
"""simple docstring"""





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.distilbert.modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
    )


class A_	(unittest.TestCase      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=13				,   lowercase_=7				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   lowercase_=99				,   lowercase_=32				,   lowercase_=5				,   lowercase_=4				,   lowercase_=37				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=512				,   lowercase_=16				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=4				,   ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size  )

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

     UpperCAmelCase_					:					int			     =    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				,   tie_weights_=lowercase_				,   )

     return config, input_ids, attention_mask







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.prepare_config_and_inputs()
     UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    config_and_inputs
     UpperCAmelCase_					:					Dict			     =    {"input_ids": input_ids, "attention_mask": attention_mask}
     return config, inputs_dict




@require_flax
class A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Dict       =		(
      (
          FlaxDistilBertModel,
          FlaxDistilBertForMaskedLM,
          FlaxDistilBertForMultipleChoice,
          FlaxDistilBertForQuestionAnswering,
          FlaxDistilBertForSequenceClassification,
          FlaxDistilBertForTokenClassification,
          FlaxDistilBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    FlaxDistilBertModelTester(self  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_class_name in self.all_model_classes:
        UpperCAmelCase_					:					int			     =    model_class_name.from_pretrained("distilbert-base-uncased"  )
        UpperCAmelCase_					:					List[Any]			     =    model(np.ones((1, 1)  )  )
        self.assertIsNotNone(lowercase_  )




@require_flax
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"  )
     UpperCAmelCase_					:					Optional[Any]			     =    np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
     UpperCAmelCase_					:					Dict			     =    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
     UpperCAmelCase_					:					Union[str, Any]			     =    model(lowercase_				,   attention_mask=lowercase_  )[0]
     UpperCAmelCase_					:					Dict			     =    (1, 11, 768)
     self.assertEqual(output.shape				,   lowercase_  )
     UpperCAmelCase_					:					int			     =    np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]  )

     self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]				,   lowercase_				,   atol=1E-4  )  )





'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase :      List[Any]					 =     version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
     from nltk import word_tokenize


lowerCamelCase :      Any					 =     "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowerCamelCase :      Optional[Any]					 =     "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowerCamelCase :      Optional[Any]					 =     "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,   _KWARGS_DESCRIPTION  )
class 							A__    (       datasets.Metric  ):





  def 						A  (		self      :	Tuple )	->    str:





       '''simple docstring'''






       return datasets.MetricInfo(
           description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
               {
                   'predictions': datasets.Value('string'							,					id='sequence' ),
                   'references': datasets.Value('string'							,					id='sequence' ),
               } )							,					codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,					reference_urls=[
               'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
               'https://en.wikipedia.org/wiki/METEOR',
           ]							,					)





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Union[str, Any] )	->    Optional[int]:





       '''simple docstring'''






       import nltk

       nltk.download('wordnet' )
       if NLTK_VERSION >= version.Version('3.6.5' ):
            nltk.download('punkt' )
       if NLTK_VERSION >= version.Version('3.6.6' ):
            nltk.download('omw-1.4' )





  def 						A  (		self      :	int							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	List[str]=0.9							,					_a      :	Dict=3							,					_a      :	Optional[int]=0.5 )	->    Optional[int]:





       '''simple docstring'''






       if NLTK_VERSION >= version.Version('3.6.5' ):
            _SCREAMING_SNAKE_CASE							    =[
                meteor_score.single_meteor_score(
                    word_tokenize(_a )							,					word_tokenize(_a )							,					alpha=_a							,					beta=_a							,					gamma=_a )
                for ref, pred in zip(_a							,					_a )
            ]
       else:
            _SCREAMING_SNAKE_CASE							    =[
                meteor_score.single_meteor_score(_a							,					_a							,					alpha=_a							,					beta=_a							,					gamma=_a )
                for ref, pred in zip(_a							,					_a )
            ]

       return {"meteor": np.mean(_a )}
