


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	):




		"""simple docstring"""






		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	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= to_pil_image(_a	)
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= pil_image.size
		lowerCAmelCase__       :      List[Any]   					= pytesseract.image_to_data(_a     ,       lang=_a     ,       output_type='''dict'''     ,       config=_a	)
		lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      str   					= data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

		# filter empty words and corresponding coordinates
		lowerCAmelCase__       :      Any   					= [idx for idx, word in enumerate(_a	) if not word.strip()]
		lowerCAmelCase__       :      int   					= [word for idx, word in enumerate(_a	) if idx not in irrelevant_indices]
		lowerCAmelCase__       :      Union[str, Any]   					= [coord for idx, coord in enumerate(_a	) if idx not in irrelevant_indices]
		lowerCAmelCase__       :      int   					= [coord for idx, coord in enumerate(_a	) if idx not in irrelevant_indices]
		lowerCAmelCase__       :      Optional[int]   					= [coord for idx, coord in enumerate(_a	) if idx not in irrelevant_indices]
		lowerCAmelCase__       :      Optional[Any]   					= [coord for idx, coord in enumerate(_a	) if idx not in irrelevant_indices]

		# turn coordinates into (left, top, left+width, top+height) format
		lowerCAmelCase__       :      List[Any]   					= []
		for x, y, w, h in zip(_a     ,       _a     ,       _a     ,       _a	):
				lowerCAmelCase__       :      Optional[Any]   					= [x, y, x + w, y + h]
				actual_boxes.append(_a	)

		# finally, normalize the bounding boxes
		lowerCAmelCase__       :      Optional[Any]   					= []
		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  _a							(    _lowercase):
	_a  :  Optional[Any]				   =							['''pixel_values''']




	def __init__( self						:		str		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Dict[str, int] = None		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = PILImageResampling.BILINEAR		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		float = 1 / 255		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]] = None		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]] = None		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		, _SCREAMING_SNAKE_CASE						:		Optional[str] = ""		, **_SCREAMING_SNAKE_CASE						:		int		, )->	None:
			super().__init__(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[Any]   					= size if size is not None else {'''height''': 224, '''width''': 224}
			lowerCAmelCase__       :      Optional[Any]   					= get_size_dict(_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      int   					= do_resize
			lowerCAmelCase__       :      str   					= size
			lowerCAmelCase__       :      str   					= resample
			lowerCAmelCase__       :      Any   					= do_rescale
			lowerCAmelCase__       :      List[Any]   					= rescale_value
			lowerCAmelCase__       :      Tuple   					= do_normalize
			lowerCAmelCase__       :      int   					= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowerCAmelCase__       :      Dict   					= image_std if image_std is not None else IMAGENET_STANDARD_STD
			lowerCAmelCase__       :      int   					= apply_ocr
			lowerCAmelCase__       :      Tuple   					= ocr_lang
			lowerCAmelCase__       :      Optional[Any]   					= tesseract_config




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Dict[str, int]		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = PILImageResampling.BILINEAR		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	np.ndarray:
			lowerCAmelCase__       :      str   					= 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()}'		)
			lowerCAmelCase__       :      Optional[int]   					= (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 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Union[int, float]		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Any		, )->	np.ndarray:
			return rescale(_SCREAMING_SNAKE_CASE		, scale=_SCREAMING_SNAKE_CASE		, data_format=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]]		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]]		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		, )->	np.ndarray:
			return normalize(_SCREAMING_SNAKE_CASE		, mean=_SCREAMING_SNAKE_CASE		, std=_SCREAMING_SNAKE_CASE		, data_format=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		ImageInput		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		Dict[str, int] = None		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		float = None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]] = None		, _SCREAMING_SNAKE_CASE						:		Union[float, Iterable[float]] = None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, TensorType]] = None		, _SCREAMING_SNAKE_CASE						:		ChannelDimension = ChannelDimension.FIRST		, **_SCREAMING_SNAKE_CASE						:		List[Any]		, )->	PIL.Image.Image:
			lowerCAmelCase__       :      str   					= do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase__       :      Dict   					= size if size is not None else self.size
			lowerCAmelCase__       :      Union[str, Any]   					= get_size_dict(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[str]   					= resample if resample is not None else self.resample
			lowerCAmelCase__       :      Tuple   					= do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase__       :      Dict   					= rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase__       :      Optional[int]   					= do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase__       :      Optional[Any]   					= image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase__       :      Optional[int]   					= image_std if image_std is not None else self.image_std
			lowerCAmelCase__       :      str   					= apply_ocr if apply_ocr is not None else self.apply_ocr
			lowerCAmelCase__       :      Any   					= ocr_lang if ocr_lang is not None else self.ocr_lang
			lowerCAmelCase__       :      Optional[Any]   					= tesseract_config if tesseract_config is not None else self.tesseract_config

			lowerCAmelCase__       :      int   					= 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.
			lowerCAmelCase__       :      Optional[Any]   					= [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'''		)
					lowerCAmelCase__       :      Union[str, Any]   					= []
					lowerCAmelCase__       :      str   					= []
					for image in images:
							lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= 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:
					lowerCAmelCase__       :      Dict   					= [self.resize(image=_SCREAMING_SNAKE_CASE		, size=_SCREAMING_SNAKE_CASE		, resample=_SCREAMING_SNAKE_CASE		) for image in images]

