

import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   old_name

     if "patch_embed" in old_name:
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   old_name.split('''.'''    )

          if layer == "0":
               SCREAMING_SNAKE_CASE__   =   old_name.replace('''0'''				,		'''convolution1'''    )
          elif layer == "1":
               SCREAMING_SNAKE_CASE__   =   old_name.replace('''1'''				,		'''batchnorm_before'''    )
          elif layer == "3":
               SCREAMING_SNAKE_CASE__   =   old_name.replace('''3'''				,		'''convolution2'''    )
          else:
               SCREAMING_SNAKE_CASE__   =   old_name.replace('''4'''				,		'''batchnorm_after'''    )

     if "network" in old_name and re.search(R'''\d\.\d'''				,		_A    ):
          SCREAMING_SNAKE_CASE__   =   R'''\b\d{2}\b'''
          if bool(re.search(_A				,		_A    )    ):
               SCREAMING_SNAKE_CASE__   =   re.search(R'''\d\.\d\d.'''				,		_A    ).group()
          else:
               SCREAMING_SNAKE_CASE__   =   re.search(R'''\d\.\d.'''				,		_A    ).group()
          if int(match[0]    ) < 6:
               SCREAMING_SNAKE_CASE__   =   old_name.replace(_A				,		''''''    )
               SCREAMING_SNAKE_CASE__   =   trimmed_name.replace('''network'''				,		match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]    )
               SCREAMING_SNAKE_CASE__   =   '''intermediate_stages.''' + trimmed_name
          else:
               SCREAMING_SNAKE_CASE__   =   old_name.replace(_A				,		''''''    )
               if int(match[2]    ) < num_meta4D_last_stage:
                    SCREAMING_SNAKE_CASE__   =   trimmed_name.replace('''network'''				,		'''meta4D_layers.blocks.''' + match[2]    )
               else:
                    SCREAMING_SNAKE_CASE__   =   str(int(match[2]    ) - num_meta4D_last_stage    )
                    SCREAMING_SNAKE_CASE__   =   trimmed_name.replace('''network'''				,		'''meta3D_layers.blocks.''' + layer_index    )
                    if "norm1" in old_name:
                         SCREAMING_SNAKE_CASE__   =   trimmed_name.replace('''norm1'''				,		'''layernorm1'''    )
                    elif "norm2" in old_name:
                         SCREAMING_SNAKE_CASE__   =   trimmed_name.replace('''norm2'''				,		'''layernorm2'''    )
                    elif "fc1" in old_name:
                         SCREAMING_SNAKE_CASE__   =   trimmed_name.replace('''fc1'''				,		'''linear_in'''    )
                    elif "fc2" in old_name:
                         SCREAMING_SNAKE_CASE__   =   trimmed_name.replace('''fc2'''				,		'''linear_out'''    )

               SCREAMING_SNAKE_CASE__   =   '''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(R'''.\d.'''				,		_A    ):
          SCREAMING_SNAKE_CASE__   =   old_name.replace('''network'''				,		'''intermediate_stages'''    )

     if "fc" in new_name:
          SCREAMING_SNAKE_CASE__   =   new_name.replace('''fc'''				,		'''convolution'''    )
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          SCREAMING_SNAKE_CASE__   =   new_name.replace('''norm1'''				,		'''batchnorm_before'''    )
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          SCREAMING_SNAKE_CASE__   =   new_name.replace('''norm2'''				,		'''batchnorm_after'''    )
     if "proj" in new_name:
          SCREAMING_SNAKE_CASE__   =   new_name.replace('''proj'''				,		'''projection'''    )
     if "dist_head" in new_name:
          SCREAMING_SNAKE_CASE__   =   new_name.replace('''dist_head'''				,		'''distillation_classifier'''    )
     elif "head" in new_name:
          SCREAMING_SNAKE_CASE__   =   new_name.replace('''head'''				,		'''classifier'''    )
     elif "patch_embed" in new_name:
          SCREAMING_SNAKE_CASE__   =   '''efficientformer.''' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          SCREAMING_SNAKE_CASE__   =   new_name.replace('''norm'''				,		'''layernorm'''    )
          SCREAMING_SNAKE_CASE__   =   '''efficientformer.''' + new_name
     else:
          SCREAMING_SNAKE_CASE__   =   '''efficientformer.encoder.''' + new_name

     return new_name
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     for key in checkpoint.copy().keys():
          SCREAMING_SNAKE_CASE__   =   checkpoint.pop(_A    )
          SCREAMING_SNAKE_CASE__   =   val

     return checkpoint
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__   =   Image.open(requests.get(_A				,		stream=_A    ).raw    )

     return image
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   torch.load(_A				,		map_location='''cpu'''    )['''model''']
     SCREAMING_SNAKE_CASE__   =   EfficientFormerConfig.from_json_file(_A    )
     SCREAMING_SNAKE_CASE__   =   EfficientFormerForImageClassificationWithTeacher(_A    )
     SCREAMING_SNAKE_CASE__   =   '''_'''.join(checkpoint_path.split('''/'''    )[-1].split('''.'''    )[0].split('''_'''    )[:-1]    )

