'''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()
UpperCamelCase__     :	str				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Dict    ):
   '''simple docstring'''
   UpperCAmelCase__							:				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 __UpperCamelCase(   _A					:				int  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   for i in range(encoder_config.num_hidden_layers    ):
      # queries, keys and values (only weights, no biases)
      UpperCAmelCase__							:				str   				=  state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''    )

      UpperCAmelCase__							:				Optional[int]   				=  in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      UpperCAmelCase__							:				Dict   				=  in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      UpperCAmelCase__							:				Optional[Any]   				=  in_proj_weight[
          -encoder_config.hidden_size :, :
      ]







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				List[str]  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  dct.pop(_A    )
   UpperCAmelCase__							:				Union[str, Any]   				=  val







def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''
   if "handwritten" in checkpoint_url:
      UpperCAmelCase__							:				Optional[int]   				=  '''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__							:				int   				=  '''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__							:				str   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    ).convert('''RGB'''    )
   return im







@torch.no_grad()
def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Dict    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  ViTConfig(image_size=3_84  ,			qkv_bias=_A    )
   UpperCAmelCase__							:				List[Any]   				=  TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      UpperCAmelCase__							:				Dict   				=  7_68
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      UpperCAmelCase__							:				Optional[int]   				=  10_24
      UpperCAmelCase__							:				Optional[int]   				=  40_96
      UpperCAmelCase__							:				Optional[int]   				=  24
      UpperCAmelCase__							:				List[str]   				=  16
      UpperCAmelCase__							:				List[Any]   				=  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:
      UpperCAmelCase__							:				Optional[int]   				=  False
      UpperCAmelCase__							:				Tuple   				=  '''relu'''
      UpperCAmelCase__							:				List[Any]   				=  10_24
      UpperCAmelCase__							:				str   				=  True
      UpperCAmelCase__							:				int   				=  False
      UpperCAmelCase__							:				Optional[int]   				=  False

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

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

   UpperCAmelCase__							:				Optional[int]   				=  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():
      UpperCAmelCase__							:				Dict   				=  state_dict.pop(_A    )
      if key.startswith('''decoder'''    ) and "output_projection" not in key:
         UpperCAmelCase__							:				int   				=  val
      else:
         UpperCAmelCase__							:				List[Any]   				=  val

    # load state dict
   model.load_state_dict(_A    )

   # Check outputs on an image
   UpperCAmelCase__							:				Any   				=  ViTImageProcessor(size=encoder_config.image_size    )
   UpperCAmelCase__							:				Optional[Any]   				=  RobertaTokenizer.from_pretrained('''roberta-large'''    )
   UpperCAmelCase__							:				Optional[int]   				=  TrOCRProcessor(_A  ,			_A    )

   UpperCAmelCase__							:				Tuple   				=  processor(images=prepare_img(_A    )  ,			return_tensors='''pt'''    ).pixel_values

   # verify logits
   UpperCAmelCase__							:				Optional[Any]   				=  torch.tensor([[model.config.decoder.decoder_start_token_id]]    )
   UpperCAmelCase__							:				int   				=  model(pixel_values=_A  ,			decoder_input_ids=_A    )
   UpperCAmelCase__							:				List[Any]   				=  outputs.logits

   UpperCAmelCase__							:				int   				=  torch.Size([1, 1, 5_02_65]    )
   if "trocr-base-handwritten" in checkpoint_url:
      UpperCAmelCase__							:				int   				=  torch.tensor(
          [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]    )
   elif "trocr-large-handwritten" in checkpoint_url:
      UpperCAmelCase__							:				Optional[int]   				=  torch.tensor(
          [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]    )
   elif "trocr-base-printed" in checkpoint_url:
      UpperCAmelCase__							:				Dict   				=  torch.tensor(
          [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]    )
   elif "trocr-large-printed" in checkpoint_url:
      UpperCAmelCase__							:				Dict   				=  torch.tensor(
          [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]    )

   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__":
  UpperCamelCase__     :	Any				=			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__     :	Union[str, Any]				=			parser.parse_args()
  convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





'''simple docstring'''

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase__     :	Tuple				=			TypeVar('T')


class 				_lowercase	(      Generic[T]   ):
      '''simple docstring'''


      UpperCAmelCase_      :      deque[T]  # Cache store of keys
      UpperCAmelCase_      :      set[T]  # References of the keys in cache
      UpperCAmelCase_      :      int      							=   10  # Maximum capacity of cache




      def __init__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  deque()
         UpperCAmelCase__							:				Optional[int]   				=  set()
         if not n:
            UpperCAmelCase__							:				List[Any]   				=  sys.maxsize
         elif n < 0:
            raise ValueError('''n should be an integer greater than 0.'''							)
         else:
            UpperCAmelCase__							:				Any   				=  n




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         if x not in self.key_reference:
            if len(self.dq_store							) == LRUCache._MAX_CAPACITY:
               UpperCAmelCase__							:				int   				=  self.dq_store.pop()
               self.key_reference.remove(lowerCamelCase_							)
         else:
            self.dq_store.remove(lowerCamelCase_							)

         self.dq_store.appendleft(lowerCamelCase_							)
         self.key_reference.add(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         for k in self.dq_store:
            print(lowerCamelCase_							)




      def __repr__(				self							)			->			str:



         '''simple docstring'''



         return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store							)}'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCamelCase__     :	LRUCache[str | int]				=			LRUCache(4)
  lru_cache.refer('A')
  lru_cache.refer(2)
  lru_cache.refer(3)
  lru_cache.refer('A')
  lru_cache.refer(4)
  lru_cache.refer(5)
  lru_cache.display()

  print(lru_cache)
  assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