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

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

			lowerCAmelCase__       :      str   					= [to_channel_dimension_format(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		) for image in images]

			lowerCAmelCase__       :      Any   					= BatchFeature(data={'''pixel_values''': images}		, tensor_type=_SCREAMING_SNAKE_CASE		)

			if apply_ocr:
					lowerCAmelCase__       :      Optional[Any]   					= words_batch
					lowerCAmelCase__       :      Tuple   					= boxes_batch
			return data








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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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






def 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= []
		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')	)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('''cls_token''', '''vit.embeddings.cls_token'''),
		        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
		        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
		        ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
		    ]	)

		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"
				lowerCAmelCase__       :      Tuple   					= [(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'''),
				    ]	)

		return rename_keys






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=False	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(_a     ,       _a	)






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= dct.pop(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= val






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__       :      Tuple   					= Image.open(requests.get(_a     ,       stream=_a	).raw	)
		return im






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= ViTConfig()
		lowerCAmelCase__       :      Optional[Any]   					= False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[Any]   					= True
				lowerCAmelCase__       :      Any   					= int(vit_name[-12:-10]	)
				lowerCAmelCase__       :      int   					= int(vit_name[-9:-6]	)
		else:
				lowerCAmelCase__       :      Dict   					= 1_000
				lowerCAmelCase__       :      str   					= '''huggingface/label-files'''
				lowerCAmelCase__       :      Dict   					= '''imagenet-1k-id2label.json'''
				lowerCAmelCase__       :      str   					= json.load(open(hf_hub_download(_a     ,       _a     ,       repo_type='''dataset'''	)     ,       '''r'''	)	)
				lowerCAmelCase__       :      Any   					= {int(_a	): v for k, v in idalabel.items()}
				lowerCAmelCase__       :      Optional[Any]   					= idalabel
				lowerCAmelCase__       :      List[str]   					= {v: k for k, v in idalabel.items()}
				lowerCAmelCase__       :      Tuple   					= int(vit_name[-6:-4]	)
				lowerCAmelCase__       :      Union[str, Any]   					= int(vit_name[-3:]	)
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith('''tiny'''	):
						lowerCAmelCase__       :      List[str]   					= 192
						lowerCAmelCase__       :      Tuple   					= 768
						lowerCAmelCase__       :      Optional[int]   					= 12
						lowerCAmelCase__       :      List[Any]   					= 3
				elif vit_name[9:].startswith('''small'''	):
						lowerCAmelCase__       :      Any   					= 384
						lowerCAmelCase__       :      Optional[int]   					= 1_536
						lowerCAmelCase__       :      List[Any]   					= 12
						lowerCAmelCase__       :      Tuple   					= 6
				else:
						pass
		else:
				if vit_name[4:].startswith('''small'''	):
						lowerCAmelCase__       :      List[str]   					= 768
						lowerCAmelCase__       :      Tuple   					= 2_304
						lowerCAmelCase__       :      Any   					= 8
						lowerCAmelCase__       :      Union[str, Any]   					= 8
				elif vit_name[4:].startswith('''base'''	):
						pass
				elif vit_name[4:].startswith('''large'''	):
						lowerCAmelCase__       :      str   					= 1_024
						lowerCAmelCase__       :      Optional[Any]   					= 4_096
						lowerCAmelCase__       :      Optional[int]   					= 24
						lowerCAmelCase__       :      Tuple   					= 16
				elif vit_name[4:].startswith('''huge'''	):
						lowerCAmelCase__       :      Tuple   					= 1_280
						lowerCAmelCase__       :      Tuple   					= 5_120
						lowerCAmelCase__       :      Optional[int]   					= 32
						lowerCAmelCase__       :      List[Any]   					= 16

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

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

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[str]   					= ViTModel(_a	).eval()
		else:
				lowerCAmelCase__       :      Any   					= ViTForImageClassification(_a	).eval()
		model.load_state_dict(_a	)

		# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
		if "deit" in vit_name:
				lowerCAmelCase__       :      Dict   					= DeiTImageProcessor(size=config.image_size	)
		else:
				lowerCAmelCase__       :      int   					= ViTImageProcessor(size=config.image_size	)
		lowerCAmelCase__       :      Optional[Any]   					= image_processor(images=prepare_img()     ,       return_tensors='''pt'''	)
		lowerCAmelCase__       :      List[str]   					= encoding['''pixel_values''']
		lowerCAmelCase__       :      List[Any]   					= model(_a	)

		if base_model:
				lowerCAmelCase__       :      Optional[Any]   					= 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:
				lowerCAmelCase__       :      Union[str, Any]   					= timm_model(_a	)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_a     ,       outputs.logits     ,       atol=1e-3	)

		Path(_a	).mkdir(exist_ok=_a	)
		print(f'Saving model {vit_name} to {pytorch_dump_folder_path}'	)
		model.save_pretrained(_a	)
		print(f'Saving image processor to {pytorch_dump_folder_path}'	)
		image_processor.save_pretrained(_a	)


if __name__ == "__main__":
		lowerCamelCase						=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--vit_name''',
		    default='''vit_base_patch16_224''',
		    type=str,
		    help='''Name of the 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.'''
		)

		lowerCamelCase						=		parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




