
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()
snake_case_      :					List[str]				      =     logging.get_logger()





@dataclass
class 		__lowerCamelCase     :
							lowerCamelCase__:				nn.Module
							lowerCamelCase__:				List[nn.Module]      =							field(default_factory=lowercase		)
							lowerCamelCase__:				list      =							field(default_factory=lowercase		)
							lowerCamelCase__:				Dict[str, nn.Module]      =							field(default_factory=lowercase		)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     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:					List[str] 						=     m



							def __call__(     self	,				__snake_case							)						->     Optional[Any]:






								"""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     A__ (     self							)						->     Dict:






								"""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	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



								super().__init__()

								UpperCAmelCase:					List[Tuple[str, nn.Module]] 						=     []
								# - 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:					Optional[int] 						=     len(__snake_case							) + 1
									feature_blocks.append((F'res{block_index}', v)							)

								UpperCAmelCase:					Optional[int] 						=     nn.ModuleDict(__snake_case							)




							def     A__ (     self	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



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





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]



def      __UpperCAmelCase    (  snake_case_							:		str				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     RegNetConfig(depths=[2, 7, 1_7, 1]				,					hidden_sizes=[8, 8, 8, 8]				,					groups_width=8				)
	if "in1k" in model_name:
		UpperCAmelCase:					Union[str, Any] 						=     RegNetForImageClassification(snake_case_				)
	else:
		UpperCAmelCase:					int 						=     RegNetModel(snake_case_				)
	# create from model (with small weights)
	UpperCAmelCase:					Optional[int] 						=     FakeRegNetVisslWrapper(
	    RegNet(FakeRegNetParams(depth=2_7				,					group_width=1_0_1_0				,					w_a=1_7_4_4				,					w_a=620.83				,					w_m=2.52				)				)				)

	with torch.no_grad():
		UpperCAmelCase:					List[Any] 						=     from_model.eval()
		UpperCAmelCase:					List[Any] 						=     our_model.eval()

		UpperCAmelCase:					List[str] 						=     torch.randn((1, 3, 3_2, 3_2)				)
		# trace both
		UpperCAmelCase:					Dict 						=     Tracker(snake_case_				)
		UpperCAmelCase:					List[str] 						=     dest_tracker(snake_case_				).parametrized

		pprint(dest_tracker.nameamodule				)
		UpperCAmelCase:					Optional[Any] 						=     Tracker(snake_case_				)
		UpperCAmelCase:					Any 						=     src_tracker(snake_case_				).parametrized

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

	UpperCAmelCase:					Any 						=     {}

	UpperCAmelCase:					Tuple 						=     to_params_dict(snake_case_				)
	UpperCAmelCase:					Tuple 						=     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:					List[str] 						=     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:					Dict 						=     "classifier.1.weight"
		UpperCAmelCase:					Union[str, Any] 						=     "classifier.1.bias"

	return from_to_ours_keys



def      __UpperCAmelCase    (  snake_case_							:		Path				,					snake_case_							:		str = None				,					snake_case_							:		bool = True				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     "imagenet-1k-id2label.json"
	UpperCAmelCase:					List[str] 						=     1_0_0_0

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

	UpperCAmelCase:					Dict 						=     idalabel
	UpperCAmelCase:					List[Any] 						=     {v: k for k, v in idalabel.items()}

	UpperCAmelCase:					List[Any] 						=     partial(snake_case_				,					num_labels=snake_case_				,					idalabel=snake_case_				,					labelaid=snake_case_				)

	UpperCAmelCase:					List[str] 						=     {
	    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1]				,					hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]				,					groups_width=1_0_1_0				),
	    # finetuned on imagenet
	    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 1_7, 1]				,					hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]				,					groups_width=1_0_1_0				),
	}

	# add seer weights logic
	def load_using_classy_vision(snake_case_							:		str				) -> Tuple[Dict, Dict]:
		UpperCAmelCase:					Union[str, Any] 						=     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:					str 						=     files["classy_state_dict"]["base_model"]["model"]
		return model_state_dict["trunk"], model_state_dict["heads"]

	UpperCAmelCase:					Optional[int] 						=     {
	    "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:					List[Any] 						=     get_from_to_our_keys(snake_case_				)

	if not (save_directory / F'{model_name}.pth').exists():
		logger.info("Loading original state_dict."				)
		UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     names_to_from_model[model_name]()
		UpperCAmelCase:					List[Any] 						=     from_state_dict_trunk
		if "in1k" in model_name:
			# add the head
			UpperCAmelCase:					int 						=     {**from_state_dict_trunk, **from_state_dict_head}
		logger.info("Done!"				)

		UpperCAmelCase:					Union[str, Any] 						=     {}

		UpperCAmelCase:					int 						=     list(from_state_dict.keys()				)
		UpperCAmelCase:					List[str] 						=     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:					Optional[Any] 						=     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:					int 						=     from_to_ours_keys[src_key]
			# store the parameter with our key
			UpperCAmelCase:					Dict 						=     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:					Any 						=     names_to_config[model_name]
		UpperCAmelCase:					str 						=     RegNetModel
		if "in1k" in model_name:
			UpperCAmelCase:					Dict 						=     RegNetForImageClassification
		UpperCAmelCase:					List[Any] 						=     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:					List[str] 						=     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:					Union[str, Any] 						=     3_8_4
		# we can use the convnext one
		UpperCAmelCase:					Optional[Any] 						=     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__":
	snake_case_      :					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 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.',
	)

	snake_case_      :					Dict				      =     parser.parse_args()

	snake_case_      :					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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_      :					Dict				      =     {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['BigBirdTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     ['BigBirdTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BigBirdForCausalLM',
	    'BigBirdForMaskedLM',
	    'BigBirdForMultipleChoice',
	    'BigBirdForPreTraining',
	    'BigBirdForQuestionAnswering',
	    'BigBirdForSequenceClassification',
	    'BigBirdForTokenClassification',
	    'BigBirdLayer',
	    'BigBirdModel',
	    'BigBirdPreTrainedModel',
	    'load_tf_weights_in_big_bird',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     [
	    'FlaxBigBirdForCausalLM',
	    'FlaxBigBirdForMaskedLM',
	    'FlaxBigBirdForMultipleChoice',
	    'FlaxBigBirdForPreTraining',
	    'FlaxBigBirdForQuestionAnswering',
	    'FlaxBigBirdForSequenceClassification',
	    'FlaxBigBirdForTokenClassification',
	    'FlaxBigBirdModel',
	    'FlaxBigBirdPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird import BigBirdTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird_fast import BigBirdTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_big_bird import (
		    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BigBirdForCausalLM,
		    BigBirdForMaskedLM,
		    BigBirdForMultipleChoice,
		    BigBirdForPreTraining,
		    BigBirdForQuestionAnswering,
		    BigBirdForSequenceClassification,
		    BigBirdForTokenClassification,
		    BigBirdLayer,
		    BigBirdModel,
		    BigBirdPreTrainedModel,
		    load_tf_weights_in_big_bird,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		    FlaxBigBirdPreTrainedModel,
		)

else:
	import sys

	snake_case_      :					int				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
