





'''simple docstring'''


import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase :					Any 							=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase :					Optional[Any] 							=[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
 )
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
 rename_keys.append(
     (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
 )
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
 rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
 )
 rename_keys.append(
     (
         F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
         F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
     )
 )
 rename_keys.append(
     (
         F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
         F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
     )
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
 )
 rename_keys.append(
     (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
 )
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
 rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Union[str, Any]     ,__lowerCamelCase : Optional[int]			):
   lowercase_      :Optional[Any]         =			state_dict.pop(__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			val
def 							UpperCAmelCase_  (						__lowerCamelCase : Any			):
   lowercase_      :int         =			OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         lowercase_      :Optional[int]         =			key.replace("backbone.0.body"     ,"backbone.conv_encoder.model"			)
         lowercase_      :int         =			value
      else:
         lowercase_      :Optional[int]         =			value

   return new_state_dict
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]			):
   lowercase_      :Union[str, Any]         =			""

   # first: transformer encoder
   for i in range(6			):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      lowercase_      :List[Any]         =			state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'			)
      lowercase_      :Optional[Any]         =			state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'			)
      # next, add query, keys and values (in that order) to the state dict
      lowercase_      :int         =			in_proj_weight[:2_56, :]
      lowercase_      :List[str]         =			in_proj_bias[:2_56]
      lowercase_      :Optional[Any]         =			in_proj_weight[2_56:5_12, :]
      lowercase_      :Optional[Any]         =			in_proj_bias[2_56:5_12]
      lowercase_      :Any         =			in_proj_weight[-2_56:, :]
      lowercase_      :str         =			in_proj_bias[-2_56:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6			):
      # read in weights + bias of input projection layer of self-attention
      lowercase_      :int         =			state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
      lowercase_      :Tuple         =			state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'			)
      # next, add query, keys and values (in that order) to the state dict
      lowercase_      :int         =			in_proj_weight[:2_56, :]
      lowercase_      :List[Any]         =			in_proj_bias[:2_56]
      lowercase_      :Any         =			in_proj_weight[2_56:5_12, :]
      lowercase_      :Optional[int]         =			in_proj_bias[2_56:5_12]
      lowercase_      :List[str]         =			in_proj_weight[-2_56:, :]
      lowercase_      :str         =			in_proj_bias[-2_56:]
      # read in weights + bias of input projection layer of cross-attention
      lowercase_      :str         =			state_dict.pop(
          F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'			)
      lowercase_      :List[Any]         =			state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'			)
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      lowercase_      :Tuple         =			in_proj_weight_cross_attn[:2_56, :]
      lowercase_      :Tuple         =			in_proj_bias_cross_attn[:2_56]
      lowercase_      :Dict         =			in_proj_weight_cross_attn[2_56:5_12, :]
      lowercase_      :str         =			in_proj_bias_cross_attn[2_56:5_12]
      lowercase_      :str         =			in_proj_weight_cross_attn[-2_56:, :]
      lowercase_      :List[str]         =			in_proj_bias_cross_attn[-2_56:]
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]     ,__lowerCamelCase : List[str]			):
   lowercase_      ,							lowercase_      :Optional[Any]         =			image.size
   lowercase_      :int         =			max(__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :int         =			8_00 if "detection" in checkpoint_url else 10_00
   lowercase_      :str         =			target_max_size / current_max_size
   lowercase_      :List[str]         =			image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

   return resized_image
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]			):
   lowercase_      :Optional[int]         =			F.to_tensor(__lowerCamelCase			)
   lowercase_      :str         =			F.normalize(__lowerCamelCase     ,mean=[0.485, 0.456, 0.406]     ,std=[0.229, 0.224, 0.225]			)
   return image
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]     ,__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Optional[int]			):
   logger.info("Converting model..."			)

