
"""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()
lowerCAmelCase :    Optional[int]								=						logging.get_logger(__name__)

@dataclass
class    A_     :



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       42
							SCREAMING_SNAKE_CASE_     				=       field(default_factory=lowerCAmelCase_       )
							SCREAMING_SNAKE_CASE_     				=       field(default_factory=lowerCAmelCase_       )





							def 				UpperCAmelCase__							(  self  :Dict					,							lowerCamelCase_  :List[Any]					,							lowerCamelCase_  :Tensor					,							lowerCamelCase_  :Tensor	):





										"""simple docstring"""

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





							def __call__(  self  :Optional[Any]					,							lowerCamelCase_  :Tensor	):





										"""simple docstring"""

										for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook	)	)
										self.module(_snake_case	)
										[x.remove() for x in self.handles]
										return self





							@property
							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

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






@dataclass
class    A_     :



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       42
							SCREAMING_SNAKE_CASE_     				=       42
							SCREAMING_SNAKE_CASE_     				=       0
							SCREAMING_SNAKE_CASE_     				=       field(default_factory=lowerCAmelCase_       )
							SCREAMING_SNAKE_CASE_     				=       field(default_factory=lowerCAmelCase_       )





							def __call__(  self  :Any					,							lowerCamelCase_  :Tensor	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=Tracker(self.dest	)(_snake_case	).parametrized
										lowerCamelCase__     :					str		=Tracker(self.src	)(_snake_case	).parametrized

										lowerCamelCase__     :					Dict		=list(filter(lambda lowerCamelCase_	: type(_snake_case	) not in self.src_skip					,							_snake_case	)	)
										lowerCamelCase__     :					List[Any]		=list(filter(lambda lowerCamelCase_	: type(_snake_case	) not in self.dest_skip					,							_snake_case	)	)

										if len(_snake_case	) != len(_snake_case	):
													raise Exception(
													    f"""Numbers of operations are different. Source module has {len(_snake_case	)} operations while"""
													    f""" destination module has {len(_snake_case	)}."""	)

										for dest_m, src_m in zip(_snake_case					,							_snake_case	):
													dest_m.load_state_dict(src_m.state_dict()	)
													if self.verbose == 1:
																print(f"""Transfered from={src_m} to={dest_m}"""	)






def        lowerCAmelCase_       (		snake_case_		:      nn.Module							,   snake_case_		:      nn.Module      )       ->Tuple:
			# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
			lowerCamelCase__     :					Optional[Any]		=from_model.state_dict()
			lowerCamelCase__     :					int		=our_model.state_dict()
			lowerCamelCase__     :					Union[str, Any]		=our_model.config
			lowerCamelCase__     :					Dict		=[]
			for stage_idx in range(len(config.hidden_sizes      )      ):
						for block_id in range(config.depths[stage_idx]      ):
									lowerCamelCase__     :					Dict		=f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
									lowerCamelCase__     :					Optional[Any]		=f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

									all_keys.append((from_key, to_key)      )
									lowerCamelCase__     :					Dict		=f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
									lowerCamelCase__     :					int		=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__     :					Union[str, Any]		=from_state_dict.pop(snake_case_      )

			our_model.load_state_dict(snake_case_      )
			return our_model






def        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      VanConfig							,   snake_case_		:      str							,   snake_case_		:      nn.Module							,   snake_case_		:      Path							,   snake_case_		:      bool = True							,   )       ->Optional[Any]:
			print(f"""Downloading weights for {name}..."""      )
			lowerCamelCase__     :					Dict		=cached_download(snake_case_      )
			print(f"""Converting {name}..."""      )
			lowerCamelCase__     :					Optional[Any]		=torch.load(snake_case_      )['state_dict']
			from_model.load_state_dict(snake_case_      )
			from_model.eval()
			with torch.no_grad():
						lowerCamelCase__     :					Union[str, Any]		=VanForImageClassification(snake_case_      ).eval()
						lowerCamelCase__     :					Tuple		=ModuleTransfer(src=snake_case_							,   dest=snake_case_      )
						lowerCamelCase__     :					Optional[Any]		=torch.randn((1, 3, 2_2_4, 2_2_4)      )
						module_transfer(snake_case_      )
						lowerCamelCase__     :					int		=copy_parameters(snake_case_							,   snake_case_      )

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

			lowerCamelCase__     :					int		=name
			print(snake_case_      )

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

						# we can use the convnext one
						lowerCamelCase__     :					Any		=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=snake_case_							,   )

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






def        lowerCAmelCase_       (		snake_case_		:      Path							,   snake_case_		:      str = None							,   snake_case_		:      bool = True      )       ->Union[str, Any]:
			lowerCamelCase__     :					Dict		='imagenet-1k-id2label.json'
			lowerCamelCase__     :					List[str]		=1_0_0_0

			lowerCamelCase__     :					Dict		='huggingface/label-files'
			lowerCamelCase__     :					Optional[int]		=num_labels
			lowerCamelCase__     :					Optional[Any]		=json.load(open(hf_hub_download(snake_case_							,   snake_case_							,   repo_type='dataset'      )							,   'r'      )      )
			lowerCamelCase__     :					Dict		={int(snake_case_      ): v for k, v in idalabel.items()}

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

			lowerCamelCase__     :					Union[str, Any]		=partial(snake_case_							,   num_labels=snake_case_							,   idalabel=snake_case_							,   labelaid=snake_case_      )

			lowerCamelCase__     :					Union[str, Any]		={
			    'van-tiny': ImageNetPreTrainedConfig(
			        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]							,   depths=[3, 3, 5, 2]							,   mlp_ratios=[8, 8, 4, 4]							,   ),
			    'van-small': ImageNetPreTrainedConfig(
			        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]							,   depths=[2, 2, 4, 2]							,   mlp_ratios=[8, 8, 4, 4]							,   ),
			    'van-base': ImageNetPreTrainedConfig(
			        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]							,   depths=[3, 3, 1_2, 3]							,   mlp_ratios=[8, 8, 4, 4]							,   ),
			    'van-large': ImageNetPreTrainedConfig(
			        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]							,   depths=[3, 5, 2_7, 3]							,   mlp_ratios=[8, 8, 4, 4]							,   ),
			}

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

			lowerCamelCase__     :					int		={
			    '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(
						    snake_case_							,   names_to_config[model_name]							,   checkpoint=names_to_original_checkpoints[model_name]							,   from_model=names_to_original_models[model_name]()							,   save_directory=snake_case_							,   push_to_hub=snake_case_							,   )
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(
									    snake_case_							,   snake_case_							,   checkpoint=names_to_original_checkpoints[model_name]							,   from_model=names_to_original_models[model_name]()							,   save_directory=snake_case_							,   push_to_hub=snake_case_							,   )


if __name__ == "__main__":
							lowerCAmelCase :    int								=						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.""",
							)

							lowerCAmelCase :    Dict								=						parser.parse_args()
							lowerCAmelCase :    Path								=						args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							lowerCAmelCase :    Optional[int]								=						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_torch_available


lowerCAmelCase								=						{
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DecisionTransformerGPT2Model""",
							    """DecisionTransformerGPT2PreTrainedModel""",
							    """DecisionTransformerModel""",
							    """DecisionTransformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_decision_transformer import (
							    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DecisionTransformerConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_decision_transformer import (
														    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DecisionTransformerGPTaModel,
														    DecisionTransformerGPTaPreTrainedModel,
														    DecisionTransformerModel,
														    DecisionTransformerPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)