



from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 		lowercase						(			_lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase):






	"""simple docstring"""


	a__				: Optional[int]		       =							[r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]




	@register_to_config
	def __init__(     self     :						Tuple						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Optional[int] = None						,  __UpperCAmelCase     :						int = 50_257						,  __UpperCAmelCase     :						int = 1_024						,  __UpperCAmelCase     :						int = 768						,  __UpperCAmelCase     :						int = 12						,  __UpperCAmelCase     :						int = 12						,  __UpperCAmelCase     :						Optional[int] = None						,  __UpperCAmelCase     :						str = "gelu_new"						,  __UpperCAmelCase     :						float = 0.1						,  __UpperCAmelCase     :						float = 0.1						,  __UpperCAmelCase     :						float = 0.1						,  __UpperCAmelCase     :						float = 1E-5						,  __UpperCAmelCase     :						float = 0.02						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						bool = False						,  )      ->			List[str]:
							super().__init__()

							UpperCAmelCase_=       prefix_length

							if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
													raise ValueError(
													    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
													    F""" `n_embd`: {n_embd} are not equal."""					)

							UpperCAmelCase_=       prefix_inner_dim
							UpperCAmelCase_=       prefix_hidden_dim

							UpperCAmelCase_=       (
							    nn.Linear(self.prefix_inner_dim						,  self.prefix_hidden_dim					)
							    if self.prefix_hidden_dim is not None
							    else nn.Identity()
							)
							UpperCAmelCase_=       (
							    nn.Linear(self.prefix_hidden_dim						,  _lowercase					) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							UpperCAmelCase_=       GPTaConfig(
							    vocab_size=_lowercase						,  n_positions=_lowercase						,  n_embd=_lowercase						,  n_layer=_lowercase						,  n_head=_lowercase						,  n_inner=_lowercase						,  activation_function=_lowercase						,  resid_pdrop=_lowercase						,  embd_pdrop=_lowercase						,  attn_pdrop=_lowercase						,  layer_norm_epsilon=_lowercase						,  initializer_range=_lowercase						,  scale_attn_weights=_lowercase						,  use_cache=_lowercase						,  scale_attn_by_inverse_layer_idx=_lowercase						,  reorder_and_upcast_attn=_lowercase						,  )
							UpperCAmelCase_=       GPTaLMHeadModel(_lowercase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						Optional[torch.Tensor] = None						,  __UpperCAmelCase     :						Optional[torch.Tensor] = None						,  )      ->			Tuple:
							UpperCAmelCase_=       self.transformer.transformer.wte(_lowercase					)
							UpperCAmelCase_=       self.encode_prefix(_lowercase					)
							UpperCAmelCase_=       self.decode_prefix(_lowercase					)
							UpperCAmelCase_=       torch.cat((prefix_embeds, embedding_text)						,  dim=1					)

							if labels is not None:
													UpperCAmelCase_=       self.get_dummy_token(input_ids.shape[0]						,  input_ids.device					)
													UpperCAmelCase_=       torch.cat((dummy_token, input_ids)						,  dim=1					)
							UpperCAmelCase_=       self.transformer(inputs_embeds=_lowercase						,  labels=_lowercase						,  attention_mask=_lowercase					)
							if self.prefix_hidden_dim is not None:
													return out, hidden
							else:
													return out




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						torch.device					)      ->			List[Any]:
							return torch.zeros(_lowercase						,  self.prefix_length						,  dtype=torch.intaa						,  device=_lowercase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						List[str]					)      ->			Optional[Any]:
							return self.encode_prefix(_lowercase					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Any						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       torch.split(_lowercase						,  1						,  dim=0					)
							UpperCAmelCase_=       []
							UpperCAmelCase_=       []
							for feature in features:
													UpperCAmelCase_=       self.decode_prefix(feature.to(_lowercase					)					)  # back to the clip feature
													# Only support beam search for now
													UpperCAmelCase_,       UpperCAmelCase_=       self.generate_beam(
													    input_embeds=_lowercase						,  device=_lowercase						,  eos_token_id=_lowercase					)
													generated_tokens.append(output_tokens[0]					)
													generated_seq_lengths.append(seq_lengths[0]					)
							UpperCAmelCase_=       torch.stack(_lowercase					)
							UpperCAmelCase_=       torch.stack(_lowercase					)
							return generated_tokens, generated_seq_lengths




