'''simple docstring'''





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def 						__A							(     UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Tuple   			=						[
						    "decoder.version",
						    "decoder.output_projection.weight",
						    "_float_tensor",
						    "decoder.embed_positions._float_tensor",
						]
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase     ,UpperCAmelCase					)
def 						__A							(     UpperCAmelCase					)     ->			Union[str, Any]:




						'''simple docstring'''
						_UpperCamelCase			,       _UpperCamelCase			:			Dict   			=						emb.weight.shape
						_UpperCamelCase			:			Optional[int]   			=						nn.Linear(UpperCAmelCase     ,UpperCAmelCase     ,bias=UpperCAmelCase					)
						_UpperCamelCase			:			List[Any]   			=						emb.weight.data
						return lin_layer
def 						__A							(     UpperCAmelCase					)     ->			Union[str, Any]:




						'''simple docstring'''
						_UpperCamelCase			:			Union[str, Any]   			=						torch.load(UpperCAmelCase     ,map_location="cpu"					)
						_UpperCamelCase			:			List[str]   			=						Namespace(**checkpoint["cfg"]["model"]					)
						_UpperCamelCase			:			List[str]   			=						checkpoint["model"]
						remove_ignore_keys_(UpperCAmelCase					)
						_UpperCamelCase			:			Optional[int]   			=						state_dict["decoder.embed_tokens.weight"].shape[0]

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

						_UpperCamelCase			:			Any   			=						XGLMConfig(
						    vocab_size=UpperCAmelCase     ,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			:			Optional[Any]   			=						XGLMForCausalLM(UpperCAmelCase					)
						_UpperCamelCase			:			str   			=						model.load_state_dict(UpperCAmelCase     ,strict=UpperCAmelCase					)
						print(UpperCAmelCase					)
						_UpperCamelCase			:			Optional[int]   			=						make_linear_from_emb(model.model.embed_tokens					)

						return model


if __name__ == "__main__":
			lowerCAmelCase_	:		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_	:		Union[str, Any]			=     parser.parse_args()
			lowerCAmelCase_	:		Union[str, Any]			=     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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_	:		Union[str, Any]			=     {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Tuple			=     [
			    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """OPTForCausalLM""",
			    """OPTModel""",
			    """OPTPreTrainedModel""",
			    """OPTForSequenceClassification""",
			    """OPTForQuestionAnswering""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		str			=     ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		List[Any]			=     [
			    """FlaxOPTForCausalLM""",
			    """FlaxOPTModel""",
			    """FlaxOPTPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_opt import (
						    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OPTForCausalLM,
						    OPTForQuestionAnswering,
						    OPTForSequenceClassification,
						    OPTModel,
						    OPTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
			import sys

			lowerCAmelCase_	:		Any			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


