



'''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											=						logging.get_logger(__name__)

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

    __lowerCamelCase       :       List[str]           = in_proj_weight[
        : encoder_config.hidden_size, :
    ]
    __lowerCamelCase       :       Dict           = in_proj_weight[
        encoder_config.hidden_size : encoder_config.hidden_size * 2, :
    ]
    __lowerCamelCase       :       str           = in_proj_weight[
        -encoder_config.hidden_size :, :
    ]

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      str:
  __lowerCamelCase       :       Dict           = dct.pop(_lowerCAmelCase       )
  __lowerCamelCase       :       Any           = val

def        a_     (							_lowerCAmelCase       )      ->      List[str]:
  if "handwritten" in checkpoint_url:
    __lowerCamelCase       :       Dict           = '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       :       Optional[int]           = Image.open(requests.get(_lowerCAmelCase       ,stream=_lowerCAmelCase       ).raw       ).convert('RGB'       )
  return im

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

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

  # load HuggingFace model
  __lowerCamelCase       :       List[str]           = ViTModel(_lowerCAmelCase       ,add_pooling_layer=_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[int]           = TrOCRForCausalLM(_lowerCAmelCase       )
  __lowerCamelCase       :       List[str]           = VisionEncoderDecoderModel(encoder=_lowerCAmelCase       ,decoder=_lowerCAmelCase       )
  model.eval()

  # load state_dict of original model, rename some keys
  __lowerCamelCase       :       Union[str, Any]           = torch.hub.load_state_dict_from_url(_lowerCAmelCase       ,map_location='cpu'       ,check_hash=_lowerCAmelCase       )['model']

  __lowerCamelCase       :       List[str]           = create_rename_keys(_lowerCAmelCase       ,_lowerCAmelCase       )
  for src, dest in rename_keys:
    rename_key(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
  read_in_q_k_v(_lowerCAmelCase       ,_lowerCAmelCase       )

  # 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       :       Union[str, Any]           = state_dict.pop(_lowerCAmelCase       )
    if key.startswith('decoder'       ) and "output_projection" not in key:
      __lowerCamelCase       :       Dict           = val
    else:
      __lowerCamelCase       :       Dict           = val

    # load state dict
  model.load_state_dict(_lowerCAmelCase       )

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

  __lowerCamelCase       :       Tuple           = processor(images=prepare_img(_lowerCAmelCase       )       ,return_tensors='pt'       ).pixel_values

  # verify logits
  __lowerCamelCase       :       List[Any]           = torch.tensor([[model.config.decoder.decoder_start_token_id]]       )
  __lowerCamelCase       :       str           = model(pixel_values=_lowerCAmelCase       ,decoder_input_ids=_lowerCAmelCase       )
  __lowerCamelCase       :       str           = outputs.logits

  __lowerCamelCase       :       Optional[Any]           = torch.Size([1, 1, 50265]       )
  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       :       Dict           = 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       :       Dict           = 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       :       str           = 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]       ,_lowerCAmelCase       ,atol=1E-3       ), "First elements of logits not as expected"

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


if __name__ == "__main__":
   _UpperCamelCase											=						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											=						parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











'''simple docstring'''





from collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






