

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				:   List[str]					   =							logging.get_logger(__name__)

def 			a__     (		UpperCAmelCase							:			Optional[int]				, UpperCAmelCase							:			str			)  ->						str:
  UpperCAmelCase       :							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 			a__     (		UpperCAmelCase							:			int				, UpperCAmelCase							:			Dict			)  ->						Any:
  for i in range(encoder_config.num_hidden_layers			):
    # queries, keys and values (only weights, no biases)
    UpperCAmelCase       :							int  = state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight'''			)

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

def 			a__     (		UpperCAmelCase							:			Tuple				, UpperCAmelCase							:			Optional[Any]				, UpperCAmelCase							:			List[Any]			)  ->						int:
  UpperCAmelCase       :							List[str]  = dct.pop(snake_case__			)
  UpperCAmelCase       :							str  = val

def 			a__     (		UpperCAmelCase							:			Optional[int]			)  ->						Optional[Any]:
  if "handwritten" in checkpoint_url:
    UpperCAmelCase       :							Dict  = '''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:
    UpperCAmelCase       :							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'''
  UpperCAmelCase       :							Union[str, Any]  = Image.open(requests.get(snake_case__				, stream=snake_case__			).raw			).convert('''RGB'''			)
  return im

@torch.no_grad()
def 			a__     (		UpperCAmelCase							:			List[Any]				, UpperCAmelCase							:			List[Any]			)  ->						str:
  UpperCAmelCase       :							List[Any]  = ViTConfig(image_size=384				, qkv_bias=snake_case__			)
  UpperCAmelCase       :							List[Any]  = TrOCRConfig()

  # size of the architecture
  if "base" in checkpoint_url:
    UpperCAmelCase       :							str  = 768
  elif "large" in checkpoint_url:
    # use ViT-large encoder
    UpperCAmelCase       :							Dict  = 1_024
    UpperCAmelCase       :							Optional[Any]  = 4_096
    UpperCAmelCase       :							Any  = 24
    UpperCAmelCase       :							Any  = 16
    UpperCAmelCase       :							Any  = 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:
    UpperCAmelCase       :							Optional[int]  = False
    UpperCAmelCase       :							str  = '''relu'''
    UpperCAmelCase       :							Optional[Any]  = 1_024
    UpperCAmelCase       :							List[str]  = True
    UpperCAmelCase       :							int  = False
    UpperCAmelCase       :							List[Any]  = False

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

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

  UpperCAmelCase       :							Tuple  = 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():
    UpperCAmelCase       :							Optional[int]  = state_dict.pop(snake_case__			)
    if key.startswith('''decoder'''			) and "output_projection" not in key:
      UpperCAmelCase       :							Tuple  = val
    else:
      UpperCAmelCase       :							Union[str, Any]  = val

    # load state dict
  model.load_state_dict(snake_case__			)

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

  UpperCAmelCase       :							List[Any]  = processor(images=prepare_img(snake_case__			)				, return_tensors='''pt'''			).pixel_values

  # verify logits
  UpperCAmelCase       :							int  = torch.tensor([[model.config.decoder.decoder_start_token_id]]			)
  UpperCAmelCase       :							str  = model(pixel_values=snake_case__				, decoder_input_ids=snake_case__			)
  UpperCAmelCase       :							List[Any]  = outputs.logits

  UpperCAmelCase       :							Optional[Any]  = torch.Size([1, 1, 50_265]			)
  if "trocr-base-handwritten" in checkpoint_url:
    UpperCAmelCase       :							Optional[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:
    UpperCAmelCase       :							List[Any]  = 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:
    UpperCAmelCase       :							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:
    UpperCAmelCase       :							int  = 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]				, 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__":
     _lowerCamelCase				:   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."
     )
     _lowerCamelCase				:   Optional[int]					   =							parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
						from fairscale.optim import OSS


__a  			=					logging.get_logger(__name__)

__a  			=					{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=None       ,							*SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     List[str]:
					super().__init__(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if config is None:
									assert isinstance(self.model       ,							SCREAMING_SNAKE_CASE_       ), (
									    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
									    f''' {self.model.__class__}'''
									)
									lowercase_       =					self.model.config
					else:
									lowercase_       =					config

					lowercase_       =					data_args
					lowercase_       =					self.config.tgt_vocab_size if isinstance(self.config       ,							SCREAMING_SNAKE_CASE_       ) else self.config.vocab_size

					if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
									assert self.config.pad_token_id is not None, (
									    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
									    " calculation or doing label smoothing."
									)

					if self.config.pad_token_id is None and self.config.eos_token_id is not None:
									logger.warning(
									    f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
									    ''' padding..'''       )

					if self.args.label_smoothing == 0:
									lowercase_       =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
					else:
									# dynamically import label_smoothed_nll_loss
									from utils import label_smoothed_nll_loss

