


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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase      						=    logging.get_logger(__name__)







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Any					=						['pixel_values']




			def __init__(							self ,  snake_case = True ,  snake_case = None ,  snake_case = PILImageResampling.BILINEAR ,  snake_case = True ,  snake_case = 1 / 255 ,  snake_case = True ,  snake_case = None ,  snake_case = None ,  **snake_case ,  ):

						'''simple docstring'''


						super().__init__(**snake_case       )
						UpperCamelCase__   =    size if size is not None else {"height": 224, "width": 224}
						UpperCamelCase__   =    get_size_dict(snake_case       )
						UpperCamelCase__   =    do_resize
						UpperCamelCase__   =    do_rescale
						UpperCamelCase__   =    do_normalize
						UpperCamelCase__   =    size
						UpperCamelCase__   =    resample
						UpperCamelCase__   =    rescale_factor
						UpperCamelCase__   =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCamelCase__   =    image_std if image_std is not None else IMAGENET_STANDARD_STD




			def 			snake_case__      (							self ,  snake_case ,  snake_case ,  snake_case = PILImageResampling.BILINEAR ,  snake_case = None ,  **snake_case ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    get_size_dict(snake_case       )
						if "height" not in size or "width" not in size:
									raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''       )
						return resize(
						    snake_case ,  size=(size["height"], size["width"]) ,  resample=snake_case ,  data_format=snake_case ,  **snake_case       )




			def 			snake_case__      (							self ,  snake_case ,  snake_case ,  snake_case = None ,  **snake_case       ):

						'''simple docstring'''


						return rescale(snake_case ,  scale=snake_case ,  data_format=snake_case ,  **snake_case       )




			def 			snake_case__      (							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case = None ,  **snake_case ,  ):

						'''simple docstring'''


						return normalize(snake_case ,  mean=snake_case ,  std=snake_case ,  data_format=snake_case ,  **snake_case       )







			def 			snake_case__      (							self ,  snake_case ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = ChannelDimension.FIRST ,  **snake_case ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    do_resize if do_resize is not None else self.do_resize
						UpperCamelCase__   =    do_rescale if do_rescale is not None else self.do_rescale
						UpperCamelCase__   =    do_normalize if do_normalize is not None else self.do_normalize
						UpperCamelCase__   =    resample if resample is not None else self.resample
						UpperCamelCase__   =    rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCamelCase__   =    image_mean if image_mean is not None else self.image_mean
						UpperCamelCase__   =    image_std if image_std is not None else self.image_std

						UpperCamelCase__   =    size if size is not None else self.size
						UpperCamelCase__   =    get_size_dict(snake_case       )

						UpperCamelCase__   =    make_list_of_images(snake_case       )

						if not valid_images(snake_case       ):
									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:
									raise ValueError("Size must be specified if do_resize is True."       )

						if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True."       )

						# All transformations expect numpy arrays.
						UpperCamelCase__   =    [to_numpy_array(snake_case       ) for image in images]

						if do_resize:
									UpperCamelCase__   =    [self.resize(image=snake_case ,  size=snake_case ,  resample=snake_case       ) for image in images]

						if do_rescale:
									UpperCamelCase__   =    [self.rescale(image=snake_case ,  scale=snake_case       ) for image in images]

						if do_normalize:
									UpperCamelCase__   =    [self.normalize(image=snake_case ,  mean=snake_case ,  std=snake_case       ) for image in images]

						UpperCamelCase__   =    [to_channel_dimension_format(snake_case ,  snake_case       ) for image in images]

						UpperCamelCase__   =    {"pixel_values": images}
						return BatchFeature(data=snake_case ,  tensor_type=snake_case       )






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


__UpperCamelCase      						=    [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

__UpperCamelCase      						=    get_logger(__name__)


__UpperCamelCase      						=    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}
def     UpperCamelCase_(			_A				:dict					)->						Optional[TaskTemplate]:
			UpperCamelCase__   =    task_template_dict.get("task"					)
			if task_name is None:
						logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_A					)}'''					)
						return None
			UpperCamelCase__   =    NAMEaTEMPLATE.get(_A					)
			return template.from_dict(_A					)


