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    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     Optional[int]	):
 UpperCamelCase_					:						Dict =					[]
 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					:     Optional[Any]  ,  lowerCamelCase					:     Union[str, Any]	):

 for i in range(encoder_config.num_hidden_layers	):
  # queries, keys and values (only weights, no biases)
  UpperCamelCase_					:						Dict =					state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"	)

  UpperCamelCase_					:						Tuple =					in_proj_weight[
      : encoder_config.hidden_size, :
  ]
  UpperCamelCase_					:						List[str] =					in_proj_weight[
      encoder_config.hidden_size : encoder_config.hidden_size * 2, :
  ]
  UpperCamelCase_					:						List[str] =					in_proj_weight[
      -encoder_config.hidden_size :, :
  ]





def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					dct.pop(SCREAMING_SNAKE_CASE_	)
 UpperCamelCase_					:						List[str] =					val





def    __lowercase     (						lowerCamelCase					:     Optional[int]	):

 if "handwritten" in checkpoint_url:
  UpperCamelCase_					:						List[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_					:						str =					"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_					:						Optional[Any] =					Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,  stream=SCREAMING_SNAKE_CASE_	).raw	).convert('RGB'	)
 return im





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						List[Any] =					ViTConfig(image_size=384  ,  qkv_bias=SCREAMING_SNAKE_CASE_	)
 UpperCamelCase_					:						Optional[int] =					TrOCRConfig()

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

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

 # load state_dict of original model, rename some keys
 UpperCamelCase_					:						str =					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_  ,  map_location='cpu'  ,  check_hash=SCREAMING_SNAKE_CASE_	)["model"]

 UpperCamelCase_					:						Union[str, Any] =					create_rename_keys(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_	)
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_	)
 read_in_q_k_v(SCREAMING_SNAKE_CASE_  ,  SCREAMING_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_					:						Any =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
  if key.startswith('decoder'	) and "output_projection" not in key:
   UpperCamelCase_					:						Union[str, Any] =					val
  else:
   UpperCamelCase_					:						Tuple =					val

  # load state dict
 model.load_state_dict(SCREAMING_SNAKE_CASE_	)

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

 UpperCamelCase_					:						int =					processor(images=prepare_img(SCREAMING_SNAKE_CASE_	)  ,  return_tensors='pt'	).pixel_values

 # verify logits
 UpperCamelCase_					:						Dict =					torch.tensor([[model.config.decoder.decoder_start_token_id]]	)
 UpperCamelCase_					:						int =					model(pixel_values=SCREAMING_SNAKE_CASE_  ,  decoder_input_ids=SCREAMING_SNAKE_CASE_	)
 UpperCamelCase_					:						Tuple =					outputs.logits

 UpperCamelCase_					:						Optional[int] =					torch.Size([1, 1, 50265]	)
 if "trocr-base-handwritten" in checkpoint_url:
  UpperCamelCase_					:						Any =					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_					:						str =					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_					:						List[Any] =					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_					:						Optional[Any] =					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]  ,  SCREAMING_SNAKE_CASE_  ,  atol=1e-3	), "First elements of logits not as expected"

 Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
 print(F"Saving model to {pytorch_dump_folder_path}"	)
 model.save_pretrained(SCREAMING_SNAKE_CASE_	)
 print(F"Saving processor to {pytorch_dump_folder_path}"	)
 processor.save_pretrained(SCREAMING_SNAKE_CASE_	)


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)







def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for i in range(len(SCREAMING_SNAKE_CASE_	) - 1      ,						0      ,						-1	):
    lowerCamelCase :					Tuple    =				False

    for j in range(SCREAMING_SNAKE_CASE_      ,						0      ,						-1	):
      if unsorted[j] < unsorted[j - 1]:
        lowerCamelCase    ,	lowerCamelCase :					int    =				unsorted[j - 1], unsorted[j]
        lowerCamelCase :					Optional[int]    =				True

    for j in range(SCREAMING_SNAKE_CASE_	):
      if unsorted[j] > unsorted[j + 1]:
        lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				unsorted[j + 1], unsorted[j]
        lowerCamelCase :					Optional[Any]    =				True

    if not swapped:
      break
  return unsorted


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case             =     input('''Enter numbers separated by a comma:\n''').strip()
   _snake_case             =     [int(item) for item in user_input.split(''',''')]
   print(f'''{cocktail_shaker_sort(unsorted) = }''')



