





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     int     =		[
	    'decoder.version',
	    'decoder.output_projection.weight',
	    '_float_tensor',
	    'decoder.embed_positions._float_tensor',
	]
	for k in ignore_keys:
		state_dict.pop(__a				,	__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_  ,snake_case_						:     Optional[Any]     =		emb.weight.shape
	snake_case_						:     str     =		nn.Linear(__a				,	__a				,	bias=__a				)
	snake_case_						:     List[str]     =		emb.weight.data
	return lin_layer


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Dict     =		torch.load(__a				,	map_location='cpu'				)
	snake_case_						:     Optional[Any]     =		Namespace(**checkpoint['cfg']['model']				)
	snake_case_						:     List[Any]     =		checkpoint['model']
	remove_ignore_keys_(__a				)
	snake_case_						:     Dict     =		state_dict['decoder.embed_tokens.weight'].shape[0]

	snake_case_						:     Union[str, Any]     =		{key.replace('decoder'				,	'model'				): val for key, val in state_dict.items()}

	snake_case_						:     List[str]     =		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				,	)

	snake_case_						:     Tuple     =		XGLMForCausalLM(__a				)
	snake_case_						:     Optional[Any]     =		model.load_state_dict(__a				,	strict=__a				)
	print(__a				)
	snake_case_						:     int     =		make_linear_from_emb(model.model.embed_tokens				)

	return model


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						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.""")
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						_SCREAMING_SNAKE_CASE 		=						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_   ):






						@register_to_config
						def __init__( self : Optional[Any]						,				*,
						    _A : int = 4						,				_A : int = 768						,				_A : int						,				_A : Tuple						,				)							->      Dict:







							"""simple docstring"""


							super().__init__()

							snake_case_						:     int     =		nn.Parameter(torch.zeros(_A		)		)

							# parameters for additional clip time embeddings
							snake_case_						:     Tuple     =		nn.Linear(_A						,				_A		)
							snake_case_						:     List[Any]     =		nn.Linear(_A						,				_A		)

							# parameters for encoder hidden states
							snake_case_						:     Union[str, Any]     =		clip_extra_context_tokens
							snake_case_						:     str     =		nn.Linear(
							    _A						,				self.clip_extra_context_tokens * cross_attention_dim		)
							snake_case_						:     Any     =		nn.Linear(_A						,				_A		)
							snake_case_						:     Tuple     =		nn.LayerNorm(_A		)






						def UpperCAmelCase_			( self : List[str]						,				*, _A : Tuple						,				_A : List[Any]						,				_A : str						,				_A : Optional[Any]		)							->      List[Any]:







							"""simple docstring"""


							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								snake_case_						:     Optional[int]     =		image_embeddings.shape[0]
								snake_case_						:     Optional[Any]     =		self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
								snake_case_						:     Optional[Any]     =		classifier_free_guidance_embeddings.expand(
								    _A						,				-1		)
								snake_case_						:     Any     =		torch.cat([classifier_free_guidance_embeddings, image_embeddings]						,				dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							snake_case_						:     str     =		prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							snake_case_						:     str     =		self.embedding_proj(_A		)
							snake_case_						:     Dict     =		self.clip_image_embeddings_project_to_time_embeddings(_A		)
							snake_case_						:     Tuple     =		time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							snake_case_						:     List[str]     =		self.clip_extra_context_tokens_proj(_A		)
							snake_case_						:     Optional[Any]     =		clip_extra_context_tokens.reshape(_A						,				-1						,				self.clip_extra_context_tokens		)
							snake_case_						:     int     =		clip_extra_context_tokens.permute(0						,				2						,				1		)

							snake_case_						:     Optional[int]     =		self.encoder_hidden_states_proj(_A		)
							snake_case_						:     Any     =		self.text_encoder_hidden_states_norm(_A		)
							snake_case_						:     Dict     =		torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]						,				dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings

