


"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE      		=    logging.get_logger(__name__)



def     __a   (	a,		a=False       ):





       """simple docstring"""







       _a							 =					[]

       # fmt: off
       # stem:
       rename_keys.append(("cls_token", "vit.embeddings.cls_token")       )
       rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")       )

       rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")       )
       rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")       )

       # backbone
       rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")       )
       rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")       )
       rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")       )

       for stage_idx in range(len(config.backbone_config.depths       )       ):
              for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')       )
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')       )
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')       )
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')       )
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')       )
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')       )
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')       )
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')       )
                     rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')       )

              rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')       )
              rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')       )
              rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')       )

       # transformer encoder
       for i in range(config.num_hidden_layers       ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')       )
              rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')       )
              rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')       )
              rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')       )
              rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')       )
              rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')       )
              rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')       )
              rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')       )
              rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')       )
              rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')       )

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                      ("pre_logits.fc.weight", "pooler.dense.weight"),
                      ("pre_logits.fc.bias", "pooler.dense.bias"),
                  ]       )

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              _a							 =					[(pair[0], pair[1][4:]) if pair[1].startswith("vit"       ) else pair for pair in rename_keys]
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ("norm.weight", "vit.layernorm.weight"),
                      ("norm.bias", "vit.layernorm.bias"),
                      ("head.weight", "classifier.weight"),
                      ("head.bias", "classifier.bias"),
                  ]       )
       # fmt: on

       return rename_keys



def     __a   (	a,		a,		a=False       ):





       """simple docstring"""







       for i in range(config.num_hidden_layers       ):
              if base_model:
                     _a							 =					""
              else:
                     _a							 =					"vit."
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              _a							 =					state_dict.pop(F'blocks.{i}.attn.qkv.weight'       )
              _a							 =					state_dict.pop(F'blocks.{i}.attn.qkv.bias'       )
              # next, add query, keys and values (in that order) to the state dict
              _a							 =					in_proj_weight[
                  : config.hidden_size, :
              ]
              _a							 =					in_proj_bias[: config.hidden_size]
              _a							 =					in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _a							 =					in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              _a							 =					in_proj_weight[
                  -config.hidden_size :, :
              ]
              _a							 =					in_proj_bias[-config.hidden_size :]



def     __a   (	a       ):





       """simple docstring"""







       _a							 =					["head.weight", "head.bias"]
       for k in ignore_keys:
              state_dict.pop(__a,		__a       )



def     __a   (	a,		a,		a       ):





       """simple docstring"""







       _a							 =					dct.pop(__a       )
       _a							 =					val



def     __a   (	):





       """simple docstring"""







       _a							 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       _a							 =					Image.open(requests.get(__a,		stream=__a       ).raw       )
       return im



@torch.no_grad()
def     __a   (	a,		a,		a=False       ):





       """simple docstring"""







       _a							 =					BitConfig(
           global_padding="same",		layer_type="bottleneck",		depths=(3, 4, 9),		out_features=["stage3"],		embedding_dynamic_padding=__a,		)
       _a							 =					ViTHybridConfig(backbone_config=__a,		image_size=3_8_4,		num_labels=1_0_0_0       )
       _a							 =					False

       # load original model from timm
       _a							 =					timm.create_model(__a,		pretrained=__a       )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       _a							 =					timm_model.state_dict()
       if base_model:
              remove_classification_head_(__a       )
       _a							 =					create_rename_keys(__a,		__a       )
       for src, dest in rename_keys:
              rename_key(__a,		__a,		__a       )
       read_in_q_k_v(__a,		__a,		__a       )

       _a							 =					"huggingface/label-files"
       _a							 =					"imagenet-1k-id2label.json"
       _a							 =					json.load(open(hf_hub_download(__a,		__a,		repo_type="dataset"       ),		"r"       )       )
       _a							 =					{int(__a       ): v for k, v in idalabel.items()}
       _a							 =					idalabel
       _a							 =					{v: k for k, v in idalabel.items()}

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              _a							 =					ViTHybridModel(__a       ).eval()
       else:
              _a							 =					ViTHybridForImageClassification(__a       ).eval()
       model.load_state_dict(__a       )

       # create image processor
       _a							 =					create_transform(**resolve_data_config({},		model=__a       )       )
       _a							 =					transform.transforms

       _a							 =					{
           "bilinear": PILImageResampling.BILINEAR,
           "bicubic": PILImageResampling.BICUBIC,
           "nearest": PILImageResampling.NEAREST,
       }

       _a							 =					ViTHybridImageProcessor(
           do_resize=__a,		size={"shortest_edge": timm_transforms[0].size},		resample=pillow_resamplings[timm_transforms[0].interpolation.value],		do_center_crop=__a,		crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]},		do_normalize=__a,		image_mean=timm_transforms[-1].mean.tolist(),		image_std=timm_transforms[-1].std.tolist(),		)

       _a							 =					prepare_img()
       _a							 =					transform(__a       ).unsqueeze(0       )
       _a							 =					processor(__a,		return_tensors="pt"       ).pixel_values

