


"""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     A:

		"""simple docstring"""





		A         =       42
		A         =       field(default_factory=UpperCAmelCase_		)
		A         =       field(default_factory=UpperCAmelCase_		)
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:List[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				,							SCREAMING_SNAKE_CASE__      )			->  Union[str, 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      _UpperCamelCase(   self      )			->  Optional[Any]:

					"""simple docstring"""



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






@dataclass
class     A:

		"""simple docstring"""





		A         =       42
		A         =       42
		A         =       0
		A         =       field(default_factory=UpperCAmelCase_		)
		A         =       field(default_factory=UpperCAmelCase_		)
		def __call__(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  Tracker(self.dest      )(__UpperCamelCase      ).parametrized
					_UpperCamelCase				:Tuple				    =  Tracker(self.src      )(__UpperCamelCase      ).parametrized

					_UpperCamelCase				:Union[str, Any]				    =  list(filter(lambda SCREAMING_SNAKE_CASE__      : type(__UpperCamelCase      ) not in self.src_skip				,							__UpperCamelCase      )      )
					_UpperCamelCase				:Tuple				    =  list(filter(lambda SCREAMING_SNAKE_CASE__      : 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 							A_					(   snake_case__					,				snake_case__		)				->     List[Any]:
			_UpperCamelCase				:Any				    =  from_model.state_dict()
			_UpperCamelCase				:List[Any]				    =  our_model.state_dict()
			_UpperCamelCase				:Any				    =  our_model.config
			_UpperCamelCase				:Union[str, Any]				    =  []
			for stage_idx in range(len(config.hidden_sizes		)		):
						for block_id in range(config.depths[stage_idx]		):
									_UpperCamelCase				:Dict				    =  f"block{stage_idx + 1}.{block_id}.layer_scale_1"
									_UpperCamelCase				:Tuple				    =  f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

									all_keys.append((from_key, to_key)		)
									_UpperCamelCase				:int				    =  f"block{stage_idx + 1}.{block_id}.layer_scale_2"
									_UpperCamelCase				:Optional[Any]				    =  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:
						_UpperCamelCase				:Optional[int]				    =  from_state_dict.pop(lowercase_		)

			our_model.load_state_dict(lowercase_		)
			return our_model





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				snake_case__					,				snake_case__					,				snake_case__ = True					,				)				->     Dict:
			print(f"Downloading weights for {name}..."		)
			_UpperCamelCase				:Optional[Any]				    =  cached_download(lowercase_		)
			print(f"Converting {name}..."		)
			_UpperCamelCase				:Union[str, Any]				    =  torch.load(lowercase_		)['''state_dict''']
			from_model.load_state_dict(lowercase_		)
			from_model.eval()
			with torch.no_grad():
						_UpperCamelCase				:List[str]				    =  VanForImageClassification(lowercase_		).eval()
						_UpperCamelCase				:Dict				    =  ModuleTransfer(src=lowercase_					,				dest=lowercase_		)
						_UpperCamelCase				:str				    =  torch.randn((1, 3, 2_24, 2_24)		)
						module_transfer(lowercase_		)
						_UpperCamelCase				:Any				    =  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.'''		)

			_UpperCamelCase				:List[Any]				    =  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
						_UpperCamelCase				:Any				    =  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_					(   snake_case__					,				snake_case__ = None					,				snake_case__ = True		)				->     Optional[int]:
			_UpperCamelCase				:Optional[Any]				    =  '''imagenet-1k-id2label.json'''
			_UpperCamelCase				:Optional[Any]				    =  10_00

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

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

			_UpperCamelCase				:int				    =  partial(lowercase_					,				num_labels=lowercase_					,				idalabel=lowercase_					,				labelaid=lowercase_		)

			_UpperCamelCase				:Dict				    =  {
			    '''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]					,				),
			}

			_UpperCamelCase				:Optional[Any]				    =  {
			    '''van-tiny''': van_tiny,
			    '''van-small''': van_small,
			    '''van-base''': van_base,
			    '''van-large''': van_large,
			}

			_UpperCamelCase				:Optional[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(
						    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__":
					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__							:str       =  parser.parse_args()
					UpperCamelCase__							:Path       =  args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					UpperCamelCase__							: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)







