
"""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()
UpperCAmelCase__				:   str   							=			logging.get_logger(__name__)



@dataclass
class      lowerCAmelCase_						:


				"""simple docstring"""



				__UpperCamelCase				:						nn.Module
				__UpperCamelCase				:						List[nn.Module] 	=       field(default_factory=a__  )
				__UpperCamelCase				:						list 	=       field(default_factory=a__  )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       len(list(m.modules()   )   ) == 1 or isinstance(SCREAMING_SNAKE_CASE__       ,	nn.Convad   ) or isinstance(SCREAMING_SNAKE_CASE__       ,	nn.BatchNormad   )
											if has_not_submodules:
																		if not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																									self.traced.append(SCREAMING_SNAKE_CASE__   )



				def __call__(self       ,	SCREAMING_SNAKE_CASE__   ) ->		List[Any]:



											"""simple docstring"""




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



				@property
				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




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






@dataclass
class      lowerCAmelCase_						:


				"""simple docstring"""



				__UpperCamelCase				:						nn.Module
				__UpperCamelCase				:						nn.Module
				__UpperCamelCase				:						int 	=       0
				__UpperCamelCase				:						List 	=       field(default_factory=a__  )
				__UpperCamelCase				:						List 	=       field(default_factory=a__  )



				def __call__(self       ,	SCREAMING_SNAKE_CASE__   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       Tracker(self.dest   )(SCREAMING_SNAKE_CASE__   ).parametrized
											SCREAMING_SNAKE_CASE__						:	int									=       Tracker(self.src   )(SCREAMING_SNAKE_CASE__   ).parametrized

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       list(filter(lambda SCREAMING_SNAKE_CASE__   : type(SCREAMING_SNAKE_CASE__   ) not in self.src_skip       ,	SCREAMING_SNAKE_CASE__   )   )
											SCREAMING_SNAKE_CASE__						:	Tuple									=       list(filter(lambda SCREAMING_SNAKE_CASE__   : type(SCREAMING_SNAKE_CASE__   ) not in self.dest_skip       ,	SCREAMING_SNAKE_CASE__   )   )

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

											for dest_m, src_m in zip(SCREAMING_SNAKE_CASE__       ,	SCREAMING_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 lowercase_  (       _snake_case       ,_snake_case						):
							# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
							SCREAMING_SNAKE_CASE__						:	str									=       from_model.state_dict()
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       our_model.state_dict()
							SCREAMING_SNAKE_CASE__						:	List[str]									=       our_model.config
							SCREAMING_SNAKE_CASE__						:	List[str]									=       []
							for stage_idx in range(len(config.hidden_sizes						)						):
														for block_id in range(config.depths[stage_idx]						):
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
																					SCREAMING_SNAKE_CASE__						:	List[Any]									=       f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

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

							our_model.load_state_dict(_snake_case						)
							return our_model

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case = True       ,):
							print(f'''Downloading weights for {name}...'''						)
							SCREAMING_SNAKE_CASE__						:	Tuple									=       cached_download(_snake_case						)
							print(f'''Converting {name}...'''						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.load(_snake_case						)["""state_dict"""]
							from_model.load_state_dict(_snake_case						)
							from_model.eval()
							with torch.no_grad():
														SCREAMING_SNAKE_CASE__						:	Tuple									=       VanForImageClassification(_snake_case						).eval()
														SCREAMING_SNAKE_CASE__						:	Tuple									=       ModuleTransfer(src=_snake_case       ,dest=_snake_case						)
														SCREAMING_SNAKE_CASE__						:	Any									=       torch.randn((1, 3, 224, 224)						)
														module_transfer(_snake_case						)
														SCREAMING_SNAKE_CASE__						:	str									=       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."""						)

							SCREAMING_SNAKE_CASE__						:	Any									=       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
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       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 lowercase_  (       _snake_case       ,_snake_case = None       ,_snake_case = True						):
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       """imagenet-1k-id2label.json"""
							SCREAMING_SNAKE_CASE__						:	str									=       1_000

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

							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       idalabel
							SCREAMING_SNAKE_CASE__						:	List[str]									=       {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__						:	Dict									=       partial(_snake_case       ,num_labels=_snake_case       ,idalabel=_snake_case       ,labelaid=_snake_case						)

							SCREAMING_SNAKE_CASE__						:	List[Any]									=       {
							    """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]       ,),
							}

							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {
							    """van-tiny""": van_tiny,
							    """van-small""": van_small,
							    """van-base""": van_base,
							    """van-large""": van_large,
							}

							SCREAMING_SNAKE_CASE__						:	str									=       {
							    """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[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.',
					)

					UpperCAmelCase__				:   Optional[Any]   							=			parser.parse_args()
					UpperCAmelCase__				:   Path   							=			args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					UpperCAmelCase__				:   List[str]   							=			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"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       FNetConfig.from_json_file(_snake_case						)
							print(f'''Building PyTorch model from configuration: {config}'''						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       FNetForPreTraining(_snake_case						)

							SCREAMING_SNAKE_CASE__						:	Tuple									=       restore_checkpoint(_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       checkpoint_dict["""target"""]

							# Embeddings
							# Position IDs
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fnet_pretraining_model.state_dict()

							SCREAMING_SNAKE_CASE__						:	Any									=       state_dict["""fnet.embeddings.position_ids"""]
							SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""fnet.embeddings.position_ids""": position_ids}
							# Embedding Layers
							SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Any									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]						)

							# Encoder Layers
							for layer in range(config.num_hidden_layers						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]						)

							# Pooler Layers
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]						)

							# Masked LM Layers
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_output"""]["""output_bias"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]						)

							# Seq Relationship Layers
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_kernel"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_bias"""]						)

							# Load State Dict
							fnet_pretraining_model.load_state_dict(_snake_case						)

							# Save PreTrained
							print(f'''Saving pretrained model to {save_path}'''						)
							fnet_pretraining_model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[int]   							=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--fnet_config_file',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        'The config json file corresponding to the pre-trained FNet model. \n'
					        'This specifies the model architecture.'
					    ),
					)
					parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
					UpperCAmelCase__				:   Optional[Any]   							=			parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




