


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__				:	Tuple					       =					logging.get_logger(__name__)


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

      lowerCamelCase_						:   Dict		= in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      lowerCamelCase_						:   Tuple		= in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      lowerCamelCase_						:   Optional[Any]		= in_proj_weight[
          -encoder_config.hidden_size :, :
      ]


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Dict						,						A__						: Optional[int]					)     ->    Tuple:
   lowerCamelCase_						:   Optional[int]		= dct.pop(A__					)
   lowerCamelCase_						:   Union[str, Any]		= val


def    __lowerCamelCase   (      A__						: Any					)     ->    Any:
   if "handwritten" in checkpoint_url:
      lowerCamelCase_						:   Tuple		= """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:
      lowerCamelCase_						:   Optional[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"""
   lowerCamelCase_						:   Any		= Image.open(requests.get(A__						,						stream=A__					).raw					).convert("""RGB"""					)
   return im


@torch.no_grad()
def    __lowerCamelCase   (      A__						: Any						,						A__						: Optional[Any]					)     ->    Tuple:
   lowerCamelCase_						:   Any		= ViTConfig(image_size=384						,						qkv_bias=A__					)
   lowerCamelCase_						:   Tuple		= TrOCRConfig()

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

   # load HuggingFace model
   lowerCamelCase_						:   Union[str, Any]		= ViTModel(A__						,						add_pooling_layer=A__					)
   lowerCamelCase_						:   List[str]		= TrOCRForCausalLM(A__					)
   lowerCamelCase_						:   str		= VisionEncoderDecoderModel(encoder=A__						,						decoder=A__					)
   model.eval()

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

   lowerCamelCase_						:   Union[str, Any]		= create_rename_keys(A__						,						A__					)
   for src, dest in rename_keys:
      rename_key(A__						,						A__						,						A__					)
   read_in_q_k_v(A__						,						A__					)

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

    # load state dict
   model.load_state_dict(A__					)

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

   lowerCamelCase_						:   int		= processor(images=prepare_img(A__					)						,						return_tensors="""pt"""					).pixel_values

   # verify logits
   lowerCamelCase_						:   Union[str, Any]		= torch.tensor([[model.config.decoder.decoder_start_token_id]]					)
   lowerCamelCase_						:   str		= model(pixel_values=A__						,						decoder_input_ids=A__					)
   lowerCamelCase_						:   Tuple		= outputs.logits

   lowerCamelCase_						:   List[str]		= torch.Size([1, 1, 5_0265]					)
   if "trocr-base-handwritten" in checkpoint_url:
      lowerCamelCase_						:   List[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:
      lowerCamelCase_						:   Optional[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:
      lowerCamelCase_						:   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:
      lowerCamelCase_						:   Optional[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]						,						A__						,						atol=1e-3					), "First elements of logits not as expected"

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


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









class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Tuple		,				__a				:				int							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= n
          lowerCamelCase_						:   Dict		= [None] * self.n
          lowerCamelCase_						:   int		= 0  # index of the first element
          lowerCamelCase_						:   str		= 0
          lowerCamelCase_						:   Optional[Any]		= 0



       def __len__(						self				:				List[str]							)					->int:
          return self.size



       def 				_lowerCAmelCase							(						self				:				str							)					->bool:
          return self.size == 0



       def 				_lowerCAmelCase							(						self				:				str							)					->Any:
          return False if self.is_empty() else self.array[self.front]



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				int							)					->List[Any]:
          if self.size >= self.n:
             raise Exception("""QUEUE IS FULL"""							)

          lowerCamelCase_						:   Tuple		= data
          lowerCamelCase_						:   Tuple		= (self.rear + 1) % self.n
          self.size += 1
          return self




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->int:
          if self.size == 0:
             raise Exception("""UNDERFLOW"""							)

          lowerCamelCase_						:   str		= self.array[self.front]
          lowerCamelCase_						:   str		= None
          lowerCamelCase_						:   Optional[Any]		= (self.front + 1) % self.n
          self.size -= 1
          return temp





