"""simple docstring"""







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()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   []
	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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	for i in range(encoder_config.num_hidden_layers	):
		# queries, keys and values (only weights, no biases)
		__lowerCAmelCase    :		Optional[int]		    =   state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"	)

		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		__lowerCAmelCase    :		Tuple		    =   in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		__lowerCAmelCase    :		int		    =   in_proj_weight[
		    -encoder_config.hidden_size :, :
		]
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[Any]		    =   dct.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		Union[str, Any]		    =   val
def 					__lowerCAmelCase       (_UpperCamelCase	):
	if "handwritten" in checkpoint_url:
		__lowerCAmelCase    :		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:
		__lowerCAmelCase    :		List[Any]		    =   'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
	__lowerCAmelCase    :		str		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	).convert('RGB'	)
	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Dict		    =   ViTConfig(image_size=384 ,						qkv_bias=_UpperCamelCase	)
	__lowerCAmelCase    :		List[str]		    =   TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		__lowerCAmelCase    :		Optional[int]		    =   768
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		__lowerCAmelCase    :		int		    =   1024
		__lowerCAmelCase    :		str		    =   4096
		__lowerCAmelCase    :		Optional[Any]		    =   24
		__lowerCAmelCase    :		Dict		    =   16
		__lowerCAmelCase    :		List[Any]		    =   1024
	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:
		__lowerCAmelCase    :		List[Any]		    =   False
		__lowerCAmelCase    :		Union[str, Any]		    =   'relu'
		__lowerCAmelCase    :		Optional[Any]		    =   1024
		__lowerCAmelCase    :		Optional[Any]		    =   True
		__lowerCAmelCase    :		Optional[int]		    =   False
		__lowerCAmelCase    :		str		    =   False

	# load HuggingFace model
	__lowerCAmelCase    :		Optional[int]		    =   ViTModel(_UpperCamelCase ,						add_pooling_layer=_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   TrOCRForCausalLM(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[int]		    =   VisionEncoderDecoderModel(encoder=_UpperCamelCase ,						decoder=_UpperCamelCase	)
	model.eval()

	# load state_dict of original model, rename some keys
	__lowerCAmelCase    :		Optional[int]		    =   torch.hub.load_state_dict_from_url(_UpperCamelCase ,						map_location='cpu' ,						check_hash=_UpperCamelCase	)['model']

	__lowerCAmelCase    :		Dict		    =   create_rename_keys(_UpperCamelCase ,						_UpperCamelCase	)
	for src, dest in rename_keys:
		rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	read_in_q_k_v(_UpperCamelCase ,						_UpperCamelCase	)

	# 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():
		__lowerCAmelCase    :		Union[str, Any]		    =   state_dict.pop(_UpperCamelCase	)
		if key.startswith('decoder'	) and "output_projection" not in key:
			__lowerCAmelCase    :		List[str]		    =   val
		else:
			__lowerCAmelCase    :		List[Any]		    =   val

    # load state dict
	model.load_state_dict(_UpperCamelCase	)

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

	__lowerCAmelCase    :		Optional[int]		    =   processor(images=prepare_img(_UpperCamelCase	) ,						return_tensors='pt'	).pixel_values

	# verify logits
	__lowerCAmelCase    :		int		    =   torch.tensor([[model.config.decoder.decoder_start_token_id]]	)
	__lowerCAmelCase    :		List[str]		    =   model(pixel_values=_UpperCamelCase ,						decoder_input_ids=_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[int]		    =   outputs.logits

	__lowerCAmelCase    :		List[Any]		    =   torch.Size([1, 1, 5_0265]	)
	if "trocr-base-handwritten" in checkpoint_url:
		__lowerCAmelCase    :		Tuple		    =   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:
		__lowerCAmelCase    :		Tuple		    =   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:
		__lowerCAmelCase    :		Optional[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:
		__lowerCAmelCase    :		List[Any]		    =   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] ,						_UpperCamelCase ,						atol=1e-3	), "First elements of logits not as expected"

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


if __name__ == "__main__":
							lowerCamelCase__           =	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."""
							)
							lowerCamelCase__           =	parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

"""simple docstring"""







class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   0
			__lowerCAmelCase    :		Any		    =   0
			__lowerCAmelCase    :		List[Any]		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			if vertex not in self.adjacency:
				__lowerCAmelCase    :		Dict		    =   {}
				self.num_vertices += 1

