




"""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				=							logging.get_logger(__name__)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""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					,							UpperCamelCase				)      ->      int:


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

						__UpperCAmelCase						:			List[Any]												=       in_proj_weight[
						    : encoder_config.hidden_size, :
						]
						__UpperCAmelCase						:			List[str]												=       in_proj_weight[
						    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
						]
						__UpperCAmelCase						:			List[Any]												=       in_proj_weight[
						    -encoder_config.hidden_size :, :
						]

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Any:


			"""simple docstring"""
			__UpperCAmelCase						:			str												=       dct.pop(UpperCamelCase				)
			__UpperCAmelCase						:			List[Any]												=       val

def        _UpperCamelCase     (      UpperCamelCase				)      ->      str:


			"""simple docstring"""
			if "handwritten" in checkpoint_url:
						__UpperCAmelCase						:			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						:			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						:			Tuple												=       Image.open(requests.get(UpperCamelCase					,							stream=UpperCamelCase				).raw				).convert("RGB"				)
			return im

@torch.no_grad()
def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      List[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			List[str]												=       ViTConfig(image_size=384					,							qkv_bias=UpperCamelCase				)
			__UpperCAmelCase						:			Any												=       TrOCRConfig()

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

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

			# load state_dict of original model, rename some keys
			__UpperCAmelCase						:			Optional[int]												=       torch.hub.load_state_dict_from_url(UpperCamelCase					,							map_location="cpu"					,							check_hash=UpperCamelCase				)["model"]

			__UpperCAmelCase						:			Optional[Any]												=       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						:			Optional[int]												=       state_dict.pop(UpperCamelCase				)
						if key.startswith("decoder"				) and "output_projection" not in key:
									__UpperCAmelCase						:			Any												=       val
						else:
									__UpperCAmelCase						:			int												=       val

    # load state dict
			model.load_state_dict(UpperCamelCase				)

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

			__UpperCAmelCase						:			Optional[Any]												=       processor(images=prepare_img(UpperCamelCase				)					,							return_tensors="pt"				).pixel_values

			# verify logits
			__UpperCAmelCase						:			Dict												=       torch.tensor([[model.config.decoder.decoder_start_token_id]]				)
			__UpperCAmelCase						:			Optional[Any]												=       model(pixel_values=UpperCamelCase					,							decoder_input_ids=UpperCamelCase				)
			__UpperCAmelCase						:			List[Any]												=       outputs.logits

			__UpperCAmelCase						:			List[str]												=       torch.Size([1, 1, 5_0265]				)
			if "trocr-base-handwritten" in checkpoint_url:
						__UpperCAmelCase						:			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:
						__UpperCAmelCase						:			int												=       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:
						__UpperCAmelCase						:			str												=       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:
						__UpperCAmelCase						:			Union[str, Any]												=       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]					,							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__":
					A				=							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				=							parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def 		a__      (		_UpperCamelCase      :			List[Any]				):
   if "cls_token" in name:
      __lowerCamelCase    				=     name.replace('''cls_token'''			,'''vit.embeddings.cls_token'''				)
   if "mask_token" in name:
      __lowerCamelCase    				=     name.replace('''mask_token'''			,'''decoder.mask_token'''				)
   if "decoder_pos_embed" in name:
      __lowerCamelCase    				=     name.replace('''decoder_pos_embed'''			,'''decoder.decoder_pos_embed'''				)
   if "pos_embed" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''pos_embed'''			,'''vit.embeddings.position_embeddings'''				)
   if "patch_embed.proj" in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.proj'''			,'''vit.embeddings.patch_embeddings.projection'''				)
   if "patch_embed.norm" in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.norm'''			,'''vit.embeddings.norm'''				)
   if "decoder_blocks" in name:
      __lowerCamelCase    				=     name.replace('''decoder_blocks'''			,'''decoder.decoder_layers'''				)
   if "blocks" in name:
      __lowerCamelCase    				=     name.replace('''blocks'''			,'''vit.encoder.layer'''				)
   if "attn.proj" in name:
      __lowerCamelCase    				=     name.replace('''attn.proj'''			,'''attention.output.dense'''				)
   if "attn" in name:
      __lowerCamelCase    				=     name.replace('''attn'''			,'''attention.self'''				)
   if "norm1" in name:
      __lowerCamelCase    				=     name.replace('''norm1'''			,'''layernorm_before'''				)
   if "norm2" in name:
      __lowerCamelCase    				=     name.replace('''norm2'''			,'''layernorm_after'''				)
   if "mlp.fc1" in name:
      __lowerCamelCase    				=     name.replace('''mlp.fc1'''			,'''intermediate.dense'''				)
   if "mlp.fc2" in name:
      __lowerCamelCase    				=     name.replace('''mlp.fc2'''			,'''output.dense'''				)
   if "decoder_embed" in name:
      __lowerCamelCase    				=     name.replace('''decoder_embed'''			,'''decoder.decoder_embed'''				)
   if "decoder_norm" in name:
      __lowerCamelCase    				=     name.replace('''decoder_norm'''			,'''decoder.decoder_norm'''				)
   if "decoder_pred" in name:
      __lowerCamelCase    				=     name.replace('''decoder_pred'''			,'''decoder.decoder_pred'''				)
   if "norm.weight" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''norm.weight'''			,'''vit.layernorm.weight'''				)
   if "norm.bias" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''norm.bias'''			,'''vit.layernorm.bias'''				)

