'''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()
__a:					Dict			  =  logging.get_logger(__name__)
def      __UpperCamelCase			(  UpperCAmelCase						,	UpperCAmelCase	):
 lowercase__							:       str										=   []
 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						,	UpperCAmelCase	):





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

  lowercase__							:       Any										=   in_proj_weight[
      : encoder_config.hidden_size, :
  ]
  lowercase__							:       Union[str, Any]										=   in_proj_weight[
      encoder_config.hidden_size : encoder_config.hidden_size * 2, :
  ]
  lowercase__							:       str										=   in_proj_weight[
      -encoder_config.hidden_size :, :
  ]
def      __UpperCamelCase			(  UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase	):
 lowercase__							:       int										=   dct.pop(a__	)
 lowercase__							:       Any										=   val
def      __UpperCamelCase			(  UpperCAmelCase	):





 if "handwritten" in checkpoint_url:
  lowercase__							:       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:
  lowercase__							:       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'''
 lowercase__							:       str										=   Image.open(requests.get(a__						,	stream=a__	).raw	).convert('''RGB'''	)
 return im
@torch.no_grad()
def      __UpperCamelCase			(  UpperCAmelCase						,	UpperCAmelCase	):
 lowercase__							:       List[str]										=   ViTConfig(image_size=384						,	qkv_bias=a__	)
 lowercase__							:       Optional[Any]										=   TrOCRConfig()

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

 # load HuggingFace model
 lowercase__							:       Any										=   ViTModel(a__						,	add_pooling_layer=a__	)
 lowercase__							:       Optional[Any]										=   TrOCRForCausalLM(a__	)
 lowercase__							:       Dict										=   VisionEncoderDecoderModel(encoder=a__						,	decoder=a__	)
 model.eval()

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

 lowercase__							:       Optional[Any]										=   create_rename_keys(a__						,	a__	)
 for src, dest in rename_keys:
  rename_key(a__						,	a__						,	a__	)
 read_in_q_k_v(a__						,	a__	)

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

    # load state dict
 model.load_state_dict(a__	)

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

 lowercase__							:       str										=   processor(images=prepare_img(a__	)						,	return_tensors='''pt'''	).pixel_values

 # verify logits
 lowercase__							:       Tuple										=   torch.tensor([[model.config.decoder.decoder_start_token_id]]	)
 lowercase__							:       Union[str, Any]										=   model(pixel_values=a__						,	decoder_input_ids=a__	)
 lowercase__							:       Tuple										=   outputs.logits

 lowercase__							:       int										=   torch.Size([1, 1, 5_0265]	)
 if "trocr-base-handwritten" in checkpoint_url:
  lowercase__							:       Optional[int]										=   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:
  lowercase__							:       List[str]										=   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:
  lowercase__							:       Union[str, 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:
  lowercase__							:       Tuple										=   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]						,	a__						,	atol=1E-3	), "First elements of logits not as expected"

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


if __name__ == "__main__":
  __a:					Optional[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."""
  )
  __a:					List[str]			  =  parser.parse_args()
  convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def      _UpperCamelCase  (a__ :Tuple ,      a__ :List[str]=False							):






							"""simple docstring"""





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

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""module.cls_token""", """vit.embeddings.cls_token"""),
							        ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
							        ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
							        ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
							    ]							)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""module.norm.weight""", """layernorm.weight"""),
														        ("""module.norm.bias""", """layernorm.bias"""),
														    ]							)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														UpperCamelCase__					  =      [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""							) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""norm.weight""", """vit.layernorm.weight"""),
														        ("""norm.bias""", """vit.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]							)

							return rename_keys





def      _UpperCamelCase  (a__ :int ,      a__ :Tuple ,      a__ :Union[str, Any]=False							):






							"""simple docstring"""





							for i in range(config.num_hidden_layers							):
														if base_model:
																					UpperCamelCase__					  =      """"""
														else:
																					UpperCamelCase__					  =      """vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														UpperCamelCase__					  =      state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""							)
														UpperCamelCase__					  =      state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""							)
														# next, add query, keys and values (in that order) to the state dict
														UpperCamelCase__					  =      in_proj_weight[
														    : config.hidden_size, :
														]
														UpperCamelCase__					  =      in_proj_bias[: config.hidden_size]
														UpperCamelCase__					  =      in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCamelCase__					  =      in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCamelCase__					  =      in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCamelCase__					  =      in_proj_bias[-config.hidden_size :]





def      _UpperCamelCase  (a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(a__ ,      a__							)





def      _UpperCamelCase  (a__ :Optional[int]							):






							"""simple docstring"""