     SCREAMING_SNAKE_CASE__   =   config.depths[-1] - config.num_metaad_blocks + 1
     SCREAMING_SNAKE_CASE__   =   convert_torch_checkpoint(_A				,		_A    )

     model.load_state_dict(_A    )
     model.eval()

     SCREAMING_SNAKE_CASE__   =   {
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     SCREAMING_SNAKE_CASE__   =   prepare_img()
     SCREAMING_SNAKE_CASE__   =   2_56
     SCREAMING_SNAKE_CASE__   =   2_24
     SCREAMING_SNAKE_CASE__   =   EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size}				,		crop_size={'''height''': crop_size, '''width''': crop_size}				,		resample=pillow_resamplings['''bicubic''']				,		)
     SCREAMING_SNAKE_CASE__   =   processor(images=_A				,		return_tensors='''pt'''    ).pixel_values

     # original processing pipeline
     SCREAMING_SNAKE_CASE__   =   Compose(
         [
             Resize(_A				,		interpolation=pillow_resamplings['''bicubic''']    ),
             CenterCrop(_A    ),
             ToTensor(),
             Normalize(_A				,		_A    ),
         ]    )
     SCREAMING_SNAKE_CASE__   =   image_transforms(_A    ).unsqueeze(0    )

     assert torch.allclose(_A				,		_A    )

     SCREAMING_SNAKE_CASE__   =   model(_A    )
     SCREAMING_SNAKE_CASE__   =   outputs.logits

     SCREAMING_SNAKE_CASE__   =   (1, 10_00)

     if "l1" in model_name:
          SCREAMING_SNAKE_CASE__   =   torch.Tensor(
              [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]    )
          assert torch.allclose(logits[0, :10]				,		_A				,		atol=1e-3    )
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          SCREAMING_SNAKE_CASE__   =   torch.Tensor(
              [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]    )
          assert torch.allclose(logits[0, :10]				,		_A				,		atol=1e-3    )
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          SCREAMING_SNAKE_CASE__   =   torch.Tensor(
              [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]    )
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''    )

     # Save Checkpoints
     Path(_A    ).mkdir(exist_ok=_A    )
     model.save_pretrained(_A    )
     print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''    )
     processor.save_pretrained(_A    )
     print(F'''Processor successfuly saved at {pytorch_dump_path}'''    )

     if push_to_hub:
          print('''Pushing model to the hub...'''    )

          model.push_to_hub(
              repo_id=F'''Bearnardd/{pytorch_dump_path}'''				,		commit_message='''Add model'''				,		use_temp_dir=_A				,		)
          processor.push_to_hub(
              repo_id=F'''Bearnardd/{pytorch_dump_path}'''				,		commit_message='''Add image processor'''				,		use_temp_dir=_A				,		)


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Any             =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--pytorch_model_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to EfficientFormer pytorch checkpoint.''',
       )
       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help='''The json file for EfficientFormer model config.''',
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )

       parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
       parser.add_argument(
           '''--no-push_to_hub''',
           dest='''push_to_hub''',
           action='''store_false''',
           help='''Do not push model and image processor to the hub''',
       )
       parser.set_defaults(push_to_hub=True)

       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()
       convert_efficientformer_checkpoint(
           checkpoint_path=args.pytorch_model_path,
           efficientformer_config_file=args.config_file,
           pytorch_dump_path=args.pytorch_dump_path,
           push_to_hub=args.push_to_hub,
       )





import comet  # From: unbabel-comet
import torch

import datasets


_SCREAMING_SNAKE_CASE					:  List[str]             =      datasets.logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Any             =      '''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION	)
class 	UpperCAmelCase__		(	datasets.Metric	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							List[str]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       homepage='''https://unbabel.github.io/COMET/html/index.html'''     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''sources''': datasets.Value('''string'''     ,       id='''sequence''' ),
                   '''predictions''': datasets.Value('''string'''     ,       id='''sequence''' ),
                   '''references''': datasets.Value('''string'''     ,       id='''sequence''' ),
               } )     ,       codebase_urls=['''https://github.com/Unbabel/COMET''']     ,       reference_urls=[
               '''https://github.com/Unbabel/COMET''',
               '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
               '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
           ]     ,       )






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Dict )		->							Tuple:
       if self.config_name == "default":
            SCREAMING_SNAKE_CASE__   =   comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
       else:
            SCREAMING_SNAKE_CASE__   =   comet.load_from_checkpoint(comet.download_model(self.config_name ) )







  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Dict=None     ,       __lowerCamelCase						:		Optional[int]=False )		->							str:
       if gpus is None:
            SCREAMING_SNAKE_CASE__   =   1 if torch.cuda.is_available() else 0
       SCREAMING_SNAKE_CASE__   =   {'''src''': sources, '''mt''': predictions, '''ref''': references}
       SCREAMING_SNAKE_CASE__   =   [dict(zip(__lowerCamelCase     ,       __lowerCamelCase ) ) for t in zip(*data.values() )]
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.scorer.predict(__lowerCamelCase     ,       gpus=__lowerCamelCase     ,       progress_bar=__lowerCamelCase )
       return {"mean_score": mean_score, "scores": scores}


