






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    				=		logging.get_logger(__name__)


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    []
  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__			(     lowercase						,     lowercase				):

  """simple docstring"""






  for i in range(encoder_config.num_hidden_layers				):
    # queries, keys and values (only weights, no biases)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''				)

    SCREAMING_SNAKE_CASE       :      Tuple							=    in_proj_weight[
        : encoder_config.hidden_size, :
    ]
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    in_proj_weight[
        encoder_config.hidden_size : encoder_config.hidden_size * 2, :
    ]
    SCREAMING_SNAKE_CASE       :      int							=    in_proj_weight[
        -encoder_config.hidden_size :, :
    ]


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Any							=    dct.pop(lowercase				)
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    val


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  if "handwritten" in checkpoint_url:
    SCREAMING_SNAKE_CASE       :      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:
    SCREAMING_SNAKE_CASE       :      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"
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    Image.open(requests.get(lowercase						,     stream=lowercase				).raw				).convert("RGB"				)
  return im


@torch.no_grad()
def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    ViTConfig(image_size=384						,     qkv_bias=lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    TrOCRConfig()

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

  # load HuggingFace model
  SCREAMING_SNAKE_CASE       :      int							=    ViTModel(lowercase						,     add_pooling_layer=lowercase				)
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    TrOCRForCausalLM(lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    VisionEncoderDecoderModel(encoder=lowercase						,     decoder=lowercase				)
  model.eval()

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

  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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():
    SCREAMING_SNAKE_CASE       :      str							=    state_dict.pop(lowercase				)
    if key.startswith("decoder"				) and "output_projection" not in key:
      SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    val
    else:
      SCREAMING_SNAKE_CASE       :      Any							=    val

    # load state dict
  model.load_state_dict(lowercase				)

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

  SCREAMING_SNAKE_CASE       :      Tuple							=    processor(images=prepare_img(lowercase				)						,     return_tensors="pt"				).pixel_values

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

  SCREAMING_SNAKE_CASE       :      List[str]							=    torch.Size([1, 1, 50265]				)
  if "trocr-base-handwritten" in checkpoint_url:
    SCREAMING_SNAKE_CASE       :      List[Any]							=    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:
    SCREAMING_SNAKE_CASE       :      Tuple							=    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:
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    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:
    SCREAMING_SNAKE_CASE       :      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]						,     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    				=		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    				=		parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)














from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