   # load original state dict
   lowercase_      :Dict         =			torch.hub.load_state_dict_from_url(__lowerCamelCase     ,map_location="cpu"			)
   # rename keys
   for src, dest in rename_keys:
      rename_key(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			rename_backbone_keys(__lowerCamelCase			)
   # query, key and value matrices need special treatment
   read_in_q_k_v(__lowerCamelCase			)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   lowercase_      :int         =			"model."
   for key in state_dict.copy().keys():
      if not key.startswith("class_labels_classifier"			) and not key.startswith("bbox_predictor"			):
         lowercase_      :Union[str, Any]         =			state_dict.pop(__lowerCamelCase			)
         lowercase_      :List[Any]         =			val
    # create HuggingFace model and load state dict
   lowercase_      :int         =			TableTransformerConfig(
       backbone="resnet18"     ,mask_loss_coefficient=1     ,dice_loss_coefficient=1     ,ce_loss_coefficient=1     ,bbox_loss_coefficient=5     ,giou_loss_coefficient=2     ,eos_coefficient=0.4     ,class_cost=1     ,bbox_cost=5     ,giou_cost=2     ,)

   if "detection" in checkpoint_url:
      lowercase_      :Optional[Any]         =			15
      lowercase_      :Union[str, Any]         =			2
      lowercase_      :Tuple         =			{0: "table", 1: "table rotated"}
      lowercase_      :str         =			idalabel
      lowercase_      :Union[str, Any]         =			{v: k for k, v in idalabel.items()}
   else:
      lowercase_      :Optional[int]         =			1_25
      lowercase_      :Optional[Any]         =			6
      lowercase_      :List[Any]         =			{
          0: "table",
          1: "table column",
          2: "table row",
          3: "table column header",
          4: "table projected row header",
          5: "table spanning cell",
      }
      lowercase_      :Dict         =			idalabel
      lowercase_      :str         =			{v: k for k, v in idalabel.items()}

   lowercase_      :Dict         =			DetrImageProcessor(
       format="coco_detection"     ,max_size=8_00 if "detection" in checkpoint_url else 10_00			)
   lowercase_      :List[Any]         =			TableTransformerForObjectDetection(__lowerCamelCase			)
   model.load_state_dict(__lowerCamelCase			)
   model.eval()

   # verify our conversion
   lowercase_      :Tuple         =			"example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
   lowercase_      :List[str]         =			hf_hub_download(repo_id="nielsr/example-pdf"     ,repo_type="dataset"     ,filename=__lowerCamelCase			)
   lowercase_      :Optional[Any]         =			Image.open(__lowerCamelCase			).convert("RGB"			)
   lowercase_      :str         =			normalize(resize(__lowerCamelCase     ,__lowerCamelCase			)			).unsqueeze(0			)

   lowercase_      :int         =			model(__lowerCamelCase			)

   if "detection" in checkpoint_url:
      lowercase_      :Dict         =			(1, 15, 3)
      lowercase_      :List[str]         =			torch.tensor(
          [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]			)
      lowercase_      :int         =			torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]			)

   else:
      lowercase_      :str         =			(1, 1_25, 7)
      lowercase_      :List[str]         =			torch.tensor(
          [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]			)
      lowercase_      :Tuple         =			torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]			)

   assert outputs.logits.shape == expected_shape
   assert torch.allclose(outputs.logits[0, :3, :3]     ,__lowerCamelCase     ,atol=1e-4			)
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,__lowerCamelCase     ,atol=1e-4			)
   print("Looks ok!"			)

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'			)
      Path(__lowerCamelCase			).mkdir(exist_ok=__lowerCamelCase			)
      model.save_pretrained(__lowerCamelCase			)
      image_processor.save_pretrained(__lowerCamelCase			)

   if push_to_hub:
      # Push model to HF hub
      logger.info("Pushing model to the hub..."			)
      lowercase_      :int         =			(
          "microsoft/table-transformer-detection"
          if "detection" in checkpoint_url
          else "microsoft/table-transformer-structure-recognition"
      )
      model.push_to_hub(__lowerCamelCase			)
      image_processor.push_to_hub(__lowerCamelCase			)


if __name__ == "__main__":
 lowerCAmelCase :					str 							=argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
     type=str,
     choices=[
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
     ],
     help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 lowerCAmelCase :					str 							=parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''


