




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


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






@dataclass
class    __lowerCAmelCase			:
			snake_case_				:			nn.Module
			snake_case_				:			List[nn.Module]								=					field(default_factory=UpperCAmelCase__			)
			snake_case_				:			list								=					field(default_factory=UpperCAmelCase__			)
			snake_case_				:			Dict[str, nn.Module]								=					field(default_factory=UpperCAmelCase__			)







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      Tensor   ,   snake_case__   :      Tensor   ,   snake_case__   :      str				):




									"""simple docstring"""


									_UpperCAmelCase													=					len(list(m.modules()				)				) == 1 or isinstance(snake_case__   ,   nn.Convad				) or isinstance(snake_case__   ,   nn.BatchNormad				)
									if has_not_submodules:
															self.traced.append(snake_case__				)
															_UpperCAmelCase													=					m







			def __call__(	self   :      str   ,   snake_case__   :      Tensor				):




									"""simple docstring"""


									for name, m in self.module.named_modules():
															self.handles.append(m.register_forward_hook(partial(self._forward_hook   ,   name=snake_case__				)				)				)
									self.module(snake_case__				)
									[x.remove() for x in self.handles]
									return self







			@property
			def UpperCamelCase      (	self   :      List[Any]				):




									"""simple docstring"""


									return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()				)				) > 0}






class    __lowerCAmelCase			(	nn.Module			):







			def __init__(	self   :      int   ,   snake_case__   :      nn.Module				):




									"""simple docstring"""


									super().__init__()

									_UpperCAmelCase													=					[]
									# - get the stem
									feature_blocks.append(("conv1", model.stem)				)
									# - get all the feature blocks
									for k, v in model.trunk_output.named_children():
															assert k.startswith("block"				), F"""Unexpected layer name {k}"""
															_UpperCAmelCase													=					len(snake_case__				) + 1
															feature_blocks.append((F"""res{block_index}""", v)				)

									_UpperCAmelCase													=					nn.ModuleDict(snake_case__				)







			def UpperCamelCase      (	self   :      Dict   ,   snake_case__   :      Tensor				):




									"""simple docstring"""


									return get_trunk_forward_outputs(
									    snake_case__   ,   out_feat_keys=snake_case__   ,   feature_blocks=self._feature_blocks   ,   )






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]




def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					RegNetConfig(depths=[2, 7, 17, 1]     ,   hidden_sizes=[8, 8, 8, 8]     ,   groups_width=8						)
						if "in1k" in model_name:
												_UpperCAmelCase													=					RegNetForImageClassification(snake_case_						)
						else:
												_UpperCAmelCase													=					RegNetModel(snake_case_						)
						# create from model (with small weights)
						_UpperCAmelCase													=					FakeRegNetVisslWrapper(
						    RegNet(FakeRegNetParams(depth=27     ,   group_width=1010     ,   w_a=1744     ,   w_a=620.83     ,   w_m=2.52						)						)						)

						with torch.no_grad():
												_UpperCAmelCase													=					from_model.eval()
												_UpperCAmelCase													=					our_model.eval()

												_UpperCAmelCase													=					torch.randn((1, 3, 32, 32)						)
												# trace both
												_UpperCAmelCase													=					Tracker(snake_case_						)
												_UpperCAmelCase													=					dest_tracker(snake_case_						).parametrized

												pprint(dest_tracker.nameamodule						)
												_UpperCAmelCase													=					Tracker(snake_case_						)
												_UpperCAmelCase													=					src_tracker(snake_case_						).parametrized

						# convert the keys -> module dict to keys -> params
						def to_params_dict(snake_case_						):
												_UpperCAmelCase													=					OrderedDict()
												for name, module in dict_with_modules.items():
																		for param_name, param in module.state_dict().items():
																								_UpperCAmelCase													=					param
												return params_dict

						_UpperCAmelCase													=					{}

						_UpperCAmelCase													=					to_params_dict(snake_case_						)
						_UpperCAmelCase													=					to_params_dict(snake_case_						)

						for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()     ,   dst_state_dict.items()						):
												_UpperCAmelCase													=					dest_key
												logger.info(f"""{src_key} -> {dest_key}"""						)
						# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
						if "in1k" in model_name:
												_UpperCAmelCase													=					"classifier.1.weight"
												_UpperCAmelCase													=					"classifier.1.bias"

						return from_to_ours_keys


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_ = None     ,   snake_case_ = True						):

