
'''simple docstring'''

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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    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


UpperCAmelCase__ :			int											=		logging.get_logger(__name__)






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Union[str, Any]   =  ['pixel_values']





	def __init__(       self      :							Union[str, Any]   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							int = 0.9   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BICUBIC   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							Union[int, float] = 1 / 255   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				**__magic_name__      :							List[Any]   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)
				lowerCAmelCase__										=				size if size is not None else {"shortest_edge": 224}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				lowerCAmelCase__										=				crop_size if crop_size is not None else {"height": 224, "width": 224}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				param_name="crop_size"				)

				lowerCAmelCase__										=				do_resize
				lowerCAmelCase__										=				size
				lowerCAmelCase__										=				crop_pct
				lowerCAmelCase__										=				resample
				lowerCAmelCase__										=				do_center_crop
				lowerCAmelCase__										=				crop_size
				lowerCAmelCase__										=				do_rescale
				lowerCAmelCase__										=				rescale_factor
				lowerCAmelCase__										=				do_normalize
				lowerCAmelCase__										=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				lowerCAmelCase__										=				image_std if image_std is not None else IMAGENET_DEFAULT_STD





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							Optional[float] = None   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BICUBIC   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Optional[int]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				if "shortest_edge" not in size and ("height" not in size or "width" not in size):
							raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
				if crop_pct is not None:
							if "shortest_edge" in size:
										lowerCAmelCase__										=				int(size["shortest_edge"] / crop_pct				)
							elif "height" in size and "width" in size:
										if size["height"] == size["width"]:
													lowerCAmelCase__										=				int(size["height"] / crop_pct				)
										else:
													lowerCAmelCase__										=				(int(size["height"] / crop_pct				), int(size["width"] / crop_pct				))
							else:
										raise ValueError("Invalid size for resize: {}".format(__magic_name__				)				)

							lowerCAmelCase__										=				get_resize_output_image_size(__magic_name__   ,				size=__magic_name__   ,				default_to_square=__magic_name__				)
				else:
							if "shortest_edge" in size:
										lowerCAmelCase__										=				get_resize_output_image_size(__magic_name__   ,				size=size["shortest_edge"]   ,				default_to_square=__magic_name__				)
							elif "height" in size and "width" in size:
										lowerCAmelCase__										=				(size["height"], size["width"])
							else:
										raise ValueError("Invalid size for resize: {}".format(__magic_name__				)				)

				return resize(__magic_name__   ,				size=__magic_name__   ,				resample=__magic_name__   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__				)
				if "height" not in size or "width" not in size:
							raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""				)
				return center_crop(__magic_name__   ,				size=(size["height"], size["width"])   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Union[int, float]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							str   ,				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Union[float, List[float]]   ,				__magic_name__      :							Union[float, List[float]]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

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







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							ImageInput   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							int = None   ,				__magic_name__      :							PILImageResampling = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							float = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[str, TensorType]] = None   ,				__magic_name__      :							ChannelDimension = ChannelDimension.FIRST   ,				**__magic_name__      :							Any   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				do_resize if do_resize is not None else self.do_resize
				lowerCAmelCase__										=				crop_pct if crop_pct is not None else self.crop_pct
				lowerCAmelCase__										=				resample if resample is not None else self.resample
				lowerCAmelCase__										=				do_center_crop if do_center_crop is not None else self.do_center_crop
				lowerCAmelCase__										=				do_rescale if do_rescale is not None else self.do_rescale
				lowerCAmelCase__										=				rescale_factor if rescale_factor is not None else self.rescale_factor
				lowerCAmelCase__										=				do_normalize if do_normalize is not None else self.do_normalize
				lowerCAmelCase__										=				image_mean if image_mean is not None else self.image_mean
				lowerCAmelCase__										=				image_std if image_std is not None else self.image_std

				lowerCAmelCase__										=				size if size is not None else self.size
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				lowerCAmelCase__										=				crop_size if crop_size is not None else self.crop_size
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				param_name="crop_size"				)

				lowerCAmelCase__										=				make_list_of_images(__magic_name__				)

				if not valid_images(__magic_name__				):
							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_center_crop and crop_pct is None:
							raise ValueError("Crop_pct must be specified if do_center_crop is True."				)

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

				if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("Image mean and std must be specified if do_normalize is True."				)

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

				if do_resize:
							lowerCAmelCase__										=				[self.resize(image=__magic_name__   ,				size=__magic_name__   ,				crop_pct=__magic_name__   ,				resample=__magic_name__				) for image in images]

				if do_center_crop:
							lowerCAmelCase__										=				[self.center_crop(image=__magic_name__   ,				size=__magic_name__				) for image in images]

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

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

				lowerCAmelCase__										=				[to_channel_dimension_format(__magic_name__   ,				__magic_name__				) for image in images]

				lowerCAmelCase__										=				{"pixel_values": images}
				return BatchFeature(data=__magic_name__   ,				tensor_type=__magic_name__				)












'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)



