






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__      :	Optional[Any]       	=      logging.get_logger(__name__)






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

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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     dct.pop(__lowercase)
   UpperCamelCase_									=     val






def     _snake_case    (__lowercase):
   if "handwritten" in checkpoint_url:
      UpperCamelCase_									=     '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_									=     '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_									=     Image.open(requests.get(__lowercase    ,      stream=__lowercase).raw).convert('RGB')
   return im






@torch.no_grad()
def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     ViTConfig(image_size=384    ,      qkv_bias=__lowercase)
   UpperCamelCase_									=     TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      UpperCamelCase_									=     768
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      UpperCamelCase_									=     1024
      UpperCamelCase_									=     4096
      UpperCamelCase_									=     24
      UpperCamelCase_									=     16
      UpperCamelCase_									=     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_									=     False
      UpperCamelCase_									=     'relu'
      UpperCamelCase_									=     1024
      UpperCamelCase_									=     True
      UpperCamelCase_									=     False
      UpperCamelCase_									=     False

   # load HuggingFace model
   UpperCamelCase_									=     ViTModel(__lowercase    ,      add_pooling_layer=__lowercase)
   UpperCamelCase_									=     TrOCRForCausalLM(__lowercase)
   UpperCamelCase_									=     VisionEncoderDecoderModel(encoder=__lowercase    ,      decoder=__lowercase)
   model.eval()

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

   UpperCamelCase_									=     create_rename_keys(__lowercase    ,      __lowercase)
   for src, dest in rename_keys:
      rename_key(__lowercase    ,      __lowercase    ,      __lowercase)
   read_in_q_k_v(__lowercase    ,      __lowercase)

   # 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_									=     state_dict.pop(__lowercase)
      if key.startswith('decoder') and "output_projection" not in key:
         UpperCamelCase_									=     val
      else:
         UpperCamelCase_									=     val

    # load state dict
   model.load_state_dict(__lowercase)

   # Check outputs on an image
   UpperCamelCase_									=     ViTImageProcessor(size=encoder_config.image_size)
   UpperCamelCase_									=     RobertaTokenizer.from_pretrained('roberta-large')
   UpperCamelCase_									=     TrOCRProcessor(__lowercase    ,      __lowercase)

   UpperCamelCase_									=     processor(images=prepare_img(__lowercase)    ,      return_tensors='pt').pixel_values

   # verify logits
   UpperCamelCase_									=     torch.tensor([[model.config.decoder.decoder_start_token_id]])
   UpperCamelCase_									=     model(pixel_values=__lowercase    ,      decoder_input_ids=__lowercase)
   UpperCamelCase_									=     outputs.logits

   UpperCamelCase_									=     torch.Size([1, 1, 50265])
   if "trocr-base-handwritten" in checkpoint_url:
      UpperCamelCase_									=     torch.tensor(
          [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311])
   elif "trocr-large-handwritten" in checkpoint_url:
      UpperCamelCase_									=     torch.tensor(
          [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170])
   elif "trocr-base-printed" in checkpoint_url:
      UpperCamelCase_									=     torch.tensor(
          [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210])
   elif "trocr-large-printed" in checkpoint_url:
      UpperCamelCase_									=     torch.tensor(
          [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535])

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :10]    ,      __lowercase    ,      atol=1e-3), "First elements of logits not as expected"

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


if __name__ == "__main__":
 snake_case__      :	Dict       	=      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)













snake_case__      :	List[Any]       	=      """Tobias Carryer"""

from time import time


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=int(time() ) )			-> Tuple:  # noqa: B008
     UpperCamelCase_									=     multiplier
     UpperCamelCase_									=     increment
     UpperCamelCase_									=     modulo
     UpperCamelCase_									=     seed




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     (self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__      :	List[Any]       	=      LinearCongruentialGenerator(1_6_6_4_5_2_5, 1_0_1_3_9_0_4_2_2_3, 2 << 3_1)
 while True:
  print(lcg.next_number())





