






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()
UpperCAmelCase_ :						Union[str, Any]      =      logging.get_logger()

@dataclass
class     _SCREAMING_SNAKE_CASE       :
	snake_case__							:     nn.Module
	snake_case__							:     List[nn.Module]     	=  field(default_factory=_a      )
	snake_case__							:     list     	=  field(default_factory=_a      )





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Tensor ,  __lowerCamelCase				:					Tensor	):
		UpperCamelCase     :List[Any]          =	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				:					Optional[Any] ,  __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 						_A (		self				:					Optional[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     _SCREAMING_SNAKE_CASE       :
	snake_case__							:     nn.Module
	snake_case__							:     nn.Module
	snake_case__							:     int     	=  0
	snake_case__							:     List     	=  field(default_factory=_a      )
	snake_case__							:     List     	=  field(default_factory=_a      )





	def __call__(		self				:					Optional[int] ,  __lowerCamelCase				:					Tensor	):
		UpperCamelCase     :List[Any]          =	Tracker(self.dest	)(__lowerCamelCase	).parametrized
		UpperCamelCase     :Optional[Any]          =	Tracker(self.src	)(__lowerCamelCase	).parametrized

		UpperCamelCase     :Optional[int]          =	list(filter(lambda __lowerCamelCase	: type(__lowerCamelCase	) not in self.src_skip ,  __lowerCamelCase	)	)
		UpperCamelCase     :Optional[Any]          =	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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  ResNetConfig	,			__magic_name__					:  Path	,			__magic_name__					:  bool = True					)		->			Optional[int]:






	"""simple docstring"""



	print(f"""Converting {name}..."""					)
	with torch.no_grad():
		UpperCamelCase     :Optional[int]          =	timm.create_model(__magic_name__	,			pretrained=__magic_name__					).eval()
		UpperCamelCase     :Optional[int]          =	ResNetForImageClassification(__magic_name__					).eval()
		UpperCamelCase     :Tuple          =	ModuleTransfer(src=__magic_name__	,			dest=__magic_name__					)
		UpperCamelCase     :int          =	torch.randn((1, 3, 224, 224)					)
		module_transfer(__magic_name__					)

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

	UpperCamelCase     :List[Any]          =	f"""resnet{'-'.join(name.split('resnet'					)					)}"""
	print(__magic_name__					)

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

		# we can use the convnext one
		UpperCamelCase     :str          =	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=__magic_name__	,			)

		print(f"""Pushed {checkpoint_name}"""					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Path	,			__magic_name__					:  str = None	,			__magic_name__					:  bool = True					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Any          =	"""imagenet-1k-id2label.json"""
	UpperCamelCase     :Tuple          =	1000
	UpperCamelCase     :Any          =	(1, num_labels)

	UpperCamelCase     :List[str]          =	"""huggingface/label-files"""
	UpperCamelCase     :Tuple          =	num_labels
	UpperCamelCase     :Union[str, Any]          =	json.load(open(hf_hub_download(__magic_name__	,			__magic_name__	,			repo_type="""dataset"""					)	,			"""r"""					)					)
	UpperCamelCase     :int          =	{int(__magic_name__					): v for k, v in idalabel.items()}

	UpperCamelCase     :Optional[int]          =	idalabel
	UpperCamelCase     :List[str]          =	{v: k for k, v in idalabel.items()}

	UpperCamelCase     :Union[str, Any]          =	partial(__magic_name__	,			num_labels=__magic_name__	,			idalabel=__magic_name__	,			labelaid=__magic_name__					)

	UpperCamelCase     :Dict          =	{
	    """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(__magic_name__	,			names_to_config[model_name]	,			__magic_name__	,			__magic_name__					)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)
	return config, expected_shape


if __name__ == "__main__":
	UpperCAmelCase_ :						Optional[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.''',
	)

	UpperCAmelCase_ :						str      =      parser.parse_args()
	UpperCAmelCase_ :						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)








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_ :						Any      =      '''Create a default config file for Accelerate with only a few flags set.'''

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]="no"	,			__magic_name__					:  str = default_json_config_file	,			__magic_name__					:  bool = False					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :Any          =	Path(__magic_name__					)
	path.parent.mkdir(parents=__magic_name__	,			exist_ok=__magic_name__					)
	if path.exists():
		print(
		    f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""					)
		return False
	UpperCamelCase     :Dict          =	mixed_precision.lower()
	if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
		raise ValueError(
		    f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""					)
	UpperCamelCase     :Optional[Any]          =	{
	    """compute_environment""": """LOCAL_MACHINE""",
	    """mixed_precision""": mixed_precision,
	}
	if torch.cuda.is_available():
		UpperCamelCase     :Union[str, Any]          =	torch.cuda.device_count()
		UpperCamelCase     :List[Any]          =	num_gpus
		UpperCamelCase     :Dict          =	False
		if num_gpus > 1:
			UpperCamelCase     :Any          =	"""MULTI_GPU"""
		else:
			UpperCamelCase     :Any          =	"""NO"""
	elif is_xpu_available() and use_xpu:
		UpperCamelCase     :Optional[Any]          =	torch.xpu.device_count()
		UpperCamelCase     :Optional[int]          =	num_xpus
		UpperCamelCase     :int          =	False
		if num_xpus > 1:
			UpperCamelCase     :Union[str, Any]          =	"""MULTI_XPU"""
		else:
			UpperCamelCase     :Union[str, Any]          =	"""NO"""
	elif is_npu_available():
		UpperCamelCase     :List[Any]          =	torch.npu.device_count()
		UpperCamelCase     :Optional[Any]          =	num_npus
		UpperCamelCase     :Tuple          =	False
		if num_npus > 1:
			UpperCamelCase     :Optional[Any]          =	"""MULTI_NPU"""
		else:
			UpperCamelCase     :List[Any]          =	"""NO"""
	else:
		UpperCamelCase     :Any          =	0
		UpperCamelCase     :Optional[Any]          =	True
		UpperCamelCase     :Optional[Any]          =	1
		UpperCamelCase     :List[str]          =	"""NO"""
	UpperCamelCase     :int          =	ClusterConfig(**__magic_name__					)
	config.to_json_file(__magic_name__					)
	return path

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Tuple					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	parser.add_parser("""default"""	,			parents=__magic_name__	,			help=__magic_name__	,			formatter_class=__magic_name__					)
	parser.add_argument(
	    """--config_file"""	,			default=__magic_name__	,			help=(
	        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )	,			dest="""save_location"""	,			)

	parser.add_argument(
	    """--mixed_precision"""	,			choices=["""no""", """fp16""", """bf16"""]	,			type=__magic_name__	,			help="""Whether or not to use mixed precision training. """
	    """Choose between FP16 and BF16 (bfloat16) training. """
	    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""	,			default="""no"""	,			)
	parser.set_defaults(func=__magic_name__					)
	return parser

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	write_basic_config(args.mixed_precision	,			args.save_location					)
	if config_file:
		print(f"""accelerate configuration saved at {config_file}"""					)
