



"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__snake_case							:     Tuple   =					logging.get_logger(__name__)  # pylint: disable=invalid-name



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def __init__(  self:	Any ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	int=768)       -> str:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= proj_size

				__lowerCAmelCase   :						Tuple				= CLIPVisionModel(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= PaintByExampleMapper(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Tuple				= nn.LayerNorm(config.hidden_size)
				__lowerCAmelCase   :						Optional[int]				= nn.Linear(config.hidden_size ,    self.proj_size)

				# uncondition for scaling
				__lowerCAmelCase   :						str				= nn.Parameter(torch.randn((1, 1, self.proj_size)))







	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	List[Any]=False)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.model(pixel_values=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= clip_output.pooler_output
				__lowerCAmelCase   :						List[str]				= self.mapper(latent_states[:, None])
				__lowerCAmelCase   :						Optional[int]				= self.final_layer_norm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= self.proj_out(_SCREAMING_SNAKE_CASE)
				if return_uncond_vector:
							return latent_states, self.uncond_vector

				return latent_states






class     A__     (						nn.Module							):




	'''simple docstring'''





	def __init__(  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> str:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase   :						Any				= (config.num_hidden_layers + 1) // 5
				__lowerCAmelCase   :						List[str]				= config.hidden_size
				__lowerCAmelCase   :						Optional[Any]				= 1
				__lowerCAmelCase   :						List[Any]				= nn.ModuleList(
				    [
				        BasicTransformerBlock(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    activation_fn="gelu" ,    attention_bias=_SCREAMING_SNAKE_CASE)
				        for _ in range(_SCREAMING_SNAKE_CASE)
				    ])







	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> Union[str, Any]:

				"""simple docstring"""

				for block in self.blocks:
							__lowerCAmelCase   :						Any				= block(_SCREAMING_SNAKE_CASE)

				return hidden_states




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,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   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


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