


"""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()
lowercase_			     =  logging.get_logger()
@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			nn.Module
		__UpperCAmelCase		:			List[nn.Module]										=				field(default_factory=__SCREAMING_SNAKE_CASE       )
		__UpperCAmelCase		:			list										=				field(default_factory=__SCREAMING_SNAKE_CASE       )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a      ):
				__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      ):
				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 			__UpperCAmelCase       (  self      ):
				# 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 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			nn.Module
		__UpperCAmelCase		:			nn.Module
		__UpperCAmelCase		:			int										=				0
		__UpperCAmelCase		:			List										=				field(default_factory=__SCREAMING_SNAKE_CASE       )
		__UpperCAmelCase		:			List										=				field(default_factory=__SCREAMING_SNAKE_CASE       )

		def __call__(  self							, _a      ):
				__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        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     ResNetConfig    ,					lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     bool = True			) ->      Tuple:
		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, 224, 224)			)
				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			) ->      Optional[Any]:
		__a           =			'''imagenet-1k-id2label.json'''
		__a           =			1000
		__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=[64, 128, 256, 512]    ,					layer_type='''basic'''			),
		    '''resnet26''': ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]    ,					hidden_sizes=[256, 512, 1024, 2048]    ,					layer_type='''bottleneck'''			),
		    '''resnet34''': ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]    ,					hidden_sizes=[64, 128, 256, 512]    ,					layer_type='''basic'''			),
		    '''resnet50''': ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]    ,					hidden_sizes=[256, 512, 1024, 2048]    ,					layer_type='''bottleneck'''			),
		    '''resnet101''': ImageNetPreTrainedConfig(
		        depths=[3, 4, 23, 3]    ,					hidden_sizes=[256, 512, 1024, 2048]    ,					layer_type='''bottleneck'''			),
		    '''resnet152''': ImageNetPreTrainedConfig(
		        depths=[3, 8, 36, 3]    ,					hidden_sizes=[256, 512, 1024, 2048]    ,					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__":
				lowercase_			     =  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.",
				)

				lowercase_			     =  parser.parse_args()
				lowercase_			     =  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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_			     =  {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "XGLMForCausalLM",
				    "XGLMModel",
				    "XGLMPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FlaxXGLMForCausalLM",
				    "FlaxXGLMModel",
				    "FlaxXGLMPreTrainedModel",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFXGLMForCausalLM",
				    "TFXGLMModel",
				    "TFXGLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm import XGLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm_fast import XGLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xglm import (
								    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXGLMForCausalLM,
								    TFXGLMModel,
								    TFXGLMPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure)






