


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    :   int	    =					logging.get_logger(__name__)
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      a						: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     __lowerCamelCase						(			UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





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

            a						:Dict										=	in_proj_weight[
                : encoder_config.hidden_size, :
            ]
            a						:Tuple										=	in_proj_weight[
                encoder_config.hidden_size : encoder_config.hidden_size * 2, :
            ]
            a						:Union[str, Any]										=	in_proj_weight[
                -encoder_config.hidden_size :, :
            ]
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:Union[str, Any]										=	dct.pop(UpperCAmelCase_						)
      a						:Dict										=	val
def     __lowerCamelCase						(			UpperCAmelCase_						:		Any						):




      """simple docstring"""





      if "handwritten" in checkpoint_url:
            a						:int										=	'''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:
            a						: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'''
      a						:Union[str, Any]										=	Image.open(requests.get(UpperCAmelCase_			,  stream=UpperCAmelCase_						).raw						).convert('''RGB'''						)
      return im
@torch.no_grad()
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      a						:Any										=	ViTConfig(image_size=384			,  qkv_bias=UpperCAmelCase_						)
      a						:List[str]										=	TrOCRConfig()

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

      # load HuggingFace model
      a						:int										=	ViTModel(UpperCAmelCase_			,  add_pooling_layer=UpperCAmelCase_						)
      a						:List[str]										=	TrOCRForCausalLM(UpperCAmelCase_						)
      a						:Tuple										=	VisionEncoderDecoderModel(encoder=UpperCAmelCase_			,  decoder=UpperCAmelCase_						)
      model.eval()

      # load state_dict of original model, rename some keys
      a						:List[Any]										=	torch.hub.load_state_dict_from_url(UpperCAmelCase_			,  map_location='''cpu'''			,  check_hash=UpperCAmelCase_						)['''model''']

      a						:Optional[int]										=	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():
            a						:Any										=	state_dict.pop(UpperCAmelCase_						)
            if key.startswith('''decoder'''						) and "output_projection" not in key:
                  a						:Optional[Any]										=	val
            else:
                  a						:List[Any]										=	val

    # load state dict
      model.load_state_dict(UpperCAmelCase_						)

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

      a						:Union[str, Any]										=	processor(images=prepare_img(UpperCAmelCase_						)			,  return_tensors='''pt'''						).pixel_values

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

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









import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


snake_case    :   Any	    =					pytest.mark.integration
@pytest.mark.parametrize('''path'''			,  ['''paws''', '''csv''']						)
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      inspect_dataset(UpperCAmelCase_			,  UpperCAmelCase_						)
      a						:List[Any]										=	path + '''.py'''
      assert script_name in os.listdir(UpperCAmelCase_						)
      assert "__pycache__" not in os.listdir(UpperCAmelCase_						)
@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''						)
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''						)
@pytest.mark.parametrize('''path'''			,  ['''accuracy''']						)
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      inspect_metric(UpperCAmelCase_			,  UpperCAmelCase_						)
      a						:Dict										=	path + '''.py'''
      assert script_name in os.listdir(UpperCAmelCase_						)
      assert "__pycache__" not in os.listdir(UpperCAmelCase_						)
@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''			,  [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:List[str]										=	get_dataset_config_info(UpperCAmelCase_			,  config_name=UpperCAmelCase_						)
      assert info.config_name == config_name
      assert list(info.splits.keys()						) == expected_splits
@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''			,  [
        ('''paws''', None, ValueError),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      with pytest.raises(UpperCAmelCase_						):
            get_dataset_config_info(UpperCAmelCase_			,  config_name=UpperCAmelCase_						)
@pytest.mark.parametrize(
    '''path, expected'''			,  [
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      a						:List[str]										=	get_dataset_config_names(UpperCAmelCase_						)
      assert expected in config_names
@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''			,  [
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Optional[int]										=	get_dataset_infos(UpperCAmelCase_						)
      assert list(infos.keys()						) == expected_configs
      a						:Union[str, Any]										=	expected_configs[0]
      assert expected_config in infos
      a						:List[Any]										=	infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()						) == expected_splits_in_first_config
@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''			,  [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Any			,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Union[str, Any]										=	get_dataset_infos(UpperCAmelCase_						)
      assert expected_config in infos
      a						:int										=	infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()						) == expected_splits






@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''			,  [
        ('''paws''', None, ValueError),
    ]			,  )
def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      with pytest.raises(UpperCAmelCase_						):
            get_dataset_split_names(UpperCAmelCase_			,  config_name=UpperCAmelCase_						)





