

# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


A_		:      List[str]					    = get_logger(__name__)
class        A_ :


    '''simple docstring'''






    a__  				=							"dummy_data"
    a__  				=							"datasets"
    a__  				=							False


    def __init__(self  ,   lowercase__  ,   lowercase__  ,   lowercase__  ,   lowercase__ = None  ,   lowercase__ = False  ,   lowercase__ = True  ,   lowercase__ = None  ,   )  ->   Tuple:
        __UpperCAmelCase					       =      0
        __UpperCAmelCase					       =      dataset_name
        __UpperCAmelCase					       =      cache_dir
        __UpperCAmelCase					       =      use_local_dummy_data
        __UpperCAmelCase					       =      config
        # download_callbacks take a single url as input
        __UpperCAmelCase					       =      download_callbacks or []
        # if False, it doesn't load existing files and it returns the paths of the dummy files relative
        # to the dummy_data zip file root
        __UpperCAmelCase					       =      load_existing_dummy_data

        # TODO(PVP, QL) might need to make this more general
        __UpperCAmelCase					       =      str(__lowerCamelCase	)
        # to be downloaded
        __UpperCAmelCase					       =      None
        __UpperCAmelCase					       =      None


    @property
    def   lowerCAmelCase_    (self	)  ->   str:







        if self._dummy_file is None:
            __UpperCAmelCase					       =      self.download_dummy_data()
        return self._dummy_file


    @property
    def   lowerCAmelCase_    (self	)  ->   Tuple:







        if self.config is not None:
            # structure is dummy / config_name / version_name
            return os.path.join('''dummy'''  ,   self.config.name  ,   self.version_name	)
        # structure is dummy / version_name
        return os.path.join('''dummy'''  ,   self.version_name	)


    @property
    def   lowerCAmelCase_    (self	)  ->   List[str]:
        return os.path.join(self.dummy_data_folder  ,   '''dummy_data.zip'''	)


    def   lowerCAmelCase_    (self	)  ->   Optional[int]:
        __UpperCAmelCase					       =      (
            self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
        )

        __UpperCAmelCase					       =      cached_path(
            __lowerCamelCase  ,   cache_dir=self.cache_dir  ,   extract_compressed_file=__lowerCamelCase  ,   force_extract=__lowerCamelCase	)

        return os.path.join(__lowerCamelCase  ,   self.dummy_file_name	)


    @property
    def   lowerCAmelCase_    (self	)  ->   Dict:
        return os.path.join(self.datasets_scripts_dir  ,   self.dataset_name  ,   self.dummy_zip_file	)


    @property
    def   lowerCAmelCase_    (self	)  ->   str:







        if self._bucket_url is None:
            __UpperCAmelCase					       =      hf_github_url(self.dataset_name  ,   self.dummy_zip_file.replace(os.sep  ,   '''/'''	)	)
        return self._bucket_url


    @property
    def   lowerCAmelCase_    (self	)  ->   int:







        if os.path.isdir(self.dummy_file	):
            return self.dummy_file
        # else cut off path to file -> example `xsum`.
        return "/".join(self.dummy_file.replace(os.sep  ,   '''/'''	).split('''/'''	)[:-1]	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   *lowercase__	)  ->   List[str]:







        if self.load_existing_dummy_data:
            # dummy data is downloaded and tested
            __UpperCAmelCase					       =      self.dummy_file
        else:
            # dummy data cannot be downloaded and only the path to dummy file is returned
            __UpperCAmelCase					       =      self.dummy_file_name

        # special case when data_url is a dict
        if isinstance(__lowerCamelCase  ,   __lowerCamelCase	):
            return self.create_dummy_data_dict(__lowerCamelCase  ,   __lowerCamelCase	)
        elif isinstance(__lowerCamelCase  ,   (list, tuple)	):
            return self.create_dummy_data_list(__lowerCamelCase  ,   __lowerCamelCase	)
        else:
            return self.create_dummy_data_single(__lowerCamelCase  ,   __lowerCamelCase	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   *lowercase__	)  ->   str:
        return self.download_and_extract(__lowerCamelCase	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__	)  ->   Any:
        return self.download_and_extract(__lowerCamelCase	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   *lowercase__  ,   **lowercase__	)  ->   int:
        return path


