


"""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()
lowerCamelCase_   :   str									=  logging.get_logger()






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  42
 __lowerCAmelCase 					=  field(default_factory=_SCREAMING_SNAKE_CASE			)
 __lowerCAmelCase 					=  field(default_factory=_SCREAMING_SNAKE_CASE			)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A						)					->     Any:
     a 		=len(list(m.modules()						)						) == 1 or isinstance(__A ,    nn.Convad						) or isinstance(__A ,    nn.BatchNormad						)
     if has_not_submodules:
         self.traced.append(__A						)


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





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






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  42
 __lowerCAmelCase 					=  42
 __lowerCAmelCase 					=  0
 __lowerCAmelCase 					=  field(default_factory=_SCREAMING_SNAKE_CASE			)
 __lowerCAmelCase 					=  field(default_factory=_SCREAMING_SNAKE_CASE			)


 def __call__(		self ,    __A						)					->     int:
     a 		=Tracker(self.dest						)(__A						).parametrized
     a 		=Tracker(self.src						)(__A						).parametrized

     a 		=list(filter(lambda __A						: type(__A						) not in self.src_skip ,    __A						)						)
     a 		=list(filter(lambda __A						: type(__A						) not in self.dest_skip ,    __A						)						)

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

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

def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase = True		):







    """simple docstring"""
    print(f'''Converting {name}...'''		)
    with torch.no_grad():
        a 		=timm.create_model(lowercase       ,    pretrained=lowercase		).eval()
        a 		=ResNetForImageClassification(lowercase		).eval()
        a 		=ModuleTransfer(src=lowercase       ,    dest=lowercase		)
        a 		=torch.randn((1, 3, 2_24, 2_24)		)
        module_transfer(lowercase		)

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

    a 		=f'''resnet{"-".join(name.split("resnet"		)		)}'''
    print(lowercase		)

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

        # 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=lowercase       ,    )

        print(f'''Pushed {checkpoint_name}'''		)
def _A						(						lowercase       ,    lowercase = None       ,    lowercase = True		):







    """simple docstring"""
    a 		='''imagenet-1k-id2label.json'''
    a 		=10_00
    a 		=(1, num_labels)

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

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

    a 		=partial(lowercase       ,    num_labels=lowercase       ,    idalabel=lowercase       ,    labelaid=lowercase		)

    a 		={
        '''resnet18''': ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]       ,    hidden_sizes=[64, 1_28, 2_56, 5_12]       ,    layer_type='''basic'''		),
        '''resnet26''': ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]       ,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]       ,    layer_type='''bottleneck'''		),
        '''resnet34''': ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]       ,    hidden_sizes=[64, 1_28, 2_56, 5_12]       ,    layer_type='''basic'''		),
        '''resnet50''': ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]       ,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]       ,    layer_type='''bottleneck'''		),
        '''resnet101''': ImageNetPreTrainedConfig(
            depths=[3, 4, 23, 3]       ,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]       ,    layer_type='''bottleneck'''		),
        '''resnet152''': ImageNetPreTrainedConfig(
            depths=[3, 8, 36, 3]       ,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]       ,    layer_type='''bottleneck'''		),
    }

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


if __name__ == "__main__":
       lowerCamelCase_   :   Union[str, 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.""",
       )

       lowerCamelCase_   :   Dict									=  parser.parse_args()
       lowerCamelCase_   :   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)







"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__A		:       Union[str, Any]  				=		{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''NllbTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''NllbTokenizerFast''']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_nllb import NllbTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_nllb_fast import NllbTokenizerFast

else:
						import sys

						__A		:       List[Any]  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



