
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


UpperCAmelCase__						:	Optional[Any]	     = getLogger(__name__)


UpperCAmelCase__						:	Optional[int]	     = 'cuda' if torch.cuda.is_available() else 'cpu'






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

   _A:    str						     =			AutoTokenizer.from_pretrained(a )
   logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

   _A:    Optional[Any]						     =			time.time()
   # update config with task specific params
   use_task_specific_params(a						,						a )
   if prefix is None:
      _A:    Any						     =			prefix or getattr(model.config						,						'''prefix'''						,						'''''' ) or ''''''
   for examples_chunk in tqdm(list(chunks(a						,						a ) ) ):
      _A:    Dict						     =			[prefix + text for text in examples_chunk]
      _A:    str						     =			tokenizer(a						,						return_tensors='''pt'''						,						truncation=a						,						padding='''longest''' ).to(a )
      _A:    Dict						     =			model.generate(
          input_ids=batch.input_ids						,						attention_mask=batch.attention_mask						,						**a						,						)
      _A:    List[str]						     =			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()
   _A:    Dict						     =			int(time.time() - start_time )  # seconds
   _A:    int						     =			len(a )
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs						,						4 )}






def     lowerCamelCase__				(   ) ->  List[Any]:
   return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )






def     lowerCamelCase__				(   a=True ) ->  List[str]:
   _A:    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
   _A     ,    _A:    Tuple						     =			parser.parse_known_args()
   _A:    Any						     =			parse_numeric_n_bool_cl_kwargs(a )
   if parsed_args and verbose:
      print(f"""parsed the following generate kwargs: {parsed_args}""" )
   with open(args.input_path ) as f:
      _A:    str						     =			[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
   if args.n_obs > 0:
      _A:    str						     =			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.""" )
   _A:    Dict						     =			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
   _A:    Optional[Any]						     =			calculate_bleu if '''translation''' in args.task else calculate_rouge
   _A:    Union[str, Any]						     =			[x.rstrip() for x in open(args.save_path ).readlines()]
   _A:    Dict						     =			[x.rstrip() for x in open(args.reference_path ).readlines()][: len(a )]
   _A:    dict						     =			score_fn(a						,						a )
   scores.update(a )

   if args.dump_args:
      scores.update(a )
   if args.info:
      _A:    Optional[Any]						     =			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)





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


UpperCAmelCase__						:	Any	     = logging.get_logger(__name__)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				List[str]	):

      """simple docstring"""
      super().__init__()
      if version.parse(torch.__version__	) < version.parse('''1.12.0'''	):
         raise ImportError(
             F"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
             '''PytorchGELUTanh. Please upgrade torch.'''	)


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return nn.functional.gelu(lowerCAmelCase_     ,     approximate='''tanh'''	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.044715 * torch.pow(lowerCAmelCase_     ,     3.0	))	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Tuple     ,     lowerCAmelCase_	:				bool = False	):

      """simple docstring"""
      super().__init__()
      if use_gelu_python:
         _A:    Union[str, Any]						     =			self._gelu_python
      else:
         _A:    Union[str, Any]						     =			nn.functional.gelu


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0	)	))


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return self.act(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * torch.sigmoid(1.702 * input	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				str     ,     lowerCAmelCase_	:				float     ,     lowerCAmelCase_	:				float	):

      """simple docstring"""
      if min > max:
         raise ValueError(F"""min should be < max (got min: {min}, max: {max})"""	)

      super().__init__()
      _A:    Tuple						     =			min
      _A:    str						     =			max


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return torch.clip(gelu(lowerCAmelCase_	)     ,     self.min     ,     self.max	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Optional[Any]	):

      """simple docstring"""
      super().__init__()
      _A:    Any						     =			math.sqrt(2 / math.pi	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(lowerCAmelCase_     ,     3	))	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return nn.functional.silu(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Union[str, Any]	):

      """simple docstring"""
      super().__init__()
      if version.parse(torch.__version__	) < version.parse('''1.9.0'''	):
         _A:    Optional[Any]						     =			self._mish_python
      else:
         _A:    int						     =			nn.functional.mish


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * torch.tanh(nn.functional.softplus(lowerCAmelCase_	)	)


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return self.act(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				List[str]     ,     lowerCAmelCase_	:				Tuple=0.707107     ,     lowerCAmelCase_	:				Union[str, Any]=0.282095	):

      """simple docstring"""
      _A:    Any						     =			(input - mu).div(sigma * math.sqrt(2.0	)	)
      return 0.5 * (1.0 + torch.erf(lowerCAmelCase_	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				List[Any]	):

      """simple docstring"""
      _A:    Optional[int]						     =			nn.functional.relu(lowerCAmelCase_	)
      _A:    Any						     =			torch.square(lowerCAmelCase_	)
      return squared






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   def __getitem__(   self	:				List[Any]     ,     lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			super().__getitem__(lowerCAmelCase_	)
      _A     ,    _A:    List[str]						     =			content if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else (content, {})
      return cls(**lowerCAmelCase_	)

UpperCAmelCase__						:	List[str]	     = {
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -10, 'max': 10}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
UpperCAmelCase__						:	List[str]	     = ClassInstantier(ACTaCLS)






def     lowerCamelCase__				(   a ) ->  List[str]:
   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )


# For backwards compatibility with: from activations import gelu_python
UpperCAmelCase__						:	Any	     = get_activation('gelu_python')
UpperCAmelCase__						:	Dict	     = get_activation('gelu_new')
UpperCAmelCase__						:	List[Any]	     = get_activation('gelu')
UpperCAmelCase__						:	Dict	     = get_activation('gelu_fast')
UpperCAmelCase__						:	str	     = get_activation('quick_gelu')
UpperCAmelCase__						:	Union[str, Any]	     = get_activation('silu')
UpperCAmelCase__						:	List[Any]	     = get_activation('mish')





UpperCAmelCase__						:	int	     = get_activation('linear')