    def   lowerCAmelCase_    (self	)  ->   Union[str, Any]:
        return {}


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__	)  ->   int:
        __UpperCAmelCase					       =      {}
        for key, single_urls in data_url.items():
            for download_callback in self.download_callbacks:
                if isinstance(__lowerCamelCase  ,   __lowerCamelCase	):
                    for single_url in single_urls:
                        download_callback(__lowerCamelCase	)
                else:
                    __UpperCAmelCase					       =      single_urls
                    download_callback(__lowerCamelCase	)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
            if isinstance(__lowerCamelCase  ,   __lowerCamelCase	):
                __UpperCAmelCase					       =      [os.path.join(__lowerCamelCase  ,   urllib.parse.quote_plus(Path(__lowerCamelCase	).name	)	) for x in single_urls]
            else:
                __UpperCAmelCase					       =      single_urls
                __UpperCAmelCase					       =      os.path.join(__lowerCamelCase  ,   urllib.parse.quote_plus(Path(__lowerCamelCase	).name	)	)
            __UpperCAmelCase					       =      value

        # make sure that values are unique
        if all(isinstance(__lowerCamelCase  ,   __lowerCamelCase	) for i in dummy_data_dict.values()	) and len(set(dummy_data_dict.values()	)	) < len(
            dummy_data_dict.values()	):
            # append key to value to make its name unique
            __UpperCAmelCase					       =      {key: value + key for key, value in dummy_data_dict.items()}

        return dummy_data_dict


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__	)  ->   int:
        __UpperCAmelCase					       =      []
        # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
        __UpperCAmelCase					       =      all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''  ,   __lowerCamelCase	)	) for url in data_url	)
        __UpperCAmelCase					       =      all(
            url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''	) for url in data_url	)
        if data_url and (is_tf_records or is_pubmed_records):
            __UpperCAmelCase					       =      [data_url[0]] * len(__lowerCamelCase	)
        for single_url in data_url:
            for download_callback in self.download_callbacks:
                download_callback(__lowerCamelCase	)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
            __UpperCAmelCase					       =      os.path.join(__lowerCamelCase  ,   urllib.parse.quote_plus(single_url.split('''/'''	)[-1]	)	)
            dummy_data_list.append(__lowerCamelCase	)
        return dummy_data_list


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__	)  ->   Optional[Any]:







        for download_callback in self.download_callbacks:
            download_callback(__lowerCamelCase	)
        # we force the name of each key to be the last file / folder name of the url path
        # if the url has arguments, we need to encode them with urllib.parse.quote_plus
        __UpperCAmelCase					       =      os.path.join(__lowerCamelCase  ,   urllib.parse.quote_plus(data_url.split('''/'''	)[-1]	)	)
        if os.path.exists(__lowerCamelCase	) or not self.load_existing_dummy_data:
            return value
        else:
            # Backward compatibility, maybe deprecate at one point.
            # For many datasets with single url calls to dl_manager.download_and_extract,
            # the dummy_data.zip file is actually the zipped downloaded file
            # while now we expected the dummy_data.zip file to be a directory containing
            # the downloaded file.
            return path_to_dummy_data


    def   lowerCAmelCase_    (self	)  ->   Dict:
        pass


    def   lowerCAmelCase_    (self	)  ->   Union[str, Any]:
        pass


    def   lowerCAmelCase_    (self  ,   lowercase__	)  ->   Dict:







        def _iter_archive_members(lowercase__	):
            # this preserves the order of the members inside the ZIP archive
            __UpperCAmelCase					       =      Path(self.dummy_file	).parent
            __UpperCAmelCase					       =      path.relative_to(__lowerCamelCase	)
            with ZipFile(self.local_path_to_dummy_data	) as zip_file:
                __UpperCAmelCase					       =      zip_file.namelist()
            for member in members:
                if member.startswith(relative_path.as_posix()	):
                    yield dummy_parent_path.joinpath(__lowerCamelCase	)