						'''simple docstring'''


						_UpperCAmelCase													=					"imagenet-1k-id2label.json"
						_UpperCAmelCase													=					1000

						_UpperCAmelCase													=					"huggingface/label-files"
						_UpperCAmelCase													=					num_labels
						_UpperCAmelCase													=					json.load(open(cached_download(hf_hub_url(snake_case_     ,   snake_case_     ,   repo_type="dataset"						)						)     ,   "r"						)						)
						_UpperCAmelCase													=					{int(snake_case_						): v for k, v in idalabel.items()}

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

						_UpperCAmelCase													=					partial(snake_case_     ,   num_labels=snake_case_     ,   idalabel=snake_case_     ,   labelaid=snake_case_						)

						_UpperCAmelCase													=					{
						    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]     ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]     ,   groups_width=1010						),
						    # finetuned on imagenet
						    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]     ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]     ,   groups_width=1010						),
						}

						# add seer weights logic
						def load_using_classy_vision(snake_case_						) -> Tuple[Dict, Dict]:
												_UpperCAmelCase													=					torch.hub.load_state_dict_from_url(snake_case_     ,   model_dir=str(snake_case_						)     ,   map_location="cpu"						)
												# check if we have a head, if yes add it
												_UpperCAmelCase													=					files["classy_state_dict"]["base_model"]["model"]
												return model_state_dict["trunk"], model_state_dict["heads"]

						_UpperCAmelCase													=					{
						    "regnet-y-10b-seer": partial(
						        snake_case_     ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"     ,   ),
						    "regnet-y-10b-seer-in1k": partial(
						        snake_case_     ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"     ,   ),
						}

						_UpperCAmelCase													=					get_from_to_our_keys(snake_case_						)

						if not (save_directory / f"""{model_name}.pth""").exists():
												logger.info("Loading original state_dict."						)
												_UpperCAmelCase							,			_UpperCAmelCase													=					names_to_from_model[model_name]()
												_UpperCAmelCase													=					from_state_dict_trunk
												if "in1k" in model_name:
																		# add the head
																		_UpperCAmelCase													=					{**from_state_dict_trunk, **from_state_dict_head}
												logger.info("Done!"						)

												_UpperCAmelCase													=					{}

												_UpperCAmelCase													=					list(from_state_dict.keys()						)
												_UpperCAmelCase													=					R"\.block.-part."
												# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
												for key in from_state_dict.keys():
																		# remove the weird "block[0,1]-part" from the key
																		_UpperCAmelCase													=					re.sub(snake_case_     ,   ""     ,   snake_case_						)
																		# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
																		_UpperCAmelCase													=					from_to_ours_keys[src_key]
																		# store the parameter with our key
																		_UpperCAmelCase													=					from_state_dict[key]
																		not_used_keys.remove(snake_case_						)
												# check that all keys have been updated
												assert len(snake_case_						) == 0, f"""Some keys where not used {",".join(snake_case_						)}"""

												logger.info(f"""The following keys were not used: {",".join(snake_case_						)}"""						)

												# save our state dict to disk
												torch.save(snake_case_     ,   save_directory / f"""{model_name}.pth"""						)

												del converted_state_dict
						else:
												logger.info("The state_dict was already stored on disk."						)
						if push_to_hub:
												logger.info(f"""Token is {os.environ["HF_TOKEN"]}"""						)
												logger.info("Loading our model."						)
												# create our model
												_UpperCAmelCase													=					names_to_config[model_name]
												_UpperCAmelCase													=					RegNetModel
												if "in1k" in model_name:
																		_UpperCAmelCase													=					RegNetForImageClassification
												_UpperCAmelCase													=					our_model_func(snake_case_						)
												# place our model to the meta device (so remove all the weights)
												our_model.to(torch.device("meta"						)						)
												logger.info("Loading state_dict in our model."						)
												# load state dict
												_UpperCAmelCase													=					our_model.state_dict().keys()
												PreTrainedModel._load_pretrained_model_low_mem(
												    snake_case_     ,   snake_case_     ,   [save_directory / f"""{model_name}.pth"""]						)
												logger.info("Finally, pushing!"						)
												# push it to hub
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / model_name     ,   commit_message="Add model"     ,   output_dir=save_directory / model_name     ,   )
												_UpperCAmelCase													=					384
												# we can use the convnext one
												_UpperCAmelCase													=					AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"     ,   size=snake_case_						)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / model_name     ,   commit_message="Add image processor"     ,   output_dir=save_directory / model_name     ,   )


