


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()
snake_case							: Any      =   logging.get_logger()







@dataclass
class  snake_case_     :
					UpperCAmelCase__      :					nn.Module
					UpperCAmelCase__      :					List[nn.Module]					   =    field(default_factory=lowerCamelCase_   )
					UpperCAmelCase__      :					list					   =    field(default_factory=lowerCamelCase_   )
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :str    ,__snake_case     :Tensor    ,__snake_case     :Tensor   )      ->					Any:
												a__    =  len(list(m.modules()   )   ) == 1 or isinstance(__snake_case    ,nn.Convad   ) or isinstance(__snake_case    ,nn.BatchNormad   )
												if has_not_submodules:
																			self.traced.append(__snake_case   )
					def __call__(   self     :str    ,__snake_case     :Tensor   )      ->					Tuple:
												for m in self.module.modules():
																			self.handles.append(m.register_forward_hook(self._forward_hook   )   )
												self.module(__snake_case   )
												[x.remove() for x in self.handles]
												return self



					@property
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Any:
												# check the len of the state_dict keys to see if we have learnable params
												return list(filter(lambda __snake_case   : len(list(x.state_dict().keys()   )   ) > 0    ,self.traced   )   )







@dataclass
class  snake_case_     :
					UpperCAmelCase__      :					nn.Module
					UpperCAmelCase__      :					nn.Module
					UpperCAmelCase__      :					int					   =    0
					UpperCAmelCase__      :					List					   =    field(default_factory=lowerCamelCase_   )
					UpperCAmelCase__      :					List					   =    field(default_factory=lowerCamelCase_   )
					def __call__(   self     :Union[str, Any]    ,__snake_case     :Tensor   )      ->					str:
												a__    =  Tracker(self.dest   )(__snake_case   ).parametrized
												a__    =  Tracker(self.src   )(__snake_case   ).parametrized

												a__    =  list(filter(lambda __snake_case   : type(__snake_case   ) not in self.src_skip    ,__snake_case   )   )
												a__    =  list(filter(lambda __snake_case   : type(__snake_case   ) not in self.dest_skip    ,__snake_case   )   )

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

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






def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       ResNetConfig      ,					__lowerCAmelCase							:       Path      ,					__lowerCAmelCase							:       bool = True     ):
							print(F'Converting {name}...'     )
							with torch.no_grad():
														a__    =  timm.create_model(__lowerCAmelCase      ,					pretrained=__lowerCAmelCase     ).eval()
														a__    =  ResNetForImageClassification(__lowerCAmelCase     ).eval()
														a__    =  ModuleTransfer(src=__lowerCAmelCase      ,					dest=__lowerCAmelCase     )
														a__    =  torch.randn((1, 3, 2_2_4, 2_2_4)     )
														module_transfer(__lowerCAmelCase     )

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

							a__    =  F'resnet{"-".join(name.split("resnet"     )     )}'
							print(__lowerCAmelCase     )

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

														# we can use the convnext one
														a__    =  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=__lowerCAmelCase      ,					)

														print(F'Pushed {checkpoint_name}'     )






def 							__lowercase     (				__lowerCAmelCase							:       Path      ,					__lowerCAmelCase							:       str = None      ,					__lowerCAmelCase							:       bool = True     ):
							a__    =  'imagenet-1k-id2label.json'
							a__    =  1_0_0_0
							a__    =  (1, num_labels)

							a__    =  'huggingface/label-files'
							a__    =  num_labels
							a__    =  json.load(open(hf_hub_download(__lowerCAmelCase      ,					__lowerCAmelCase      ,					repo_type='dataset'     )      ,					'r'     )     )
							a__    =  {int(__lowerCAmelCase     ): v for k, v in idalabel.items()}

							a__    =  idalabel
							a__    =  {v: k for k, v in idalabel.items()}

							a__    =  partial(__lowerCAmelCase      ,					num_labels=__lowerCAmelCase      ,					idalabel=__lowerCAmelCase      ,					labelaid=__lowerCAmelCase     )

							a__    =  {
							    '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(__lowerCAmelCase      ,					names_to_config[model_name]      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
							return config, expected_shape


if __name__ == "__main__":
						snake_case							: Any      =   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.''',
						)

						snake_case							: List[str]      =   parser.parse_args()
						snake_case							: Path      =   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)








from sklearn.metrics import fa_score

import datasets


snake_case							: Optional[int]      =   '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


snake_case							: List[Any]      =   '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


snake_case							: Union[str, Any]      =   '''
@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}
}
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,		_KWARGS_DESCRIPTION   )
class  snake_case_     (datasets.Metric   ):
					def 				lowerCamelCase__(   self     :Any   )      ->					Any:
												return datasets.MetricInfo(
												    description=_DESCRIPTION    ,citation=_CITATION    ,inputs_description=_KWARGS_DESCRIPTION    ,features=datasets.Features(
												        {
												            'predictions': datasets.Sequence(datasets.Value('int32'   )   ),
												            'references': datasets.Sequence(datasets.Value('int32'   )   ),
												        }
												        if self.config_name == 'multilabel'
												        else {
												            'predictions': datasets.Value('int32'   ),
												            'references': datasets.Value('int32'   ),
												        }   )    ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']    ,)



					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :Dict=None    ,__snake_case     :str=1    ,__snake_case     :Optional[int]="binary"    ,__snake_case     :Union[str, Any]=None   )      ->					Tuple:
												a__    =  fa_score(
												    __snake_case    ,__snake_case    ,labels=__snake_case    ,pos_label=__snake_case    ,average=__snake_case    ,sample_weight=__snake_case   )
												return {"f1": float(__snake_case   ) if score.size == 1 else score}




