
'''simple docstring'''
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def 		A_(     A						:		Any):
							UpperCamelCase   			=     [
							    'decoder.version',
							    'decoder.output_projection.weight',
							    '_float_tensor',
							    'decoder.embed_positions._float_tensor',
							]
							for k in ignore_keys:
														state_dict.pop(A		,     A)







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







def 		A_(     A						:		Optional[int]):
							UpperCamelCase   			=     torch.load(A		,     map_location='cpu')
							UpperCamelCase   			=     Namespace(**checkpoint['cfg']['model'])
							UpperCamelCase   			=     checkpoint['model']
							remove_ignore_keys_(A)
							UpperCamelCase   			=     state_dict['decoder.embed_tokens.weight'].shape[0]

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

							UpperCamelCase   			=     XGLMConfig(
							    vocab_size=A		,     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		,     )

							UpperCamelCase   			=     XGLMForCausalLM(A)
							UpperCamelCase   			=     model.load_state_dict(A		,     strict=A)
							print(A)
							UpperCamelCase   			=     make_linear_from_emb(model.model.embed_tokens)

							return model


if __name__ == "__main__":
						lowerCAmelCase					:    Optional[Any]           = 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.')
						lowerCAmelCase					:    str           = parser.parse_args()
						lowerCAmelCase					:    Dict           = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase					:    Optional[Any]           = {
    'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    str           = [
						    'FALCON_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FalconForCausalLM',
						    'FalconModel',
						    'FalconPreTrainedModel',
						    'FalconForSequenceClassification',
						    'FalconForTokenClassification',
						    'FalconForQuestionAnswering',
						]


if TYPE_CHECKING:
						from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_falcon import (
												    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FalconForCausalLM,
												    FalconForQuestionAnswering,
												    FalconForSequenceClassification,
												    FalconForTokenClassification,
												    FalconModel,
												    FalconPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




