'''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()
__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  logging.get_logger(__name__)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[str]			,	_UpperCAmelCase  :      List[Any]    )    ->    Tuple:


       """simple docstring"""







       _UpperCAmelCase     :  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 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      str    )    ->    List[Any]:


       """simple docstring"""







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

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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int			,	_UpperCAmelCase  :      Optional[int]			,	_UpperCAmelCase  :      int    )    ->    Tuple:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    dct.pop(_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    val

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    Any:


       """simple docstring"""







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

@torch.no_grad()
def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      Tuple    )    ->    List[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  Union[str, Any]							      =    ViTConfig(image_size=384			,	qkv_bias=_UpperCAmelCase    )
       _UpperCAmelCase     :  List[Any]							      =    TrOCRConfig()

       # size of the architecture
       if "base" in checkpoint_url:
              _UpperCAmelCase     :  Optional[Any]							      =    768
       elif "large" in checkpoint_url:
              # use ViT-large encoder
              _UpperCAmelCase     :  Dict							      =    1_024
              _UpperCAmelCase     :  Union[str, Any]							      =    4_096
              _UpperCAmelCase     :  int							      =    24
              _UpperCAmelCase     :  Dict							      =    16
              _UpperCAmelCase     :  int							      =    1_024
       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[str]							      =    False
              _UpperCAmelCase     :  str							      =    "relu"
              _UpperCAmelCase     :  Dict							      =    1_024
              _UpperCAmelCase     :  int							      =    True
              _UpperCAmelCase     :  int							      =    False
              _UpperCAmelCase     :  Union[str, Any]							      =    False

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

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

       _UpperCAmelCase     :  str							      =    create_rename_keys(_UpperCAmelCase			,	_UpperCAmelCase    )
       for src, dest in rename_keys:
              rename_key(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       read_in_q_k_v(_UpperCAmelCase			,	_UpperCAmelCase    )

       # 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(_UpperCAmelCase    )
              if key.startswith("decoder"    ) and "output_projection" not in key:
                     _UpperCAmelCase     :  int							      =    val
              else:
                     _UpperCAmelCase     :  int							      =    val

    # load state dict
       model.load_state_dict(_UpperCAmelCase    )

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

       _UpperCAmelCase     :  int							      =    processor(images=prepare_img(_UpperCAmelCase    )			,	return_tensors="pt"    ).pixel_values

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

       _UpperCAmelCase     :  Any							      =    torch.Size([1, 1, 50_265]    )
       if "trocr-base-handwritten" in checkpoint_url:
              _UpperCAmelCase     :  Dict							      =    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     :  Optional[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     :  int							      =    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]			,	_UpperCAmelCase			,	atol=1e-3    ), "First elements of logits not as expected"

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


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Union[str, 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."""
  )
  __SCREAMING_SNAKE_CASE       :     str     =  parser.parse_args()
  convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


"""simple docstring"""


import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Initialise PyTorch model.
  # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
  # TapasConfig to False.

  # initialize configuration from json file
  _UpperCAmelCase         =					TapasConfig.from_json_file(lowercase						)
  # set absolute/relative position embeddings parameter
  _UpperCAmelCase         =					reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WTQ":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					True
    # hparam_utils.py hparams
    _UpperCAmelCase         =					0.66_46_94
    _UpperCAmelCase         =					0.20_79_51
    _UpperCAmelCase         =					0.12_11_94
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					False
    _UpperCAmelCase         =					0.0_35_25_13

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					False
    # hparam_utils.py hparams
    _UpperCAmelCase         =					36.45_19
    _UpperCAmelCase         =					0.90_34_21
    _UpperCAmelCase         =					2_22.0_88
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					0.76_31_41

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "TABFACT":
    _UpperCAmelCase         =					TapasForSequenceClassification(config=lowercase						)
  elif task == "MLM":
    _UpperCAmelCase         =					TapasForMaskedLM(config=lowercase						)
  elif task == "INTERMEDIATE_PRETRAINING":
    _UpperCAmelCase         =					TapasModel(config=lowercase						)
  else:
    raise ValueError(f'''Task {task} not supported.'''						)

  print(f'''Building PyTorch model from configuration: {config}'''						)
  # Load weights from tf checkpoint
  load_tf_weights_in_tapas(lowercase      ,lowercase      ,lowercase						)

  # Save pytorch-model (weights and configuration)
  print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
  model.save_pretrained(lowercase						)

  # Save tokenizer files
  print(f'''Save tokenizer files to {pytorch_dump_path}'''						)
  _UpperCAmelCase         =					TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""      ,model_max_length=5_12						)
  tokenizer.save_pretrained(lowercase						)

  print("""Used relative position embeddings:"""      ,model.config.reset_position_index_per_cell						)


if __name__ == "__main__":
    UpperCAmelCase__     						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
    )
    parser.add_argument(
        """--reset_position_index_per_cell""",
        default=False,
        action="""store_true""",
        help="""Whether to use relative position embeddings or not. Defaults to True.""",
    )
    parser.add_argument(
        """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--tapas_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained TAPAS model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    UpperCAmelCase__     						=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.task,
        args.reset_position_index_per_cell,
        args.tf_checkpoint_path,
        args.tapas_config_file,
        args.pytorch_dump_path,
    )
