

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 , lowercase , lowercase , lowercase    )					->  str:
							'''simple docstring'''






							super().__init__()
							A__            =     mega_args
							A__            =     nn.Embedding(lowercase , self.mega_args.encoder_embed_dim    )
							A__            =     nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(lowercase    )]    )
							A__            =     depth


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase=True , lowercase=0    )					->  Dict:
							'''simple docstring'''






							if batch_first:
											A__            =     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:
											A__            =     1 - attention_mask

							# get token embeddings from IDs
							A__            =     self.embedding_layer(lowercase    )

							# pass through the Mega layers
							# input is (time, batch, encoder dim) and output is the same
							for encoder in self.encoders:
											A__            =     encoder(lowercase , lowercase    )

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

class    a__  (   nn.Module     ):
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase , lowercase    )					->  Any:
							'''simple docstring'''






							super().__init__()
							A__            =     MegaLM(lowercase , lowercase , lowercase    )
							A__            =     nn.Linear(mega_args.encoder_embed_dim , lowercase    )
							A__            =     nn.Dropout(p=0.1    )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase=True , lowercase=0    )					->  Optional[int]:
							'''simple docstring'''






							A__            =     self.mega(lowercase , lowercase , lowercase , lowercase    )
							return self.mlm_head(self.dropout(lowercase    )    )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							str )     -> Optional[Any]:




				'''simple docstring'''





				with open(os.path.join(SCREAMING_SNAKE_CASE_		,    "model_args.pkl" )		,    "rb" ) as f:
								A__            =     pkl.load(SCREAMING_SNAKE_CASE_ )

				# load the original encoder
				A__            =     OriginalMegaForMaskedLM(**SCREAMING_SNAKE_CASE_ ).eval()

				# load its weights
				print(
				    "Original Mega encoder:"		,    original_mlm.mega.load_state_dict(
				        torch.load(os.path.join(SCREAMING_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(SCREAMING_SNAKE_CASE_		,    "mlm_head_weights.pt" )		,    map_location="cpu" ) )		,    )

				# create a new config from the old one
				A__            =     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=SCREAMING_SNAKE_CASE_		,    norm_affine=SCREAMING_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=SCREAMING_SNAKE_CASE_		,    add_lm_hidden_dense_layer=SCREAMING_SNAKE_CASE_		,    )

				A__            =     MegaForMaskedLM(SCREAMING_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
				A__            =     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
				A__            =     original_mlm.mega.encoders.state_dict()
				A__            =     {}
				for module_name in original_state_dict.keys():
								A__            =     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:
																A__            =     module_name.replace("move.beta"		,    "ema_gate.ema_expansion_matrix" )
												elif "mega_layer.beta" in module_name:
																A__            =     module_name.replace("beta"		,    "qk_bias" )
												else:
																A__            =     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:
																A__            =     module_name.replace("move.gamma"		,    "ema_gate.kernel_projection_matrix" )
												elif "mega_layer.gamma" in module_name:
																A__            =     module_name.replace("gamma"		,    "qk_weight" )
												else:
																A__            =     module_name.replace("gamma"		,    "g_param" )
        # alpha is used in EMA and positional bias; renaming to improve readability
								elif "move.alpha" in module_name:
												A__            =     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:
												A__            =     module_name.replace("move.delta"		,    "ema_gate.damping_factor" )
								# omega is only used in EMA; renaming to improve readability
								elif "omega" in module_name:
												A__            =     module_name.replace("move.omega"		,    "ema_gate.residual_weight" )

								if new_module_name:
												A__            =     new_module_name

				if len(SCREAMING_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():
								A__            =     original_state_dict.pop(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE_		,    "mlm_head_weights.pt" )		,    map_location="cpu" ) )		,    )

				# test on a randomly generated input sequence
				A__            =     torch.randint(0		,    hf_config.vocab_size		,    size=(4, 2_5_6) )
				A__            =     torch.ones_like(SCREAMING_SNAKE_CASE_ )
				# mask a few tokens to make sure masking is applied appropriately :)
				A__            =     0

				# run forward passes
				A__            =     original_mlm(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    batch_first=SCREAMING_SNAKE_CASE_		,    ignore_mask_value=0 )
				A__            =     hf_mlm(SCREAMING_SNAKE_CASE_		,    SCREAMING_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
				A__            =     torch.allclose(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    atol=1e-3 )

				if success:
								print("Yay!" )
								hf_mlm.save_pretrained(SCREAMING_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" )
								A__            =     AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
								tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
	lowerCAmelCase__						=	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""",
	)

	lowerCAmelCase__						=	parser.parse_args()

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




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							A__            =     num_channels
							A__            =     depths
							A__            =     embed_dims
							A__            =     mlp_ratio
							A__            =     downsamples
							A__            =     hidden_act
							A__            =     down_patch_size
							A__            =     down_stride
							A__            =     down_pad
							A__            =     drop_path_rate
							A__            =     use_layer_scale
							A__            =     layer_scale_init_value
							A__            =     batch_norm_eps

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