"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
					from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
					raise ImportError("""You need to install the version of fairseq from the Mega repo!""")






class     A(     nn.Module		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase				:List[str]				    =  mega_args
					_UpperCamelCase				:Dict				    =  nn.Embedding(SCREAMING_SNAKE_CASE__				,							self.mega_args.encoder_embed_dim      )
					_UpperCamelCase				:Optional[Any]				    =  nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(SCREAMING_SNAKE_CASE__      )]      )
					_UpperCamelCase				:Tuple				    =  depth





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=0      )			->  Dict:

					"""simple docstring"""



					if batch_first:
								_UpperCamelCase				:Tuple				    =  input_ids.T

					# to make things more confusing, Mega expects the attention mask to
					# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
					# which is the opposite of what HF returns
					if ignore_mask_value == 0:
								_UpperCamelCase				:str				    =  1 - attention_mask

					# get token embeddings from IDs
					_UpperCamelCase				:Union[str, Any]				    =  self.embedding_layer(SCREAMING_SNAKE_CASE__      )

					# pass through the Mega layers
					# input is (time, batch, encoder dim) and output is the same
					for encoder in self.encoders:
								_UpperCamelCase				:Union[str, Any]				    =  encoder(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					# return according to the shape specified
					if batch_first:
								# (T, B, H) --> (B, T, H)
								return torch.transpose(SCREAMING_SNAKE_CASE__				,							0				,							1      )
					else:
								return embeds






class     A(     nn.Module		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Any:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase				:Dict				    =  MegaLM(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:str				    =  nn.Linear(mega_args.encoder_embed_dim				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  nn.Dropout(p=0.1      )





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=0      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.mega(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					return self.mlm_head(self.dropout(SCREAMING_SNAKE_CASE__      )      )





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__		)				->     Union[str, Any]:
			with open(os.path.join(snake_case__					,				'''model_args.pkl'''		)					,				'''rb'''		) as f:
						_UpperCamelCase				:List[str]				    =  pkl.load(snake_case__		)

			# load the original encoder
			_UpperCamelCase				:Tuple				    =  OriginalMegaForMaskedLM(**snake_case__		).eval()

			# load its weights
			print(
			    '''Original Mega encoder:'''					,				original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''encoder_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)
			print(
			    '''Original Mega MLM layer:'''					,				original_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''mlm_head_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)

			# create a new config from the old one
			_UpperCamelCase				:Optional[int]				    =  MegaConfig(
			    num_hidden_layers=mega_original_args['''depth''']					,				vocab_size=mega_original_args['''vocab_size''']					,				hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim					,				shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim					,				intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim					,				ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim					,				dropout_prob=mega_original_args['''mega_args'''].dropout					,				attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout					,				hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout					,				activation=mega_original_args['''mega_args'''].activation_fn					,				attention_activation=mega_original_args['''mega_args'''].attention_activation_fn					,				bidirectional=mega_original_args['''mega_args'''].bidirectional					,				use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0					,				chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size					,				truncation=mega_original_args['''mega_args'''].truncation_length					,				normalization_type=mega_original_args['''mega_args'''].normalization_type					,				normalize_before_mega=snake_case__					,				norm_affine=snake_case__					,				use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout					,				relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias					,				max_positions=mega_original_args['''mega_args'''].max_source_positions					,				nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim					,				normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before					,				nffn_activation_dropout_prob=0.0					,				add_token_type_embeddings=snake_case__					,				add_lm_hidden_dense_layer=snake_case__					,				)

			_UpperCamelCase				:Optional[Any]				    =  MegaForMaskedLM(snake_case__		).eval()

			# the originl checkpoint just uses nn.Embedding for the word embeddings
			# we use a wrapper module for embeddings to add support for positional embeddings
			_UpperCamelCase				:Dict				    =  original_mlm.mega.embedding_layer.weight

			# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
			# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
			# also renaming previously confusing parameter names
			_UpperCamelCase				:Union[str, Any]				    =  original_mlm.mega.encoders.state_dict()
			_UpperCamelCase				:List[str]				    =  {}
			for module_name in original_state_dict.keys():
						_UpperCamelCase				:Any				    =  None
						# have to handle gamma, beta, and alpha differently due to their use
						# in multiple modules within the original repository;
						# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
						# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
						if "beta" in module_name:
									# EMA sub-layers were always called "move" in the original repo
									if "move.beta" in module_name:
												_UpperCamelCase				:List[Any]				    =  module_name.replace('''move.beta'''					,				'''ema_gate.ema_expansion_matrix'''		)
									elif "mega_layer.beta" in module_name:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''beta'''					,				'''qk_bias'''		)
									else:
												_UpperCamelCase				:Union[str, Any]				    =  module_name.replace('''beta'''					,				'''b_param'''		)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
						elif "gamma" in module_name:
									if "move.gamma" in module_name:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''move.gamma'''					,				'''ema_gate.kernel_projection_matrix'''		)
									elif "mega_layer.gamma" in module_name:
												_UpperCamelCase				:str				    =  module_name.replace('''gamma'''					,				'''qk_weight'''		)
									else:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''gamma'''					,				'''g_param'''		)
        # alpha is used in EMA and positional bias; renaming to improve readability
						elif "move.alpha" in module_name:
									_UpperCamelCase				:Optional[Any]				    =  module_name.replace('''move.alpha'''					,				'''ema_gate.decay_factor'''		)
						# delta is only used in EMA; renaming to improve readability
						elif "move.delta" in module_name:
									_UpperCamelCase				:Union[str, Any]				    =  module_name.replace('''move.delta'''					,				'''ema_gate.damping_factor'''		)
						# omega is only used in EMA; renaming to improve readability
						elif "omega" in module_name:
									_UpperCamelCase				:int				    =  module_name.replace('''move.omega'''					,				'''ema_gate.residual_weight'''		)

						if new_module_name:
									_UpperCamelCase				:List[Any]				    =  new_module_name

			if len(snake_case__		) != 0:
						print(f"Renaming these keys: {updated_keys.keys()}"		)
			else:
						print('''No need to rename state dict entries'''		)
			for old, new in updated_keys.items():
						_UpperCamelCase				:List[str]				    =  original_state_dict.pop(snake_case__		)

			# now attempt to load the state dictionary with updated names
			# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
			print('''HF Mega encoder:'''					,				hf_mlm.mega.layers.load_state_dict(snake_case__		)		)

			# load the MLM head weights directly
			print(
			    '''HF Mega MLM layer:'''					,				hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''mlm_head_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)

			# test on a randomly generated input sequence
			_UpperCamelCase				:Dict				    =  torch.randint(0					,				hf_config.vocab_size					,				size=(4, 2_56)		)
			_UpperCamelCase				:Tuple				    =  torch.ones_like(snake_case__		)
			# mask a few tokens to make sure masking is applied appropriately :)
			_UpperCamelCase				:str				    =  0

			# run forward passes
			_UpperCamelCase				:str				    =  original_mlm(snake_case__					,				snake_case__					,				batch_first=snake_case__					,				ignore_mask_value=0		)
			_UpperCamelCase				:str				    =  hf_mlm(snake_case__					,				snake_case__		)[0]

			# print shapes and diff
			print(f"original output {original_output.shape}"		)
			print(f"hf output {hf_output.shape}"		)
			print(f"max diff: {(original_output - hf_output).max()}"		)  # 0.0
			_UpperCamelCase				:str				    =  torch.allclose(snake_case__					,				snake_case__					,				atol=1E-3		)

			if success:
						print('''Yay!'''		)
						hf_mlm.save_pretrained(snake_case__		)
			else:
						raise RuntimeError(f"Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"		)

			if includes_tokenizer:
						print('''Transferring tokenizer'''		)
						_UpperCamelCase				:Optional[Any]				    =  AutoTokenizer.from_pretrained(snake_case__		)
						tokenizer.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__							:Optional[int]       =  argparse.ArgumentParser()

					parser.add_argument(
					    """--pretrained_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Point to the directory containing your model weights using the official Mega repo""",
					)

					parser.add_argument(
					    """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
					)

					parser.add_argument(
					    """--includes_tokenizer""",
					    action="""store_true""",
					    help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
					)

					UpperCamelCase__							:Dict       =  parser.parse_args()

					convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)

