






'''simple docstring'''







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			List[str]:
	_snake_case     		=	[
	    "decoder.version",
	    "decoder.output_projection.weight",
	    "_float_tensor",
	    "decoder.embed_positions._float_tensor",
	]
	for k in ignore_keys:
		state_dict.pop(_lowerCamelCase    ,       _lowerCamelCase			)







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			List[Any]:
	_snake_case     		=	emb.weight.shape
	_snake_case     		=	nn.Linear(_lowerCamelCase    ,       _lowerCamelCase    ,       bias=_lowerCamelCase			)
	_snake_case     		=	emb.weight.data
	return lin_layer







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Any:
	_snake_case     		=	torch.load(_lowerCamelCase    ,       map_location='cpu'			)
	_snake_case     		=	Namespace(**checkpoint['cfg']['model']			)
	_snake_case     		=	checkpoint["model"]
	remove_ignore_keys_(_lowerCamelCase			)
	_snake_case     		=	state_dict["decoder.embed_tokens.weight"].shape[0]

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

	_snake_case     		=	XGLMConfig(
	    vocab_size=_lowerCamelCase    ,       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    ,       )

	_snake_case     		=	XGLMForCausalLM(_lowerCamelCase			)
	_snake_case     		=	model.load_state_dict(_lowerCamelCase    ,       strict=_lowerCamelCase			)
	print(_lowerCamelCase			)
	_snake_case     		=	make_linear_from_emb(model.model.embed_tokens			)

	return model


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



'''simple docstring'''







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel

