
"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[Any]     =     [
							    """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		):
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     emb.weight.shape
							SCREAMING_SNAKE_CASE__		: int     =     nn.Linear(_snake_case     ,_snake_case     ,bias=_snake_case		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     emb.weight.data
							return lin_layer

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     torch.load(_snake_case     ,map_location="""cpu"""		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Namespace(**checkpoint["""cfg"""]["""model"""]		)
							SCREAMING_SNAKE_CASE__		: Any     =     checkpoint["""model"""]
							remove_ignore_keys_(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

							SCREAMING_SNAKE_CASE__		: Union[str, 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     ,)

							SCREAMING_SNAKE_CASE__		: Dict     =     XGLMForCausalLM(_snake_case		)
							SCREAMING_SNAKE_CASE__		: int     =     model.load_state_dict(_snake_case     ,strict=_snake_case		)
							print(_snake_case		)
							SCREAMING_SNAKE_CASE__		: str     =     make_linear_from_emb(model.model.embed_tokens		)

							return model


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






"""simple docstring"""




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__ = 16     , SCREAMING_SNAKE_CASE__ = 88     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 1     , SCREAMING_SNAKE_CASE__ = 0.0     , SCREAMING_SNAKE_CASE__ = 32     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = "geglu"     , SCREAMING_SNAKE_CASE__ = None     , )      ->	Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     nn.ModuleList(
											    [
											        TransformeraDModel(
											            num_attention_heads=SCREAMING_SNAKE_CASE__     , attention_head_dim=SCREAMING_SNAKE_CASE__     , in_channels=SCREAMING_SNAKE_CASE__     , num_layers=SCREAMING_SNAKE_CASE__     , dropout=SCREAMING_SNAKE_CASE__     , norm_num_groups=SCREAMING_SNAKE_CASE__     , cross_attention_dim=SCREAMING_SNAKE_CASE__     , attention_bias=SCREAMING_SNAKE_CASE__     , sample_size=SCREAMING_SNAKE_CASE__     , num_vector_embeds=SCREAMING_SNAKE_CASE__     , activation_fn=SCREAMING_SNAKE_CASE__     , num_embeds_ada_norm=SCREAMING_SNAKE_CASE__     , )
											        for _ in range(2					)
											    ]					)

											# Variables that can be set by a pipeline:

											# The ratio of transformer1 to transformer2's output states to be combined during inference
											SCREAMING_SNAKE_CASE__		: Tuple     =     0.5

											# The shape of `encoder_hidden_states` is expected to be
											# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [77, 2_57]

											# Which transformer to use to encode which condition.
											# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
											SCREAMING_SNAKE_CASE__		: int     =     [1, 0]



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     hidden_states

											SCREAMING_SNAKE_CASE__		: Tuple     =     []
											SCREAMING_SNAKE_CASE__		: List[str]     =     0
											# attention_mask is not used yet
											for i in range(2					):
																		# for each of the two transformers, pass the corresponding condition tokens
																		SCREAMING_SNAKE_CASE__		: str     =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																		SCREAMING_SNAKE_CASE__		: Any     =     self.transformer_index_for_condition[i]
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.transformers[transformer_index](
																		    SCREAMING_SNAKE_CASE__     , encoder_hidden_states=SCREAMING_SNAKE_CASE__     , timestep=SCREAMING_SNAKE_CASE__     , cross_attention_kwargs=SCREAMING_SNAKE_CASE__     , return_dict=SCREAMING_SNAKE_CASE__     , )[0]
																		encoded_states.append(encoded_state - input_states					)
																		tokens_start += self.condition_lengths[i]

											SCREAMING_SNAKE_CASE__		: Tuple     =     encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     output_states + input_states

											if not return_dict:
																		return (output_states,)

											return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE__					)




