






'''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


SCREAMING_SNAKE_CASE						=	getLogger(__name__)


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


def 							lowercase_				(    __A :       List[str]					,      __A :       str					,      __A :       str					,      __A :       int = 8					,      __A :       str = DEFAULT_DEVICE					,      __A :       str=False					,      __A :       Union[str, Any]="summarization"					,      __A :       Tuple=None					,      **__A :       Dict					,      )       ->      Dict:



 """simple docstring"""




 lowercase					:   List[str]				  =Path(__A			).open('''w'''					,      encoding='''utf-8'''			)
 lowercase					:   int				  =str(__A			)
 lowercase					:   Optional[Any]				  =AutoModelForSeqaSeqLM.from_pretrained(__A			).to(__A			)
 if fpaa:
  lowercase					:   List[str]				  =model.half()

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

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


def 							lowercase_				(    )       ->      Optional[Any]:



 """simple docstring"""




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


def 							lowercase_				(    __A :       Tuple=True			)       ->      List[Any]:



 """simple docstring"""




 lowercase					:   Optional[int]				  =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
 lowercase  ,		lowercase					:   Dict				  =parser.parse_known_args()
 lowercase					:   Any				  =parse_numeric_n_bool_cl_kwargs(__A			)
 if parsed_args and verbose:
  print(F'parsed the following generate kwargs: {parsed_args}'			)
 lowercase					:   Tuple				  =[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path			).readlines()]
 if args.n_obs > 0:
  lowercase					:   List[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.'			)

 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'''			)

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

 if args.dump_args:
  scores.update(__A			)
 if args.info:
  lowercase					:   Tuple				  =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 __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.distilbert.modeling_tf_distilbert import (
          TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDistilBertForMaskedLM,
          TFDistilBertForMultipleChoice,
          TFDistilBertForQuestionAnswering,
          TFDistilBertForSequenceClassification,
          TFDistilBertForTokenClassification,
          TFDistilBertModel,
      )
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		List[str]     ,      )  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   int				  =parent
       lowercase					:   Any				  =13
       lowercase					:   Any				  =7
       lowercase					:   Optional[int]				  =True
       lowercase					:   Optional[int]				  =True
       lowercase					:   Tuple				  =False
       lowercase					:   Optional[Any]				  =True
       lowercase					:   Dict				  =99
       lowercase					:   Union[str, Any]				  =32
       lowercase					:   Union[str, Any]				  =2
       lowercase					:   Union[str, Any]				  =4
       lowercase					:   List[str]				  =37
       lowercase					:   str				  ='''gelu'''
       lowercase					:   Dict				  =0.1
       lowercase					:   List[Any]				  =0.1
       lowercase					:   List[str]				  =512
       lowercase					:   Optional[int]				  =16
       lowercase					:   Optional[Any]				  =2
       lowercase					:   List[str]				  =0.0_2
       lowercase					:   Any				  =3
       lowercase					:   Optional[Any]				  =4
       lowercase					:   int				  =None






      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Tuple				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size		)

       lowercase					:   Any				  =None
       if self.use_input_mask:
        lowercase					:   List[str]				  =random_attention_mask([self.batch_size, self.seq_length]		)

       lowercase					:   Union[str, Any]				  =None
       lowercase					:   Any				  =None
       lowercase					:   str				  =None
       if self.use_labels:
        lowercase					:   Union[str, Any]				  =ids_tensor([self.batch_size]     ,      self.type_sequence_label_size		)
        lowercase					:   Tuple				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels		)
        lowercase					:   List[Any]				  =ids_tensor([self.batch_size]     ,      self.num_choices		)

       lowercase					:   Dict				  =DistilBertConfig(
           vocab_size=self.vocab_size     ,      dim=self.hidden_size     ,      n_layers=self.num_hidden_layers     ,      n_heads=self.num_attention_heads     ,      hidden_dim=self.intermediate_size     ,      hidden_act=self.hidden_act     ,      dropout=self.hidden_dropout_prob     ,      attention_dropout=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      initializer_range=self.initializer_range     ,      )

