code: 
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import AlbertConfig, is_tf_available
  from transformers.models.auto import get_values
  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 import TF_MODEL_FOR_PRETRAINING_MAPPING
  			from transformers.models.albert.modeling_tf_albert import (
  			    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  			    TFAlbertForMaskedLM,
  			    TFAlbertForMultipleChoice,
  			    TFAlbertForPreTraining,
  			    TFAlbertForQuestionAnswering,
  			    TFAlbertForSequenceClassification,
  			    TFAlbertForTokenClassification,
  			    TFAlbertModel,
  			)
  
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						def __init__(					self   ,							_A   ,							_A=13   ,							_A=7   ,							_A=True   ,							_A=True   ,							_A=True   ,							_A=True   ,							_A=99   ,							_A=16   ,							_A=32   ,							_A=2   ,							_A=4   ,							_A=37   ,							_A="gelu"   ,							_A=0.1   ,							_A=0.1   ,							_A=512   ,							_A=16   ,							_A=2   ,							_A=0.0_2   ,							_A=3   ,							_A=4   ,							_A=None   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      parent
  													__SCREAMING_SNAKE_CASE								=      13
  													__SCREAMING_SNAKE_CASE								=      7
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      99
  													__SCREAMING_SNAKE_CASE								=      16
  													__SCREAMING_SNAKE_CASE								=      32
  													__SCREAMING_SNAKE_CASE								=      2
  													__SCREAMING_SNAKE_CASE								=      4
  													__SCREAMING_SNAKE_CASE								=      37
  													__SCREAMING_SNAKE_CASE								=      'gelu'
  													__SCREAMING_SNAKE_CASE								=      0.1
  													__SCREAMING_SNAKE_CASE								=      0.1
  													__SCREAMING_SNAKE_CASE								=      512
  													__SCREAMING_SNAKE_CASE								=      16
  													__SCREAMING_SNAKE_CASE								=      2
  													__SCREAMING_SNAKE_CASE								=      0.0_2
  													__SCREAMING_SNAKE_CASE								=      3
  													__SCREAMING_SNAKE_CASE								=      4
  													__SCREAMING_SNAKE_CASE								=      None
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size	)
  
  													__SCREAMING_SNAKE_CASE								=      None
  													if self.use_input_mask:
  																				__SCREAMING_SNAKE_CASE								=      random_attention_mask([self.batch_size, self.seq_length]	)
  
  													__SCREAMING_SNAKE_CASE								=      None
  													if self.use_token_type_ids:
  																				__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size, self.seq_length]   ,							self.type_vocab_size	)
  
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      None
  													if self.use_labels:
  																				__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size]   ,							self.type_sequence_label_size	)
  																				__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size, self.seq_length]   ,							self.num_labels	)
  																				__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size]   ,							self.num_choices	)
  
  													__SCREAMING_SNAKE_CASE								=      AlbertConfig(
  													    vocab_size=self.vocab_size   ,							hidden_size=self.hidden_size   ,							embedding_size=self.embedding_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   ,							initializer_range=self.initializer_range   ,							)
  
