





import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Optional[Any]											=	[]
	for i in range(encoder_config.num_hidden_layers   ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.norm1.weight""", f"""encoder.encoder.layer.{i}.layernorm_before.weight""")   )
		rename_keys.append((f"""encoder.deit.blocks.{i}.norm1.bias""", f"""encoder.encoder.layer.{i}.layernorm_before.bias""")   )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.attn.proj.weight""", f"""encoder.encoder.layer.{i}.attention.output.dense.weight""")   )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.attn.proj.bias""", f"""encoder.encoder.layer.{i}.attention.output.dense.bias""")   )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.norm2.weight""", f"""encoder.encoder.layer.{i}.layernorm_after.weight""")   )
		rename_keys.append((f"""encoder.deit.blocks.{i}.norm2.bias""", f"""encoder.encoder.layer.{i}.layernorm_after.bias""")   )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.mlp.fc1.weight""", f"""encoder.encoder.layer.{i}.intermediate.dense.weight""")   )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.mlp.fc1.bias""", f"""encoder.encoder.layer.{i}.intermediate.dense.bias""")   )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.mlp.fc2.weight""", f"""encoder.encoder.layer.{i}.output.dense.weight""")   )
		rename_keys.append((f"""encoder.deit.blocks.{i}.mlp.fc2.bias""", f"""encoder.encoder.layer.{i}.output.dense.bias""")   )

	# cls token, position embeddings and patch embeddings of encoder
	rename_keys.extend(
	    [
	        ('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
	        ('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
	        ('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
	        ('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
	        ('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
	        ('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
	    ]   )

	return rename_keys


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	for i in range(encoder_config.num_hidden_layers   ):
		# queries, keys and values (only weights, no biases)
		snake_case_		:			int											=	state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""   )

		snake_case_		:			Optional[int]											=	in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		snake_case_		:			Optional[Any]											=	in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		snake_case_		:			Any											=	in_proj_weight[
		    -encoder_config.hidden_size :, :
		]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	dct.pop(__a   )
	snake_case_		:			List[str]											=	val


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if "handwritten" in checkpoint_url:
		snake_case_		:			Optional[int]											=	'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'  # industry
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
	elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
		snake_case_		:			Optional[int]											=	'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
	snake_case_		:			Optional[Any]											=	Image.open(requests.get(__a   , stream=__a   ).raw   ).convert('RGB'   )
	return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Optional[int]											=	ViTConfig(image_size=3_84   , qkv_bias=__a   )
	snake_case_		:			Union[str, Any]											=	TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		snake_case_		:			Dict											=	7_68
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		snake_case_		:			Dict											=	10_24
		snake_case_		:			Union[str, Any]											=	40_96
		snake_case_		:			str											=	24
		snake_case_		:			str											=	16
		snake_case_		:			Optional[Any]											=	10_24
	else:
		raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'   )

	# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
	if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
		snake_case_		:			str											=	False
		snake_case_		:			Union[str, Any]											=	'relu'
		snake_case_		:			int											=	10_24
		snake_case_		:			Any											=	True
		snake_case_		:			int											=	False
		snake_case_		:			Optional[int]											=	False

	# load HuggingFace model
	snake_case_		:			str											=	ViTModel(__a   , add_pooling_layer=__a   )
	snake_case_		:			Tuple											=	TrOCRForCausalLM(__a   )
	snake_case_		:			List[str]											=	VisionEncoderDecoderModel(encoder=__a   , decoder=__a   )
	model.eval()

	# load state_dict of original model, rename some keys
	snake_case_		:			Tuple											=	torch.hub.load_state_dict_from_url(__a   , map_location='cpu'   , check_hash=__a   )['model']

	snake_case_		:			str											=	create_rename_keys(__a   , __a   )
	for src, dest in rename_keys:
		rename_key(__a   , __a   , __a   )
	read_in_q_k_v(__a   , __a   )

	# remove parameters we don't need
	del state_dict["encoder.deit.head.weight"]
	del state_dict["encoder.deit.head.bias"]
	del state_dict["decoder.version"]

	# add prefix to decoder keys
	for key, val in state_dict.copy().items():
		snake_case_		:			int											=	state_dict.pop(__a   )
		if key.startswith('decoder'   ) and "output_projection" not in key:
			snake_case_		:			Any											=	val
		else:
			snake_case_		:			Tuple											=	val

    # load state dict
	model.load_state_dict(__a   )

	# Check outputs on an image
	snake_case_		:			str											=	ViTImageProcessor(size=encoder_config.image_size   )
	snake_case_		:			Tuple											=	RobertaTokenizer.from_pretrained('roberta-large'   )
	snake_case_		:			List[Any]											=	TrOCRProcessor(__a   , __a   )

	snake_case_		:			Union[str, Any]											=	processor(images=prepare_img(__a   )   , return_tensors='pt'   ).pixel_values

	# verify logits
	snake_case_		:			Optional[int]											=	torch.tensor([[model.config.decoder.decoder_start_token_id]]   )
	snake_case_		:			Union[str, Any]											=	model(pixel_values=__a   , decoder_input_ids=__a   )
	snake_case_		:			int											=	outputs.logits

	snake_case_		:			Union[str, Any]											=	torch.Size([1, 1, 5_02_65]   )
	if "trocr-base-handwritten" in checkpoint_url:
		snake_case_		:			Union[str, Any]											=	torch.tensor(
		    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]   )
	elif "trocr-large-handwritten" in checkpoint_url:
		snake_case_		:			Dict											=	torch.tensor(
		    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]   )
	elif "trocr-base-printed" in checkpoint_url:
		snake_case_		:			List[Any]											=	torch.tensor(
		    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]   )
	elif "trocr-large-printed" in checkpoint_url:
		snake_case_		:			Dict											=	torch.tensor(
		    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]   )

	if "stage1" not in checkpoint_url:
		assert logits.shape == expected_shape, "Shape of logits not as expected"
		assert torch.allclose(logits[0, 0, :10]   , __a   , atol=1E-3   ), "First elements of logits not as expected"

