

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						: str											=							logging.get_logger(__name__)





def    _A							(						__snake_case  :Optional[Any]   , __snake_case  :int  )   -> Dict:



   """simple docstring"""


   __SCREAMING_SNAKE_CASE       =				[]
   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    _A							(						__snake_case  :List[Any]   , __snake_case  :str  )   -> Optional[Any]:



   """simple docstring"""


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

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





def    _A							(						__snake_case  :List[Any]   , __snake_case  :Tuple   , __snake_case  :List[Any]  )   -> Dict:



   """simple docstring"""


   __SCREAMING_SNAKE_CASE       =				dct.pop(UpperCamelCase_  )
   __SCREAMING_SNAKE_CASE       =				val





def    _A							(						__snake_case  :Dict  )   -> Optional[int]:



   """simple docstring"""


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





@torch.no_grad()
def    _A							(						__snake_case  :Optional[Any]   , __snake_case  :Optional[int]  )   -> str:



   """simple docstring"""


   __SCREAMING_SNAKE_CASE       =				ViTConfig(image_size=384   , qkv_bias=UpperCamelCase_  )
   __SCREAMING_SNAKE_CASE       =				TrOCRConfig()

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

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

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

   __SCREAMING_SNAKE_CASE       =				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():
      __SCREAMING_SNAKE_CASE       =				state_dict.pop(UpperCamelCase_  )
      if key.startswith("decoder"  ) and "output_projection" not in key:
         __SCREAMING_SNAKE_CASE       =				val
      else:
         __SCREAMING_SNAKE_CASE       =				val

    # load state dict
   model.load_state_dict(UpperCamelCase_  )

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

   __SCREAMING_SNAKE_CASE       =				processor(images=prepare_img(UpperCamelCase_  )   , return_tensors="pt"  ).pixel_values

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

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





'''simple docstring'''





import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
    from .features import FeatureType


__lowerCamelCase					,		__lowerCamelCase					,		__lowerCamelCase												=						False, False, False




@dataclass
class 				UpperCAmelCase			:
 UpperCAmelCase				=					None
 UpperCAmelCase				=					True
 UpperCAmelCase				=					True
 UpperCAmelCase				=					None
 # Automatically constructed
 UpperCAmelCase				=					"dict"
 UpperCAmelCase				=					pa.struct({"bytes": pa.binary(), "path": pa.string()}						)
 UpperCAmelCase				=					field(default="Audio"						, init=_snake_case						, repr=_snake_case						)


 def __call__(      self							:	Any ):
    return self.pa_type


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Union[str, bytes, dict] ):
    try:
       import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
    except ImportError as err:
       raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
    if isinstance(__lowerCamelCase			,       __lowerCamelCase ):
       return {"bytes": None, "path": value}
    elif isinstance(__lowerCamelCase			,       __lowerCamelCase ):
       return {"bytes": value, "path": None}
    elif "array" in value:
       # convert the audio array to wav bytes
       UpperCAmelCase__	:int      =      BytesIO()
       sf.write(__lowerCamelCase			,       value['''array''']			,       value['''sampling_rate''']			,       format='''wav''' )
       return {"bytes": buffer.getvalue(), "path": None}
    elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
       # we set "bytes": None to not duplicate the data if they're already available locally
       if value["path"].endswith('''pcm''' ):
          # "PCM" only has raw audio bytes
          if value.get('''sampling_rate''' ) is None:
             # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
             raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
          if value.get('''bytes''' ):
             # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
             UpperCAmelCase__	:List[Any]      =      np.frombuffer(value['''bytes''']			,       dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
          else:
             UpperCAmelCase__	:Optional[Any]      =      np.memmap(value['''path''']			,       dtype='''h'''			,       mode='''r''' ).astype(np.floataa ) / 3_2_7_6_7

          UpperCAmelCase__	:Optional[Any]      =      BytesIO(bytes() )
          sf.write(__lowerCamelCase			,       __lowerCamelCase			,       value['''sampling_rate''']			,       format='''wav''' )
          return {"bytes": buffer.getvalue(), "path": None}
       else:
          return {"bytes": None, "path": value.get('''path''' )}
    elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
       # store the audio bytes, and path is used to infer the audio format using the file extension
       return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
    else:
       raise ValueError(
           f'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	dict			,       __lowerCamelCase							:	Optional[Dict[str, Union[str, bool, None]]] = None ):
    if not self.decode:
       raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )

    UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      (value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
    if path is None and file is None:
       raise ValueError(f'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''' )

    try:
       import librosa
       import soundfile as sf
    except ImportError as err:
       raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err

    UpperCAmelCase__	:List[str]      =      xsplitext(__lowerCamelCase )[1][1:].lower() if path is not None else None
    if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
       raise RuntimeError(
           '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
    elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
       raise RuntimeError(
           '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )

    if file is None:
       UpperCAmelCase__	:Optional[Any]      =      token_per_repo_id or {}
       UpperCAmelCase__	:str      =      path.split('''::''' )[-1]
       try:
          UpperCAmelCase__	:Tuple      =      string_to_dict(__lowerCamelCase			,       config.HUB_DATASETS_URL )['''repo_id''']
          UpperCAmelCase__	:str      =      token_per_repo_id[repo_id]
       except (ValueError, KeyError):
          UpperCAmelCase__	:Tuple      =      None

       with xopen(__lowerCamelCase			,       '''rb'''			,       use_auth_token=__lowerCamelCase ) as f:
          UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      sf.read(__lowerCamelCase )

    else:
       UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      sf.read(__lowerCamelCase )

    UpperCAmelCase__	:Optional[int]      =      array.T
    if self.mono:
       UpperCAmelCase__	:Any      =      librosa.to_mono(__lowerCamelCase )
    if self.sampling_rate and self.sampling_rate != sampling_rate:
       UpperCAmelCase__	:Union[str, Any]      =      librosa.resample(__lowerCamelCase			,       orig_sr=__lowerCamelCase			,       target_sr=self.sampling_rate )
       UpperCAmelCase__	:List[str]      =      self.sampling_rate

    return {"path": path, "array": array, "sampling_rate": sampling_rate}


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    from .features import Value

    if self.decode:
       raise ValueError('''Cannot flatten a decoded Audio feature.''' )
    return {
        "bytes": Value('''binary''' ),
        "path": Value('''string''' ),
    }


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	Union[pa.StringArray, pa.StructArray] ):
    if pa.types.is_string(storage.type ):
       UpperCAmelCase__	:List[str]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.binary() )
       UpperCAmelCase__	:Tuple      =      pa.StructArray.from_arrays([bytes_array, storage]			,       ['''bytes''', '''path''']			,       mask=storage.is_null() )
    elif pa.types.is_binary(storage.type ):
       UpperCAmelCase__	:str      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.string() )
       UpperCAmelCase__	:int      =      pa.StructArray.from_arrays([storage, path_array]			,       ['''bytes''', '''path''']			,       mask=storage.is_null() )
    elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
       UpperCAmelCase__	:Any      =      pa.array([Audio().encode_example(__lowerCamelCase ) if x is not None else None for x in storage.to_pylist()] )
    elif pa.types.is_struct(storage.type ):
       if storage.type.get_field_index('''bytes''' ) >= 0:
          UpperCAmelCase__	:str      =      storage.field('''bytes''' )
       else:
          UpperCAmelCase__	:List[str]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.binary() )
       if storage.type.get_field_index('''path''' ) >= 0:
          UpperCAmelCase__	:Optional[int]      =      storage.field('''path''' )
       else:
          UpperCAmelCase__	:Optional[int]      =      pa.array([None] * len(__lowerCamelCase )			,       type=pa.string() )
       UpperCAmelCase__	:List[str]      =      pa.StructArray.from_arrays([bytes_array, path_array]			,       ['''bytes''', '''path''']			,       mask=storage.is_null() )
    return array_cast(__lowerCamelCase			,       self.pa_type )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	pa.StructArray ):

    @no_op_if_value_is_null
    def path_to_bytes(__lowerCamelCase							:	Dict ):
       with xopen(__lowerCamelCase			,       '''rb''' ) as f:
          UpperCAmelCase__	:Any      =      f.read()
       return bytes_

    UpperCAmelCase__	:Union[str, Any]      =      pa.array(
        [
            (path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
            for x in storage.to_pylist()
        ]			,       type=pa.binary()			,       )
    UpperCAmelCase__	:Optional[int]      =      pa.array(
        [os.path.basename(__lowerCamelCase ) if path is not None else None for path in storage.field('''path''' ).to_pylist()]			,       type=pa.string()			,       )
    UpperCAmelCase__	:Optional[int]      =      pa.StructArray.from_arrays([bytes_array, path_array]			,       ['''bytes''', '''path''']			,       mask=bytes_array.is_null() )
    return array_cast(__lowerCamelCase			,       self.pa_type )