									lowercase_       =					label_smoothed_nll_loss
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Optional[Any]:
					if self.optimizer is None:
									lowercase_       =					['''bias''', '''LayerNorm.weight''']
									lowercase_       =					[
									    {
									        '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
									        '''weight_decay''': self.args.weight_decay,
									    },
									    {
									        '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
									        '''weight_decay''': 0.0,
									    },
									]
									lowercase_       =					Adafactor if self.args.adafactor else AdamW
									if self.args.adafactor:
													lowercase_       =					Adafactor
													lowercase_       =					{'''scale_parameter''': False, '''relative_step''': False}
									else:
													lowercase_       =					AdamW
													lowercase_       =					{
													    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
													    '''eps''': self.args.adam_epsilon,
													}
									lowercase_       =					self.args.learning_rate
									if self.sharded_ddp:
													lowercase_       =					OSS(
													    params=SCREAMING_SNAKE_CASE_       ,							optim=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							)
									else:
													lowercase_       =					optimizer_cls(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if self.lr_scheduler is None:
									lowercase_       =					self._get_lr_scheduler(SCREAMING_SNAKE_CASE_       )
					else:  # ignoring --lr_scheduler
									logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''       )
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     Dict:
					lowercase_       =					arg_to_scheduler[self.args.lr_scheduler]
					if self.args.lr_scheduler == "constant":
									lowercase_       =					schedule_func(self.optimizer       )
					elif self.args.lr_scheduler == "constant_w_warmup":
									lowercase_       =					schedule_func(self.optimizer       ,							num_warmup_steps=self.args.warmup_steps       )
					else:
									lowercase_       =					schedule_func(
									    self.optimizer       ,							num_warmup_steps=self.args.warmup_steps       ,							num_training_steps=SCREAMING_SNAKE_CASE_       )
					return scheduler
	def 						_lowercase  (       self  :			Tuple       )       ->     Optional[torch.utils.data.Sampler]:
					if isinstance(self.train_dataset       ,							torch.utils.data.IterableDataset       ):
									return None
					elif is_torch_tpu_available():
									return get_tpu_sampler(self.train_dataset       )
					else:
									if self.args.sortish_sampler:
													self.train_dataset.make_sortish_sampler(
													    self.args.per_device_train_batch_size       ,							distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)       ,							)

									return (
									    RandomSampler(self.train_dataset       )
									    if self.args.local_rank == -1
									    else DistributedSampler(self.train_dataset       )
									)
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Any:
					if self.args.label_smoothing == 0:
									if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
													# force training to ignore pad token
													lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[0]
													lowercase_       =					self.loss_fn(logits.view(-1       ,							logits.shape[-1]       )       ,							labels.view(-1       )       )
									else:
													# compute usual loss via models
													lowercase_  ,	lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							labels=SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[:2]
					else:
									# compute label smoothed loss
									lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[0]
									lowercase_       =					torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,							dim=-1       )
									lowercase_  ,	lowercase_       =					self.loss_fn(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							self.args.label_smoothing       ,							ignore_index=self.config.pad_token_id       )
					return loss, logits
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     List[Any]:
					lowercase_       =					inputs.pop('''labels'''       )
					lowercase_  ,	lowercase_       =					self._compute_loss(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					return loss
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			nn.Module       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, Union[torch.Tensor, Any]]       ,							SCREAMING_SNAKE_CASE_  :			bool       ,							SCREAMING_SNAKE_CASE_  :			Optional[List[str]] = None       ,							)       ->     Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
					lowercase_       =					self._prepare_inputs(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					{
					    '''max_length''': self.data_args.val_max_target_length
					    if self.data_args is not None
					    else self.config.max_length,
					    '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
					}

					if self.args.predict_with_generate and not self.args.prediction_loss_only:
									lowercase_       =					self.model.generate(
									    inputs['''input_ids''']       ,							attention_mask=inputs['''attention_mask''']       ,							**SCREAMING_SNAKE_CASE_       ,							)
									# in case the batch is shorter than max length, the output should be padded
									if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
													lowercase_       =					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_       ,							gen_kwargs['''max_length''']       )

					lowercase_       =					inputs.pop('''labels'''       )
					with torch.no_grad():
									# compute loss on predict data
									lowercase_  ,	lowercase_       =					self._compute_loss(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					loss.mean().detach()
					if self.args.prediction_loss_only:
									return (loss, None, None)

					lowercase_       =					generated_tokens if self.args.predict_with_generate else logits

					if labels.shape[-1] < gen_kwargs["max_length"]:
									lowercase_       =					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_       ,							gen_kwargs['''max_length''']       )

					return (loss, logits, labels)




	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Tuple:
					# If PAD token is not defined at least EOS token has to be defined
					lowercase_       =					self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

					if pad_token_id is None:
									raise ValueError(
									    '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
									    f''' padded to `max_length`={max_length}'''       )

					lowercase_       =					pad_token_id * torch.ones(
					    (tensor.shape[0], max_length)       ,							dtype=tensor.dtype       ,							device=tensor.device       )
					lowercase_       =					tensor
					return padded_tensor




