

'''simple docstring'''





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__snake_case	: Union[str, Any]       				=							logging.get_logger(__name__)

__snake_case	: Any       				=							{
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class        A  (	__A ):
						__UpperCAmelCase				:	List[str]   =   """instructblip_vision_model"""





						def __init__(	self	,    snake_case_=1_4_0_8	,    snake_case_=6_1_4_4	,    snake_case_=3_9	,    snake_case_=1_6	,    snake_case_=2_2_4	,    snake_case_=1_4	,    snake_case_="gelu"	,    snake_case_=1E-6	,    snake_case_=0.0	,    snake_case_=1E-1_0	,    snake_case_=True	,    **snake_case_	,    )			->							Optional[int]:
										super().__init__(**__A			)

										_a        =   hidden_size
										_a        =   intermediate_size
										_a        =   num_hidden_layers
										_a        =   num_attention_heads
										_a        =   patch_size
										_a        =   image_size
										_a        =   initializer_range
										_a        =   attention_dropout
										_a        =   layer_norm_eps
										_a        =   hidden_act
										_a        =   qkv_bias





						@classmethod
						def __lowerCAmelCase   (	cls	,    snake_case_	,    **snake_case_			)			->							str:
										cls._set_token_in_kwargs(__A			)

										_a      ,     _a        =   cls.get_config_dict(__A	,    **__A			)

										# get the vision config dict if we are loading from InstructBlipConfig
										if config_dict.get("model_type"			) == "instructblip":
														_a        =   config_dict["vision_config"]

										if "model_type" in config_dict and hasattr(cls	,    "model_type"			) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
														    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

										return cls.from_dict(__A	,    **__A			)



class        A  (	__A ):
						__UpperCAmelCase				:	Union[str, Any]   =   """instructblip_qformer"""





						def __init__(	self	,    snake_case_=3_0_5_2_2	,    snake_case_=7_6_8	,    snake_case_=1_2	,    snake_case_=1_2	,    snake_case_=3_0_7_2	,    snake_case_="gelu"	,    snake_case_=0.1	,    snake_case_=0.1	,    snake_case_=5_1_2	,    snake_case_=0.02	,    snake_case_=1E-1_2	,    snake_case_=0	,    snake_case_="absolute"	,    snake_case_=2	,    snake_case_=1_4_0_8	,    **snake_case_	,    )			->							Union[str, Any]:
										super().__init__(pad_token_id=__A	,    **__A			)

										_a        =   vocab_size
										_a        =   hidden_size
										_a        =   num_hidden_layers
										_a        =   num_attention_heads
										_a        =   hidden_act
										_a        =   intermediate_size
										_a        =   hidden_dropout_prob
										_a        =   attention_probs_dropout_prob
										_a        =   max_position_embeddings
										_a        =   initializer_range
										_a        =   layer_norm_eps
										_a        =   position_embedding_type
										_a        =   cross_attention_frequency
										_a        =   encoder_hidden_size





						@classmethod
						def __lowerCAmelCase   (	cls	,    snake_case_	,    **snake_case_			)			->							Dict:
										cls._set_token_in_kwargs(__A			)

										_a      ,     _a        =   cls.get_config_dict(__A	,    **__A			)

										# get the qformer config dict if we are loading from InstructBlipConfig
										if config_dict.get("model_type"			) == "instructblip":
														_a        =   config_dict["qformer_config"]

										if "model_type" in config_dict and hasattr(cls	,    "model_type"			) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
														    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

										return cls.from_dict(__A	,    **__A			)



class        A  (	__A ):
						__UpperCAmelCase				:	Dict   =   """instructblip"""
						__UpperCAmelCase				:	Optional[Any]   =   True





						def __init__(	self	,    snake_case_=None	,    snake_case_=None	,    snake_case_=None	,    snake_case_=3_2	,    **snake_case_			)			->							Tuple:
										super().__init__(**__A			)

										if vision_config is None:
														_a        =   {}
														logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."			)

										if qformer_config is None:
														_a        =   {}
														logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."			)

										if text_config is None:
														_a        =   {}
														logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."			)

										_a        =   InstructBlipVisionConfig(**__A			)
										_a        =   InstructBlipQFormerConfig(**__A			)
										_a        =   text_config["model_type"] if "model_type" in text_config else "opt"
										_a        =   CONFIG_MAPPING[text_model_type](**__A			)

										_a        =   self.text_config.tie_word_embeddings
										_a        =   self.text_config.is_encoder_decoder

										_a        =   num_query_tokens
										_a        =   self.vision_config.hidden_size
										_a        =   self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
										_a        =   1.0
										_a        =   0.02





						@classmethod
						def __lowerCAmelCase   (	cls	,    snake_case_	,    snake_case_	,    snake_case_	,    **snake_case_	,    )			->							List[str]:
										return cls(
										    vision_config=vision_config.to_dict()	,    qformer_config=qformer_config.to_dict()	,    text_config=text_config.to_dict()	,    **__A	,    )





						def __lowerCAmelCase   (	self			)			->							str:
										_a        =   copy.deepcopy(self.__dict__			)
										_a        =   self.vision_config.to_dict()
										_a        =   self.qformer_config.to_dict()
										_a        =   self.text_config.to_dict()
										_a        =   self.__class__.model_type
										return output









import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


SCREAMING_SNAKE_CASE							    =							getLogger(__name__)


SCREAMING_SNAKE_CASE							    =							'cuda' if torch.cuda.is_available() else 'cpu'




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = 8		,				lowerCAmelCase__ = DEFAULT_DEVICE		,				lowerCAmelCase__=False		,				lowerCAmelCase__="summarization"		,				lowerCAmelCase__=None		,				**lowerCAmelCase__		,				):
   __a					=  Path(lowerCAmelCase__   ).open("""w"""		,				encoding="""utf-8"""   )
   __a					=  str(lowerCAmelCase__   )
   __a					=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__   ).to(lowerCAmelCase__   )
   if fpaa:
      __a					=  model.half()

   __a					=  AutoTokenizer.from_pretrained(lowerCAmelCase__   )
   logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''   )  # if this is wrong, check config.model_type.

