
"""simple docstring"""
from typing import TYPE_CHECKING

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


_a   :      Any       =				{
    """configuration_sam""": [
        """SAM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SamConfig""",
        """SamMaskDecoderConfig""",
        """SamPromptEncoderConfig""",
        """SamVisionConfig""",
    ],
    """processing_sam""": ["""SamProcessor"""],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a   :      Dict       =				[
        """SAM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SamModel""",
        """SamPreTrainedModel""",
    ]
try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a   :      List[str]       =				[
        """TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFSamModel""",
        """TFSamPreTrainedModel""",
    ]
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a   :      List[Any]       =				["""SamImageProcessor"""]


if TYPE_CHECKING:
    from .configuration_sam import (
        SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SamConfig,
        SamMaskDecoderConfig,
        SamPromptEncoderConfig,
        SamVisionConfig,
    )
    from .processing_sam import SamProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_sam import SamImageProcessor

else:
    import sys

    _a   :      Union[str, Any]       =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









"""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()
lowercase           =							logging.get_logger(__name__)

@dataclass
class     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					field(default_factory=snake_case_	)
				UpperCAmelCase__										=					field(default_factory=snake_case_	)







				def      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Tensor    ,     __lowerCamelCase				:    Tensor						)					->       Dict:
										__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				:    int    ,     __lowerCamelCase				:    Tensor						)					->       List[str]:
										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      _snake_case       (				self				:    List[Any]						)					->       int:
										# 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     A_				:
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					42
				UpperCAmelCase__										=					0
				UpperCAmelCase__										=					field(default_factory=snake_case_	)
				UpperCAmelCase__										=					field(default_factory=snake_case_	)







				def __call__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    Tensor						)					->       str:
										__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 _lowerCAmelCase						(	__lowerCamelCase:nn.Module			,    __lowerCamelCase:nn.Module ):

						'''simple docstring'''






						__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(__lowerCamelCase )

						our_model.load_state_dict(__lowerCamelCase )
						return our_model






def _lowerCAmelCase						(	__lowerCamelCase:str			,    __lowerCamelCase:VanConfig			,    __lowerCamelCase:str			,    __lowerCamelCase:nn.Module			,    __lowerCamelCase:Path			,    __lowerCamelCase:bool = True			,    ):

						'''simple docstring'''






						print(f'''Downloading weights for {name}...''' )
						__magic_name__      =  cached_download(__lowerCamelCase )
						print(f'''Converting {name}...''' )
						__magic_name__      =  torch.load(__lowerCamelCase )["state_dict"]
						from_model.load_state_dict(__lowerCamelCase )
						from_model.eval()
						with torch.no_grad():
												__magic_name__      =  VanForImageClassification(__lowerCamelCase ).eval()
												__magic_name__      =  ModuleTransfer(src=__lowerCamelCase			,    dest=__lowerCamelCase )
												__magic_name__      =  torch.randn((1, 3, 2_2_4, 2_2_4) )
												module_transfer(__lowerCamelCase )
												__magic_name__      =  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." )

						__magic_name__      =  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
												__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=__lowerCamelCase			,    )

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






def _lowerCAmelCase						(	__lowerCamelCase:Path			,    __lowerCamelCase:str = None			,    __lowerCamelCase:bool = True ):

						'''simple docstring'''






						__magic_name__      =  "imagenet-1k-id2label.json"
						__magic_name__      =  1_0_0_0

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

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

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

						__magic_name__      =  {
						    "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]			,    ),
						}

						__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(
												    __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__":
			lowercase           =							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.''',
			)

			lowercase           =							parser.parse_args()
			lowercase           =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			lowercase           =							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)



