
'''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      snake_case__    :







		"""simple docstring"""

		lowerCamelCase							=		42
		lowerCamelCase							=		field(default_factory=__SCREAMING_SNAKE_CASE )
		lowerCamelCase							=		field(default_factory=__SCREAMING_SNAKE_CASE )



		def 	lowerCAmelCase							(	self							:       int   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Tensor   ,     UpperCamelCase__							:       Tensor	)		->       Tuple:






				"""simple docstring"""







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



		def __call__(	self							:       Optional[int]   ,     UpperCamelCase__							:       Tensor	)		->       Any:






				"""simple docstring"""







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



		@property
		def 	lowerCAmelCase							(	self							:       int	)		->       Optional[Any]:






				"""simple docstring"""







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







@dataclass
class      snake_case__    :







		"""simple docstring"""

		lowerCamelCase							=		42
		lowerCamelCase							=		42
		lowerCamelCase							=		0
		lowerCamelCase							=		field(default_factory=__SCREAMING_SNAKE_CASE )
		lowerCamelCase							=		field(default_factory=__SCREAMING_SNAKE_CASE )



		def __call__(	self							:       str   ,     UpperCamelCase__							:       Tensor	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    Dict			       =		Tracker(self.dest	)(UpperCamelCase__	).parametrized
				snake_case   :    List[str]			       =		Tracker(self.src	)(UpperCamelCase__	).parametrized

				snake_case   :    Optional[int]			       =		list(filter(lambda UpperCamelCase__	: type(UpperCamelCase__	) not in self.src_skip   ,     UpperCamelCase__	)	)
				snake_case   :    List[Any]			       =		list(filter(lambda UpperCamelCase__	: type(UpperCamelCase__	) not in self.dest_skip   ,     UpperCamelCase__	)	)

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

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







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				nn.Module:


		'''simple docstring'''


		snake_case   :    Union[str, Any]			       =		from_model.state_dict()
		snake_case   :    Optional[int]			       =		our_model.state_dict()
		snake_case   :    int			       =		our_model.config
		snake_case   :    int			       =		[]
		for stage_idx in range(len(config.hidden_sizes       )       ):
				for block_id in range(config.depths[stage_idx]       ):
						snake_case   :    List[Any]			       =		F'block{stage_idx + 1}.{block_id}.layer_scale_1'
						snake_case   :    List[Any]			       =		F'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

						all_keys.append((from_key, to_key)       )
						snake_case   :    int			       =		F'block{stage_idx + 1}.{block_id}.layer_scale_2'
						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:
				snake_case   :    Dict			       =		from_state_dict.pop(SCREAMING_SNAKE_CASE__       )

		our_model.load_state_dict(SCREAMING_SNAKE_CASE__       )
		return our_model




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__ = True      ,    )   ->				List[Any]:


		'''simple docstring'''


		print(F'Downloading weights for {name}...'       )
		snake_case   :    int			       =		cached_download(SCREAMING_SNAKE_CASE__       )
		print(F'Converting {name}...'       )
		snake_case   :    List[Any]			       =		torch.load(SCREAMING_SNAKE_CASE__       )['''state_dict''']
		from_model.load_state_dict(SCREAMING_SNAKE_CASE__       )
		from_model.eval()
		with torch.no_grad():
				snake_case   :    Optional[int]			       =		VanForImageClassification(SCREAMING_SNAKE_CASE__       ).eval()
				snake_case   :    Union[str, Any]			       =		ModuleTransfer(src=SCREAMING_SNAKE_CASE__      ,    dest=SCREAMING_SNAKE_CASE__       )
				snake_case   :    Dict			       =		torch.randn((1, 3, 224, 224)       )
				module_transfer(SCREAMING_SNAKE_CASE__       )
				snake_case   :    List[str]			       =		copy_parameters(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )

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

		snake_case   :    Dict			       =		name
		print(SCREAMING_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=SCREAMING_SNAKE_CASE__      ,    )

				# we can use the convnext one
				snake_case   :    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=SCREAMING_SNAKE_CASE__      ,    )

				print(F'Pushed {checkpoint_name}'       )




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__ = None      ,    SCREAMING_SNAKE_CASE__ = True       )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Tuple			       =		'''imagenet-1k-id2label.json'''
		snake_case   :    List[str]			       =		1000

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

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

		snake_case   :    Tuple			       =		partial(SCREAMING_SNAKE_CASE__      ,    num_labels=SCREAMING_SNAKE_CASE__      ,    idalabel=SCREAMING_SNAKE_CASE__      ,    labelaid=SCREAMING_SNAKE_CASE__       )

		snake_case   :    str			       =		{
		    '''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   :    List[Any]			       =		{
		    '''van-tiny''': van_tiny,
		    '''van-small''': van_small,
		    '''van-base''': van_base,
		    '''van-large''': van_large,
		}

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


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)









"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