if __name__ == "__main__":
				lowercase_     :    List[str]      =			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 regnet* architecture,'
				        ' currently: regnetx-*, regnety-*. 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_     :    Any      =			parser.parse_args()

				lowercase_     :    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.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


lowercase_     :    List[Any]      =			os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
lowercase_     :    Optional[int]      =			os.path.join(hf_cache_home, 'accelerate')
lowercase_     :    int      =			os.path.join(cache_dir, 'default_config.yaml')
lowercase_     :    int      =			os.path.join(cache_dir, 'default_config.yaml')

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
				lowercase_     :    List[str]      =			default_yaml_config_file
else:
				lowercase_     :    Optional[Any]      =			default_json_config_file


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if config_file is not None:
												if not os.path.isfile(snake_case_						):
																		raise FileNotFoundError(
																		    f"""The passed configuration file `{config_file}` does not exist. """
																		    "Please pass an existing file to `accelerate launch`, or use the the default one "
																		    "created through `accelerate config` and run `accelerate launch` "
																		    "without the `--config_file` argument."						)
						else:
												_UpperCAmelCase													=					default_config_file
						with open(snake_case_     ,   "r"     ,   encoding="utf-8"						) as f:
												if config_file.endswith(".json"						):
																		if (
																		    json.load(snake_case_						).get("compute_environment"     ,   ComputeEnvironment.LOCAL_MACHINE						)
																		    == ComputeEnvironment.LOCAL_MACHINE
																		):
																								_UpperCAmelCase													=					ClusterConfig
																		else:
																								_UpperCAmelCase													=					SageMakerConfig
																		return config_class.from_json_file(json_file=snake_case_						)
												else:
																		if (
																		    yaml.safe_load(snake_case_						).get("compute_environment"     ,   ComputeEnvironment.LOCAL_MACHINE						)
																		    == ComputeEnvironment.LOCAL_MACHINE
																		):
																								_UpperCAmelCase													=					ClusterConfig
																		else:
																								_UpperCAmelCase													=					SageMakerConfig
																		return config_class.from_yaml_file(yaml_file=snake_case_						)






@dataclass
class    __lowerCAmelCase			:
			snake_case_				:			ComputeEnvironment
			snake_case_				:			Union[DistributedType, SageMakerDistributedType]
			snake_case_				:			str
			snake_case_				:			bool







			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.__dict__
									# For serialization, it's best to convert Enums to strings (or their underlying value type).
									for key, value in result.items():
															if isinstance(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					value.value
															if isinstance(snake_case__   ,   snake_case__				) and not bool(snake_case__				):
																					_UpperCAmelCase													=					None
									_UpperCAmelCase													=					{k: v for k, v in result.items() if v is not None}
									return result







			@classmethod
			def UpperCamelCase      (	cls   :      Optional[Any]   ,   snake_case__   :      Optional[Any]=None				):




									"""simple docstring"""


									_UpperCAmelCase													=					default_json_config_file if json_file is None else json_file
									with open(snake_case__   ,   "r"   ,   encoding="utf-8"				) as f:
															_UpperCAmelCase													=					json.load(snake_case__				)
									if "compute_environment" not in config_dict:
															_UpperCAmelCase													=					ComputeEnvironment.LOCAL_MACHINE
									if "mixed_precision" not in config_dict:
															_UpperCAmelCase													=					"fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
									if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
									if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															_UpperCAmelCase													=					config_dict.pop("dynamo_backend"				)
															_UpperCAmelCase													=					{} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
									if "use_cpu" not in config_dict:
															_UpperCAmelCase													=					False
									return cls(**snake_case__				)







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      Tuple				):




									"""simple docstring"""


									with open(snake_case__   ,   "w"   ,   encoding="utf-8"				) as f:
															_UpperCAmelCase													=					json.dumps(self.to_dict()   ,   indent=2   ,   sort_keys=snake_case__				) + "\n"
															f.write(snake_case__				)







			@classmethod
			def UpperCamelCase      (	cls   :      Union[str, Any]   ,   snake_case__   :      List[str]=None				):