        __UpperCAmelCase					       =      Path(__lowerCamelCase	)
        __UpperCAmelCase					       =      _iter_archive_members(__lowerCamelCase	) if self.use_local_dummy_data else path.rglob('''*'''	)
        for file_path in file_paths:
            if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')	):
                yield file_path.relative_to(__lowerCamelCase	).as_posix(), file_path.open('''rb'''	)






    def   lowerCAmelCase_    (self  ,   lowercase__	)  ->   str:







        if not isinstance(__lowerCamelCase  ,   __lowerCamelCase	):
            __UpperCAmelCase					       =      [paths]
        for path in paths:
            if os.path.isfile(__lowerCamelCase	):
                if os.path.basename(__lowerCamelCase	).startswith(('''.''', '''__''')	):
                    return
                yield path
            else:
                for dirpath, dirnames, filenames in os.walk(__lowerCamelCase	):
                    if os.path.basename(__lowerCamelCase	).startswith(('''.''', '''__''')	):
                        continue
                    dirnames.sort()
                    for filename in sorted(__lowerCamelCase	):
                        if filename.startswith(('''.''', '''__''')	):
                            continue
                        yield os.path.join(__lowerCamelCase  ,   __lowerCamelCase	)








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					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Union[str, Any]:
   UpperCAmelCase      					=  []
   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					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Any:
   for i in range(encoder_config.num_hidden_layers ):
      # queries, keys and values (only weights, no biases)
      UpperCAmelCase      					=  state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight""" )

      UpperCAmelCase      					=  in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      UpperCAmelCase      					=  in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      UpperCAmelCase      					=  in_proj_weight[
          -encoder_config.hidden_size :, :
      ]
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   UpperCAmelCase      					=  dct.pop(lowerCAmelCase_ )
   UpperCAmelCase      					=  val
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   if "handwritten" in checkpoint_url:
      UpperCAmelCase      					=  """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      					=  """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      					=  Image.open(requests.get(lowerCAmelCase_	,    stream=lowerCAmelCase_ ).raw ).convert("""RGB""" )
   return im
@torch.no_grad()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   UpperCAmelCase      					=  ViTConfig(image_size=3_8_4	,    qkv_bias=lowerCAmelCase_ )
   UpperCAmelCase      					=  TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      UpperCAmelCase      					=  7_6_8
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      UpperCAmelCase      					=  1_0_2_4
      UpperCAmelCase      					=  4_0_9_6
      UpperCAmelCase      					=  2_4
      UpperCAmelCase      					=  1_6
      UpperCAmelCase      					=  1_0_2_4
   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      					=  False
      UpperCAmelCase      					=  """relu"""
      UpperCAmelCase      					=  1_0_2_4
      UpperCAmelCase      					=  True
      UpperCAmelCase      					=  False
      UpperCAmelCase      					=  False

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

   # load state_dict of original model, rename some keys
   UpperCAmelCase      					=  torch.hub.load_state_dict_from_url(lowerCAmelCase_	,    map_location="""cpu"""	,    check_hash=lowerCAmelCase_ )["""model"""]

   UpperCAmelCase      					=  create_rename_keys(lowerCAmelCase_	,    lowerCAmelCase_ )
   for src, dest in rename_keys:
      rename_key(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )
   read_in_q_k_v(lowerCAmelCase_	,    lowerCAmelCase_ )

   # 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      					=  state_dict.pop(lowerCAmelCase_ )
      if key.startswith("""decoder""" ) and "output_projection" not in key:
         UpperCAmelCase      					=  val
      else:
         UpperCAmelCase      					=  val

    # load state dict
   model.load_state_dict(lowerCAmelCase_ )

   # Check outputs on an image
   UpperCAmelCase      					=  ViTImageProcessor(size=encoder_config.image_size )
   UpperCAmelCase      					=  RobertaTokenizer.from_pretrained("""roberta-large""" )
   UpperCAmelCase      					=  TrOCRProcessor(lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  processor(images=prepare_img(lowerCAmelCase_ )	,    return_tensors="""pt""" ).pixel_values

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

   UpperCAmelCase      					=  torch.Size([1, 1, 5_0_2_6_5] )
   if "trocr-base-handwritten" in checkpoint_url:
      UpperCAmelCase      					=  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      					=  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      					=  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      					=  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, :1_0]	,    lowerCAmelCase_	,    atol=1e-3 ), "First elements of logits not as expected"

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


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)



