






"""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()
UpperCamelCase_	:				Union[str, Any]										= logging.get_logger(__name__)



def     __lowercase					(    a :				Dict       ,		a :				Union[str, Any] )					->					List[Any]:
      __snake_case      :    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     __lowercase					(    a :				Optional[int]       ,		a :				Tuple )					->					str:
      for i in range(encoder_config.num_hidden_layers ):
            # queries, keys and values (only weights, no biases)
            __snake_case      :    Optional[Any]  			=state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight''' )

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



def     __lowercase					(    a :				Union[str, Any]       ,		a :				List[str]       ,		a :				Optional[int] )					->					str:
      __snake_case      :    Optional[Any]  			=dct.pop(a )
      __snake_case      :    List[str]  			=val



def     __lowercase					(    a :				List[Any] )					->					int:
      if "handwritten" in checkpoint_url:
            __snake_case      :    Dict  			='''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:
            __snake_case      :    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'''
      __snake_case      :    Tuple  			=Image.open(requests.get(a       ,		stream=a ).raw ).convert('''RGB''' )
      return im



@torch.no_grad()
def     __lowercase					(    a :				Optional[int]       ,		a :				str )					->					str:
      __snake_case      :    Any  			=ViTConfig(image_size=384       ,		qkv_bias=a )
      __snake_case      :    Optional[Any]  			=TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            __snake_case      :    Optional[int]  			=768
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            __snake_case      :    Dict  			=1_024
            __snake_case      :    str  			=4_096
            __snake_case      :    Optional[int]  			=24
            __snake_case      :    int  			=16
            __snake_case      :    int  			=1_024
      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:
            __snake_case      :    Any  			=False
            __snake_case      :    Union[str, Any]  			='''relu'''
            __snake_case      :    Any  			=1_024
            __snake_case      :    Tuple  			=True
            __snake_case      :    Optional[Any]  			=False
            __snake_case      :    Optional[Any]  			=False

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

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

      __snake_case      :    List[str]  			=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():
            __snake_case      :    Union[str, Any]  			=state_dict.pop(a )
            if key.startswith('''decoder''' ) and "output_projection" not in key:
                  __snake_case      :    Tuple  			=val
            else:
                  __snake_case      :    int  			=val

    # load state dict
      model.load_state_dict(a )

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

      __snake_case      :    Dict  			=processor(images=prepare_img(a )       ,		return_tensors='''pt''' ).pixel_values

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

      __snake_case      :    Tuple  			=torch.Size([1, 1, 50_265] )
      if "trocr-base-handwritten" in checkpoint_url:
            __snake_case      :    Dict  			=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:
            __snake_case      :    Optional[int]  			=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:
            __snake_case      :    Dict  			=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:
            __snake_case      :    List[str]  			=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]       ,		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__":
    UpperCamelCase_	:				Optional[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."""
    )
    UpperCamelCase_	:				Union[str, Any]										= parser.parse_args()
    convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase_	:				Union[str, Any]										= {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


