


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :     List[Any]								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Tuple=False,				UpperCamelCase__       :					List[str]=False			):
 __A  :		Union[str, Any]       =			'backbone.' if is_semantic else ''

 __A  :		List[Any]       =			[]
 for i in range(config.num_hidden_layers			):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")			)
  rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")			)
  rename_keys.append(
      (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")			)
  rename_keys.append(
      (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")			)
  rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")			)
  rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")			)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")			)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")			)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")			)
  rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")			)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (f"""{prefix}cls_token""", 'beit.embeddings.cls_token'),
         (f"""{prefix}patch_embed.proj.weight""", 'beit.embeddings.patch_embeddings.projection.weight'),
         (f"""{prefix}patch_embed.proj.bias""", 'beit.embeddings.patch_embeddings.projection.bias'),
         (f"""{prefix}pos_embed""", 'beit.embeddings.position_embeddings'),
     ]			)

 if has_lm_head:
  # mask token + layernorm
  rename_keys.extend(
      [
          ('mask_token', 'beit.embeddings.mask_token'),
          ('norm.weight', 'layernorm.weight'),
          ('norm.bias', 'layernorm.bias'),
      ]			)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
          ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
          ('head.weight', 'classifier.weight'),
          ('head.bias', 'classifier.bias'),
      ]			)

 return rename_keys


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Optional[int]=False,				UpperCamelCase__       :					int=False			):
 for i in range(config.num_hidden_layers			):
  __A  :		str       =			'backbone.' if is_semantic else ''
  # queries, keys and values
  __A  :		Union[str, Any]       =			state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""			)
  __A  :		str       =			state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""			)
  __A  :		Dict       =			state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""			)

  __A  :		int       =			in_proj_weight[
      : config.hidden_size, :
  ]
  __A  :		Tuple       =			q_bias
  __A  :		List[str]       =			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __A  :		str       =			in_proj_weight[
      -config.hidden_size :, :
  ]
  __A  :		List[str]       =			v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __A  :		List[str]       =			state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""			)
  __A  :		Optional[int]       =			state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""			)

  __A  :		Any       =			gamma_a
  __A  :		List[str]       =			gamma_a


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					List[Any]			):
 __A  :		str       =			dct.pop(UpperCamelCase__			)
 __A  :		int       =			val


def 			_lowercase       (				):
 __A  :		Dict       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		Optional[int]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					List[Any]=False			):
 __A  :		Optional[int]       =			False if 'rvlcdip' in checkpoint_url else True
 __A  :		Any       =			BeitConfig(use_absolute_position_embeddings=UpperCamelCase__,				use_mask_token=UpperCamelCase__			)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __A  :		List[str]       =			1024
  __A  :		Dict       =			4096
  __A  :		Optional[Any]       =			24
  __A  :		List[Any]       =			16

 # labels
 if "rvlcdip" in checkpoint_url:
  __A  :		str       =			16
  __A  :		Tuple       =			'huggingface/label-files'
  __A  :		Any       =			'rvlcdip-id2label.json'
  __A  :		List[Any]       =			json.load(open(hf_hub_download(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			),				'r'			)			)
  __A  :		List[Any]       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
  __A  :		Union[str, Any]       =			idalabel
  __A  :		str       =			{v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 __A  :		str       =			torch.hub.load_state_dict_from_url(UpperCamelCase__,				map_location='cpu'			)['model']

 __A  :		int       =			create_rename_keys(UpperCamelCase__,				has_lm_head=UpperCamelCase__			)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 read_in_q_k_v(UpperCamelCase__,				UpperCamelCase__,				has_lm_head=UpperCamelCase__			)

 # load HuggingFace model
 __A  :		Tuple       =			BeitForMaskedImageModeling(UpperCamelCase__			) if has_lm_head else BeitForImageClassification(UpperCamelCase__			)
 model.eval()
 model.load_state_dict(UpperCamelCase__			)

 # Check outputs on an image
 __A  :		Dict       =			BeitImageProcessor(
     size=config.image_size,				resample=PILImageResampling.BILINEAR,				do_center_crop=UpperCamelCase__			)
 __A  :		Any       =			prepare_img()

 __A  :		int       =			image_processor(images=UpperCamelCase__,				return_tensors='pt'			)
 __A  :		int       =			encoding['pixel_values']

 __A  :		Optional[Any]       =			model(UpperCamelCase__			)
 __A  :		Optional[Any]       =			outputs.logits

 # verify logits
 __A  :		str       =			[1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8192]
 assert logits.shape == torch.Size(UpperCamelCase__			), "Shape 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 image processor to {pytorch_dump_folder_path}"""			)
 image_processor.save_pretrained(UpperCamelCase__			)

 if push_to_hub:
  if has_lm_head:
   __A  :		List[Any]       =			'dit-base' if 'base' in checkpoint_url else 'dit-large'
  else:
   __A  :		str       =			'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
  image_processor.push_to_hub(
      repo_path_or_name=Path(UpperCamelCase__,				UpperCamelCase__			),				organization='nielsr',				commit_message='Add image processor',				use_temp_dir=UpperCamelCase__,				)
  model.push_to_hub(
      repo_path_or_name=Path(UpperCamelCase__,				UpperCamelCase__			),				organization='nielsr',				commit_message='Add model',				use_temp_dir=UpperCamelCase__,				)


if __name__ == "__main__":
 UpperCAmelCase_ :     str								=      argparse.ArgumentParser()

 parser.add_argument(
     '--checkpoint_url',
     default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
     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.'
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
 )
 UpperCAmelCase_ :     Optional[Any]								=      parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










'''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 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[int]			):
 # 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
 __A  :		Optional[int]       =			TapasConfig.from_json_file(UpperCamelCase__			)
 # set absolute/relative position embeddings parameter
 __A  :		List[str]       =			reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __A  :		Optional[int]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WTQ":
  # run_task_main.py hparams
  __A  :		List[str]       =			4
  __A  :		Any       =			True
  # hparam_utils.py hparams
  __A  :		Any       =			0.664694
  __A  :		Dict       =			0.207951
  __A  :		Optional[Any]       =			0.121194
  __A  :		Any       =			True
  __A  :		Any       =			True
  __A  :		int       =			False
  __A  :		Optional[int]       =			0.0352513

  __A  :		List[Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __A  :		Tuple       =			4
  __A  :		Optional[int]       =			False
  # hparam_utils.py hparams
  __A  :		List[str]       =			36.4519
  __A  :		Union[str, Any]       =			0.903421
  __A  :		List[str]       =			222.088
  __A  :		Optional[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Union[str, Any]       =			True
  __A  :		str       =			0.763141

  __A  :		Union[str, Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "TABFACT":
  __A  :		Union[str, Any]       =			TapasForSequenceClassification(config=UpperCamelCase__			)
 elif task == "MLM":
  __A  :		Any       =			TapasForMaskedLM(config=UpperCamelCase__			)
 elif task == "INTERMEDIATE_PRETRAINING":
  __A  :		List[Any]       =			TapasModel(config=UpperCamelCase__			)
 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(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

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

 # Save tokenizer files
 print(f"""Save tokenizer files to {pytorch_dump_path}"""			)
 __A  :		int       =			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',				model_max_length=512			)
 tokenizer.save_pretrained(UpperCamelCase__			)

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


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      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_ :     Any								=      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,
 )






