'''simple docstring'''

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_     :						int    							=getLogger(__name__)


A_     :						Dict    							='''cuda''' if torch.cuda.is_available() else '''cpu'''



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

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

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



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



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

   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''')

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

   if args.reference_path is None:
      return {}

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

   if args.dump_args:
      scores.update(__snake_case)
   if args.info:
      lowerCAmelCase_			=      args.info

   if verbose:
      print(__snake_case)

   if args.score_path is not None:
      json.dump(__snake_case	,   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 collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_     :						Tuple    							=logging.get_logger(__name__)

A_     :						Dict    							={
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}


class     __UpperCAmelCase       ( __a		):
  __A :				Union[str, Any]											= 'efficientnet'





  def __init__(	self    ,     _lowerCamelCase = 3    ,     _lowerCamelCase = 600    ,     _lowerCamelCase = 2.0    ,     _lowerCamelCase = 3.1    ,     _lowerCamelCase = 8    ,     _lowerCamelCase = [3, 3, 5, 3, 5, 5, 3]    ,     _lowerCamelCase = [32, 16, 24, 40, 80, 112, 192]    ,     _lowerCamelCase = [16, 24, 40, 80, 112, 192, 320]    ,     _lowerCamelCase = []    ,     _lowerCamelCase = [1, 2, 2, 2, 1, 2, 1]    ,     _lowerCamelCase = [1, 2, 2, 3, 3, 4, 1]    ,     _lowerCamelCase = [1, 6, 6, 6, 6, 6, 6]    ,     _lowerCamelCase = 0.25    ,     _lowerCamelCase = "swish"    ,     _lowerCamelCase = 2560    ,     _lowerCamelCase = "mean"    ,     _lowerCamelCase = 0.02    ,     _lowerCamelCase = 0.0_01    ,     _lowerCamelCase = 0.99    ,     _lowerCamelCase = 0.5    ,     _lowerCamelCase = 0.2    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )

     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      image_size
     lowerCAmelCase_			=      width_coefficient
     lowerCAmelCase_			=      depth_coefficient
     lowerCAmelCase_			=      depth_divisor
     lowerCAmelCase_			=      kernel_sizes
     lowerCAmelCase_			=      in_channels
     lowerCAmelCase_			=      out_channels
     lowerCAmelCase_			=      depthwise_padding
     lowerCAmelCase_			=      strides
     lowerCAmelCase_			=      num_block_repeats
     lowerCAmelCase_			=      expand_ratios
     lowerCAmelCase_			=      squeeze_expansion_ratio
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dim
     lowerCAmelCase_			=      pooling_type
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      batch_norm_eps
     lowerCAmelCase_			=      batch_norm_momentum
     lowerCAmelCase_			=      dropout_rate
     lowerCAmelCase_			=      drop_connect_rate
     lowerCAmelCase_			=      sum(_lowerCamelCase ) * 4






class     __UpperCAmelCase       ( __a		):
  __A :				str											= version.parse('1.11'		)





  @property
  def 						UpperCAmelCase_	(	self ):
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ] )







  @property
  def 						UpperCAmelCase_	(	self ):
     return 1E-5





