






"""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()
lowercase_				     =     logging.get_logger(__name__)



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= []
  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		,       __UpperCamelCase ):




  """simple docstring"""







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

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



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= dct.pop(__UpperCamelCase )
  __A									= val



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if "handwritten" in checkpoint_url:
    __A									= '''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									= '''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									= Image.open(requests.get(__UpperCamelCase		,       stream=__UpperCamelCase ).raw ).convert('''RGB''' )
  return im



@torch.no_grad()
def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= ViTConfig(image_size=3_8_4		,       qkv_bias=__UpperCamelCase )
  __A									= TrOCRConfig()

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

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

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

  __A									= 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									= state_dict.pop(__UpperCamelCase )
    if key.startswith('''decoder''' ) and "output_projection" not in key:
      __A									= val
    else:
      __A									= val

    # load state dict
  model.load_state_dict(__UpperCamelCase )

  # Check outputs on an image
  __A									= ViTImageProcessor(size=encoder_config.image_size )
  __A									= RobertaTokenizer.from_pretrained('''roberta-large''' )
  __A									= TrOCRProcessor(__UpperCamelCase		,       __UpperCamelCase )

  __A									= processor(images=prepare_img(__UpperCamelCase )		,       return_tensors='''pt''' ).pixel_values

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

  __A									= torch.Size([1, 1, 5_0_2_6_5] )
  if "trocr-base-handwritten" in checkpoint_url:
    __A									= 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									= 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									= 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									= 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]		,       __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__":
       lowercase_				     =     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.'
       )
       lowercase_				     =     parser.parse_args()
       convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
       from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     for model_result in results.values():
       for batch_size, sequence_length in zip(model_result['''bs'''],       model_result['''ss''']  ):
         __A									= model_result['''result'''][batch_size][sequence_length]
         self.assertIsNotNone(_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sgugger/tiny-distilbert-classification'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       only_pretrain_model=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       torchscript=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   @unittest.skipIf(torch_device == '''cpu''',       '''Cant do half precision'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       fpaa=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     # set architectures equal to `None`
     __A									= None
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   @unittest.skipIf(torch_device == '''cpu''',       '''Can\'t do half precision'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       fpaa=_lowerCamelCase,       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
       __A									= PyTorchBenchmarkArguments(
           models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       save_to_csv=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       inference_time_csv_file=os.path.join(_lowerCamelCase,       '''inf_time.csv'''  ),       train_memory_csv_file=os.path.join(_lowerCamelCase,       '''train_mem.csv'''  ),       inference_memory_csv_file=os.path.join(_lowerCamelCase,       '''inf_mem.csv'''  ),       train_time_csv_file=os.path.join(_lowerCamelCase,       '''train_time.csv'''  ),       env_info_csv_file=os.path.join(_lowerCamelCase,       '''env.csv'''  ),       multi_process=_lowerCamelCase,       )
       __A									= PyTorchBenchmark(_lowerCamelCase  )
       benchmark.run()
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''inf_time.csv'''  )  ).exists()  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''train_time.csv'''  )  ).exists()  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''inf_mem.csv'''  )  ).exists()  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''train_mem.csv'''  )  ).exists()  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''env.csv'''  )  ).exists()  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase :     List[Any]  ):
       self.assertTrue(hasattr(_lowerCamelCase,       '''sequential'''  )  )
       self.assertTrue(hasattr(_lowerCamelCase,       '''cumulative'''  )  )
       self.assertTrue(hasattr(_lowerCamelCase,       '''current'''  )  )
       self.assertTrue(hasattr(_lowerCamelCase,       '''total'''  )  )

     with tempfile.TemporaryDirectory() as tmp_dir:
       __A									= PyTorchBenchmarkArguments(
           models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       log_filename=os.path.join(_lowerCamelCase,       '''log.txt'''  ),       log_print=_lowerCamelCase,       trace_memory_line_by_line=_lowerCamelCase,       multi_process=_lowerCamelCase,       )
       __A									= PyTorchBenchmark(_lowerCamelCase  )
       __A									= benchmark.run()
       _check_summary_is_not_empty(result.inference_summary  )
       _check_summary_is_not_empty(result.train_summary  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''log.txt'''  )  ).exists()  )




