


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Any=True ,   __lowerCAmelCase							:    Optional[Any]="pt"       ):
							a__					=					{'add_prefix_space': True} if isinstance(__lowerCAmelCase ,   __lowerCAmelCase       ) and not line.startswith(' '       ) else {}
							a__					=					padding_side
							return tokenizer(
							    [line] ,   max_length=__lowerCAmelCase ,   padding='max_length' if pad_to_max_length else None ,   truncation=__lowerCAmelCase ,   return_tensors=__lowerCAmelCase ,   add_special_tokens=__lowerCAmelCase ,   **__lowerCAmelCase ,   )






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[str]=None ,   ):
							a__					=					input_ids.ne(__lowerCAmelCase       ).any(dim=0       )
							if attention_mask is None:
														return input_ids[:, keep_column_mask]
							else:
														return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Any      ,__snake_case       :Union[str, Any]      ,__snake_case       :Optional[Any]      ,__snake_case       :List[Any]      ,__snake_case       :Optional[int]      ,__snake_case       :Any="train"      ,__snake_case       :str=None      ,__snake_case       :Tuple=None      ,__snake_case       :Optional[int]=None      ,__snake_case       :Union[str, Any]=""      ,) ->   int:
												super().__init__()
												a__					=					Path(__snake_case				).joinpath(type_path + '.source'				)
												a__					=					Path(__snake_case				).joinpath(type_path + '.target'				)
												a__					=					self.get_char_lens(self.src_file				)
												a__					=					max_source_length
												a__					=					max_target_length
												assert min(self.src_lens				) > 0, F'found empty line in {self.src_file}'
												a__					=					tokenizer
												a__					=					prefix
												if n_obs is not None:
																			a__					=					self.src_lens[:n_obs]
												a__					=					src_lang
												a__					=					tgt_lang
					def __len__(				self       :Union[str, Any]				) ->   str:
												return len(self.src_lens				)
					def __getitem__(				self       :List[Any]      ,__snake_case       :str				) ->   Dict[str, torch.Tensor]:
												a__					=					index + 1  # linecache starts at 1
												a__					=					self.prefix + linecache.getline(str(self.src_file				)      ,__snake_case				).rstrip('\n'				)
												a__					=					linecache.getline(str(self.tgt_file				)      ,__snake_case				).rstrip('\n'				)
												assert source_line, F'empty source line for index {index}'
												assert tgt_line, F'empty tgt line for index {index}'

												# Need to add eos token manually for T5
												if isinstance(self.tokenizer      ,__snake_case				):
																			source_line += self.tokenizer.eos_token
																			tgt_line += self.tokenizer.eos_token

												# Pad source and target to the right
												a__					=					(
												    self.tokenizer.question_encoder if isinstance(self.tokenizer      ,__snake_case				) else self.tokenizer
												)
												a__					=					self.tokenizer.generator if isinstance(self.tokenizer      ,__snake_case				) else self.tokenizer

												a__					=					encode_line(__snake_case      ,__snake_case      ,self.max_source_length      ,'right'				)
												a__					=					encode_line(__snake_case      ,__snake_case      ,self.max_target_length      ,'right'				)

												a__					=					source_inputs['input_ids'].squeeze()
												a__					=					target_inputs['input_ids'].squeeze()
												a__					=					source_inputs['attention_mask'].squeeze()
												return {
												    "input_ids": source_ids,
												    "attention_mask": src_mask,
												    "decoder_input_ids": target_ids,
												}
					@staticmethod
					def 			lowerCamelCase__(				__snake_case       :int				) ->   str:
												return [len(__snake_case				) for x in Path(__snake_case				).open().readlines()]



					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Tuple				) ->   Dict[str, torch.Tensor]:
												a__					=					torch.stack([x['input_ids'] for x in batch]				)
												a__					=					torch.stack([x['attention_mask'] for x in batch]				)
												a__					=					torch.stack([x['decoder_input_ids'] for x in batch]				)
												a__					=					(
												    self.tokenizer.generator.pad_token_id
												    if isinstance(self.tokenizer      ,__snake_case				)
												    else self.tokenizer.pad_token_id
												)
												a__					=					(
												    self.tokenizer.question_encoder.pad_token_id
												    if isinstance(self.tokenizer      ,__snake_case				)
												    else self.tokenizer.pad_token_id
												)
												a__					=					trim_batch(__snake_case      ,__snake_case				)
												a__				, a__					=					trim_batch(__snake_case      ,__snake_case      ,attention_mask=__snake_case				)
												a__					=					{
												    'input_ids': source_ids,
												    'attention_mask': source_mask,
												    'decoder_input_ids': y,
												}
												return batch





snake_case							:	Any					      =				getLogger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    List[List]       ):
							return list(itertools.chain.from_iterable(__lowerCAmelCase       )       )






def 							__lowercase       (			__lowerCAmelCase							:    str       ):
							a__					=					get_git_info()
							save_json(__lowerCAmelCase ,   os.path.join(__lowerCAmelCase ,   'git_log.json'       )       )






