




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

            _A							:     Tuple			=						in_proj_weight[
                : encoder_config.hidden_size, :
            ]
            _A							:     Optional[int]			=						in_proj_weight[
                encoder_config.hidden_size : encoder_config.hidden_size * 2, :
            ]
            _A							:     Dict			=						in_proj_weight[
                -encoder_config.hidden_size :, :
            ]







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     List[Any]			=						dct.pop(snake_case_     )
      _A							:     Dict			=						val







def 				lowerCAmelCase_	(     snake_case_     ):
      if "handwritten" in checkpoint_url:
            _A							:     Union[str, Any]			=						"""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							:     Dict			=						"""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(snake_case_,stream=snake_case_     ).raw     ).convert("""RGB"""     )
      return im







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Any			=						ViTConfig(image_size=384,qkv_bias=snake_case_     )
      _A							:     Tuple			=						TrOCRConfig()

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

      # load HuggingFace model
      _A							:     List[str]			=						ViTModel(snake_case_,add_pooling_layer=snake_case_     )
      _A							:     List[str]			=						TrOCRForCausalLM(snake_case_     )
      _A							:     Any			=						VisionEncoderDecoderModel(encoder=snake_case_,decoder=snake_case_     )
      model.eval()

      # load state_dict of original model, rename some keys
      _A							:     Dict			=						torch.hub.load_state_dict_from_url(snake_case_,map_location="""cpu""",check_hash=snake_case_     )["""model"""]

      _A							:     Optional[int]			=						create_rename_keys(snake_case_,snake_case_     )
      for src, dest in rename_keys:
            rename_key(snake_case_,snake_case_,snake_case_     )
      read_in_q_k_v(snake_case_,snake_case_     )

      # 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							:     List[str]			=						state_dict.pop(snake_case_     )
            if key.startswith("""decoder"""     ) and "output_projection" not in key:
                  _A							:     Optional[int]			=						val
            else:
                  _A							:     Dict			=						val

    # load state dict
      model.load_state_dict(snake_case_     )

      # Check outputs on an image
      _A							:     List[Any]			=						ViTImageProcessor(size=encoder_config.image_size     )
      _A							:     List[str]			=						RobertaTokenizer.from_pretrained("""roberta-large"""     )
      _A							:     Dict			=						TrOCRProcessor(snake_case_,snake_case_     )

      _A							:     Tuple			=						processor(images=prepare_img(snake_case_     ),return_tensors="""pt"""     ).pixel_values

      # verify logits
      _A							:     Dict			=						torch.tensor([[model.config.decoder.decoder_start_token_id]]     )
      _A							:     Optional[int]			=						model(pixel_values=snake_case_,decoder_input_ids=snake_case_     )
      _A							:     int			=						outputs.logits

      _A							:     Optional[int]			=						torch.Size([1, 1, 50265]     )
      if "trocr-base-handwritten" in checkpoint_url:
            _A							:     Dict			=						torch.tensor(
                [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]     )
      elif "trocr-large-handwritten" in checkpoint_url:
            _A							:     Tuple			=						torch.tensor(
                [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]     )
      elif "trocr-base-printed" in checkpoint_url:
            _A							:     int			=						torch.tensor(
                [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]     )
      elif "trocr-large-printed" in checkpoint_url:
            _A							:     Optional[int]			=						torch.tensor(
                [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]     )

      if "stage1" not in checkpoint_url:
            assert logits.shape == expected_shape, "Shape of logits not as expected"
            assert torch.allclose(logits[0, 0, :10],snake_case_,atol=1e-3     ), "First elements of logits not as expected"

      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )
      print(f'''Saving model to {pytorch_dump_folder_path}'''     )
      model.save_pretrained(snake_case_     )
      print(f'''Saving processor to {pytorch_dump_folder_path}'''     )
      processor.save_pretrained(snake_case_     )


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)







from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy







@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							Optional[int]:
           super().__init__(_a				)

           _A							:     Union[str, Any]			=						RobertaEmbeddings(_a				)
           self.init_weights()







@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							str:
           super().__init__(_a				)
           _A							:     Any			=						config.num_labels
           _A							:     Dict			=						config.num_hidden_layers

           _A							:     List[str]			=						DeeRobertaModel(_a				)
           _A							:     int			=						nn.Dropout(config.hidden_dropout_prob				)
           _A							:     int			=						nn.Linear(config.hidden_size		,     self.config.num_labels				)



     @add_start_docstrings_to_model_forward(_a				)
     def 					a__	(  self		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=-1		,     _a=False		,     )						->							Any:
           _A							:     Optional[int]			=						self.num_layers
           try:
                 _A							:     List[str]			=						self.roberta(
                     _a		,     attention_mask=_a		,     token_type_ids=_a		,     position_ids=_a		,     head_mask=_a		,     inputs_embeds=_a		,     )

                 _A							:     List[str]			=						outputs[1]

                 _A							:     List[str]			=						self.dropout(_a				)
                 _A							:     Optional[Any]			=						self.classifier(_a				)
                 _A							:     List[Any]			=						(logits,) + outputs[2:]  # add hidden states and attention if they are here
           except HighwayException as e:
                 _A							:     List[Any]			=						e.message
                 _A							:     Optional[int]			=						e.exit_layer
                 _A							:     Optional[int]			=						outputs[0]

           if not self.training:
                 _A							:     int			=						entropy(_a				)
                 _A							:     int			=						[]
                 _A							:     int			=						[]
           if labels is not None:
                 if self.num_labels == 1:
                       #  We are doing regression
                       _A							:     Union[str, Any]			=						MSELoss()
                       _A							:     Tuple			=						loss_fct(logits.view(-1				)		,     labels.view(-1				)				)
                 else:
                       _A							:     List[Any]			=						CrossEntropyLoss()
                       _A							:     Dict			=						loss_fct(logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)

                 # work with highway exits
                 _A							:     Optional[Any]			=						[]
                 for highway_exit in outputs[-1]:
                       _A							:     Tuple			=						highway_exit[0]
                       if not self.training:
                             highway_logits_all.append(_a				)
                             highway_entropy.append(highway_exit[2]				)
                       if self.num_labels == 1:
                             #  We are doing regression
                             _A							:     List[str]			=						MSELoss()
                             _A							:     Optional[int]			=						loss_fct(highway_logits.view(-1				)		,     labels.view(-1				)				)
                       else:
                             _A							:     List[Any]			=						CrossEntropyLoss()
                             _A							:     Tuple			=						loss_fct(highway_logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)
                       highway_losses.append(_a				)

                 if train_highway:
                       _A							:     Dict			=						(sum(highway_losses[:-1]				),) + outputs
                       # exclude the final highway, of course
                 else:
                       _A							:     int			=						(loss,) + outputs
           if not self.training:
                 _A							:     Optional[Any]			=						outputs + ((original_entropy, highway_entropy), exit_layer)
                 if output_layer >= 0:
                       _A							:     Union[str, Any]			=						(
                           (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                       )  # use the highway of the last layer

           return outputs  # (loss), logits, (hidden_states), (attentions), entropy

