
'''simple docstring'''





import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel






class __A    :



 '''simple docstring'''







 def __init__(self				,     A				,     A=13				,     A=7				,     A=True				,     A=True				,     A=True				,     A=True				,     A=99				,     A=32				,     A=5				,     A=4				,     A=4				,     A="gelu"				,     A=0.0				,     A=0.1				,     A=True				,     A=512				,     A=16				,     A=2				,     A=0.02				,     A=3				,     A=4				,     A=None				,     )					->  int:


      """simple docstring"""



      _a											=						parent
      _a											=						batch_size
      _a											=						seq_length
      _a											=						is_training
      _a											=						use_input_mask
      _a											=						use_token_type_ids
      _a											=						use_labels
      _a											=						vocab_size
      _a											=						hidden_size
      _a											=						num_hidden_layers
      _a											=						num_attention_heads
      _a											=						intermediate_multiple_size
      _a											=						hidden_act
      _a											=						hidden_dropout
      _a											=						attention_dropout
      _a											=						weight_tying
      _a											=						max_position_embeddings
      _a											=						type_vocab_size
      _a											=						type_sequence_label_size
      _a											=						initializer_range
      _a											=						num_labels
      _a											=						num_choices
      _a											=						scope



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


      """simple docstring"""



      _a											=						ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size )

      _a											=						None
      if self.use_input_mask:
           _a											=						random_attention_mask([self.batch_size, self.seq_length] )

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

      _a											=						self.get_config()

      return config, input_ids, input_mask, token_labels



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      return GPTNeoXJapaneseConfig(
          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_multiple_size=self.intermediate_multiple_size				,     hidden_act=self.hidden_act				,     hidden_dropout=self.hidden_dropout				,     attention_dropout=self.attention_dropout				,     weight_tying=self.weight_tying				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     is_decoder=A				,     initializer_range=self.initializer_range				,     )



 def       a__						(self )					->  str:


      """simple docstring"""



      _a						,					_a						,					_a						,					_a											=						self.prepare_config_and_inputs()

      _a											=						True

      return config, input_ids, input_mask, token_labels



 def       a__						(self				,     A				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						GPTNeoXJapaneseModel(config=A )
      model.to(A )
      model.eval()
      _a											=						model(A				,     attention_mask=A )
      _a											=						model(A )
      self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size) )



 def       a__						(self				,     A				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						True
      _a											=						GPTNeoXJapaneseModel(A )
      model.to(A )
      model.eval()
      _a											=						model(A				,     attention_mask=A )
      self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size) )



 def       a__						(self				,     A				,     A				,     A				,     A )					->  str:


      """simple docstring"""



      _a											=						GPTNeoXJapaneseForCausalLM(config=A )
      model.to(A )
      model.eval()
      _a											=						model(A				,     attention_mask=A				,     labels=A )
      self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size) )



 def       a__						(self				,     A				,     A				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						True
      _a											=						GPTNeoXJapaneseForCausalLM(config=A )
      model.to(A )
      model.eval()

      # first forward pass
      _a											=						model(A				,     attention_mask=A				,     use_cache=A )
      _a											=						outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _a											=						ids_tensor((self.batch_size, 3)				,     config.vocab_size )
      _a											=						ids_tensor((self.batch_size, 3)				,     vocab_size=2 )

      # append to next input_ids and
      _a											=						torch.cat([input_ids, next_tokens]				,     dim=-1 )
      _a											=						torch.cat([input_mask, next_mask]				,     dim=-1 )

      _a											=						model(A				,     attention_mask=A				,     output_hidden_states=A )
      _a											=						output_from_no_past['''hidden_states'''][0]
      _a											=						model(
          A				,     attention_mask=A				,     past_key_values=A				,     output_hidden_states=A				,     )['''hidden_states'''][0]

      # select random slice
      _a											=						ids_tensor((1,)				,     output_from_past.shape[-1] ).item()
      _a											=						output_from_no_past[:, -3:, random_slice_idx].detach()
      _a											=						output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(A				,     A				,     atol=1E-3 ) )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						self.prepare_config_and_inputs()
      _a						,					_a						,					_a						,					_a											=						config_and_inputs
      _a											=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_torch
class __A    (						A     ,     A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: List[Any]    = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
 __lowerCamelCase					: str    = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
 __lowerCamelCase					: Dict    = (
     {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
     if is_torch_available()
     else {}
 )
 __lowerCamelCase					: Any    = False
 __lowerCamelCase					: Union[str, Any]    = False
 __lowerCamelCase					: int    = False
 __lowerCamelCase					: int    = False



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						GPTNeoXJapaneseModelTester(self )
      _a											=						ConfigTester(self				,     config_class=A				,     hidden_size=37 )



 def       a__						(self )					->  Any:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a						,					_a						,					_a						,					_a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(A				,     A				,     A )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a						,					_a						,					_a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(A				,     A				,     A )



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a						,					_a						,					_a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_decoder()

      _a											=						None

      self.model_tester.create_and_check_model_as_decoder(A				,     A				,     A )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a						,					_a						,					_a						,					_a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(A				,     A				,     A )



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_causal_lm(*A )



 @slow
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						'''abeja/gpt-neox-japanese-2.7b'''

      _a											=						['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']

      _a											=						[
          '''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
          '''100年後に必要とされる会社は、「人」が中心の会社です。''',
          '''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
          '''国境の長いトンネルを抜けると、そこは雪国だった。''',
          '''美味しい日本食といえば、やっぱりお寿司ですよね。''',
      ]

      _a											=						GPTNeoXJapaneseTokenizer.from_pretrained(A )
      _a											=						GPTNeoXJapaneseForCausalLM.from_pretrained(A )

      _a											=						[]
      for prompt in prompts:
           _a											=						tokenizer(A				,     return_tensors='''pt''' ).input_ids
           _a											=						model.generate(A				,     max_length=50 )
           _a											=						tokenizer.batch_decode(A				,     skip_special_tokens=A )
           predicted_outputs += generated_string
      self.assertListEqual(A				,     A )








'''simple docstring'''





import datasets

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


lowercase_	 =						"\\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"

lowercase_	 =						"\\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"

lowercase_	 =						"\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 __A    (						datasets.Metric		):



 '''simple docstring'''







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


      """simple docstring"""



      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       a__						(self				,     A				,     A				,     A=4				,     A=False )					->  Tuple:


      """simple docstring"""



      _a											=						compute_bleu(
          reference_corpus=A				,     translation_corpus=A				,     max_order=A				,     smooth=A )
      ((_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,
      }



