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_           =				getLogger(__name__)


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




def    lowerCAmelCase_  (		lowercase:				List[str]	,       lowercase:				str	,       lowercase:				str	,       lowercase:				int = 8	,       lowercase:				str = DEFAULT_DEVICE	,       lowercase:				Optional[Any]=False	,       lowercase:				Any="summarization"	,       lowercase:				Any=None	,       **lowercase:				List[str]	,       )     ->					Dict:







 '''simple docstring'''
 _UpperCamelCase:  List[Any]    						=			Path(lowercase		).open('''w'''	,       encoding='''utf-8'''		)
 _UpperCamelCase:  List[str]    						=			str(lowercase		)
 _UpperCamelCase:  Dict    						=			AutoModelForSeqaSeqLM.from_pretrained(lowercase		).to(lowercase		)
 if fpaa:
  _UpperCamelCase:  Tuple    						=			model.half()

 _UpperCamelCase:  List[Any]    						=			AutoTokenizer.from_pretrained(lowercase		)
 logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""		)  # if this is wrong, check config.model_type.

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




def    lowerCAmelCase_  (		)     ->					Optional[int]:







 '''simple docstring'''
 return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''		)




def    lowerCAmelCase_  (		lowercase:				List[str]=True		)     ->					List[str]:







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

 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'''		)

 _UpperCamelCase:  Optional[Any]    						=			generate_summaries_or_translations(
     lowercase	,       args.save_path	,       args.model_name	,       batch_size=args.bs	,       device=args.device	,       fpaa=args.fpaa	,       task=args.task	,       prefix=args.prefix	,       **lowercase	,       )

 if args.reference_path is None:
  return {}

 # Compute scores
 _UpperCamelCase:  str    						=			calculate_bleu if '''translation''' in args.task else calculate_rouge
 _UpperCamelCase:  Any    						=			[x.rstrip() for x in open(args.save_path		).readlines()]
 _UpperCamelCase:  Optional[int]    						=			[x.rstrip() for x in open(args.reference_path		).readlines()][: len(lowercase		)]
 _UpperCamelCase:  dict    						=			score_fn(lowercase	,       lowercase		)
 scores.update(lowercase		)

 if args.dump_args:
  scores.update(lowercase		)
 if args.info:
  _UpperCamelCase:  int    						=			args.info

 if verbose:
  print(lowercase		)

 if args.score_path is not None:
  json.dump(lowercase	,       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 unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.bert.modeling_flax_bert import (
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
  )




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Tuple		,	_lowercase       :	List[str]		,	_lowercase       :	Dict=13		,	_lowercase       :	Any=7		,	_lowercase       :	List[str]=True		,	_lowercase       :	Any=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	int=True		,	_lowercase       :	str=99		,	_lowercase       :	Union[str, Any]=32		,	_lowercase       :	Any=5		,	_lowercase       :	Dict=4		,	_lowercase       :	Tuple=37		,	_lowercase       :	Optional[Any]="gelu"		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	Any=0.1		,	_lowercase       :	Union[str, Any]=512		,	_lowercase       :	Dict=16		,	_lowercase       :	int=2		,	_lowercase       :	Union[str, Any]=0.02		,	_lowercase       :	Dict=4		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			parent
    _UpperCamelCase:  Optional[int]    						=			batch_size
    _UpperCamelCase:  Any    						=			seq_length
    _UpperCamelCase:  List[str]    						=			is_training
    _UpperCamelCase:  Union[str, Any]    						=			use_attention_mask
    _UpperCamelCase:  Union[str, Any]    						=			use_token_type_ids
    _UpperCamelCase:  str    						=			use_labels
    _UpperCamelCase:  List[str]    						=			vocab_size
    _UpperCamelCase:  Dict    						=			hidden_size
    _UpperCamelCase:  List[Any]    						=			num_hidden_layers
    _UpperCamelCase:  List[Any]    						=			num_attention_heads
    _UpperCamelCase:  Tuple    						=			intermediate_size
    _UpperCamelCase:  Optional[Any]    						=			hidden_act
    _UpperCamelCase:  Any    						=			hidden_dropout_prob
    _UpperCamelCase:  int    						=			attention_probs_dropout_prob
    _UpperCamelCase:  Optional[int]    						=			max_position_embeddings
    _UpperCamelCase:  List[Any]    						=			type_vocab_size
    _UpperCamelCase:  List[Any]    						=			type_sequence_label_size
    _UpperCamelCase:  List[Any]    						=			initializer_range
    _UpperCamelCase:  Optional[int]    						=			num_choices



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  Optional[int]    						=			None
    if self.use_attention_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  Optional[int]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Tuple    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  str    						=			BertConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	is_decoder=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, token_type_ids, attention_mask



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Any    						=			config_and_inputs
    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  str    						=			config_and_inputs

    _UpperCamelCase:  int    						=			True
    _UpperCamelCase:  Optional[Any]    						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
    _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2				)

    return (
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        encoder_attention_mask,
    )




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = True

   lowerCAmelCase   :     List[Any]	      = (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxBertModelTester(self				)



   @slow
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxBertModel.from_pretrained('''bert-base-cased'''				)
    _UpperCamelCase:  int    						=			model(np.ones((1, 1)				)				)
    self.assertIsNotNone(_lowercase				)