



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
		import PIL

snake_case    :						Optional[int]  =						logging.get_logger(__name__)
def 	snake_case__       (					__lowercase       )		->		List[List[ImageInput]]:







						"""simple docstring"""

						if isinstance(__lowercase							,     (list, tuple)       ) and isinstance(videos[0]							,     (list, tuple)       ) and is_valid_image(videos[0][0]       ):
												return videos

						elif isinstance(__lowercase							,     (list, tuple)       ) and is_valid_image(videos[0]       ):
												return [videos]

						elif is_valid_image(__lowercase       ):
												return [[videos]]

						raise ValueError(F'Could not make batched video from {videos}'       )




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Dict           =						['pixel_values']







					def __init__(       self      :     List[str]			,		_A      :     bool = True			,		_A      :     Dict[str, int] = None			,		_A      :     PILImageResampling = PILImageResampling.BILINEAR			,		_A      :     bool = True			,		_A      :     Dict[str, int] = None			,		_A      :     bool = True			,		_A      :     Union[int, float] = 1 / 255			,		_A      :     bool = True			,		_A      :     bool = True			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     Optional[Union[float, List[float]]] = None			,		**_A      :     List[Any]			,		):
											super().__init__(**_A)
											A__	:							Optional[int]						    = size if size is not None else {"shortest_edge": 256}
											A__	:							List[str]						    = get_size_dict(_A			,		default_to_square=_A)
											A__	:							Union[str, Any]						    = crop_size if crop_size is not None else {"height": 224, "width": 224}
											A__	:							Union[str, Any]						    = get_size_dict(_A			,		param_name="crop_size")

											A__	:							str						    = do_resize
											A__	:							Union[str, Any]						    = size
											A__	:							str						    = do_center_crop
											A__	:							Optional[Any]						    = crop_size
											A__	:							Optional[int]						    = resample
											A__	:							Union[str, Any]						    = do_rescale
											A__	:							Any						    = rescale_factor
											A__	:							Optional[int]						    = offset
											A__	:							List[Any]						    = do_normalize
											A__	:							str						    = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											A__	:							str						    = image_std if image_std is not None else IMAGENET_STANDARD_STD







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     np.ndarray			,		_A      :     Dict[str, int]			,		_A      :     PILImageResampling = PILImageResampling.BILINEAR			,		_A      :     Optional[Union[str, ChannelDimension]] = None			,		**_A      :     Dict			,		):
											A__	:							str						    = get_size_dict(_A			,		default_to_square=_A)
											if "shortest_edge" in size:
																	A__	:							Dict						    = get_resize_output_image_size(_A			,		size["shortest_edge"]			,		default_to_square=_A)
											elif "height" in size and "width" in size:
																	A__	:							Tuple						    = (size["height"], size["width"])
											else:
																	raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
											return resize(_A			,		size=_A			,		resample=_A			,		data_format=_A			,		**_A)







					def  _lowercase			(       self      :     Dict			,		_A      :     np.ndarray			,		_A      :     Dict[str, int]			,		_A      :     Optional[Union[str, ChannelDimension]] = None			,		**_A      :     List[str]			,		):
											A__	:							Tuple						    = get_size_dict(_A)
											if "height" not in size or "width" not in size:
																	raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}')
											return center_crop(_A			,		size=(size["height"], size["width"])			,		data_format=_A			,		**_A)







					def  _lowercase			(       self      :     str			,		_A      :     np.ndarray			,		_A      :     Union[int, float]			,		_A      :     bool = True			,		_A      :     Optional[Union[str, ChannelDimension]] = None			,		**_A      :     Any			,		):
											A__	:							List[Any]						    = image.astype(np.floataa)
											if offset:
																	A__	:							Tuple						    = image - (scale / 2)
											return rescale(_A			,		scale=_A			,		data_format=_A			,		**_A)







					def  _lowercase			(       self      :     Tuple			,		_A      :     np.ndarray			,		_A      :     Union[float, List[float]]			,		_A      :     Union[float, List[float]]			,		_A      :     Optional[Union[str, ChannelDimension]] = None			,		**_A      :     List[str]			,		):
											return normalize(_A			,		mean=_A			,		std=_A			,		data_format=_A			,		**_A)







					def  _lowercase			(       self      :     str			,		_A      :     ImageInput			,		_A      :     bool = None			,		_A      :     Dict[str, int] = None			,		_A      :     PILImageResampling = None			,		_A      :     bool = None			,		_A      :     Dict[str, int] = None			,		_A      :     bool = None			,		_A      :     float = None			,		_A      :     bool = None			,		_A      :     bool = None			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     Optional[ChannelDimension] = ChannelDimension.FIRST			,		):
											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_size is None:
																	raise ValueError("Crop size 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.")

											if offset and not do_rescale:
																	raise ValueError("For offset, do_rescale must also be set to True.")

