






'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class 	SCREAMING_SNAKE_CASE					:
			snake_case__  =				field(
			    metadata={"help": "The output directory where the model will be written."}	,   )
			snake_case__  =				field(
			    metadata={
			        "help": (
			            "The encoder model checkpoint for weights initialization."
			            "Don't set if you want to train an encoder model from scratch."
			        )
			    }	,   )
			snake_case__  =				field(
			    metadata={
			        "help": (
			            "The decoder model checkpoint for weights initialization."
			            "Don't set if you want to train a decoder model from scratch."
			        )
			    }	,   )
			snake_case__  =				field(
			    default=__A	,   metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}       )
			snake_case__  =				field(
			    default=__A	,   metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}       )





def 		_UpperCAmelCase   (							):
				a_		:  int											=      HfArgumentParser((ModelArguments,)      )
				(a_       )		:  Optional[Any]											=      parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

				# Use explicit specified encoder config
				if model_args.encoder_config_name:
								a_		:  str											=      AutoConfig.from_pretrained(model_args.encoder_config_name      )
				# Use pretrained encoder model's config
				else:
								a_		:  Any											=      AutoConfig.from_pretrained(model_args.encoder_model_name_or_path      )

				# Use explicit specified decoder config
				if model_args.decoder_config_name:
								a_		:  Any											=      AutoConfig.from_pretrained(model_args.decoder_config_name      )
				# Use pretrained decoder model's config
				else:
								a_		:  Optional[int]											=      AutoConfig.from_pretrained(model_args.decoder_model_name_or_path      )

				# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
				a_		:  int											=      True
				a_		:  Union[str, Any]											=      True

				a_		:  Tuple											=      FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
				    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,		decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,		encoder_config=lowerCAmelCase__						,		decoder_config=lowerCAmelCase__						,		)

				# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
				a_		:  List[str]											=      decoder_config.decoder_start_token_id
				a_		:  Union[str, Any]											=      decoder_config.pad_token_id
				if decoder_start_token_id is None:
								a_		:  Optional[int]											=      decoder_config.bos_token_id
				if pad_token_id is None:
								a_		:  Dict											=      decoder_config.eos_token_id

				# This is necessary to make Flax's generate() work
				a_		:  int											=      decoder_config.eos_token_id
				a_		:  str											=      decoder_start_token_id
				a_		:  Optional[Any]											=      pad_token_id

				a_		:  str											=      AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path      )

				a_		:  List[str]											=      AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path      )
				a_		:  Any											=      tokenizer.convert_ids_to_tokens(model.config.pad_token_id      )

				model.save_pretrained(model_args.output_dir      )
				image_processor.save_pretrained(model_args.output_dir      )
				tokenizer.save_pretrained(model_args.output_dir      )


if __name__ == "__main__":
		main()












'''simple docstring'''







import torch

from transformers import AutoModel





class 	SCREAMING_SNAKE_CASE					(					torch.nn.Module       ):






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int="sayef/fsner-bert-base-uncased"					)			->			str:
							super(__SCREAMING_SNAKE_CASE  ,						self					).__init__()

							a_		:  str											=      AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      torch.nn.CosineSimilarity(3  ,						1e-08					)
							a_		:  Dict											=      torch.nn.Softmax(dim=1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						**__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							return self.bert(**__SCREAMING_SNAKE_CASE					).last_hidden_state






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Optional[int]:
							return token_embeddings.sum(2  ,						keepdim=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=1					)			->			Dict:
							return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Any:
							a_		:  Dict											=      W_supports['''sizes'''].tolist()
							a_		:  Tuple											=      W_supports['''start_token_id'''].item()
							a_		:  List[Any]											=      W_supports['''end_token_id'''].item()

							del W_supports["sizes"]
							del W_supports["start_token_id"]
							del W_supports["end_token_id"]

							a_		:  int											=      self.BERT(**__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      self.BERT(**__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      None
							a_		:  Tuple											=      None

							a_		:  List[str]											=      W_supports['''input_ids'''] == start_token_id
							a_		:  Dict											=      W_supports['''input_ids'''] == end_token_id

							for i, size in enumerate(__SCREAMING_SNAKE_CASE					):
											if i == 0:
															a_		:  str											=      0
											else:
															a_		:  str											=      support_sizes[i - 1]

											a_		:  Union[str, Any]											=      S[s : s + size][start_token_masks[s : s + size]]
											a_		:  Tuple											=      S[s : s + size][end_token_masks[s : s + size]]

											a_		:  Tuple											=      torch.matmul(q[i]  ,						s_start.T					).sum(1					).softmax(0					)
											a_		:  Optional[Any]											=      torch.matmul(q[i]  ,						s_end.T					).sum(1					).softmax(0					)

											if p_starts is not None:
															a_		:  Any											=      torch.vstack((p_starts, p_start)					)
															a_		:  Dict											=      torch.vstack((p_ends, p_end)					)
											else:
															a_		:  Optional[int]											=      p_start
															a_		:  List[Any]											=      p_end

							return p_starts, p_ends




