
'''simple docstring'''



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def A				(__lowerCamelCase				:List[Any]		):
						_lowerCAmelCase  						=			[
						    "decoder.version",
						    "decoder.output_projection.weight",
						    "_float_tensor",
						    "decoder.embed_positions._float_tensor",
						]
						for k in ignore_keys:
												state_dict.pop(_lowercase			,     _lowercase		)


def A				(__lowerCamelCase				:Union[str, Any]		):
						_lowerCAmelCase  						=			emb.weight.shape
						_lowerCAmelCase  						=			nn.Linear(_lowercase			,     _lowercase			,     bias=_lowercase		)
						_lowerCAmelCase  						=			emb.weight.data
						return lin_layer


def A				(__lowerCamelCase				:Tuple		):
						_lowerCAmelCase  						=			torch.load(_lowercase			,     map_location="""cpu"""		)
						_lowerCAmelCase  						=			Namespace(**checkpoint["""cfg"""]["""model"""]		)
						_lowerCAmelCase  						=			checkpoint["model"]
						remove_ignore_keys_(_lowercase		)
						_lowerCAmelCase  						=			state_dict["decoder.embed_tokens.weight"].shape[0]

						_lowerCAmelCase  						=			{key.replace("""decoder"""			,     """model"""		): val for key, val in state_dict.items()}

						_lowerCAmelCase  						=			XGLMConfig(
						    vocab_size=_lowercase			,     max_position_embeddings=args.max_target_positions			,     num_layers=args.decoder_layers			,     attention_heads=args.decoder_attention_heads			,     ffn_dim=args.decoder_ffn_embed_dim			,     d_model=args.decoder_embed_dim			,     layerdrop=args.decoder_layerdrop			,     dropout=args.dropout			,     attention_dropout=args.attention_dropout			,     activation_dropout=args.activation_dropout			,     activation_function="""gelu"""			,     scale_embedding=not args.no_scale_embedding			,     tie_word_embeddings=args.share_decoder_input_output_embed			,     )

						_lowerCAmelCase  						=			XGLMForCausalLM(_lowercase		)
						_lowerCAmelCase  						=			model.load_state_dict(_lowercase			,     strict=_lowercase		)
						print(_lowercase		)
						_lowerCAmelCase  						=			make_linear_from_emb(model.model.embed_tokens		)

						return model


if __name__ == "__main__":
						_lowercase    =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						_lowercase    =		parser.parse_args()
						_lowercase    =		convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


_lowercase    =		logging.get_logger(__name__)

_lowercase    =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[Any]									=       '''convbert'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=768   ,       _lowercase=12   ,       _lowercase=12   ,       _lowercase=3_072   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=2   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=1   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=768   ,       _lowercase=2   ,       _lowercase=9   ,       _lowercase=1   ,       _lowercase=None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(
												    pad_token_id=_lowercase   ,       bos_token_id=_lowercase   ,       eos_token_id=_lowercase   ,       **_lowercase   ,       )

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			type_vocab_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			embedding_size
												_lowerCAmelCase  						=			head_ratio
												_lowerCAmelCase  						=			conv_kernel_size
												_lowerCAmelCase  						=			num_groups
												_lowerCAmelCase  						=			classifier_dropout






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												if self.task == "multiple-choice":
																		_lowerCAmelCase  						=			{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																		_lowerCAmelCase  						=			{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												        ("""token_type_ids""", dynamic_axis),
												    ]				)