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						Any=None						,  __UpperCAmelCase     :						Dict=None						,  __UpperCAmelCase     :						Tuple=None						,  __UpperCAmelCase     :						int = 5						,  __UpperCAmelCase     :						int = 67						,  __UpperCAmelCase     :						float = 1.0						,  __UpperCAmelCase     :						Optional[int] = None						,  )      ->			Dict:
							UpperCAmelCase_=       eos_token_id
							UpperCAmelCase_=       None
							UpperCAmelCase_=       None
							UpperCAmelCase_=       torch.ones(_lowercase						,  device=_lowercase						,  dtype=torch.int					)
							UpperCAmelCase_=       torch.zeros(_lowercase						,  device=_lowercase						,  dtype=torch.bool					)

							if input_embeds is not None:
													UpperCAmelCase_=       input_embeds
							else:
													UpperCAmelCase_=       self.transformer.transformer.wte(_lowercase					)

							for i in range(_lowercase					):
													UpperCAmelCase_=       self.transformer(inputs_embeds=_lowercase					)
													UpperCAmelCase_=       outputs.logits
													UpperCAmelCase_=       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
													UpperCAmelCase_=       logits.softmax(-1					).log()

													if scores is None:
																			UpperCAmelCase_,       UpperCAmelCase_=       logits.topk(_lowercase						,  -1					)
																			UpperCAmelCase_=       generated.expand(_lowercase						,  *generated.shape[1:]					)
																			UpperCAmelCase_,       UpperCAmelCase_=       next_tokens.permute(1						,  0					), scores.squeeze(0					)
																			if tokens is None:
																									UpperCAmelCase_=       next_tokens
																			else:
																									UpperCAmelCase_=       tokens.expand(_lowercase						,  *tokens.shape[1:]					)
																									UpperCAmelCase_=       torch.cat((tokens, next_tokens)						,  dim=1					)
													else:
																			UpperCAmelCase_=       -float(np.inf					)
																			UpperCAmelCase_=       0
																			UpperCAmelCase_=       scores[:, None] + logits
																			seq_lengths[~is_stopped] += 1
																			UpperCAmelCase_=       scores_sum / seq_lengths[:, None]
																			UpperCAmelCase_,       UpperCAmelCase_=       scores_sum_average.view(-1					).topk(_lowercase						,  -1					)
																			UpperCAmelCase_=       next_tokens // scores_sum.shape[1]
																			UpperCAmelCase_=       seq_lengths[next_tokens_source]
																			UpperCAmelCase_=       next_tokens % scores_sum.shape[1]
																			UpperCAmelCase_=       next_tokens.unsqueeze(1					)
																			UpperCAmelCase_=       tokens[next_tokens_source]
																			UpperCAmelCase_=       torch.cat((tokens, next_tokens)						,  dim=1					)
																			UpperCAmelCase_=       generated[next_tokens_source]
																			UpperCAmelCase_=       scores_sum_average * seq_lengths
																			UpperCAmelCase_=       is_stopped[next_tokens_source]

													UpperCAmelCase_=       self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0]						,  1						,  -1					)
													UpperCAmelCase_=       torch.cat((generated, next_token_embed)						,  dim=1					)
													UpperCAmelCase_=       is_stopped + next_tokens.eq(_lowercase					).squeeze()
													if is_stopped.all():
																			break

							UpperCAmelCase_=       scores / seq_lengths
							UpperCAmelCase_=       scores.argsort(descending=_lowercase					)
							# tokens tensors are already padded to max_seq_length
							UpperCAmelCase_=       [tokens[i] for i in order]
							UpperCAmelCase_=       torch.stack(_lowercase						,  dim=0					)
							UpperCAmelCase_=       torch.tensor([seq_lengths[i] for i in order]						,  dtype=seq_lengths.dtype					)
							return output_texts, seq_lengths









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       [
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						    """_float_tensor""",
						    """decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   lowerCAmelCase_       : Any    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : List[str]    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       Namespace(**checkpoint["""cfg"""]["""model"""]    )
						UpperCAmelCase_=       checkpoint["""model"""]
						remove_ignore_keys_(lowerCAmelCase_    )
						UpperCAmelCase_=       state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

						UpperCAmelCase_=       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      ,)

						UpperCAmelCase_=       XGLMForCausalLM(lowerCAmelCase_    )
						UpperCAmelCase_=       model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						print(lowerCAmelCase_    )
						UpperCAmelCase_=       make_linear_from_emb(model.model.embed_tokens    )

						return model


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




