"""simple docstring"""


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
a							=logging.get_logger(__name__)






@dataclass
class        __UpperCAmelCase						:
				A__				:       nn.Module
				A__				:       List[nn.Module]  							=	field(default_factory=__lowerCAmelCase     )
				A__				:       list  							=	field(default_factory=__lowerCAmelCase     )

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =len(list(m.modules()				)				) == 1 or isinstance(_lowerCamelCase     ,			nn.Convad				) or isinstance(_lowerCamelCase     ,			nn.BatchNormad				)
										if has_not_submodules:
																if not isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																						self.traced.append(_lowerCamelCase				)

				def __call__(				self     ,			_lowerCamelCase				):
										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				):
										# 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        __UpperCAmelCase						:
				A__				:       nn.Module
				A__				:       nn.Module
				A__				:       int  							=	0
				A__				:       List  							=	field(default_factory=__lowerCAmelCase     )
				A__				:       List  							=	field(default_factory=__lowerCAmelCase     )

				def __call__(				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =Tracker(self.dest				)(_lowerCamelCase				).parametrized
										lowerCamelCase__ =Tracker(self.src				)(_lowerCamelCase				).parametrized

										lowerCamelCase__ =list(filter(lambda _lowerCamelCase				: type(_lowerCamelCase				) not in self.src_skip     ,			_lowerCamelCase				)				)
										lowerCamelCase__ =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       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> nn.Module:
						lowerCamelCase__ =from_model.state_dict()
						lowerCamelCase__ =our_model.state_dict()
						lowerCamelCase__ =our_model.config
						lowerCamelCase__ =[]
						for stage_idx in range(len(config.hidden_sizes    )    ):
												for block_id in range(config.depths[stage_idx]    ):
																		lowerCamelCase__ =F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
																		lowerCamelCase__ =F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

																		all_keys.append((from_key, to_key)    )
																		lowerCamelCase__ =F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																		lowerCamelCase__ =F'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

																		all_keys.append((from_key, to_key)    )

						for from_key, to_key in all_keys:
												lowerCamelCase__ =from_state_dict.pop(__lowerCAmelCase    )

						our_model.load_state_dict(__lowerCAmelCase    )
						return our_model





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase = True       ,   )							-> Tuple:
						print(F'''Downloading weights for {name}...'''    )
						lowerCamelCase__ =cached_download(__lowerCAmelCase    )
						print(F'''Converting {name}...'''    )
						lowerCamelCase__ =torch.load(__lowerCAmelCase    )["state_dict"]
						from_model.load_state_dict(__lowerCAmelCase    )
						from_model.eval()
						with torch.no_grad():
												lowerCamelCase__ =VanForImageClassification(__lowerCAmelCase    ).eval()
												lowerCamelCase__ =ModuleTransfer(src=__lowerCAmelCase       ,   dest=__lowerCAmelCase    )
												lowerCamelCase__ =torch.randn((1, 3, 224, 224)    )
												module_transfer(__lowerCAmelCase    )
												lowerCamelCase__ =copy_parameters(__lowerCAmelCase       ,   __lowerCAmelCase    )

						if not torch.allclose(from_model(__lowerCAmelCase    )       ,   our_model(__lowerCAmelCase    ).logits    ):
												raise ValueError("The model logits don't match the original one."    )

						lowerCamelCase__ =name
						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
												lowerCamelCase__ =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       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase = None       ,   __lowerCAmelCase = True    )							-> List[str]:
						lowerCamelCase__ ="imagenet-1k-id2label.json"
						lowerCamelCase__ =1000

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

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

						lowerCamelCase__ =partial(__lowerCAmelCase       ,   num_labels=__lowerCAmelCase       ,   idalabel=__lowerCAmelCase       ,   labelaid=__lowerCAmelCase    )

						lowerCamelCase__ ={
						    "van-tiny": ImageNetPreTrainedConfig(
						        hidden_sizes=[32, 64, 160, 256]       ,   depths=[3, 3, 5, 2]       ,   mlp_ratios=[8, 8, 4, 4]       ,   ),
						    "van-small": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]       ,   depths=[2, 2, 4, 2]       ,   mlp_ratios=[8, 8, 4, 4]       ,   ),
						    "van-base": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]       ,   depths=[3, 3, 12, 3]       ,   mlp_ratios=[8, 8, 4, 4]       ,   ),
						    "van-large": ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 128, 320, 512]       ,   depths=[3, 5, 27, 3]       ,   mlp_ratios=[8, 8, 4, 4]       ,   ),
						}

						lowerCamelCase__ ={
						    "van-tiny": van_tiny,
						    "van-small": van_small,
						    "van-base": van_base,
						    "van-large": van_large,
						}

						lowerCamelCase__ ={
						    "van-tiny": (
						        "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
						    ),
						    "van-small": (
						        "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
						    ),
						    "van-base": (
						        "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
						    ),
						    "van-large": (
						        "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
						    ),
						}

						if model_name:
												convert_weight_and_push(
												    __lowerCAmelCase       ,   names_to_config[model_name]       ,   checkpoint=names_to_original_checkpoints[model_name]       ,   from_model=names_to_original_models[model_name]()       ,   save_directory=__lowerCAmelCase       ,   push_to_hub=__lowerCAmelCase       ,   )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    __lowerCAmelCase       ,   __lowerCAmelCase       ,   checkpoint=names_to_original_checkpoints[model_name]       ,   from_model=names_to_original_models[model_name]()       ,   save_directory=__lowerCAmelCase       ,   push_to_hub=__lowerCAmelCase       ,   )


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: van-tiny/small/base/large. 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(
	    '--van_dir',
	    required=True,
	    type=Path,
	    help=(
	        'A path to VAN\'s original implementation directory. You can download from here:'
	        ' https://github.com/Visual-Attention-Network/VAN-Classification'
	    ),
	)
	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)
	a							=args.van_dir
	# append the path to the parents to maskformer dir
	sys.path.append(str(van_dir.parent))
	from van.models.van import van_base, van_large, van_small, van_tiny

	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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a							={
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['OpenAIGPTTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OpenAIGPTDoubleHeadsModel',
	    'OpenAIGPTForSequenceClassification',
	    'OpenAIGPTLMHeadModel',
	    'OpenAIGPTModel',
	    'OpenAIGPTPreTrainedModel',
	    'load_tf_weights_in_openai_gpt',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFOpenAIGPTDoubleHeadsModel',
	    'TFOpenAIGPTForSequenceClassification',
	    'TFOpenAIGPTLMHeadModel',
	    'TFOpenAIGPTMainLayer',
	    'TFOpenAIGPTModel',
	    'TFOpenAIGPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
	from .tokenization_openai import OpenAIGPTTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_openai_fast import OpenAIGPTTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_openai import (
		    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OpenAIGPTDoubleHeadsModel,
		    OpenAIGPTForSequenceClassification,
		    OpenAIGPTLMHeadModel,
		    OpenAIGPTModel,
		    OpenAIGPTPreTrainedModel,
		    load_tf_weights_in_openai_gpt,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTMainLayer,
		    TFOpenAIGPTModel,
		    TFOpenAIGPTPreTrainedModel,
		)

else:
	import sys

	a							=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