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[int]=4 ,   **__lowerCAmelCase							:    Optional[Any]       ):
							with open(__lowerCAmelCase ,   'w'       ) as f:
														json.dump(__lowerCAmelCase ,   __lowerCAmelCase ,   indent=__lowerCAmelCase ,   **__lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    Any       ):
							with open(__lowerCAmelCase       ) as f:
														return json.load(__lowerCAmelCase       )






def 							__lowercase       (			):
							a__					=					git.Repo(search_parent_directories=__lowerCAmelCase       )
							a__					=					{
							    'repo_id': str(__lowerCAmelCase       ),
							    'repo_sha': str(repo.head.object.hexsha       ),
							    'repo_branch': str(repo.active_branch       ),
							    'hostname': str(socket.gethostname()       ),
							}
							return repo_infos






def 							__lowercase       (			__lowerCAmelCase							:    Callable ,   __lowerCAmelCase							:    Iterable       ):
							return list(map(__lowerCAmelCase ,   __lowerCAmelCase       )       )






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    int       ):
							with open(__lowerCAmelCase ,   'wb'       ) as f:
														return pickle.dump(__lowerCAmelCase ,   __lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    str       ):

							def remove_articles(__lowerCAmelCase							:    Union[str, Any]       ):
														return re.sub(R'\b(a|an|the)\b' ,   ' ' ,   __lowerCAmelCase       )

							def white_space_fix(__lowerCAmelCase							:    Optional[Any]       ):
														return " ".join(text.split()       )

							def remove_punc(__lowerCAmelCase							:    Union[str, Any]       ):
														a__					=					set(string.punctuation       )
														return "".join(ch for ch in text if ch not in exclude       )

							def lower(__lowerCAmelCase							:    Optional[Any]       ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase       )       )       )       )






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    Union[str, Any]       ):
							a__					=					normalize_answer(__lowerCAmelCase       ).split()
							a__					=					normalize_answer(__lowerCAmelCase       ).split()
							a__					=					Counter(__lowerCAmelCase       ) & Counter(__lowerCAmelCase       )
							a__					=					sum(common.values()       )
							if num_same == 0:
														return 0
							a__					=					1.0 * num_same / len(__lowerCAmelCase       )
							a__					=					1.0 * num_same / len(__lowerCAmelCase       )
							a__					=					(2 * precision * recall) / (precision + recall)
							return fa






def 							__lowercase       (			__lowerCAmelCase							:    int ,   __lowerCAmelCase							:    List[str]       ):
							return normalize_answer(__lowerCAmelCase       ) == normalize_answer(__lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[str]       ):
							assert len(__lowerCAmelCase       ) == len(__lowerCAmelCase       )
							a__					=					0
							for hypo, pred in zip(__lowerCAmelCase ,   __lowerCAmelCase       ):
														em += exact_match_score(__lowerCAmelCase ,   __lowerCAmelCase       )
							if len(__lowerCAmelCase       ) > 0:
														em /= len(__lowerCAmelCase       )
							return {"em": em}






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any]       ):
							return model_prefix.startswith('rag'       )






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Optional[int]       ):
							a__					=					{p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							a__					=					'dropout_rate'
							for p in extra_params:
														if getattr(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       ):
																					if not hasattr(__lowerCAmelCase ,   __lowerCAmelCase       ) and not hasattr(__lowerCAmelCase ,   equivalent_param[p]       ):
																												logger.info('config doesn\'t have a `{}` attribute'.format(__lowerCAmelCase       )       )
																												delattr(__lowerCAmelCase ,   __lowerCAmelCase       )
																												continue
																					a__					=					p if hasattr(__lowerCAmelCase ,   __lowerCAmelCase       ) else equivalent_param[p]
																					setattr(__lowerCAmelCase ,   __lowerCAmelCase ,   getattr(__lowerCAmelCase ,   __lowerCAmelCase       )       )
																					delattr(__lowerCAmelCase ,   __lowerCAmelCase       )
							return hparams, config








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()
snake_case							:	int					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Dict       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							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(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       ).convert('RGB'       )
							return im






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int       ):
							a__					=					ViTConfig(image_size=3_8_4 ,   qkv_bias=__lowerCAmelCase       )
							a__					=					TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														a__					=					7_6_8
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														a__					=					1_0_2_4
														a__					=					4_0_9_6
														a__					=					2_4
														a__					=					1_6
														a__					=					1_0_2_4
							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_0_2_4
														a__					=					True
														a__					=					False
														a__					=					False

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

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

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

							# 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(__lowerCAmelCase       )
														if key.startswith('decoder'       ) and "output_projection" not in key:
																					a__					=					val
														else:
																					a__					=					val

    # load state dict
							model.load_state_dict(__lowerCAmelCase       )

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

							a__					=					processor(images=prepare_img(__lowerCAmelCase       ) ,   return_tensors='pt'       ).pixel_values

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

							a__					=					torch.Size([1, 1, 5_0_2_6_5]       )
							if "trocr-base-handwritten" in checkpoint_url:
														a__					=					torch.tensor(
														    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]       )
							elif "trocr-large-handwritten" in checkpoint_url:
														a__					=					torch.tensor(
														    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]       )
							elif "trocr-base-printed" in checkpoint_url:
														a__					=					torch.tensor(
														    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]       )
							elif "trocr-large-printed" in checkpoint_url:
														a__					=					torch.tensor(
														    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]       )

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :1_0] ,   __lowerCAmelCase ,   atol=1E-3       ), "First elements of logits not as expected"

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


if __name__ == "__main__":
						snake_case							:	Optional[int]					      =				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.'''
						)
						snake_case							:	int					      =				parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