  													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFAlbertModel(config=_A	)
  													# inputs = {'input_ids': input_ids,
  													#           'attention_mask': input_mask,
  													#           'token_type_ids': token_type_ids}
  													# sequence_output, pooled_output = model(**inputs)
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  
  													__SCREAMING_SNAKE_CASE								=      [input_ids, input_mask]
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  
  													self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)	)
  													self.parent.assertEqual(result.pooler_output.shape   ,							(self.batch_size, self.hidden_size)	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.num_labels
  													__SCREAMING_SNAKE_CASE								=      TFAlbertForPreTraining(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  													self.parent.assertEqual(result.prediction_logits.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)	)
  													self.parent.assertEqual(result.sop_logits.shape   ,							(self.batch_size, self.num_labels)	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFAlbertForMaskedLM(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  													self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.num_labels
  													__SCREAMING_SNAKE_CASE								=      TFAlbertForSequenceClassification(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  													self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFAlbertForQuestionAnswering(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  													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   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.num_choices
  													__SCREAMING_SNAKE_CASE								=      TFAlbertForMultipleChoice(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      tf.tile(tf.expand_dims(_A   ,							1	)   ,							(1, self.num_choices, 1)	)
  													__SCREAMING_SNAKE_CASE								=      tf.tile(tf.expand_dims(_A   ,							1	)   ,							(1, self.num_choices, 1)	)
  													__SCREAMING_SNAKE_CASE								=      tf.tile(tf.expand_dims(_A   ,							1	)   ,							(1, self.num_choices, 1)	)
  													__SCREAMING_SNAKE_CASE								=      {
  													    'input_ids': multiple_choice_inputs_ids,
  													    'attention_mask': multiple_choice_input_mask,
  													    'token_type_ids': multiple_choice_token_type_ids,
  													}
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  													self.parent.assertListEqual(list(result['logits'].shape	)   ,							[self.batch_size, self.num_choices]	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.num_labels
  													__SCREAMING_SNAKE_CASE								=      TFAlbertForTokenClassification(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {
  													    'input_ids': input_ids,
  													    'attention_mask': input_mask,
  													    'token_type_ids': token_type_ids,
  													}
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  													self.parent.assertListEqual(list(result['logits'].shape	)   ,							[self.batch_size, self.seq_length, self.num_labels]	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.prepare_config_and_inputs()
  													(
  													    (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							,  
  													)								=      config_and_inputs
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
  													return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase_						(     UpperCamelCase_					,					UpperCamelCase_					,					unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				Optional[Any]       						=						(
  						    (
  						        TFAlbertModel,
  						        TFAlbertForPreTraining,
  						        TFAlbertForMaskedLM,
  						        TFAlbertForSequenceClassification,
  						        TFAlbertForQuestionAnswering,
  						        TFAlbertForTokenClassification,
  						        TFAlbertForMultipleChoice,
  						    )
  						    if is_tf_available()
  						    else ()
  						)
  						UpperCamelCase__    :				str       						=						(
  						    {
  						        '''feature-extraction''': TFAlbertModel,
  						        '''fill-mask''': TFAlbertForMaskedLM,
  						        '''question-answering''': TFAlbertForQuestionAnswering,
  						        '''text-classification''': TFAlbertForSequenceClassification,
  						        '''token-classification''': TFAlbertForTokenClassification,
  						        '''zero-shot''': TFAlbertForSequenceClassification,
  						    }
  						    if is_tf_available()
  						    else {}
  						)
  						UpperCamelCase__    :				Union[str, Any]       						=						False
  						UpperCamelCase__    :				Any       						=						False
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A=False	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      super()._prepare_for_class(_A   ,							_A   ,							return_labels=_A	)
  
  													if return_labels:
  																				if model_class in get_values(_A	):
  																											__SCREAMING_SNAKE_CASE								=      tf.zeros(self.model_tester.batch_size   ,							dtype=tf.intaa	)
  
  													return inputs_dict
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFAlbertModelTester(self	)
  													__SCREAMING_SNAKE_CASE								=      ConfigTester(self   ,							config_class=_A   ,							hidden_size=37	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													self.config_tester.run_common_tests()
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_albert_model(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_albert_for_pretraining(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_albert_for_masked_lm(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_albert_for_multiple_choice(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_albert_for_sequence_classification(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_albert_for_question_answering(*_A	)
  
  
  
  
  
  
  						@slow
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  																				__SCREAMING_SNAKE_CASE								=      TFAlbertModel.from_pretrained(_A	)
  																				self.assertIsNotNone(_A	)
  
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase_						(     unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						@slow
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFAlbertForPreTraining.from_pretrained('albert-base-v2'	)
  													__SCREAMING_SNAKE_CASE								=      tf.constant([[0, 1, 2, 3, 4, 5]]	)
  													__SCREAMING_SNAKE_CASE								=      model(_A	)[0]
  
