



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()
__A   			=logging.get_logger()

@dataclass
class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       42
			UpperCamelCase									=       field(default_factory=__UpperCamelCase   )
			UpperCamelCase									=       field(default_factory=__UpperCamelCase   )




			def  snake_case__     (   self							: List[str]       ,       a_							: Dict       ,       a_							: Tensor       ,       a_							: Tensor ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	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							: List[Any]       ,       a_							: Tensor ):
					'''simple docstring'''





					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  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					return list(filter(lambda a_ : len(list(x.state_dict().keys() ) ) > 0       ,       self.traced ) )

@dataclass
class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       42
			UpperCamelCase									=       42
			UpperCamelCase									=       0
			UpperCamelCase									=       field(default_factory=__UpperCamelCase   )
			UpperCamelCase									=       field(default_factory=__UpperCamelCase   )




			def __call__(   self							: str       ,       a_							: Tensor ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	Tracker(self.dest )(a_ ).parametrized
					__UpperCAmelCase    :     Tuple													=	Tracker(self.src )(a_ ).parametrized

					__UpperCAmelCase    :     int													=	list(filter(lambda a_ : type(a_ ) not in self.src_skip       ,       a_ ) )
					__UpperCAmelCase    :     str													=	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						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					ResNetConfig ,   _UpperCAmelCase							:					Path ,   _UpperCAmelCase							:					bool = True						):







		'''simple docstring'''





		print(f'Converting {name}...'						)
		with torch.no_grad():
				__UpperCAmelCase    :     Optional[Any]													=	timm.create_model(_UpperCAmelCase ,   pretrained=_UpperCAmelCase						).eval()
				__UpperCAmelCase    :     int													=	ResNetForImageClassification(_UpperCAmelCase						).eval()
				__UpperCAmelCase    :     List[Any]													=	ModuleTransfer(src=_UpperCAmelCase ,   dest=_UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	torch.randn((1, 3, 2_24, 2_24)						)
				module_transfer(_UpperCAmelCase						)

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

		__UpperCAmelCase    :     Optional[int]													=	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
				__UpperCAmelCase    :     Optional[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        a						(      _UpperCAmelCase							:					Path ,   _UpperCAmelCase							:					str = None ,   _UpperCAmelCase							:					bool = True						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	'''imagenet-1k-id2label.json'''
		__UpperCAmelCase    :     int													=	10_00
		__UpperCAmelCase    :     Optional[int]													=	(1, num_labels)

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

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

		__UpperCAmelCase    :     List[str]													=	partial(_UpperCAmelCase ,   num_labels=_UpperCAmelCase ,   idalabel=_UpperCAmelCase ,   labelaid=_UpperCAmelCase						)

		__UpperCAmelCase    :     Any													=	{
		    '''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(_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__":
				__A   			=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.",
				)

				__A   			=parser.parse_args()
				__A   			=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)










import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
				import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class 		UpperCAmelCase__			(			unittest.TestCase						,__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	load_tool('''text-to-speech''' )
					self.tool.setup()




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     Union[str, Any]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Optional[int]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     List[str]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )





