






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets



class 				__UpperCAmelCase		(__lowercase   ):







			@slow
			@require_torch
			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""				,     """prajjwal1/bert-tiny"""					)
									_SCREAMING_SNAKE_CASE     				=  BertTokenizer.from_pretrained("""bert-base-uncased"""					)

									_SCREAMING_SNAKE_CASE     				=  bertabert.config.encoder.vocab_size
									_SCREAMING_SNAKE_CASE     				=  tokenizer.sep_token_id
									_SCREAMING_SNAKE_CASE     				=  tokenizer.cls_token_id
									_SCREAMING_SNAKE_CASE     				=  128

									_SCREAMING_SNAKE_CASE     				=  datasets.load_dataset("""cnn_dailymail"""				,     """3.0.0"""				,     split="""train[:1%]"""					)
									_SCREAMING_SNAKE_CASE     				=  datasets.load_dataset("""cnn_dailymail"""				,     """3.0.0"""				,     split="""validation[:1%]"""					)

									_SCREAMING_SNAKE_CASE     				=  train_dataset.select(range(32					)					)
									_SCREAMING_SNAKE_CASE     				=  val_dataset.select(range(16					)					)

									_SCREAMING_SNAKE_CASE     				=  4

									def _map_to_encoder_decoder_inputs(UpperCAmelCase_:  Optional[Any]					):
															# Tokenizer will automatically set [BOS] <text> [EOS]
															_SCREAMING_SNAKE_CASE     				=  tokenizer(batch["""article"""]				,     padding="""max_length"""				,     truncation=_a				,     max_length=512					)
															_SCREAMING_SNAKE_CASE     				=  tokenizer(batch["""highlights"""]				,     padding="""max_length"""				,     truncation=_a				,     max_length=128					)
															_SCREAMING_SNAKE_CASE     				=  inputs.input_ids
															_SCREAMING_SNAKE_CASE     				=  inputs.attention_mask

															_SCREAMING_SNAKE_CASE     				=  outputs.input_ids
															_SCREAMING_SNAKE_CASE     				=  outputs.input_ids.copy()
															_SCREAMING_SNAKE_CASE     				=  [
															    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
															]
															_SCREAMING_SNAKE_CASE     				=  outputs.attention_mask

															assert all(len(_a					) == 512 for x in inputs.input_ids					)
															assert all(len(_a					) == 128 for x in outputs.input_ids					)

															return batch

									def _compute_metrics(UpperCAmelCase_:  Any					):
															_SCREAMING_SNAKE_CASE     				=  pred.label_ids
															_SCREAMING_SNAKE_CASE     				=  pred.predictions

															# all unnecessary tokens are removed
															_SCREAMING_SNAKE_CASE     				=  tokenizer.batch_decode(_a				,     skip_special_tokens=_a					)
															_SCREAMING_SNAKE_CASE     				=  tokenizer.batch_decode(_a				,     skip_special_tokens=_a					)

															_SCREAMING_SNAKE_CASE     				=  sum([int(pred_str[i] == label_str[i]					) for i in range(len(_a					)					)]					) / len(_a					)

															return {"accuracy": accuracy}

									# map train dataset
									_SCREAMING_SNAKE_CASE     				=  train_dataset.map(
									    _map_to_encoder_decoder_inputs				,     batched=_a				,     batch_size=_a				,     remove_columns=["""article""", """highlights"""]				,     )
									train_dataset.set_format(
									    type="""torch"""				,     columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				,     )

									# same for validation dataset
									_SCREAMING_SNAKE_CASE     				=  val_dataset.map(
									    _map_to_encoder_decoder_inputs				,     batched=_a				,     batch_size=_a				,     remove_columns=["""article""", """highlights"""]				,     )
									val_dataset.set_format(
									    type="""torch"""				,     columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				,     )

									_SCREAMING_SNAKE_CASE     				=  self.get_auto_remove_tmp_dir()

									_SCREAMING_SNAKE_CASE     				=  SeqaSeqTrainingArguments(
									    output_dir=_a				,     per_device_train_batch_size=_a				,     per_device_eval_batch_size=_a				,     predict_with_generate=_a				,     evaluation_strategy="""steps"""				,     do_train=_a				,     do_eval=_a				,     warmup_steps=0				,     eval_steps=2				,     logging_steps=2				,     )

									# instantiate trainer
									_SCREAMING_SNAKE_CASE     				=  SeqaSeqTrainer(
									    model=_a				,     args=_a				,     compute_metrics=_compute_metrics				,     train_dataset=_a				,     eval_dataset=_a				,     tokenizer=_a				,     )

									# start training
									trainer.train()









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()
UpperCamelCase			    =     logging.get_logger(__name__)

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  []
						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      (		snake_case__	,snake_case__		)							->  Dict:






						"""simple docstring"""





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

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  dct.pop(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  val

def 	__lowerCamelCase      (		snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





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

@torch.no_grad()
def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ViTConfig(image_size=3_84	,qkv_bias=snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  TrOCRConfig()

						# size of the architecture
						if "base" in checkpoint_url:
												_SCREAMING_SNAKE_CASE     				=  7_68
						elif "large" in checkpoint_url:
												# use ViT-large encoder
												_SCREAMING_SNAKE_CASE     				=  10_24
												_SCREAMING_SNAKE_CASE     				=  40_96
												_SCREAMING_SNAKE_CASE     				=  24
												_SCREAMING_SNAKE_CASE     				=  16
												_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  False
												_SCREAMING_SNAKE_CASE     				=  """relu"""
												_SCREAMING_SNAKE_CASE     				=  10_24
												_SCREAMING_SNAKE_CASE     				=  True
												_SCREAMING_SNAKE_CASE     				=  False
												_SCREAMING_SNAKE_CASE     				=  False

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

						# load state_dict of original model, rename some keys
						_SCREAMING_SNAKE_CASE     				=  torch.hub.load_state_dict_from_url(snake_case__	,map_location="""cpu"""	,check_hash=snake_case__		)["""model"""]

						_SCREAMING_SNAKE_CASE     				=  create_rename_keys(snake_case__	,snake_case__		)
						for src, dest in rename_keys:
												rename_key(snake_case__	,snake_case__	,snake_case__		)
						read_in_q_k_v(snake_case__	,snake_case__		)

						# 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():
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(snake_case__		)
												if key.startswith("""decoder"""		) and "output_projection" not in key:
																		_SCREAMING_SNAKE_CASE     				=  val
												else:
																		_SCREAMING_SNAKE_CASE     				=  val

    # load state dict
						model.load_state_dict(snake_case__		)

						# Check outputs on an image
						_SCREAMING_SNAKE_CASE     				=  ViTImageProcessor(size=encoder_config.image_size		)
						_SCREAMING_SNAKE_CASE     				=  RobertaTokenizer.from_pretrained("""roberta-large"""		)
						_SCREAMING_SNAKE_CASE     				=  TrOCRProcessor(snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  processor(images=prepare_img(snake_case__		)	,return_tensors="""pt"""		).pixel_values

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

						_SCREAMING_SNAKE_CASE     				=  torch.Size([1, 1, 5_02_65]		)
						if "trocr-base-handwritten" in checkpoint_url:
												_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  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, :10]	,snake_case__	,atol=1e-3		), "First elements of logits not as expected"

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


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

