'''simple docstring'''







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_      	=	logging.get_logger()




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							List[nn.Module]     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							list     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Tensor							,							UpperCamelCase_ :       Tensor      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = len(list(m.modules()      )      ) == 1 or isinstance(UpperCamelCase_							,							nn.Convad      ) or isinstance(UpperCamelCase_							,							nn.BatchNormad      )
													if has_not_submodules:
																				self.traced.append(UpperCamelCase_      )




						def __call__(    self :       Tuple							,							UpperCamelCase_ :       Tensor      )     ->				int:
													for m in self.module.modules():
																				self.handles.append(m.register_forward_hook(self._forward_hook      )      )
													self.module(UpperCamelCase_      )
													[x.remove() for x in self.handles]
													return self




						@property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													# check the len of the state_dict keys to see if we have learnable params
													return list(filter(lambda UpperCamelCase_      : len(list(x.state_dict().keys()      )      ) > 0							,							self.traced      )      )




@dataclass
class _SCREAMING_SNAKE_CASE:
						A_			:							nn.Module
						A_			:							nn.Module
						A_			:							int     		= 0
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)
						A_			:							List     		= field(default_factory=_SCREAMING_SNAKE_CASE					)




						def __call__(    self :       List[Any]							,							UpperCamelCase_ :       Tensor      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[str]						    = Tracker(self.dest      )(UpperCamelCase_      ).parametrized
													SCREAMING_SNAKE_CASE__     :Any						    = Tracker(self.src      )(UpperCamelCase_      ).parametrized

													SCREAMING_SNAKE_CASE__     :Dict						    = list(filter(lambda UpperCamelCase_      : type(UpperCamelCase_      ) not in self.src_skip							,							UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :str						    = list(filter(lambda UpperCamelCase_      : type(UpperCamelCase_      ) not in self.dest_skip							,							UpperCamelCase_      )      )

													if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
																				raise Exception(
																				    f'''Numbers of operations are different. Source module has {len(UpperCamelCase_      )} operations while'''
																				    f''' destination module has {len(UpperCamelCase_      )}.'''      )

													for dest_m, src_m in zip(UpperCamelCase_							,							UpperCamelCase_      ):
																				dest_m.load_state_dict(src_m.state_dict()      )
																				if self.verbose == 1:
																											print(f'''Transfered from={src_m} to={dest_m}'''      )


def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  ResNetConfig				,						UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  bool = True		)			->	Union[str, Any]:


							'''simple docstring'''






							print(F'''Converting {name}...'''		)
							with torch.no_grad():
														SCREAMING_SNAKE_CASE__     :str						    = timm.create_model(UpperCAmelCase__				,						pretrained=UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :List[str]						    = ResNetForImageClassification(UpperCAmelCase__		).eval()
														SCREAMING_SNAKE_CASE__     :Dict						    = ModuleTransfer(src=UpperCAmelCase__				,						dest=UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.randn((1, 3, 2_2_4, 2_2_4)		)
														module_transfer(UpperCAmelCase__		)

							assert torch.allclose(from_model(UpperCAmelCase__		)				,						our_model(UpperCAmelCase__		).logits		), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE__     :List[str]						    = F'''resnet{"-".join(name.split("resnet"		)		)}'''
							print(UpperCAmelCase__		)

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name				,						commit_message='Add model'				,						use_temp_dir=UpperCAmelCase__				,						)

														# we can use the convnext one
														SCREAMING_SNAKE_CASE__     :int						    = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'		)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name				,						commit_message='Add image processor'				,						use_temp_dir=UpperCAmelCase__				,						)

														print(F'''Pushed {checkpoint_name}'''		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Path				,						UpperCAmelCase__				:  str = None				,						UpperCAmelCase__				:  bool = True		)			->	int:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = 'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE__     :Any						    = 1_0_0_0
							SCREAMING_SNAKE_CASE__     :List[Any]						    = (1, num_labels)

							SCREAMING_SNAKE_CASE__     :List[str]						    = 'huggingface/label-files'
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = num_labels
							SCREAMING_SNAKE_CASE__     :Tuple						    = json.load(open(hf_hub_download(UpperCAmelCase__				,						UpperCAmelCase__				,						repo_type='dataset'		)				,						'r'		)		)
							SCREAMING_SNAKE_CASE__     :int						    = {int(UpperCAmelCase__		): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__     :str						    = idalabel
							SCREAMING_SNAKE_CASE__     :Any						    = {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = partial(UpperCAmelCase__				,						num_labels=UpperCAmelCase__				,						idalabel=UpperCAmelCase__				,						labelaid=UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :List[str]						    = {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,						layer_type='basic'		),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 2_3, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 3_6, 3]				,						hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						layer_type='bottleneck'		),
							}

							if model_name:
														convert_weight_and_push(UpperCAmelCase__				,						names_to_config[model_name]				,						UpperCAmelCase__				,						UpperCAmelCase__		)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__		)
							return config, expected_shape


if __name__ == "__main__":
		UpperCamelCase_      	=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default=None,
		    type=str,
		    help=(
		        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
		        ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=Path,
		    required=True,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    default=True,
		    type=bool,
		    required=False,
		    help='''If True, push model and image processor to the hub.''',
		)

		UpperCamelCase_      	=	parser.parse_args()
		UpperCamelCase_      	=	args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''simple docstring'''







import comet  # From: unbabel-comet
import torch

import datasets


UpperCamelCase_      	=	datasets.logging.get_logger(__name__)

UpperCamelCase_      	=	'''\
@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",
}
'''

UpperCamelCase_      	=	'''\
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.
'''

UpperCamelCase_      	=	'''
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 _SCREAMING_SNAKE_CASE(							datasets.Metric					):




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




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