									"""simple docstring"""


									_UpperCAmelCase													=					default_yaml_config_file if yaml_file is None else yaml_file
									with open(snake_case__   ,   "r"   ,   encoding="utf-8"				) as f:
															_UpperCAmelCase													=					yaml.safe_load(snake_case__				)
									if "compute_environment" not in config_dict:
															_UpperCAmelCase													=					ComputeEnvironment.LOCAL_MACHINE

									if "mixed_precision" not in config_dict:
															_UpperCAmelCase													=					"fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
									if isinstance(config_dict["mixed_precision"]   ,   snake_case__				) and not config_dict["mixed_precision"]:
															_UpperCAmelCase													=					"no"
									if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
									if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															_UpperCAmelCase													=					config_dict.pop("dynamo_backend"				)
															_UpperCAmelCase													=					{} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
									if "use_cpu" not in config_dict:
															_UpperCAmelCase													=					False
									return cls(**snake_case__				)







			def UpperCamelCase      (	self   :      List[str]   ,   snake_case__   :      Dict				):




									"""simple docstring"""


									with open(snake_case__   ,   "w"   ,   encoding="utf-8"				) as f:
															yaml.safe_dump(self.to_dict()   ,   snake_case__				)







			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									if isinstance(self.compute_environment   ,   snake_case__				):
															_UpperCAmelCase													=					ComputeEnvironment(self.compute_environment				)
									if isinstance(self.distributed_type   ,   snake_case__				):
															if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																					_UpperCAmelCase													=					SageMakerDistributedType(self.distributed_type				)
															else:
																					_UpperCAmelCase													=					DistributedType(self.distributed_type				)
									if self.dynamo_config is None:
															_UpperCAmelCase													=					{}






@dataclass
class    __lowerCAmelCase			(	UpperCAmelCase__			):
			snake_case_				:			int
			snake_case_				:			int								=					0
			snake_case_				:			int								=					1
			snake_case_				:			Optional[str]								=					None
			snake_case_				:			Optional[str]								=					None
			snake_case_				:			Optional[int]								=					None
			snake_case_				:			Optional[str]								=					"static"
			snake_case_				:			Optional[bool]								=					False
			snake_case_				:			str								=					"main"

			# args for deepspeed_plugin
			snake_case_				:			dict								=					None
			# args for fsdp
			snake_case_				:			dict								=					None
			# args for megatron_lm
			snake_case_				:			dict								=					None
			# args for ipex
			snake_case_				:			dict								=					None
			# args for TPU
			snake_case_				:			bool								=					False

			# args for TPU pods
			snake_case_				:			str								=					None
			snake_case_				:			str								=					None
			snake_case_				:			bool								=					False
			snake_case_				:			bool								=					False
			snake_case_				:			str								=					None
			snake_case_				:			List[str]								=					None
			snake_case_				:			List[str]								=					None
			snake_case_				:			List[str]								=					None

			# args for dynamo
			snake_case_				:			dict								=					None







			def UpperCamelCase      (	self   :      Optional[Any]				):




									"""simple docstring"""


									if self.deepspeed_config is None:
															_UpperCAmelCase													=					{}
									if self.fsdp_config is None:
															_UpperCAmelCase													=					{}
									if self.megatron_lm_config is None:
															_UpperCAmelCase													=					{}
									if self.ipex_config is None:
															_UpperCAmelCase													=					{}
									return super().__post_init__()






@dataclass
class    __lowerCAmelCase			(	UpperCAmelCase__			):
			snake_case_				:			str
			snake_case_				:			str
			snake_case_				:			Optional[str]								=					None
			snake_case_				:			Optional[str]								=					None
			snake_case_				:			str								=					"us-east-1"
			snake_case_				:			int								=					1
			snake_case_				:			str								=					"all"
			snake_case_				:			str								=					F"""accelerate-sagemaker-{num_machines}"""
			snake_case_				:			str								=					SAGEMAKER_PYTORCH_VERSION
			snake_case_				:			str								=					SAGEMAKER_TRANSFORMERS_VERSION
			snake_case_				:			str								=					SAGEMAKER_PYTHON_VERSION
			snake_case_				:			str								=					None
			snake_case_				:			str								=					None
			snake_case_				:			dict								=					None
			snake_case_				:			dict								=					None