											# All transformations expect numpy arrays.
											A__	:							Optional[Any]						    = to_numpy_array(_A)

											if do_resize:
																	A__	:							Union[str, Any]						    = self.resize(image=_A			,		size=_A			,		resample=_A)

											if do_center_crop:
																	A__	:							Any						    = self.center_crop(_A			,		size=_A)

											if do_rescale:
																	A__	:							List[str]						    = self.rescale(image=_A			,		scale=_A			,		offset=_A)

											if do_normalize:
																	A__	:							Tuple						    = self.normalize(image=_A			,		mean=_A			,		std=_A)

											A__	:							Optional[int]						    = to_channel_dimension_format(_A			,		_A)
											return image







					def  _lowercase			(       self      :     List[Any]			,		_A      :     ImageInput			,		_A      :     bool = None			,		_A      :     Dict[str, int] = None			,		_A      :     PILImageResampling = None			,		_A      :     bool = None			,		_A      :     Dict[str, int] = None			,		_A      :     bool = None			,		_A      :     float = None			,		_A      :     bool = None			,		_A      :     bool = None			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     Optional[Union[str, TensorType]] = None			,		_A      :     ChannelDimension = ChannelDimension.FIRST			,		**_A      :     Union[str, Any]			,		):
											A__	:							List[str]						    = do_resize if do_resize is not None else self.do_resize
											A__	:							Dict						    = resample if resample is not None else self.resample
											A__	:							Optional[int]						    = do_center_crop if do_center_crop is not None else self.do_center_crop
											A__	:							Optional[int]						    = do_rescale if do_rescale is not None else self.do_rescale
											A__	:							Union[str, Any]						    = rescale_factor if rescale_factor is not None else self.rescale_factor
											A__	:							Dict						    = offset if offset is not None else self.offset
											A__	:							Tuple						    = do_normalize if do_normalize is not None else self.do_normalize
											A__	:							Dict						    = image_mean if image_mean is not None else self.image_mean
											A__	:							Tuple						    = image_std if image_std is not None else self.image_std

											A__	:							List[str]						    = size if size is not None else self.size
											A__	:							Optional[int]						    = get_size_dict(_A			,		default_to_square=_A)
											A__	:							Union[str, Any]						    = crop_size if crop_size is not None else self.crop_size
											A__	:							List[str]						    = get_size_dict(_A			,		param_name="crop_size")

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

											A__	:							str						    = make_batched(_A)

											A__	:							List[Any]						    = [
											    [
											        self._preprocess_image(
											            image=_A			,		do_resize=_A			,		size=_A			,		resample=_A			,		do_center_crop=_A			,		crop_size=_A			,		do_rescale=_A			,		rescale_factor=_A			,		offset=_A			,		do_normalize=_A			,		image_mean=_A			,		image_std=_A			,		data_format=_A			,		)
											        for img in video
											    ]
											    for video in videos
											]

											A__	:							Optional[int]						    = {"pixel_values": videos}
											return BatchFeature(data=_A			,		tensor_type=_A)





from collections import OrderedDict
from typing import Mapping

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


snake_case    :						Optional[int]  =						logging.get_logger(__name__)

snake_case    :						Union[str, Any]  =						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Optional[Any]           =						'data2vec-text'







					def __init__(       self      :     Optional[Any]			,		_A      :     List[str]=3_0522			,		_A      :     Optional[int]=768			,		_A      :     Union[str, Any]=12			,		_A      :     str=12			,		_A      :     Optional[int]=3072			,		_A      :     Dict="gelu"			,		_A      :     List[str]=0.1			,		_A      :     Any=0.1			,		_A      :     str=512			,		_A      :     Any=2			,		_A      :     str=0.02			,		_A      :     List[str]=1e-12			,		_A      :     Optional[int]=1			,		_A      :     Any=0			,		_A      :     Any=2			,		_A      :     List[Any]="absolute"			,		_A      :     Tuple=True			,		_A      :     str=None			,		**_A      :     List[Any]			,		):
											super().__init__(pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A			,		**_A)

											A__	:							Optional[int]						    = vocab_size
											A__	:							Tuple						    = hidden_size
											A__	:							str						    = num_hidden_layers
											A__	:							Union[str, Any]						    = num_attention_heads
											A__	:							Dict						    = hidden_act
											A__	:							List[Any]						    = intermediate_size
											A__	:							int						    = hidden_dropout_prob
											A__	:							Dict						    = attention_probs_dropout_prob
											A__	:							Union[str, Any]						    = max_position_embeddings
											A__	:							Tuple						    = type_vocab_size
											A__	:							Union[str, Any]						    = initializer_range
											A__	:							str						    = layer_norm_eps
											A__	:							int						    = position_embedding_type
											A__	:							str						    = use_cache
											A__	:							int						    = classifier_dropout




class 							lowerCAmelCase__  (	UpperCamelCase						):







					@property
					def  _lowercase			(       self      :     Tuple):
											if self.task == "multiple-choice":
																	A__	:							List[str]						    = {0: "batch", 1: "choice", 2: "sequence"}
											else:
																	A__	:							List[str]						    = {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											    ])