


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()
UpperCamelCase			:					Dict         =							logging.get_logger(__name__)

@dataclass
class      __snake_case				:
					SCREAMING_SNAKE_CASE_		:       nn.Module
					SCREAMING_SNAKE_CASE_		:       List[nn.Module]								=			field(default_factory=_UpperCAmelCase)
					SCREAMING_SNAKE_CASE_		:       list								=			field(default_factory=_UpperCAmelCase)
					def 	a			(						self       :						Union[str, Any]	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						Tensor	,      _lowerCAmelCase       :						Tensor  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      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       :						Optional[Any]	,      _lowerCAmelCase       :						Tensor  )							->					Dict:



											'''simple docstring'''





											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       :						List[str]  )							->					Any:



											'''simple docstring'''





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







@dataclass
class      __snake_case				:
					SCREAMING_SNAKE_CASE_		:       nn.Module
					SCREAMING_SNAKE_CASE_		:       nn.Module
					SCREAMING_SNAKE_CASE_		:       int								=			0
					SCREAMING_SNAKE_CASE_		:       List								=			field(default_factory=_UpperCAmelCase)
					SCREAMING_SNAKE_CASE_		:       List								=			field(default_factory=_UpperCAmelCase)
					def __call__(						self       :						Dict	,      _lowerCAmelCase       :						Tensor  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      Tracker(self.dest  )(_lowerCAmelCase  ).parametrized
											__magic_name__					=      Tracker(self.src  )(_lowerCAmelCase  ).parametrized

											__magic_name__					=      list(filter(lambda _lowerCAmelCase  : type(_lowerCAmelCase  ) not in self.src_skip	,      _lowerCAmelCase  )  )
											__magic_name__					=      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 				UpperCamelCase_	(					__snake_case      :    nn.Module						,      __snake_case      :    nn.Module  )->   nn.Module:




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

																		all_keys.append((from_key, to_key)  )
																		__magic_name__					=      F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																		__magic_name__					=      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:
												__magic_name__					=      from_state_dict.pop(__snake_case  )

						our_model.load_state_dict(__snake_case  )
						return our_model
def 				UpperCamelCase_	(					__snake_case      :    str						,      __snake_case      :    VanConfig						,      __snake_case      :    str						,      __snake_case      :    nn.Module						,      __snake_case      :    Path						,      __snake_case      :    bool = True						,      )->   List[str]:




						'''simple docstring'''
						print(F'''Downloading weights for {name}...'''  )
						__magic_name__					=      cached_download(__snake_case  )
						print(F'''Converting {name}...'''  )
						__magic_name__					=      torch.load(__snake_case  )['state_dict']
						from_model.load_state_dict(__snake_case  )
						from_model.eval()
						with torch.no_grad():
												__magic_name__					=      VanForImageClassification(__snake_case  ).eval()
												__magic_name__					=      ModuleTransfer(src=__snake_case						,      dest=__snake_case  )
												__magic_name__					=      torch.randn((1, 3, 2_24, 2_24)  )
												module_transfer(__snake_case  )
												__magic_name__					=      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.'  )

						__magic_name__					=      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
												__magic_name__					=      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 				UpperCamelCase_	(					__snake_case      :    Path						,      __snake_case      :    str = None						,      __snake_case      :    bool = True  )->   List[str]:




						'''simple docstring'''
						__magic_name__					=      'imagenet-1k-id2label.json'
						__magic_name__					=      10_00

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

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

						__magic_name__					=      partial(__snake_case						,      num_labels=__snake_case						,      idalabel=__snake_case						,      labelaid=__snake_case  )

						__magic_name__					=      {
						    'van-tiny': ImageNetPreTrainedConfig(
						        hidden_sizes=[32, 64, 1_60, 2_56]						,      depths=[3, 3, 5, 2]						,      mlp_ratios=[8, 8, 4, 4]						,      ),
						    'van-small': ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 1_28, 3_20, 5_12]						,      depths=[2, 2, 4, 2]						,      mlp_ratios=[8, 8, 4, 4]						,      ),
						    'van-base': ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 1_28, 3_20, 5_12]						,      depths=[3, 3, 12, 3]						,      mlp_ratios=[8, 8, 4, 4]						,      ),
						    'van-large': ImageNetPreTrainedConfig(
						        hidden_sizes=[64, 1_28, 3_20, 5_12]						,      depths=[3, 5, 27, 3]						,      mlp_ratios=[8, 8, 4, 4]						,      ),
						}

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

						__magic_name__					=      {
						    '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__":
					UpperCamelCase			:					List[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 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.''',
					)

					UpperCamelCase			:					List[Any]         =							parser.parse_args()
					UpperCamelCase			:					Path         =							args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					UpperCamelCase			:					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)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:					Optional[int]         =							{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					List[str]         =							[
					    '''DPRContextEncoderTokenizerFast''',
					    '''DPRQuestionEncoderTokenizerFast''',
					    '''DPRReaderTokenizerFast''',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					Optional[Any]         =							[
					    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPRContextEncoder''',
					    '''DPRPretrainedContextEncoder''',
					    '''DPRPreTrainedModel''',
					    '''DPRPretrainedQuestionEncoder''',
					    '''DPRPretrainedReader''',
					    '''DPRQuestionEncoder''',
					    '''DPRReader''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					Optional[int]         =							[
					    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFDPRContextEncoder''',
					    '''TFDPRPretrainedContextEncoder''',
					    '''TFDPRPretrainedQuestionEncoder''',
					    '''TFDPRPretrainedReader''',
					    '''TFDPRQuestionEncoder''',
					    '''TFDPRReader''',
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					UpperCamelCase			:					List[Any]         =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

