


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   snake_case						(				_a:     List[Any]		,   _a:     List[str]							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      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   snake_case						(				_a:     Tuple		,   _a:     int							)->		int:
      '''simple docstring'''


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

            lowerCamelCase__           =		in_proj_weight[
                : encoder_config.hidden_size, :
            ]
            lowerCamelCase__           =		in_proj_weight[
                encoder_config.hidden_size : encoder_config.hidden_size * 2, :
            ]
            lowerCamelCase__           =		in_proj_weight[
                -encoder_config.hidden_size :, :
            ]



def   snake_case						(				_a:     Optional[int]		,   _a:     Union[str, Any]		,   _a:     str							)->		str:
      '''simple docstring'''


      lowerCamelCase__           =		dct.pop(_a							)
      lowerCamelCase__           =		val



def   snake_case						(				_a:     Tuple							)->		Optional[Any]:
      '''simple docstring'''


      if "handwritten" in checkpoint_url:
            lowerCamelCase__           =		'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:
            lowerCamelCase__           =		'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
      lowerCamelCase__           =		Image.open(requests.get(_a		,   stream=_a							).raw							).convert('RGB'							)
      return im



@torch.no_grad()
def   snake_case						(				_a:     Optional[int]		,   _a:     Tuple							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		ViTConfig(image_size=384		,   qkv_bias=_a							)
      lowerCamelCase__           =		TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            lowerCamelCase__           =		768
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            lowerCamelCase__           =		1024
            lowerCamelCase__           =		4096
            lowerCamelCase__           =		24
            lowerCamelCase__           =		16
            lowerCamelCase__           =		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:
            lowerCamelCase__           =		False
            lowerCamelCase__           =		'relu'
            lowerCamelCase__           =		1024
            lowerCamelCase__           =		True
            lowerCamelCase__           =		False
            lowerCamelCase__           =		False

      # load HuggingFace model
      lowerCamelCase__           =		ViTModel(_a		,   add_pooling_layer=_a							)
      lowerCamelCase__           =		TrOCRForCausalLM(_a							)
      lowerCamelCase__           =		VisionEncoderDecoderModel(encoder=_a		,   decoder=_a							)
      model.eval()

      # load state_dict of original model, rename some keys
      lowerCamelCase__           =		torch.hub.load_state_dict_from_url(_a		,   map_location='cpu'		,   check_hash=_a							)['model']

      lowerCamelCase__           =		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():
            lowerCamelCase__           =		state_dict.pop(_a							)
            if key.startswith('decoder'							) and "output_projection" not in key:
                  lowerCamelCase__           =		val
            else:
                  lowerCamelCase__           =		val

    # load state dict
      model.load_state_dict(_a							)

      # Check outputs on an image
      lowerCamelCase__           =		ViTImageProcessor(size=encoder_config.image_size							)
      lowerCamelCase__           =		RobertaTokenizer.from_pretrained('roberta-large'							)
      lowerCamelCase__           =		TrOCRProcessor(_a		,   _a							)

      lowerCamelCase__           =		processor(images=prepare_img(_a							)		,   return_tensors='pt'							).pixel_values

      # verify logits
      lowerCamelCase__           =		torch.tensor([[model.config.decoder.decoder_start_token_id]]							)
      lowerCamelCase__           =		model(pixel_values=_a		,   decoder_input_ids=_a							)
      lowerCamelCase__           =		outputs.logits

      lowerCamelCase__           =		torch.Size([1, 1, 50265]							)
      if "trocr-base-handwritten" in checkpoint_url:
            lowerCamelCase__           =		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:
            lowerCamelCase__           =		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:
            lowerCamelCase__           =		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:
            lowerCamelCase__           =		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]		,   _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__":
   _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)







"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    'swinv2'

    a_    :				Optional[int]             =    {
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int=2_24							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=3							,					SCREAMING_SNAKE_CASE__							:					str=96							,					SCREAMING_SNAKE_CASE__							:					Dict=[2, 2, 6, 2]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=[3, 6, 12, 24]							,					SCREAMING_SNAKE_CASE__							:					Tuple=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=32							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		embed_dim
          lowerCamelCase__           =		depths
          lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		num_heads
          lowerCamelCase__           =		window_size
          lowerCamelCase__           =		mlp_ratio
          lowerCamelCase__           =		qkv_bias
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		drop_path_rate
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		use_absolute_embeddings
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		encoder_stride
          # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__           =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__				) - 1)				)
          lowerCamelCase__           =		(0, 0, 0, 0)



