



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


a_      :      str    =     '\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

a_      :      int    =     '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

a_      :      int    =     '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class    __UpperCamelCase			(       datasets.Metric				):

 """simple docstring"""



 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  return datasets.MetricInfo(
      description=_DESCRIPTION		,				citation=_CITATION		,				inputs_description=_KWARGS_DESCRIPTION		,				features=datasets.Features(
          {
              '''predictions''': datasets.Sequence(datasets.Value('''string'''		,				id='''token'''      )		,				id='''sequence'''      ),
              '''references''': datasets.Sequence(
                  datasets.Sequence(datasets.Value('''string'''		,				id='''token'''      )		,				id='''sequence'''      )		,				id='''references'''      ),
          }      )		,				codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']		,				reference_urls=[
          '''https://en.wikipedia.org/wiki/BLEU''',
          '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
      ]		,				)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=False      ) ->			Optional[int]:
  a__												= compute_bleu(
      reference_corpus=SCREAMING_SNAKE_CASE		,				translation_corpus=SCREAMING_SNAKE_CASE		,				max_order=SCREAMING_SNAKE_CASE		,				smooth=SCREAMING_SNAKE_CASE      )
  ((a__)							,			(a__)							,			(a__)							,			(a__)							,			(a__)							,			(a__))												= score
  return {
      "bleu": bleu,
      "precisions": precisions,
      "brevity_penalty": bp,
      "length_ratio": ratio,
      "translation_length": translation_length,
      "reference_length": reference_length,
  }










import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1_3		,				SCREAMING_SNAKE_CASE=7		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=9_9		,				SCREAMING_SNAKE_CASE=3_2		,				SCREAMING_SNAKE_CASE=5		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=3_7		,				SCREAMING_SNAKE_CASE="gelu"		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=5_1_2		,				SCREAMING_SNAKE_CASE=1_6		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=0.02		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=None		,				) ->			Union[str, Any]:
  a__												= parent
  a__												= batch_size
  a__												= seq_length
  a__												= is_training
  a__												= use_token_type_ids
  a__												= use_labels
  a__												= vocab_size
  a__												= hidden_size
  a__												= num_hidden_layers
  a__												= num_attention_heads
  a__												= intermediate_size
  a__												= hidden_act
  a__												= hidden_dropout_prob
  a__												= attention_probs_dropout_prob
  a__												= max_position_embeddings
  a__												= type_vocab_size
  a__												= type_sequence_label_size
  a__												= initializer_range
  a__												= num_labels
  a__												= num_choices
  a__												= scope
  a__												= self.vocab_size - 1

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size      )

  a__												= None
  if self.use_token_type_ids:
   a__												= ids_tensor([self.batch_size, self.seq_length]		,				self.type_vocab_size      )

  a__												= None
  a__												= None
  a__												= None
  if self.use_labels:
   a__												= ids_tensor([self.batch_size]		,				self.type_sequence_label_size      )
   a__												= ids_tensor([self.batch_size, self.seq_length]		,				self.num_labels      )
   a__												= ids_tensor([self.batch_size]		,				self.num_choices      )

  a__												= OpenAIGPTConfig(
      vocab_size=self.vocab_size		,				n_embd=self.hidden_size		,				n_layer=self.num_hidden_layers		,				n_head=self.num_attention_heads		,				n_positions=self.max_position_embeddings		,				pad_token_id=self.pad_token_id		,				)

  a__												= ids_tensor([self.num_hidden_layers, self.num_attention_heads]		,				2      )

  return (
      config,
      input_ids,
      head_mask,
      token_type_ids,
      sequence_labels,
      token_labels,
      choice_labels,
  )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= OpenAIGPTModel(config=SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE      )
  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE      )
  a__												= model(SCREAMING_SNAKE_CASE      )

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

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE      ) ->			Tuple:
  a__												= OpenAIGPTLMHeadModel(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.loss.shape		,				()      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE      ) ->			Dict:
  a__												= OpenAIGPTDoubleHeadsModel(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.loss.shape		,				()      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= self.num_labels
  a__												= OpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE      )
  model.to(SCREAMING_SNAKE_CASE      )
  model.eval()

  a__												= ids_tensor([self.batch_size]		,				self.type_sequence_label_size      )
  a__												= model(SCREAMING_SNAKE_CASE		,				token_type_ids=SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE      )
  self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)      )



 def 						_UpperCAmelCase       (	self      ) ->			str:
  a__												= self.prepare_config_and_inputs()
  (
      (
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			(
      a__
  )							,			
  )												= config_and_inputs
  a__												= {
      '''input_ids''': input_ids,
      '''token_type_ids''': token_type_ids,
      '''head_mask''': head_mask,
  }

  return config, inputs_dict





@require_torch
class    __UpperCamelCase			(       _lowercase	,	_lowercase	,	_lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    Dict      						=					(
     (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
     if is_torch_available()
     else ()
 )
 _lowercase   :    Optional[Any]      						=					(
     (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
 )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
 _lowercase   :    Dict      						=					(
     {
         '''feature-extraction''': OpenAIGPTModel,
         '''text-classification''': OpenAIGPTForSequenceClassification,
         '''text-generation''': OpenAIGPTLMHeadModel,
         '''zero-shot''': OpenAIGPTForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
   # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
   # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
   # tiny config could not be created.
   return True

  return False

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False      ) ->			List[str]:
  a__												= super()._prepare_for_class(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				return_labels=SCREAMING_SNAKE_CASE      )

  if return_labels:
   if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
    a__												= torch.zeros(
        (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)		,				dtype=torch.long		,				device=SCREAMING_SNAKE_CASE		,				)
    a__												= inputs_dict['''labels''']
    a__												= inputs_dict['''labels''']
    a__												= torch.zeros(
        (self.model_tester.batch_size, self.model_tester.num_choices)		,				dtype=torch.long		,				device=SCREAMING_SNAKE_CASE		,				)
    a__												= torch.zeros(
        self.model_tester.batch_size		,				dtype=torch.long		,				device=SCREAMING_SNAKE_CASE      )
  return inputs_dict

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= OpenAIGPTModelTester(self      )
  a__												= ConfigTester(self		,				config_class=SCREAMING_SNAKE_CASE		,				n_embd=3_7      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  self.config_tester.run_common_tests()

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_double_lm_head_model(*SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE      )



 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   a__												= OpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE      )
   self.assertIsNotNone(SCREAMING_SNAKE_CASE      )





@require_torch
class    __UpperCamelCase			(       unittest.TestCase				):

 """simple docstring"""



 @slow
 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''      )
  model.to(SCREAMING_SNAKE_CASE      )
  a__												= torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]		,				dtype=torch.long		,				device=SCREAMING_SNAKE_CASE      )  # the president is
  a__												= [
      4_8_1,
      4_7_3_5,
      5_4_4,
      2_4_6,
      9_6_3,
      8_7_0,
      7_6_2,
      2_3_9,
      2_4_4,
      4_0_4_7_7,
      2_4_4,
      2_4_9,
      7_1_9,
      8_8_1,
      4_8_7,
      5_4_4,
      2_4_0,
      2_4_4,
      6_0_3,
      4_8_1,
  ]  # the president is a very good man. " \n " i\'m sure he is, " said the

  a__												= model.generate(SCREAMING_SNAKE_CASE		,				do_sample=SCREAMING_SNAKE_CASE      )
  self.assertListEqual(output_ids[0].tolist()		,				SCREAMING_SNAKE_CASE      )





