

'''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()
__snake_case	: List[Any]       				=							logging.get_logger(__name__)



def 					_lowercase						(   lowerCamelCase__			:		List[Any],		lowerCamelCase__			:		Tuple							):
				_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__			:		List[str],		lowerCamelCase__			:		int							):







				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__			:		str,		lowerCamelCase__			:		Optional[int],		lowerCamelCase__			:		Optional[Any]							):
				_a        =   dct.pop(__UpperCAmelCase							)
				_a        =   val



def 					_lowercase						(   lowerCamelCase__			:		Union[str, 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(__UpperCAmelCase,		stream=__UpperCAmelCase							).raw							).convert("RGB"							)
				return im



@torch.no_grad()
def 					_lowercase						(   lowerCamelCase__			:		str,		lowerCamelCase__			:		Tuple							):
				_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(
								    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]							)
				elif "trocr-large-handwritten" in checkpoint_url:
								_a        =   torch.tensor(
								    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]							)
				elif "trocr-base-printed" in checkpoint_url:
								_a        =   torch.tensor(
								    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]							)
				elif "trocr-large-printed" in checkpoint_url:
								_a        =   torch.tensor(
								    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]							)

				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__":
							__snake_case	: str       				=							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	: Optional[int]       				=							parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












'''simple docstring'''

from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a   	=    logging.get_logger(__name__)

# TODO Update this
a   	=    {
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class __a			( _snake_case      ):
   __UpperCamelCase :			str						   =						'esm'





   def __init__(       self					:      Tuple					,lowerCamelCase					:      List[Any]=None					,lowerCamelCase					:      str=None					,lowerCamelCase					:      Any=None					,lowerCamelCase					:      Union[str, Any]=768					,lowerCamelCase					:      Tuple=12					,lowerCamelCase					:      int=12					,lowerCamelCase					:      Optional[int]=3072					,lowerCamelCase					:      List[Any]=0.1					,lowerCamelCase					:      Optional[int]=0.1					,lowerCamelCase					:      Any=1026					,lowerCamelCase					:      str=0.02					,lowerCamelCase					:      int=1E-1_2					,lowerCamelCase					:      Union[str, Any]="absolute"					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      str=None					,lowerCamelCase					:      Optional[int]=False					,lowerCamelCase					:      int=False					,lowerCamelCase					:      Union[str, Any]=None					,lowerCamelCase					:      Any=None					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

      super().__init__(pad_token_id=lowerCamelCase					,mask_token_id=lowerCamelCase					,**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							position_embedding_type
      __SCREAMING_SNAKE_CASE   				=							use_cache
      __SCREAMING_SNAKE_CASE   				=							emb_layer_norm_before
      __SCREAMING_SNAKE_CASE   				=							token_dropout
      __SCREAMING_SNAKE_CASE   				=							is_folding_model
      if is_folding_model:
         if esmfold_config is None:
            logger.info("""No esmfold_config supplied for folding model, using default values."""     )
            __SCREAMING_SNAKE_CASE   				=							EsmFoldConfig()
         elif isinstance(lowerCamelCase					,lowerCamelCase     ):
            __SCREAMING_SNAKE_CASE   				=							EsmFoldConfig(**lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							esmfold_config
         if vocab_list is None:
            logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"""     )
            __SCREAMING_SNAKE_CASE   				=							get_default_vocab_list()
         else:
            __SCREAMING_SNAKE_CASE   				=							vocab_list
      else:
         __SCREAMING_SNAKE_CASE   				=							None
         __SCREAMING_SNAKE_CASE   				=							None
      if self.esmfold_config is not None and getattr(self.esmfold_config					,"""use_esm_attn_map"""					,lowerCamelCase     ):
         raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"""     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super().to_dict()
      if isinstance(self.esmfold_config					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							self.esmfold_config.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						None
   __UpperCamelCase :			bool						   =						True
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			float						   =						0

   __UpperCamelCase :			bool						   =						True
   __UpperCamelCase :			bool						   =						False

   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			"TrunkConfig"						   =						None





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      if self.trunk is None:
         __SCREAMING_SNAKE_CASE   				=							TrunkConfig()
      elif isinstance(self.trunk					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							TrunkConfig(**self.trunk     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							asdict(self     )
      __SCREAMING_SNAKE_CASE   				=							self.trunk.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						48
   __UpperCamelCase :			int						   =						1024
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			float						   =						0
   __UpperCamelCase :			float						   =						0
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			int						   =						4
   __UpperCamelCase :			Optional[int]						   =						128
   __UpperCamelCase :			"StructureModuleConfig"						   =						None





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      if self.structure_module is None:
         __SCREAMING_SNAKE_CASE   				=							StructureModuleConfig()
      elif isinstance(self.structure_module					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							StructureModuleConfig(**self.structure_module     )

      if self.max_recycles <= 0:
         raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}."""     )
      if self.sequence_state_dim % self.sequence_state_dim != 0:
         raise ValueError(
             """`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
             f""" {self.sequence_state_dim} and {self.sequence_state_dim}."""     )
      if self.pairwise_state_dim % self.pairwise_state_dim != 0:
         raise ValueError(
             """`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
             f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""     )

      __SCREAMING_SNAKE_CASE   				=							self.sequence_state_dim // self.sequence_head_width
      __SCREAMING_SNAKE_CASE   				=							self.pairwise_state_dim // self.pairwise_head_width

      if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
         raise ValueError(
             """`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
             f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""     )
      if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
         raise ValueError(
             """`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
             f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""     )
      if self.pairwise_state_dim % 2 != 0:
         raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""     )

      if self.dropout >= 0.4:
         raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}."""     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							asdict(self     )
      __SCREAMING_SNAKE_CASE   				=							self.structure_module.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						384
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						16
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						12
   __UpperCamelCase :			int						   =						4
   __UpperCamelCase :			int						   =						8
   __UpperCamelCase :			float						   =						0.1
   __UpperCamelCase :			int						   =						8
   __UpperCamelCase :			int						   =						1
   __UpperCamelCase :			int						   =						2
   __UpperCamelCase :			int						   =						7
   __UpperCamelCase :			int						   =						10
   __UpperCamelCase :			float						   =						1E-8
   __UpperCamelCase :			float						   =						1E5





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return asdict(self     )





def     __magic_name__							(				)    ->       Dict:

   '''simple docstring'''







   return (
       "<cls>",
       "<pad>",
       "<eos>",
       "<unk>",
       "L",
       "A",
       "G",
       "V",
       "S",
       "E",
       "R",
       "T",
       "I",
       "D",
       "P",
       "K",
       "Q",
       "N",
       "F",
       "Y",
       "M",
       "H",
       "W",
       "C",
       "X",
       "B",
       "U",
       "Z",
       "O",
       ".",
       "-",
       "<null_1>",
       "<mask>",
   )





