"""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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							[]
							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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							for i in range(encoder_config.num_hidden_layers ):
														# queries, keys and values (only weights, no biases)
														A__		   =							state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight''' )

														A__		   =							in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														A__		   =							in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														A__		   =							in_proj_weight[
														    -encoder_config.hidden_size :, :
														]






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							dct.pop(UpperCamelCase__ )
							A__		   =							val






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if "handwritten" in checkpoint_url:
														A__		   =							'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:
														A__		   =							'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
							A__		   =							Image.open(requests.get(UpperCamelCase__				,			stream=UpperCamelCase__ ).raw ).convert('RGB' )
							return im






@torch.no_grad()
def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							ViTConfig(image_size=384				,			qkv_bias=UpperCamelCase__ )
							A__		   =							TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														A__		   =							768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														A__		   =							1_024
														A__		   =							4_096
														A__		   =							24
														A__		   =							16
														A__		   =							1_024
							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:
														A__		   =							False
														A__		   =							'relu'
														A__		   =							1_024
														A__		   =							True
														A__		   =							False
														A__		   =							False

							# load HuggingFace model
							A__		   =							ViTModel(UpperCamelCase__				,			add_pooling_layer=UpperCamelCase__ )
							A__		   =							TrOCRForCausalLM(UpperCamelCase__ )
							A__		   =							VisionEncoderDecoderModel(encoder=UpperCamelCase__				,			decoder=UpperCamelCase__ )
							model.eval()

							# load state_dict of original model, rename some keys
							A__		   =							torch.hub.load_state_dict_from_url(UpperCamelCase__				,			map_location='cpu'				,			check_hash=UpperCamelCase__ )['model']

							A__		   =							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():
														A__		   =							state_dict.pop(UpperCamelCase__ )
														if key.startswith('decoder' ) and "output_projection" not in key:
																					A__		   =							val
														else:
																					A__		   =							val

    # load state dict
							model.load_state_dict(UpperCamelCase__ )

							# Check outputs on an image
							A__		   =							ViTImageProcessor(size=encoder_config.image_size )
							A__		   =							RobertaTokenizer.from_pretrained('roberta-large' )
							A__		   =							TrOCRProcessor(UpperCamelCase__				,			UpperCamelCase__ )

							A__		   =							processor(images=prepare_img(UpperCamelCase__ )				,			return_tensors='pt' ).pixel_values

							# verify logits
							A__		   =							torch.tensor([[model.config.decoder.decoder_start_token_id]] )
							A__		   =							model(pixel_values=UpperCamelCase__				,			decoder_input_ids=UpperCamelCase__ )
							A__		   =							outputs.logits

							A__		   =							torch.Size([1, 1, 50_265] )
							if "trocr-base-handwritten" in checkpoint_url:
														A__		   =							torch.tensor(
														    [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1] )
							elif "trocr-large-handwritten" in checkpoint_url:
														A__		   =							torch.tensor(
														    [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0] )
							elif "trocr-base-printed" in checkpoint_url:
														A__		   =							torch.tensor(
														    [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0] )
							elif "trocr-large-printed" in checkpoint_url:
														A__		   =							torch.tensor(
														    [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5] )

							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"""




from __future__ import annotations

from typing import Generic, TypeVar

__lowerCamelCase							 =     TypeVar("T")
class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self						,__UpperCAmelCase   )    -> None:
													A__		   =							data
													A__		   =							self
													A__		   =							0






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# map from node name to the node object
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# create a new set with x as its member
													A__		   =							DisjointSetTreeNode(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> DisjointSetTreeNode[T]:
													# find the set x belongs to (with path-compression)
													A__		   =							self.map[data]
													if elem_ref != elem_ref.parent:
																				A__		   =							self.find_set(elem_ref.parent.data   )
													return elem_ref.parent





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# helper function for union operation
													if nodea.rank > nodea.rank:
																				A__		   =							nodea
													else:
																				A__		   =							nodea
																				if nodea.rank == nodea.rank:
																											nodea.rank += 1





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# merge 2 disjoint sets
													self.link(self.find_set(__UpperCAmelCase   )						,self.find_set(__UpperCAmelCase   )   )






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# connections: map from the node to the neighbouring nodes (with weights)
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# add a node ONLY if its not present in the graph
													if node not in self.connections:
																				A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# add an edge with the given weight
													self.add_node(__UpperCAmelCase   )
													self.add_node(__UpperCAmelCase   )
													A__		   =							weight
													A__		   =							weight





						def  snake_case__		(				self   )    -> GraphUndirectedWeighted[T]:
													A__		   =							[]
													A__		   =							set()
													for start in self.connections:
																				for end in self.connections[start]:
																											if (start, end) not in seen:
																																		seen.add((end, start)   )
																																		edges.append((start, end, self.connections[start][end])   )
													edges.sort(key=lambda __UpperCAmelCase   : x[2]   )

													# creating the disjoint set
													A__		   =							DisjointSetTree[T]()
													for node in self.connections:
																				disjoint_set.make_set(__UpperCAmelCase   )

													# MST generation
													A__		   =							0
													A__		   =							0
													A__		   =							GraphUndirectedWeighted[T]()
													while num_edges < len(self.connections   ) - 1:
																				A__   ,     A__   ,     A__		   =							edges[index]
																				index += 1
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				if parent_u != parent_v:
																											num_edges += 1
																											graph.add_edge(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
																											disjoint_set.union(__UpperCAmelCase						,__UpperCAmelCase   )
													return graph