   __a					=  time.time()
   # update config with task specific params
   use_task_specific_params(lowerCAmelCase__		,				lowerCAmelCase__   )
   if prefix is None:
      __a					=  prefix or getattr(model.config		,				"""prefix"""		,				""""""   ) or """"""
   for examples_chunk in tqdm(list(chunks(lowerCAmelCase__		,				lowerCAmelCase__   )   )   ):
      __a					=  [prefix + text for text in examples_chunk]
      __a					=  tokenizer(lowerCAmelCase__		,				return_tensors="""pt"""		,				truncation=lowerCAmelCase__		,				padding="""longest"""   ).to(lowerCAmelCase__   )
      __a					=  model.generate(
          input_ids=batch.input_ids		,				attention_mask=batch.attention_mask		,				**lowerCAmelCase__		,				)
      __a					=  tokenizer.batch_decode(lowerCAmelCase__		,				skip_special_tokens=lowerCAmelCase__		,				clean_up_tokenization_spaces=lowerCAmelCase__   )
      for hypothesis in dec:
         fout.write(hypothesis + """\n"""   )
         fout.flush()
   fout.close()
   __a					=  int(time.time() - start_time   )  # seconds
   __a					=  len(lowerCAmelCase__   )
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,				4   )}




def  a   ():
   return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""   )




def  a   (lowerCAmelCase__=True   ):
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""model_name"""		,				type=lowerCAmelCase__		,				help="""like facebook/bart-large-cnn,t5-base, etc."""   )
   parser.add_argument("""input_path"""		,				type=lowerCAmelCase__		,				help="""like cnn_dm/test.source"""   )
   parser.add_argument("""save_path"""		,				type=lowerCAmelCase__		,				help="""where to save summaries"""   )
   parser.add_argument("""--reference_path"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				help="""like cnn_dm/test.target"""   )
   parser.add_argument("""--score_path"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default="""metrics.json"""		,				help="""where to save metrics"""   )
   parser.add_argument("""--device"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default=lowerCAmelCase__		,				help="""cuda, cuda:1, cpu etc."""   )
   parser.add_argument(
       """--prefix"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default=lowerCAmelCase__		,				help="""will be added to the begininng of src examples"""   )
   parser.add_argument("""--task"""		,				type=lowerCAmelCase__		,				default="""summarization"""		,				help="""used for task_specific_params + metrics"""   )
   parser.add_argument("""--bs"""		,				type=lowerCAmelCase__		,				default=8		,				required=lowerCAmelCase__		,				help="""batch size"""   )
   parser.add_argument(
       """--n_obs"""		,				type=lowerCAmelCase__		,				default=-1		,				required=lowerCAmelCase__		,				help="""How many observations. Defaults to all."""   )
   parser.add_argument("""--fp16"""		,				action="""store_true"""   )
   parser.add_argument("""--dump-args"""		,				action="""store_true"""		,				help="""print the custom hparams with the results"""   )
   parser.add_argument(
       """--info"""		,				nargs="""?"""		,				type=lowerCAmelCase__		,				const=datetime_now()		,				help=(
           """use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."""
           """ lang=en-ru. If no value is passed, the current datetime string will be used."""
       )		,				)
   # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
   __a	,    __a					=  parser.parse_known_args()
   __a					=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__   )
   if parsed_args and verbose:
      print(f'''parsed the following generate kwargs: {parsed_args}'''   )
   __a					=  [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
   if args.n_obs > 0:
      __a					=  examples[: args.n_obs]
   Path(args.save_path   ).parent.mkdir(exist_ok=lowerCAmelCase__   )

   if args.reference_path is None and Path(args.score_path   ).exists():
      warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''   )

   if args.device == "cpu" and args.fpaa:
      # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
      raise ValueError("""Can't mix --fp16 and --device cpu"""   )

   __a					=  generate_summaries_or_translations(
       lowerCAmelCase__		,				args.save_path		,				args.model_name		,				batch_size=args.bs		,				device=args.device		,				fpaa=args.fpaa		,				task=args.task		,				prefix=args.prefix		,				**lowerCAmelCase__		,				)

   if args.reference_path is None:
      return {}

   # Compute scores
   __a					=  calculate_bleu if """translation""" in args.task else calculate_rouge
   __a					=  [x.rstrip() for x in open(args.save_path   ).readlines()]
   __a					=  [x.rstrip() for x in open(args.reference_path   ).readlines()][: len(lowerCAmelCase__   )]
   __a					=  score_fn(lowerCAmelCase__		,				lowerCAmelCase__   )
   scores.update(lowerCAmelCase__   )

   if args.dump_args:
      scores.update(lowerCAmelCase__   )
   if args.info:
      __a					=  args.info

   if verbose:
      print(lowerCAmelCase__   )

   if args.score_path is not None:
      json.dump(lowerCAmelCase__		,				open(args.score_path		,				"""w"""   )   )

   return scores


if __name__ == "__main__":
  # Usage for MT:
  # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  run_generate(verbose=True)