   return name





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict				):
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase    				=     orig_state_dict.pop(_UpperCamelCase				)

      if "qkv" in key:
         __lowerCamelCase    				=     key.split('''.'''				)
         __lowerCamelCase    				=     int(key_split[1]				)
         if "decoder_blocks" in key:
            __lowerCamelCase    				=     config.decoder_hidden_size
            __lowerCamelCase    				=     '''decoder.decoder_layers.'''
            if "weight" in key:
               __lowerCamelCase    				=     val[:dim, :]
               __lowerCamelCase    				=     val[dim : dim * 2, :]
               __lowerCamelCase    				=     val[-dim:, :]
            elif "bias" in key:
               __lowerCamelCase    				=     val[:dim]
               __lowerCamelCase    				=     val[dim : dim * 2]
               __lowerCamelCase    				=     val[-dim:]
         else:
            __lowerCamelCase    				=     config.hidden_size
            __lowerCamelCase    				=     '''vit.encoder.layer.'''
            if "weight" in key:
               __lowerCamelCase    				=     val[:dim, :]
               __lowerCamelCase    				=     val[dim : dim * 2, :]
               __lowerCamelCase    				=     val[-dim:, :]
            elif "bias" in key:
               __lowerCamelCase    				=     val[:dim]
               __lowerCamelCase    				=     val[dim : dim * 2]
               __lowerCamelCase    				=     val[-dim:]

      else:
         __lowerCamelCase    				=     val

   return orig_state_dict





def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     ViTMAEConfig()
   if "large" in checkpoint_url:
      __lowerCamelCase    				=     10_24
      __lowerCamelCase    				=     40_96
      __lowerCamelCase    				=     24
      __lowerCamelCase    				=     16
   elif "huge" in checkpoint_url:
      __lowerCamelCase    				=     14
      __lowerCamelCase    				=     12_80
      __lowerCamelCase    				=     51_20
      __lowerCamelCase    				=     32
      __lowerCamelCase    				=     16

   __lowerCamelCase    				=     ViTMAEForPreTraining(_UpperCamelCase				)

   __lowerCamelCase    				=     torch.hub.load_state_dict_from_url(_UpperCamelCase			,map_location='''cpu'''				)['''model''']

   __lowerCamelCase    				=     ViTMAEImageProcessor(size=config.image_size				)

   __lowerCamelCase    				=     convert_state_dict(_UpperCamelCase			,_UpperCamelCase				)

   model.load_state_dict(_UpperCamelCase				)
   model.eval()

   __lowerCamelCase    				=     '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				)
   __lowerCamelCase    				=     ViTMAEImageProcessor(size=config.image_size				)
   __lowerCamelCase    				=     image_processor(images=_UpperCamelCase			,return_tensors='''pt'''				)

   # forward pass
   torch.manual_seed(2				)
   __lowerCamelCase    				=     model(**_UpperCamelCase				)
   __lowerCamelCase    				=     outputs.logits

   if "large" in checkpoint_url:
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]				)
   elif "huge" in checkpoint_url:
      __lowerCamelCase    				=     torch.tensor(
          [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]				)
   else:
      __lowerCamelCase    				=     torch.tensor(
          [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]				)

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

   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 __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      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."""
  )

  a_								=       parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