       # verify pixel values
       assert torch.allclose(__a,		__a       )

       # verify logits
       with torch.no_grad():
              _a							 =					model(__a       )
              _a							 =					outputs.logits

       print("Predicted class:",		logits.argmax(-1       ).item()       )
       if base_model:
              _a							 =					timm_model.forward_features(__a       )
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(__a,		outputs.pooler_output,		atol=1e-3       )
       else:
              _a							 =					timm_model(__a       )
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(__a,		outputs.logits,		atol=1e-3       )
       print("Looks ok!"       )

       if pytorch_dump_folder_path is not None:
              Path(__a       ).mkdir(exist_ok=__a       )
              print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'       )
              model.save_pretrained(__a       )
              print(F'Saving processor to {pytorch_dump_folder_path}'       )
              processor.save_pretrained(__a       )

       if push_to_hub:
              print(F'Pushing model and processor to the hub {vit_name}'       )
              model.push_to_hub(F'ybelkada/{vit_name}'       )
              processor.push_to_hub(F'ybelkada/{vit_name}'       )


if __name__ == "__main__":
   __SCREAMING_SNAKE_CASE      		=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--vit_name""",
       default="""vit_base_r50_s16_384""",
       type=str,
       help="""Name of the hybrid ViT timm model you\'d like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
   )

   __SCREAMING_SNAKE_CASE      		=    parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""




from typing import Dict, Iterable, 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, to_pil_image
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, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
						import PIL

# soft dependency
if is_pytesseract_available():
						import pytesseract

lowerCAmelCase__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		return [
		    int(1_000 * (box[0] / width)     ),
		    int(1_000 * (box[1] / height)     ),
		    int(1_000 * (box[2] / width)     ),
		    int(1_000 * (box[3] / height)     ),
		]



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			to_pil_image(__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			pil_image.size
		SCREAMING_SNAKE_CASE_    			=			pytesseract.image_to_data(__a,				lang=__a,				output_type='''dict''',				config=__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

		# filter empty words and corresponding coordinates
		SCREAMING_SNAKE_CASE_    			=			[idx for idx, word in enumerate(__a     ) if not word.strip()]
		SCREAMING_SNAKE_CASE_    			=			[word for idx, word in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]

		# turn coordinates into (left, top, left+width, top+height) format
		SCREAMING_SNAKE_CASE_    			=			[]
		for x, y, w, h in zip(__a,				__a,				__a,				__a     ):
				SCREAMING_SNAKE_CASE_    			=			[x, y, x + w, y + h]
				actual_boxes.append(__a     )

		# finally, normalize the bounding boxes
		SCREAMING_SNAKE_CASE_    			=			[]
		for box in actual_boxes:
				normalized_boxes.append(normalize_box(__a,				__a,				__a     )     )

		assert len(__a     ) == len(__a     ), "Not as many words as there are bounding boxes"

		return words, normalized_boxes





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   ['''pixel_values''']

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = 1 / 2_55							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ""							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			do_resize
									SCREAMING_SNAKE_CASE_    			=			size
									SCREAMING_SNAKE_CASE_    			=			resample
									SCREAMING_SNAKE_CASE_    			=			do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_value
									SCREAMING_SNAKE_CASE_    			=			do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else IMAGENET_STANDARD_STD
									SCREAMING_SNAKE_CASE_    			=			apply_ocr
									SCREAMING_SNAKE_CASE_    			=			ocr_lang
									SCREAMING_SNAKE_CASE_    			=			tesseract_config

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_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()}'     )
									SCREAMING_SNAKE_CASE_    			=			(size['''height'''], size['''width'''])
									return resize(SCREAMING_SNAKE_CASE_							,							size=SCREAMING_SNAKE_CASE_							,							resample=SCREAMING_SNAKE_CASE_							,							data_format=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else self.size
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_    			=			do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_    			=			do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_    			=			apply_ocr if apply_ocr is not None else self.apply_ocr
									SCREAMING_SNAKE_CASE_    			=			ocr_lang if ocr_lang is not None else self.ocr_lang
									SCREAMING_SNAKE_CASE_    			=			tesseract_config if tesseract_config is not None else self.tesseract_config

									SCREAMING_SNAKE_CASE_    			=			make_list_of_images(SCREAMING_SNAKE_CASE_     )

									if not valid_images(SCREAMING_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.'''     )

									if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''     )

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

									# Tesseract OCR to get words + normalized bounding boxes
									if apply_ocr:
											requires_backends(self							,							'''pytesseract'''     )
											SCREAMING_SNAKE_CASE_    			=			[]
											SCREAMING_SNAKE_CASE_    			=			[]
											for image in images:
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			apply_tesseract(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
													words_batch.append(SCREAMING_SNAKE_CASE_     )
													boxes_batch.append(SCREAMING_SNAKE_CASE_     )

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

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

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

									SCREAMING_SNAKE_CASE_    			=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ) for image in images]

									SCREAMING_SNAKE_CASE_    			=			BatchFeature(data={'''pixel_values''': images}							,							tensor_type=SCREAMING_SNAKE_CASE_     )

									if apply_ocr:
											SCREAMING_SNAKE_CASE_    			=			words_batch
											SCREAMING_SNAKE_CASE_    			=			boxes_batch
									return data