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

lowerCAmelCase :					Any 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]			):
   if isinstance(__lowerCamelCase     ,(list, tuple)			) and isinstance(videos[0]     ,(list, tuple)			) and is_valid_image(videos[0][0]			):
      return videos

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

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

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


class 				a_       (    _lowerCAmelCase							):
     __A								=      ["pixel_values"]





     def __init__(			self		: List[str]     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = PILImageResampling.BILINEAR     ,				lowercase		: bool = True     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = True     ,				lowercase		: Union[int, float] = 1 / 255     ,				lowercase		: bool = True     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				**lowercase		: Tuple     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :Any         =			size if size is not None else {"shortest_edge": 256}
        lowercase_      :int         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        lowercase_      :str         =			crop_size if crop_size is not None else {"height": 224, "width": 224}
        lowercase_      :List[str]         =			get_size_dict(lowercase     ,				param_name="crop_size"						)

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





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: PILImageResampling = PILImageResampling.BILINEAR     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Optional[Any]     ,				):





        """simple docstring"""




        lowercase_      :List[Any]         =			get_size_dict(lowercase     ,				default_to_square=lowercase						)
        if "shortest_edge" in size:
           lowercase_      :int         =			get_resize_output_image_size(lowercase     ,				size["shortest_edge"]     ,				default_to_square=lowercase						)
        elif "height" in size and "width" in size:
           lowercase_      :Union[str, Any]         =			(size["height"], size["width"])
        else:
           raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
        return resize(lowercase     ,				size=lowercase     ,				resample=lowercase     ,				data_format=lowercase     ,				**lowercase						)





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





        """simple docstring"""




        lowercase_      :Any         =			get_size_dict(lowercase						)
        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(lowercase     ,				size=(size["height"], size["width"])     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: np.ndarray     ,				lowercase		: Union[int, float]     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: List[str]     ,				):





        """simple docstring"""




        lowercase_      :List[str]         =			image.astype(np.floataa						)
        if offset:
           lowercase_      :List[str]         =			image - (scale / 2)
        return rescale(lowercase     ,				scale=lowercase     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: np.ndarray     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Union[float, List[float]]     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Dict     ,				):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: ImageInput     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: PILImageResampling = None     ,				lowercase		: bool = None     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = None     ,				lowercase		: float = None     ,				lowercase		: bool = None     ,				lowercase		: bool = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[ChannelDimension] = ChannelDimension.FIRST     ,				):





        """simple docstring"""




        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.
        lowercase_      :Optional[int]         =			to_numpy_array(lowercase						)

        if do_resize:
           lowercase_      :Tuple         =			self.resize(image=lowercase     ,				size=lowercase     ,				resample=lowercase						)

        if do_center_crop:
           lowercase_      :Any         =			self.center_crop(lowercase     ,				size=lowercase						)

        if do_rescale:
           lowercase_      :Optional[Any]         =			self.rescale(image=lowercase     ,				scale=lowercase     ,				offset=lowercase						)

        if do_normalize:
           lowercase_      :Tuple         =			self.normalize(image=lowercase     ,				mean=lowercase     ,				std=lowercase						)

        lowercase_      :Optional[Any]         =			to_channel_dimension_format(lowercase     ,				lowercase						)
        return image







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





        """simple docstring"""




        lowercase_      :str         =			do_resize if do_resize is not None else self.do_resize
        lowercase_      :Optional[Any]         =			resample if resample is not None else self.resample
        lowercase_      :Tuple         =			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_      :Dict         =			do_rescale if do_rescale is not None else self.do_rescale
        lowercase_      :Optional[int]         =			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_      :Dict         =			offset if offset is not None else self.offset
        lowercase_      :Tuple         =			do_normalize if do_normalize is not None else self.do_normalize
        lowercase_      :int         =			image_mean if image_mean is not None else self.image_mean
        lowercase_      :Optional[int]         =			image_std if image_std is not None else self.image_std

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

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

        lowercase_      :List[str]         =			make_batched(lowercase						)

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

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