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			self.add_vertex(_SCREAMING_SNAKE_CASE				)
			self.add_vertex(_SCREAMING_SNAKE_CASE				)

			if head == tail:
				return

			__lowerCAmelCase    :		Union[str, Any]		    =   weight
			__lowerCAmelCase    :		str		    =   weight

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.get_edges()
			for edge in edges:
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   edge
				edges.remove((tail, head, weight)				)
			for i in range(len(_SCREAMING_SNAKE_CASE				)				):
				__lowerCAmelCase    :		List[str]		    =   list(edges[i]				)

			edges.sort(key=lambda _SCREAMING_SNAKE_CASE				: e[2]				)
			for i in range(len(_SCREAMING_SNAKE_CASE				) - 1				):
				if edges[i][2] >= edges[i + 1][2]:
					__lowerCAmelCase    :		Dict		    =   edges[i][2] + 1
			for edge in edges:
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   edge
				__lowerCAmelCase    :		Union[str, Any]		    =   weight
				__lowerCAmelCase    :		Optional[int]		    =   weight

		def __str__(   self				):
			__lowerCAmelCase    :		List[Any]		    =   ''
			for tail in self.adjacency:
				for head in self.adjacency[tail]:
					__lowerCAmelCase    :		str		    =   self.adjacency[head][tail]
					string += f"{head} -> {tail} == {weight}\n"
			return string.rstrip('\n'				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   []
			for tail in self.adjacency:
				for head in self.adjacency[tail]:
					output.append((tail, head, self.adjacency[head][tail])				)
			return output

		def 							__lowerCamelCase							(   self				):
			return self.adjacency.keys()

		@staticmethod
		def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			__lowerCAmelCase    :		List[str]		    =   Graph()
			if vertices is None:
				__lowerCAmelCase    :		int		    =   []
			if edges is None:
				__lowerCAmelCase    :		Optional[int]		    =   []
			for vertex in vertices:
				g.add_vertex(_SCREAMING_SNAKE_CASE				)
			for edge in edges:
				g.add_edge(*_SCREAMING_SNAKE_CASE				)
			return g




		class 				A__ :

				def __init__(   self				):
					__lowerCAmelCase    :		Dict		    =   {}
					__lowerCAmelCase    :		Optional[int]		    =   {}

				def __len__(   self				):
					return len(self.parent				)

				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
					if item in self.parent:
						return self.find(_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		Dict		    =   item
					__lowerCAmelCase    :		int		    =   0
					return item

				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
					if item not in self.parent:
						return self.make_set(_SCREAMING_SNAKE_CASE				)
					if item != self.parent[item]:
						__lowerCAmelCase    :		Dict		    =   self.find(self.parent[item]				)
					return self.parent[item]







				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		Any		    =   self.find(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		List[str]		    =   self.find(_SCREAMING_SNAKE_CASE				)

					if roota == roota:
						return roota

					if self.rank[roota] > self.rank[roota]:
						__lowerCAmelCase    :		Tuple		    =   roota
						return roota

					if self.rank[roota] < self.rank[roota]:
						__lowerCAmelCase    :		str		    =   roota
						return roota

					if self.rank[roota] == self.rank[roota]:
						self.rank[roota] += 1
						__lowerCAmelCase    :		Optional[int]		    =   roota
						return roota
					return None



		@staticmethod
		def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   graph.num_vertices

			__lowerCAmelCase    :		List[Any]		    =   Graph.UnionFind()
			__lowerCAmelCase    :		Tuple		    =   []
			while num_components > 1:
				__lowerCAmelCase    :		str		    =   {}
				for vertex in graph.get_vertices():
					__lowerCAmelCase    :		Union[str, Any]		    =   -1

				__lowerCAmelCase    :		int		    =   graph.get_edges()
				for edge in edges:
					__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   edge
					edges.remove((tail, head, weight)				)
				for edge in edges:
					__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   edge
					__lowerCAmelCase    :		Optional[int]		    =   union_find.find(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		str		    =   union_find.find(_SCREAMING_SNAKE_CASE				)
					if seta != seta:
						if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
							__lowerCAmelCase    :		List[str]		    =   [head, tail, weight]

						if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
							__lowerCAmelCase    :		List[str]		    =   [head, tail, weight]
				for vertex in cheap_edge:
					if cheap_edge[vertex] != -1:
						__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   cheap_edge[vertex]
						if union_find.find(_SCREAMING_SNAKE_CASE				) != union_find.find(_SCREAMING_SNAKE_CASE				):
							union_find.union(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
							mst_edges.append(cheap_edge[vertex]				)
							__lowerCAmelCase    :		List[str]		    =   num_components - 1
			__lowerCAmelCase    :		Union[str, Any]		    =   Graph.build(edges=_SCREAMING_SNAKE_CASE				)
			return mst