	Path(__a   ).mkdir(exist_ok=__a   )
	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(__a   )
	print(f"""Saving processor to {pytorch_dump_folder_path}"""   )
	processor.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()

						parser.add_argument(
						    """--checkpoint_url""",
						    default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
						    type=str,
						    help="""URL to the original PyTorch checkpoint (.pth file).""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	return [
	    int(10_00 * (box[0] / width)   ),
	    int(10_00 * (box[1] / height)   ),
	    int(10_00 * (box[2] / width)   ),
	    int(10_00 * (box[3] / height)   ),
	]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a = None   ):
	snake_case_		:			int											=	tesseract_config if tesseract_config is not None else ''

	# apply OCR
	snake_case_		:			str											=	to_pil_image(__a   )
	snake_case_						,snake_case_		:			str											=	pil_image.size
	snake_case_		:			str											=	pytesseract.image_to_data(__a   , lang=__a   , output_type='dict'   , config=__a   )
	snake_case_						,snake_case_						,snake_case_						,snake_case_						,snake_case_		:			Dict											=	data['text'], data['left'], data['top'], data['width'], data['height']

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

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

	# finally, normalize the bounding boxes
	snake_case_		:			Dict											=	[]
	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 SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       ["pixel_values"]






						def __init__(      self      :      List[str]		,   _A      :      bool = True		,   _A      :      Dict[str, int] = None		,   _A      :      PILImageResampling = PILImageResampling.BILINEAR		,   _A      :      bool = True		,   _A      :      Optional[str] = None		,   _A      :      Optional[str] = ""		,   **_A      :      int		,   )							->    None:







							"""simple docstring"""


							super().__init__(**_A	)
							snake_case_		:			Optional[int]											=	size if size is not None else {'height': 224, 'width': 224}
							snake_case_		:			List[str]											=	get_size_dict(_A	)

							snake_case_		:			int											=	do_resize
							snake_case_		:			int											=	size
							snake_case_		:			Any											=	resample
							snake_case_		:			List[str]											=	apply_ocr
							snake_case_		:			str											=	ocr_lang
							snake_case_		:			Dict											=	tesseract_config






						def UpperCAmelCase_				(      self      :      int		,   _A      :      np.ndarray		,   _A      :      Dict[str, int]		,   _A      :      PILImageResampling = PILImageResampling.BILINEAR		,   _A      :      Optional[Union[str, ChannelDimension]] = None		,   **_A      :      str		,   )							->    np.ndarray:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	get_size_dict(_A	)
							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()}"""	)
							snake_case_		:			Optional[Any]											=	(size['height'], size['width'])
							return resize(_A		,   size=_A		,   resample=_A		,   data_format=_A		,   **_A	)






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      ImageInput		,   _A      :      bool = None		,   _A      :      Dict[str, int] = None		,   _A      :      PILImageResampling = None		,   _A      :      bool = None		,   _A      :      Optional[str] = None		,   _A      :      Optional[str] = None		,   _A      :      Optional[Union[str, TensorType]] = None		,   _A      :      ChannelDimension = ChannelDimension.FIRST		,   **_A      :      List[str]		,   )							->    PIL.Image.Image:







							"""simple docstring"""


							snake_case_		:			Dict											=	do_resize if do_resize is not None else self.do_resize
							snake_case_		:			Optional[Any]											=	size if size is not None else self.size
							snake_case_		:			Union[str, Any]											=	get_size_dict(_A	)
							snake_case_		:			Dict											=	resample if resample is not None else self.resample
							snake_case_		:			Tuple											=	apply_ocr if apply_ocr is not None else self.apply_ocr
							snake_case_		:			int											=	ocr_lang if ocr_lang is not None else self.ocr_lang
							snake_case_		:			int											=	tesseract_config if tesseract_config is not None else self.tesseract_config

							snake_case_		:			str											=	make_list_of_images(_A	)

							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.'	)

							if do_resize and size is None:
								raise ValueError('Size must be specified if do_resize is True.'	)

							# All transformations expect numpy arrays.
							snake_case_		:			List[str]											=	[to_numpy_array(_A	) for image in images]

							if apply_ocr:
								requires_backends(self		,   'pytesseract'	)
								snake_case_		:			str											=	[]
								snake_case_		:			List[Any]											=	[]
								for image in images:
									snake_case_						,snake_case_		:			Tuple											=	apply_tesseract(_A		,   _A		,   _A	)
									words_batch.append(_A	)
									boxes_batch.append(_A	)

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

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							snake_case_		:			int											=	[flip_channel_order(_A	) for image in images]
							snake_case_		:			Any											=	[to_channel_dimension_format(_A		,   _A	) for image in images]

							snake_case_		:			List[str]											=	BatchFeature(data={'pixel_values': images}		,   tensor_type=_A	)

							if apply_ocr:
								snake_case_		:			Dict											=	words_batch
								snake_case_		:			List[str]											=	boxes_batch
							return data