							UpperCamelCase__					  =      [
							    """module.fc.fc1.weight""",
							    """module.fc.fc1.bias""",
							    """module.fc.bn1.weight""",
							    """module.fc.bn1.bias""",
							    """module.fc.bn1.running_mean""",
							    """module.fc.bn1.running_var""",
							    """module.fc.bn1.num_batches_tracked""",
							    """module.fc.fc2.weight""",
							    """module.fc.fc2.bias""",
							    """module.fc.bn2.weight""",
							    """module.fc.bn2.bias""",
							    """module.fc.bn2.running_mean""",
							    """module.fc.bn2.running_var""",
							    """module.fc.bn2.num_batches_tracked""",
							    """module.fc.fc3.weight""",
							    """module.fc.fc3.bias""",
							]
							for k in ignore_keys:
														state_dict.pop(a__ ,      a__							)





def      _UpperCamelCase  (a__ :List[str] ,      a__ :Any ,      a__ :str							):






							"""simple docstring"""





							UpperCamelCase__					  =      dct.pop(a__							)
							UpperCamelCase__					  =      val





def      _UpperCamelCase  (a__ :Dict ,      a__ :Dict							):






							"""simple docstring"""





							UpperCamelCase__					  =      ViTMSNConfig()
							UpperCamelCase__					  =      1000

							UpperCamelCase__					  =      """datasets/huggingface/label-files"""
							UpperCamelCase__					  =      """imagenet-1k-id2label.json"""
							UpperCamelCase__					  =      json.load(open(hf_hub_download(a__ ,      a__							) ,      """r"""							)							)
							UpperCamelCase__					  =      {int(a__							): v for k, v in idalabel.items()}
							UpperCamelCase__					  =      idalabel
							UpperCamelCase__					  =      {v: k for k, v in idalabel.items()}

							if "s16" in checkpoint_url:
														UpperCamelCase__					  =      384
														UpperCamelCase__					  =      1536
														UpperCamelCase__					  =      6
							elif "l16" in checkpoint_url:
														UpperCamelCase__					  =      1024
														UpperCamelCase__					  =      4096
														UpperCamelCase__					  =      24
														UpperCamelCase__					  =      16
														UpperCamelCase__					  =      0.1
							elif "b4" in checkpoint_url:
														UpperCamelCase__					  =      4
							elif "l7" in checkpoint_url:
														UpperCamelCase__					  =      7
														UpperCamelCase__					  =      1024
														UpperCamelCase__					  =      4096
														UpperCamelCase__					  =      24
														UpperCamelCase__					  =      16
														UpperCamelCase__					  =      0.1

							UpperCamelCase__					  =      ViTMSNModel(a__							)

							UpperCamelCase__					  =      torch.hub.load_state_dict_from_url(a__ ,      map_location="""cpu"""							)["""target_encoder"""]

							UpperCamelCase__					  =      ViTImageProcessor(size=config.image_size							)

							remove_projection_head(a__							)
							UpperCamelCase__					  =      create_rename_keys(a__ ,      base_model=a__							)

							for src, dest in rename_keys:
														rename_key(a__ ,      a__ ,      a__							)
							read_in_q_k_v(a__ ,      a__ ,      base_model=a__							)

							model.load_state_dict(a__							)
							model.eval()

							UpperCamelCase__					  =      """http://images.cocodataset.org/val2017/000000039769.jpg"""

							UpperCamelCase__					  =      Image.open(requests.get(a__ ,      stream=a__							).raw							)
							UpperCamelCase__					  =      ViTImageProcessor(
							    size=config.image_size ,      image_mean=a__ ,      image_std=a__							)
							UpperCamelCase__					  =      image_processor(images=a__ ,      return_tensors="""pt"""							)

							# forward pass
							torch.manual_seed(2							)
							UpperCamelCase__					  =      model(**a__							)
							UpperCamelCase__					  =      outputs.last_hidden_state

							# The following Colab Notebook was used to generate these outputs:
							# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
							if "s16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[-1.0915, -1.4876, -1.1809]]							)
							elif "b16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[14.2889, -18.9045, 11.7281]]							)
							elif "l16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[41.5028, -22.8681, 45.6475]]							)
							elif "b4" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[-4.3868, 5.2932, -0.4137]]							)
							else:
														UpperCamelCase__					  =      torch.tensor([[-0.1792, -0.6465, 2.4263]]							)

							# verify logits
							assert torch.allclose(last_hidden_state[:, 0, :3] ,      a__ ,      atol=1e-4							)

							print(f"""Saving model to {pytorch_dump_folder_path}"""							)
							model.save_pretrained(a__							)

							print(f"""Saving image processor to {pytorch_dump_folder_path}"""							)
							image_processor.save_pretrained(a__							)


if __name__ == "__main__":
					UpperCamelCase__           =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_url",
					    default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
					    type=str,
					    help="URL of the checkpoint you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)

					UpperCamelCase__           =			parser.parse_args()
					convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




