"""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__  :  List[str]						     =	logging.get_logger()
@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 42
					snake_case__				    = field(default_factory=__UpperCAmelCase				)
					snake_case__				    = field(default_factory=__UpperCAmelCase				)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	Tensor    ,lowerCamelCase__   :	Tensor			):
									UpperCAmelCase__             =							len(list(m.modules()			)			) == 1 or isinstance(lowerCamelCase__    ,nn.Convad			) or isinstance(lowerCamelCase__    ,nn.BatchNormad			)
									if has_not_submodules:
													self.traced.append(lowerCamelCase__			)







					def __call__(				self   :	str    ,lowerCamelCase__   :	Tensor			):
									for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook			)			)
									self.module(lowerCamelCase__			)
									[x.remove() for x in self.handles]
									return self







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






@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 42
					snake_case__				    = 42
					snake_case__				    = 0
					snake_case__				    = field(default_factory=__UpperCAmelCase				)
					snake_case__				    = field(default_factory=__UpperCAmelCase				)







					def __call__(				self   :	Optional[Any]    ,lowerCamelCase__   :	Tensor			):
									UpperCAmelCase__             =							Tracker(self.dest			)(lowerCamelCase__			).parametrized
									UpperCAmelCase__             =							Tracker(self.src			)(lowerCamelCase__			).parametrized

									UpperCAmelCase__             =							list(filter(lambda lowerCamelCase__			: type(lowerCamelCase__			) not in self.src_skip    ,lowerCamelCase__			)			)
									UpperCAmelCase__             =							list(filter(lambda lowerCamelCase__			: type(lowerCamelCase__			) not in self.dest_skip    ,lowerCamelCase__			)			)

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

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






def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase = True       ):
				print(f'''Converting {name}...'''       )
				with torch.no_grad():
								UpperCAmelCase__             =							timm.create_model(lowerCamelCase					,			pretrained=lowerCamelCase       ).eval()
								UpperCAmelCase__             =							ResNetForImageClassification(lowerCamelCase       ).eval()
								UpperCAmelCase__             =							ModuleTransfer(src=lowerCamelCase					,			dest=lowerCamelCase       )
								UpperCAmelCase__             =							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."

				UpperCAmelCase__             =							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
								UpperCAmelCase__             =							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   a_						(					lowerCamelCase					,			lowerCamelCase = None					,			lowerCamelCase = True       ):
				UpperCAmelCase__             =							'imagenet-1k-id2label.json'
				UpperCAmelCase__             =							1_0_0_0
				UpperCAmelCase__             =							(1, num_labels)

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

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

				UpperCAmelCase__             =							partial(lowerCamelCase					,			num_labels=lowerCamelCase					,			idalabel=lowerCamelCase					,			labelaid=lowerCamelCase       )

				UpperCAmelCase__             =							{
				    '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__":
						lowerCAmelCase__  :  Tuple						     =	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__  :  List[Any]						     =	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)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      List[str]												= {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= ["Speech2TextTokenizer"]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= ["Speech2TextFeatureExtractor"]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= [
     "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFSpeech2TextForConditionalGeneration",
     "TFSpeech2TextModel",
     "TFSpeech2TextPreTrainedModel",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      str												= [
     "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Speech2TextForConditionalGeneration",
     "Speech2TextModel",
     "Speech2TextPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[Any]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


