

from sklearn.metrics import mean_squared_error

import datasets


__UpperCAmelCase								=			"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

__UpperCAmelCase								=			"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


__UpperCAmelCase								=			"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,							_KWARGS_DESCRIPTION			)
class SCREAMING_SNAKE_CASE     (  datasets.Metric			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Optional[int]:






     """simple docstring"""







     return datasets.MetricInfo(
         description=_DESCRIPTION	,							citation=_CITATION	,							inputs_description=_KWARGS_DESCRIPTION	,							features=datasets.Features(self._get_feature_types()   )	,							reference_urls=[
             """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
         ]	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Tuple:






     """simple docstring"""







     if self.config_name == "multilist":
        return {
            "predictions": datasets.Sequence(datasets.Value("""float"""   )   ),
            "references": datasets.Sequence(datasets.Value("""float"""   )   ),
        }
     else:
        return {
            "predictions": datasets.Value("""float"""   ),
            "references": datasets.Value("""float"""   ),
        }







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   List[Any]=None	,							lowerCAmelCase			:   List[Any]="uniform_average"	,							lowerCAmelCase			:   Optional[Any]=True   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						mean_squared_error(
         lowerCAmelCase	,							lowerCAmelCase	,							sample_weight=lowerCAmelCase	,							multioutput=lowerCAmelCase	,							squared=lowerCAmelCase   )

     return {"mse": mse}





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								=			logging.get_logger(__name__)





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

      __lowerCAmelCase  :      Tuple    =						in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      __lowerCAmelCase  :      str    =						in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      __lowerCAmelCase  :      str    =						in_proj_weight[
          -encoder_config.hidden_size :, :
      ]





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						str ,       __A					:						Optional[Any]					)      ->     Optional[Any]:
   __lowerCAmelCase  :      Any    =						dct.pop(__A					)
   __lowerCAmelCase  :      str    =						val





def      snake_case_				(__A					:						int					)      ->     Tuple:
   if "handwritten" in checkpoint_url:
      __lowerCAmelCase  :      Tuple    =						"""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:
      __lowerCAmelCase  :      Optional[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"""
   __lowerCAmelCase  :      Dict    =						Image.open(requests.get(__A ,       stream=__A					).raw					).convert("""RGB"""					)
   return im





@torch.no_grad()
def      snake_case_				(__A					:						Any ,       __A					:						Union[str, Any]					)      ->     Optional[int]:
   __lowerCAmelCase  :      List[Any]    =						ViTConfig(image_size=3_8_4 ,       qkv_bias=__A					)
   __lowerCAmelCase  :      List[Any]    =						TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      __lowerCAmelCase  :      Union[str, Any]    =						7_6_8
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      __lowerCAmelCase  :      Any    =						1_0_2_4
      __lowerCAmelCase  :      Any    =						4_0_9_6
      __lowerCAmelCase  :      Optional[int]    =						2_4
      __lowerCAmelCase  :      str    =						1_6
      __lowerCAmelCase  :      List[Any]    =						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:
      __lowerCAmelCase  :      Tuple    =						False
      __lowerCAmelCase  :      Union[str, Any]    =						"""relu"""
      __lowerCAmelCase  :      List[Any]    =						1_0_2_4
      __lowerCAmelCase  :      Any    =						True
      __lowerCAmelCase  :      List[Any]    =						False
      __lowerCAmelCase  :      Dict    =						False

   # load HuggingFace model
   __lowerCAmelCase  :      Dict    =						ViTModel(__A ,       add_pooling_layer=__A					)
   __lowerCAmelCase  :      Union[str, Any]    =						TrOCRForCausalLM(__A					)
   __lowerCAmelCase  :      Any    =						VisionEncoderDecoderModel(encoder=__A ,       decoder=__A					)
   model.eval()

   # load state_dict of original model, rename some keys
   __lowerCAmelCase  :      Union[str, Any]    =						torch.hub.load_state_dict_from_url(__A ,       map_location="""cpu""" ,       check_hash=__A					)["""model"""]

   __lowerCAmelCase  :      Any    =						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():
      __lowerCAmelCase  :      Tuple    =						state_dict.pop(__A					)
      if key.startswith("""decoder"""					) and "output_projection" not in key:
         __lowerCAmelCase  :      str    =						val
      else:
         __lowerCAmelCase  :      Tuple    =						val

    # load state dict
   model.load_state_dict(__A					)

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

   __lowerCAmelCase  :      List[str]    =						processor(images=prepare_img(__A					) ,       return_tensors="""pt"""					).pixel_values

   # verify logits
   __lowerCAmelCase  :      List[str]    =						torch.tensor([[model.config.decoder.decoder_start_token_id]]					)
   __lowerCAmelCase  :      List[str]    =						model(pixel_values=__A ,       decoder_input_ids=__A					)
   __lowerCAmelCase  :      Optional[Any]    =						outputs.logits

   __lowerCAmelCase  :      Union[str, Any]    =						torch.Size([1, 1, 5_0_2_6_5]					)
   if "trocr-base-handwritten" in checkpoint_url:
      __lowerCAmelCase  :      Dict    =						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:
      __lowerCAmelCase  :      List[Any]    =						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:
      __lowerCAmelCase  :      Tuple    =						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:
      __lowerCAmelCase  :      List[Any]    =						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] ,       __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								=			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								=			parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


