAKE_CASE:				str           =   aa[:, None] - 2 * ab + ba[None, :]
						return d






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   Dict					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   x.reshape(-1       ,			3					)
						__SCREAMING_SNAKE_CASE:				str           =   squared_euclidean_distance(_A       ,			_A					)
						return np.argmin(_A       ,			axis=1					)





class 				a			(      lowerCAmelCase__			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     ['''pixel_values''']






				def __init__(  self			, _lowerCAmelCase = None			, _lowerCAmelCase = True			, _lowerCAmelCase = None			, _lowerCAmelCase = PILImageResampling.BILINEAR			, _lowerCAmelCase = True			, _lowerCAmelCase = True			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**__lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   size if size is not None else {'''height''': 256, '''width''': 256}
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   get_size_dict(__lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   np.array(__lowerCAmelCase					) if clusters is not None else None
										__SCREAMING_SNAKE_CASE:				List[Any]           =   do_resize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   size
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   resample
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   do_normalize
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   do_color_quantize






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = PILImageResampling.BILINEAR			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   get_size_dict(__lowerCAmelCase					)
										if "height" not in size or "width" not in size:
																raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""					)
										return resize(
										    __lowerCAmelCase			, size=(size['''height'''], size['''width'''])			, resample=__lowerCAmelCase			, data_format=__lowerCAmelCase			, **__lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   rescale(image=__lowerCAmelCase			, scale=1 / 127.5			, data_format=__lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   image - 1
										return image






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = ChannelDimension.FIRST			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   do_resize if do_resize is not None else self.do_resize
										__SCREAMING_SNAKE_CASE:				Tuple           =   size if size is not None else self.size
										__SCREAMING_SNAKE_CASE:				str           =   get_size_dict(__lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   resample if resample is not None else self.resample
										__SCREAMING_SNAKE_CASE:				int           =   do_normalize if do_normalize is not None else self.do_normalize
										__SCREAMING_SNAKE_CASE:				str           =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   clusters if clusters is not None else self.clusters
										__SCREAMING_SNAKE_CASE:				Dict           =   np.array(__lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   make_list_of_images(__lowerCAmelCase					)

										if not valid_images(__lowerCAmelCase					):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

										if do_resize and size is None or resample is None:
																raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

										if do_color_quantize and clusters is None:
																raise ValueError('''Clusters must be specified if do_color_quantize is True.'''					)

										# All transformations expect numpy arrays.
										__SCREAMING_SNAKE_CASE:				int           =   [to_numpy_array(__lowerCAmelCase					) for image in images]

										if do_resize:
																__SCREAMING_SNAKE_CASE:				int           =   [self.resize(image=__lowerCAmelCase			, size=__lowerCAmelCase			, resample=__lowerCAmelCase					) for image in images]

										if do_normalize:
																__SCREAMING_SNAKE_CASE:				int           =   [self.normalize(image=__lowerCAmelCase					) for image in images]

										if do_color_quantize:
																__SCREAMING_SNAKE_CASE:				Dict           =   [to_channel_dimension_format(__lowerCAmelCase			, ChannelDimension.LAST					) for image in images]
																# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   np.array(__lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				str           =   color_quantize(__lowerCAmelCase			, __lowerCAmelCase					).reshape(images.shape[:-1]					)

																# flatten to (batch_size, height*width)
																__SCREAMING_SNAKE_CASE:				Any           =   images.shape[0]
																__SCREAMING_SNAKE_CASE:				Dict           =   images.reshape(__lowerCAmelCase			, -1					)

																# We need to convert back to a list of images to keep consistent behaviour across processors.
																__SCREAMING_SNAKE_CASE:				str           =   list(__lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				int           =   [to_channel_dimension_format(__lowerCAmelCase			, __lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				List[Any]           =   {'''input_ids''': images}
										return BatchFeature(data=__lowerCAmelCase			, tensor_type=__lowerCAmelCase					)




'''simple docstring'''




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate






@dataclass(frozen=lowerCAmelCase__      )
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			field(default='''automatic-speech-recognition'''					,		metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''audio''': Audio()}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''transcription''': Value('''string'''      )}      )
			UpperCAmelCase_     :	str						=			"audio"
			UpperCAmelCase_     :	str						=			"transcription"



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if self.audio_column not in features:
							raise ValueError(f"Column {self.audio_column} is not present in features.")
					if not isinstance(features[self.audio_column]			,       __lowerCAmelCase):
							raise ValueError(f"Column {self.audio_column} is not an Audio type.")
					lowerCAmelCase     =    copy.deepcopy(self)
					lowerCAmelCase     =    self.input_schema.copy()
					lowerCAmelCase     =    features[self.audio_column]
					lowerCAmelCase     =    input_schema
					return task_template







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return {self.audio_column: "audio", self.transcription_column: "transcription"}