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   int				  =TFDistilBertModel(config=UpperCAmelCase		)
       lowercase					:   int				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}

       lowercase					:   List[str]				  =model(UpperCAmelCase		)

       lowercase					:   str				  =[input_ids, input_mask]

       lowercase					:   Tuple				  =model(UpperCAmelCase		)

       self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[str]				  =TFDistilBertForMaskedLM(config=UpperCAmelCase		)
       lowercase					:   int				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   Union[str, Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Optional[int]		)  ->  Any:


       '''simple docstring'''

       lowercase					:   str				  =TFDistilBertForQuestionAnswering(config=UpperCAmelCase		)
       lowercase					:   int				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
       }
       lowercase					:   List[str]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.start_logits.shape     ,      (self.batch_size, self.seq_length)		)
       self.parent.assertEqual(result.end_logits.shape     ,      (self.batch_size, self.seq_length)		)






      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Dict		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Dict				  =self.num_labels
       lowercase					:   Optional[Any]				  =TFDistilBertForSequenceClassification(UpperCAmelCase		)
       lowercase					:   str				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   Union[str, Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.num_choices
       lowercase					:   Optional[int]				  =TFDistilBertForMultipleChoice(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =tf.tile(tf.expand_dims(UpperCAmelCase     ,      1		)     ,      (1, self.num_choices, 1)		)
       lowercase					:   List[Any]				  =tf.tile(tf.expand_dims(UpperCAmelCase     ,      1		)     ,      (1, self.num_choices, 1)		)
       lowercase					:   Tuple				  ={
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
       }
       lowercase					:   Tuple				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Dict				  =self.num_labels
       lowercase					:   Tuple				  =TFDistilBertForTokenClassification(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   str				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)		)






      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   int				  =self.prepare_config_and_inputs()
       ((lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase))					:   Union[str, Any]				  =config_and_inputs
       lowercase					:   Tuple				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict





@require_tf
class UpperCAmelCase_					(    __A				,       __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			(
          (
              TFDistilBertModel,
              TFDistilBertForMaskedLM,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertForMultipleChoice,
          )
          if is_tf_available()
          else None
      )
      UpperCamelCase_								=			(
          {
              '''feature-extraction''': TFDistilBertModel,
              '''fill-mask''': TFDistilBertForMaskedLM,
              '''question-answering''': TFDistilBertForQuestionAnswering,
              '''text-classification''': TFDistilBertForSequenceClassification,
              '''token-classification''': TFDistilBertForTokenClassification,
              '''zero-shot''': TFDistilBertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase_								=			False
      UpperCamelCase_								=			False






      def 				A__      ( self			:		Dict		)  ->  str:


       '''simple docstring'''

       lowercase					:   str				  =TFDistilBertModelTester(self		)
       lowercase					:   int				  =ConfigTester(self     ,      config_class=UpperCAmelCase     ,      dim=37		)






      def 				A__      ( self			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      def 				A__      ( self			:		Optional[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_model(*UpperCAmelCase		)






      def 				A__      ( self			:		Tuple		)  ->  Any:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCAmelCase		)






      def 				A__      ( self			:		Optional[int]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCAmelCase		)






      def 				A__      ( self			:		Any		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCAmelCase		)






      def 				A__      ( self			:		Optional[Any]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCAmelCase		)






      def 				A__      ( self			:		str		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCAmelCase		)






      @slow
      def 				A__      ( self			:		List[Any]		)  ->  Dict:


       '''simple docstring'''

       for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]		):
        lowercase					:   Union[str, Any]				  =TFDistilBertModel.from_pretrained(UpperCAmelCase		)
        self.assertIsNotNone(UpperCAmelCase		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      @slow
      def 				A__      ( self			:		List[str]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''		)
       lowercase					:   Tuple				  =tf.constant([[0, 1, 2, 3, 4, 5]]		)
       lowercase					:   List[Any]				  =model(UpperCAmelCase		)[0]

       lowercase					:   str				  =[1, 6, 768]
       self.assertEqual(output.shape     ,      UpperCAmelCase		)

       lowercase					:   Optional[int]				  =tf.constant(
           [
               [
                   [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                   [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                   [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
               ]
           ]		)
       tf.debugging.assert_near(output[:, :3, :3]     ,      UpperCAmelCase     ,      atol=1e-4		)