  													__SCREAMING_SNAKE_CASE								=      [1, 6, 30_000]
  													self.assertEqual(output.shape   ,							_A	)
  
  													__SCREAMING_SNAKE_CASE								=      tf.constant(
  													    [
  													        [
  													            [4.5_9_5_6_6_8, 0.7_4_4_6_2_7_5_4, -1.8_1_8_1_4_7],
  													            [4.5_9_5_4_3_4_7, 0.7_4_5_4_1_8_4, -1.8_1_8_8_2_5_8],
  													            [4.5_9_5_4_9_0_5, 0.7_4_4_8_2_3_5, -1.8_1_8_2_3_1_6],
  													        ]
  													    ]	)
  													tf.debugging.assert_near(output[:, :3, :3]   ,							_A   ,							atol=1e-4	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  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
  
  
  lowerCAmelCase__	:  Union[str, Any] 		=getLogger(__name__)
  
  
  lowerCAmelCase__	:  Optional[int] 		='''cuda''' if torch.cuda.is_available() else '''cpu'''
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__							,     a__ = 8							,     a__ = DEFAULT_DEVICE							,     a__=False							,     a__="summarization"							,     a__=None							,     **a__							,     )       ->				Dict:
  							__SCREAMING_SNAKE_CASE								=      Path(a__     ).open('w'							,     encoding='utf-8'     )
  							__SCREAMING_SNAKE_CASE								=      str(a__     )
  							__SCREAMING_SNAKE_CASE								=      AutoModelForSeqaSeqLM.from_pretrained(a__     ).to(a__     )
  							if fpaa:
  														__SCREAMING_SNAKE_CASE								=      model.half()
  
  							__SCREAMING_SNAKE_CASE								=      AutoTokenizer.from_pretrained(a__     )
  							logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.
  
  							__SCREAMING_SNAKE_CASE								=      time.time()
  							# update config with task specific params
  							use_task_specific_params(a__							,     a__     )
  							if prefix is None:
  														__SCREAMING_SNAKE_CASE								=      prefix or getattr(model.config							,     'prefix'							,     ''     ) or ''
  							for examples_chunk in tqdm(list(chunks(a__							,     a__     )     )     ):
  														__SCREAMING_SNAKE_CASE								=      [prefix + text for text in examples_chunk]
  														__SCREAMING_SNAKE_CASE								=      tokenizer(a__							,     return_tensors='pt'							,     truncation=a__							,     padding='longest'     ).to(a__     )
  														__SCREAMING_SNAKE_CASE								=      model.generate(
  														    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **a__							,     )
  														__SCREAMING_SNAKE_CASE								=      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()
  							__SCREAMING_SNAKE_CASE								=      int(time.time() - start_time     )  # seconds
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4     )}
  
  
  
  
  
  
  def 			__lowercase     (				)       ->				Dict:
  							return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )
  
  
  
  
  
  
  def 			__lowercase     (				a__=True     )       ->				Optional[Any]:
  							__SCREAMING_SNAKE_CASE								=      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
  							__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      parser.parse_known_args()
  							__SCREAMING_SNAKE_CASE								=      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:
  														__SCREAMING_SNAKE_CASE								=      [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  							if args.n_obs > 0:
  														__SCREAMING_SNAKE_CASE								=      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."""     )
  							__SCREAMING_SNAKE_CASE								=      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
  							__SCREAMING_SNAKE_CASE								=      calculate_bleu if 'translation' in args.task else calculate_rouge
  							__SCREAMING_SNAKE_CASE								=      [x.rstrip() for x in open(args.save_path     ).readlines()]
  							__SCREAMING_SNAKE_CASE								=      [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(a__     )]
  							__SCREAMING_SNAKE_CASE								=      score_fn(a__							,     a__     )
  							scores.update(a__     )
  
  							if args.dump_args:
  														scores.update(a__     )
  							if args.info:
  														__SCREAMING_SNAKE_CASE								=      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)
  
  
  
  
  
  
  