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




@dataclass
class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	_UpperCAmelCase :	nn.Module
	_UpperCAmelCase :	List[nn.Module]					     =      field(default_factory=UpperCAmelCase      )
	_UpperCAmelCase :	list					     =      field(default_factory=UpperCAmelCase      )



	def 				A							(						self			:					List[str]    ,						lowercase			:					Dict    ,						lowercase			:					Tensor    ,						lowercase			:					Tensor			):


				'''simple docstring'''


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



	def __call__(						self			:					Optional[int]    ,						lowercase			:					Tensor			):


				'''simple docstring'''


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





	@property
	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


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




@dataclass
class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



	_UpperCAmelCase :	nn.Module
	_UpperCAmelCase :	nn.Module
	_UpperCAmelCase :	int					     =      0
	_UpperCAmelCase :	List					     =      field(default_factory=UpperCAmelCase      )
	_UpperCAmelCase :	List					     =      field(default_factory=UpperCAmelCase      )



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


				'''simple docstring'''


				_snake_case						=    Tracker(self.dest			)(lowercase			).parametrized
				_snake_case						=    Tracker(self.src			)(lowercase			).parametrized

				_snake_case						=    list(filter(lambda lowercase			: type(lowercase			) not in self.src_skip    ,						lowercase			)			)
				_snake_case						=    list(filter(lambda lowercase			: type(lowercase			) not in self.dest_skip    ,						lowercase			)			)

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

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






def  a_	( __lowercase     :			nn.Module ,      __lowercase     :			nn.Module				)				->						nn.Module:
			# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
			_snake_case						=    from_model.state_dict()
			_snake_case						=    our_model.state_dict()
			_snake_case						=    our_model.config
			_snake_case						=    []
			for stage_idx in range(len(config.hidden_sizes				)				):
						for block_id in range(config.depths[stage_idx]				):
									_snake_case						=    f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
									_snake_case						=    f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

									all_keys.append((from_key, to_key)				)
									_snake_case						=    f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
									_snake_case						=    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:
						_snake_case						=    from_state_dict.pop(__lowercase				)

			our_model.load_state_dict(__lowercase				)
			return our_model






def  a_	( __lowercase     :			str ,      __lowercase     :			VanConfig ,      __lowercase     :			str ,      __lowercase     :			nn.Module ,      __lowercase     :			Path ,      __lowercase     :			bool = True ,      )				->						str:
			print(f'''Downloading weights for {name}...'''				)
			_snake_case						=    cached_download(__lowercase				)
			print(f'''Converting {name}...'''				)
			_snake_case						=    torch.load(__lowercase				)['state_dict']
			from_model.load_state_dict(__lowercase				)
			from_model.eval()
			with torch.no_grad():
						_snake_case						=    VanForImageClassification(__lowercase				).eval()
						_snake_case						=    ModuleTransfer(src=__lowercase ,      dest=__lowercase				)
						_snake_case						=    torch.randn((1, 3, 224, 224)				)
						module_transfer(__lowercase				)
						_snake_case						=    copy_parameters(__lowercase ,      __lowercase				)

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

			_snake_case						=    name
			print(__lowercase				)

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

						# we can use the convnext one
						_snake_case						=    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=__lowercase ,      )

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






def  a_	( __lowercase     :			Path ,      __lowercase     :			str = None ,      __lowercase     :			bool = True				)				->						int:
			_snake_case						=    'imagenet-1k-id2label.json'
			_snake_case						=    1_000

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

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

			_snake_case						=    partial(__lowercase ,      num_labels=__lowercase ,      idalabel=__lowercase ,      labelaid=__lowercase				)

			_snake_case						=    {
			    '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] ,      ),
			}

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

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


if __name__ == "__main__":
			_lowerCamelCase :       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 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 :       Optional[Any]     					=    parser.parse_args()
			_lowerCamelCase :       Path     					=    args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			_lowerCamelCase :       List[Any]     					=    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)

# Copyright 2023 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase :       int     					=    {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    [
			    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TvltModel''',
			    '''TvltForPreTraining''',
			    '''TvltForAudioVisualClassification''',
			    '''TvltPreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    ['''TvltImageProcessor''']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			_lowerCamelCase :       Dict     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)