




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Dict    =    [
				    'decoder.version',
				    'decoder.output_projection.weight',
				    '_float_tensor',
				    'decoder.embed_positions._float_tensor',
				]
				for k in ignore_keys:
								state_dict.pop(lowercase__							,    lowercase__		)







def      _snake_case			(				lowercase__		):
				_lowerCamelCase, _lowerCamelCase     :			List[str]    =    emb.weight.shape
				_lowerCamelCase     :			Union[str, Any]    =    nn.Linear(lowercase__							,    lowercase__							,    bias=lowercase__		)
				_lowerCamelCase     :			List[str]    =    emb.weight.data
				return lin_layer







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			List[str]    =    torch.load(lowercase__							,    map_location='cpu'		)
				_lowerCamelCase     :			Tuple    =    Namespace(**checkpoint['cfg']['model']		)
				_lowerCamelCase     :			Optional[int]    =    checkpoint['model']
				remove_ignore_keys_(lowercase__		)
				_lowerCamelCase     :			int    =    state_dict['decoder.embed_tokens.weight'].shape[0]

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

				_lowerCamelCase     :			Tuple    =    XGLMConfig(
				    vocab_size=lowercase__							,    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(lowercase__		)
				_lowerCamelCase     :			Optional[Any]    =    model.load_state_dict(lowercase__							,    strict=lowercase__		)
				print(lowercase__		)
				_lowerCamelCase     :			Union[str, Any]    =    make_linear_from_emb(model.model.embed_tokens		)

				return model


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





"""simple docstring"""






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

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


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






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

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






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states