




"""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()
A				=							logging.get_logger(__name__)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       []
			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				)      ->      int:


			"""simple docstring"""
			for i in range(encoder_config.num_hidden_layers				):
						# queries, keys and values (only weights, no biases)
						__UpperCAmelCase						:			Optional[Any]												=       state_dict.pop(f"encoder.deit.blocks.{i}.attn.qkv.weight"				)

						__UpperCAmelCase						:			List[Any]												=       in_proj_weight[
						    : encoder_config.hidden_size, :
						]
						__UpperCAmelCase						:			List[str]												=       in_proj_weight[
						    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
						]
						__UpperCAmelCase						:			List[Any]												=       in_proj_weight[
						    -encoder_config.hidden_size :, :
						]

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Any:


			"""simple docstring"""
			__UpperCAmelCase						:			str												=       dct.pop(UpperCamelCase				)
			__UpperCAmelCase						:			List[Any]												=       val

def        _UpperCamelCase     (      UpperCamelCase				)      ->      str:


			"""simple docstring"""
			if "handwritten" in checkpoint_url:
						__UpperCAmelCase						:			Any												=       "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						:			Tuple												=       Image.open(requests.get(UpperCamelCase					,							stream=UpperCamelCase				).raw				).convert("RGB"				)
			return im

@torch.no_grad()
def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      List[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			List[str]												=       ViTConfig(image_size=384					,							qkv_bias=UpperCamelCase				)
			__UpperCAmelCase						:			Any												=       TrOCRConfig()

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

			# load HuggingFace model
			__UpperCAmelCase						:			Optional[Any]												=       ViTModel(UpperCamelCase					,							add_pooling_layer=UpperCamelCase				)
			__UpperCAmelCase						:			str												=       TrOCRForCausalLM(UpperCamelCase				)
			__UpperCAmelCase						:			Optional[int]												=       VisionEncoderDecoderModel(encoder=UpperCamelCase					,							decoder=UpperCamelCase				)
			model.eval()

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

			__UpperCAmelCase						:			Optional[Any]												=       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():
						__UpperCAmelCase						:			Optional[int]												=       state_dict.pop(UpperCamelCase				)
						if key.startswith("decoder"				) and "output_projection" not in key:
									__UpperCAmelCase						:			Any												=       val
						else:
									__UpperCAmelCase						:			int												=       val

    # load state dict
			model.load_state_dict(UpperCamelCase				)

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

			__UpperCAmelCase						:			Optional[Any]												=       processor(images=prepare_img(UpperCamelCase				)					,							return_tensors="pt"				).pixel_values

			# verify logits
			__UpperCAmelCase						:			Dict												=       torch.tensor([[model.config.decoder.decoder_start_token_id]]				)
			__UpperCAmelCase						:			Optional[Any]												=       model(pixel_values=UpperCamelCase					,							decoder_input_ids=UpperCamelCase				)
			__UpperCAmelCase						:			List[Any]												=       outputs.logits

			__UpperCAmelCase						:			List[str]												=       torch.Size([1, 1, 5_0265]				)
			if "trocr-base-handwritten" in checkpoint_url:
						__UpperCAmelCase						:			str												=       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						:			int												=       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						:			str												=       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						:			Union[str, 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__":
					A				=							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."""
					)
					A				=							parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   params

								__magic_name__		:Any   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   np.array([len(__lowerCAmelCase  ) for t in data]  )

								self.check()
								self.remove_long_sequences()
								self.remove_empty_sequences()
								self.remove_unknown_sequences()
								self.check()
								self.print_statistics()







				def __getitem__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return (self.token_ids[index], self.lengths[index])







				def __len__(    self  ):
								"""simple docstring"""
								return len(self.lengths  )







				def       A						(    self  ):
								"""simple docstring"""
								assert len(self.token_ids  ) == len(self.lengths  )
								assert all(self.lengths[i] == len(self.token_ids[i]  ) for i in range(len(self.lengths  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   self.params.max_model_input_size
								__magic_name__		:int   =   self.lengths > max_len
								logger.info(F'''Splitting {sum(__lowerCAmelCase  )} too long sequences.'''  )

								def divide_chunks(__lowerCAmelCase							,       __lowerCAmelCase  ):
												return [l[i : i + n] for i in range(0							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )]

								__magic_name__		:Optional[int]   =   []
								__magic_name__		:List[Any]   =   []
								if self.params.mlm:
												__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
								else:
												__magic_name__  ,					__magic_name__		:Tuple   =   self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

								for seq_, len_ in zip(self.token_ids							,       self.lengths  ):
												assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
												if len_ <= max_len:
																new_tok_ids.append(seq_  )
																new_lengths.append(len_  )
												else:
																__magic_name__		:int   =   []
																for sub_s in divide_chunks(seq_							,       max_len - 2  ):
																				if sub_s[0] != cls_id:
																								__magic_name__		:List[Any]   =   np.insert(__lowerCAmelCase							,       0							,       __lowerCAmelCase  )
																				if sub_s[-1] != sep_id:
																								__magic_name__		:Union[str, Any]   =   np.insert(__lowerCAmelCase							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )
																				assert len(__lowerCAmelCase  ) <= max_len
																				assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																				sub_seqs.append(__lowerCAmelCase  )

																new_tok_ids.extend(__lowerCAmelCase  )
																new_lengths.extend([len(__lowerCAmelCase  ) for l in sub_seqs]  )

								__magic_name__		:Tuple   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   np.array(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   len(self  )
								__magic_name__		:int   =   self.lengths > 1_1
								__magic_name__		:List[str]   =   self.token_ids[indices]
								__magic_name__		:Union[str, Any]   =   self.lengths[indices]
								__magic_name__		:List[str]   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if "unk_token" not in self.params.special_tok_ids:
												return
								else:
												__magic_name__		:Tuple   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Dict   =   len(self  )
								__magic_name__		:Tuple   =   np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
								__magic_name__		:int   =   (unk_occs / self.lengths) < 0.5
								__magic_name__		:str   =   self.token_ids[indices]
								__magic_name__		:str   =   self.lengths[indices]
								__magic_name__		:Any   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if not self.params.is_master:
												return
								logger.info(F'''{len(self  )} sequences'''  )
								# data_len = sum(self.lengths)
								# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
								# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

								# unk_idx = self.params.special_tok_ids['unk_token']
								# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
								# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   [t[0] for t in batch]
								__magic_name__		:List[Any]   =   [t[1] for t in batch]
								assert len(__lowerCAmelCase  ) == len(__lowerCAmelCase  )

								# Max for paddings
								__magic_name__		:Tuple   =   max(__lowerCAmelCase  )

								# Pad token ids
								if self.params.mlm:
												__magic_name__		:Any   =   self.params.special_tok_ids['''pad_token''']
								else:
												__magic_name__		:str   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Any   =   [list(t.astype(__lowerCAmelCase  )  ) + [pad_idx] * (max_seq_len_ - len(__lowerCAmelCase  )) for t in token_ids]
								assert len(tk_  ) == len(__lowerCAmelCase  )
								assert all(len(__lowerCAmelCase  ) == max_seq_len_ for t in tk_  )

								__magic_name__		:Optional[int]   =   torch.tensor(tk_  )  # (bs, max_seq_len_)
								__magic_name__		:Optional[int]   =   torch.tensor(__lowerCAmelCase  )  # (bs)
								return tk_t, lg_t






