



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


a__			=		getLogger(__name__)


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



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		List[str] ,				SCREAMING_SNAKE_CASE__				:		Optional[Any] ,				SCREAMING_SNAKE_CASE__				:		List[Any] = 8 ,				SCREAMING_SNAKE_CASE__				:		Dict = DEFAULT_DEVICE ,				SCREAMING_SNAKE_CASE__				:		Dict=False ,				SCREAMING_SNAKE_CASE__				:		Optional[int]="summarization" ,				SCREAMING_SNAKE_CASE__				:		str=None ,				**SCREAMING_SNAKE_CASE__				:		List[str] ,				)     ->   Optional[Any]:
 _snake_case			:     Any 		=    Path(a__ ).open("""w""" ,				encoding="""utf-8""" )
 _snake_case			:     Optional[Any] 		=    str(a__ )
 _snake_case			:     Optional[Any] 		=    AutoModelForSeqaSeqLM.from_pretrained(a__ ).to(a__ )
 if fpaa:
  _snake_case			:     Dict 		=    model.half()

 _snake_case			:     str 		=    AutoTokenizer.from_pretrained(a__ )
 logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}''' )  # if this is wrong, check config.model_type.

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



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



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Union[str, Any]=True )     ->   Dict:
 _snake_case			:     List[Any] 		=    argparse.ArgumentParser()
 parser.add_argument("""model_name""" ,				type=a__ ,				help="""like facebook/bart-large-cnn,t5-base, etc.""" )
 parser.add_argument("""input_path""" ,				type=a__ ,				help="""like cnn_dm/test.source""" )
 parser.add_argument("""save_path""" ,				type=a__ ,				help="""where to save summaries""" )
 parser.add_argument("""--reference_path""" ,				type=a__ ,				required=a__ ,				help="""like cnn_dm/test.target""" )
 parser.add_argument("""--score_path""" ,				type=a__ ,				required=a__ ,				default="""metrics.json""" ,				help="""where to save metrics""" )
 parser.add_argument("""--device""" ,				type=a__ ,				required=a__ ,				default=a__ ,				help="""cuda, cuda:1, cpu etc.""" )
 parser.add_argument(
     """--prefix""" ,				type=a__ ,				required=a__ ,				default=a__ ,				help="""will be added to the begininng of src examples""" )
 parser.add_argument("""--task""" ,				type=a__ ,				default="""summarization""" ,				help="""used for task_specific_params + metrics""" )
 parser.add_argument("""--bs""" ,				type=a__ ,				default=8 ,				required=a__ ,				help="""batch size""" )
 parser.add_argument(
     """--n_obs""" ,				type=a__ ,				default=-1 ,				required=a__ ,				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=a__ ,				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
 _snake_case		,			_snake_case			:     Any 		=    parser.parse_known_args()
 _snake_case			:     Union[str, Any] 		=    parse_numeric_n_bool_cl_kwargs(a__ )
 if parsed_args and verbose:
  print(F'''parsed the following generate kwargs: {parsed_args}''' )
 _snake_case			:     Any 		=    [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
 if args.n_obs > 0:
  _snake_case			:     Dict 		=    examples[: args.n_obs]
 Path(args.save_path ).parent.mkdir(exist_ok=a__ )

 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""" )

 _snake_case			:     List[Any] 		=    generate_summaries_or_translations(
     a__ ,				args.save_path ,				args.model_name ,				batch_size=args.bs ,				device=args.device ,				fpaa=args.fpaa ,				task=args.task ,				prefix=args.prefix ,				**a__ ,				)

 if args.reference_path is None:
  return {}

 # Compute scores
 _snake_case			:     List[str] 		=    calculate_bleu if """translation""" in args.task else calculate_rouge
 _snake_case			:     Any 		=    [x.rstrip() for x in open(args.save_path ).readlines()]
 _snake_case			:     str 		=    [x.rstrip() for x in open(args.reference_path ).readlines()][: len(a__ )]
 _snake_case			:     Dict 		=    score_fn(a__ ,				a__ )
 scores.update(a__ )

 if args.dump_args:
  scores.update(a__ )
 if args.info:
  _snake_case			:     Optional[int] 		=    args.info

 if verbose:
  print(a__ )

 if args.score_path is not None:
  json.dump(a__ ,				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)



'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase :       List[str]			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "maskformer-swin"

 lowerCAmelCase__  					=      {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }
 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple=224					,							__SCREAMING_SNAKE_CASE					:       str=4					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=3					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=96					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=[2, 2, 6, 2]					,							__SCREAMING_SNAKE_CASE					:       Any=[3, 6, 12, 24]					,							__SCREAMING_SNAKE_CASE					:       Dict=7					,							__SCREAMING_SNAKE_CASE					:       Dict=4.0					,							__SCREAMING_SNAKE_CASE					:       Dict=True					,							__SCREAMING_SNAKE_CASE					:       str=0.0					,							__SCREAMING_SNAKE_CASE					:       int=0.0					,							__SCREAMING_SNAKE_CASE					:       str=0.1					,							__SCREAMING_SNAKE_CASE					:       List[Any]="gelu"					,							__SCREAMING_SNAKE_CASE					:       str=False					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=0.02					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=1E-5					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							**__SCREAMING_SNAKE_CASE					:       Tuple					,							)  -> Tuple:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      embed_dim
      __SCREAMING_SNAKE_CASE     =      depths
      __SCREAMING_SNAKE_CASE     =      len(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      num_heads
      __SCREAMING_SNAKE_CASE     =      window_size
      __SCREAMING_SNAKE_CASE     =      mlp_ratio
      __SCREAMING_SNAKE_CASE     =      qkv_bias
      __SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
      __SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      use_absolute_embeddings
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      __SCREAMING_SNAKE_CASE     =      int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE			) - 1)			)
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(__SCREAMING_SNAKE_CASE			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






