



"""simple docstring"""






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


snake_case   					=				'''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

snake_case   					=				'''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

snake_case   					=				'''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''



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



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """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'''				),
             }				)							,       )





   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	List[List[List[str]]]							,       __lowerCamelCase    :	List[List[str]]							,       __lowerCamelCase    :	int = 1							,       __lowerCamelCase    :	int = 4							,       ):



     """simple docstring"""
     return {
         "google_bleu": gleu_score.corpus_gleu(
             list_of_references=__lowerCamelCase							,       hypotheses=__lowerCamelCase							,       min_len=__lowerCamelCase							,       max_len=__lowerCamelCase				)
     }










"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     snake_case   					=				'''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->				List[Any]:
  if attention_mask is None:
    _snake_case   	=      np.where(input_ids != config.pad_token_id  ,						1  ,						0	)
  if decoder_attention_mask is None:
    _snake_case   	=      np.where(decoder_input_ids != config.pad_token_id  ,						1  ,						0	)
  if head_mask is None:
    _snake_case   	=      np.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  if cross_attn_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]=1_3							,       __lowerCamelCase    :	int=7							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	Optional[int]=9_9							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Tuple=2							,       __lowerCamelCase    :	Tuple=4							,       __lowerCamelCase    :	Optional[int]=4							,       __lowerCamelCase    :	Tuple="gelu"							,       __lowerCamelCase    :	int=0.1							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Optional[int]=2							,       __lowerCamelCase    :	Optional[Any]=1							,       __lowerCamelCase    :	Dict=0							,       __lowerCamelCase    :	List[str]=0.0_2							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      seq_length
     _snake_case   	=      is_training
     _snake_case   	=      use_labels
     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      eos_token_id
     _snake_case   	=      pad_token_id
     _snake_case   	=      bos_token_id
     _snake_case   	=      initializer_range



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,       self.vocab_size				)							,       3							,       self.vocab_size				)
     _snake_case   	=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,       dtype=np.intaa				))							,       -1				)

     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)

     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=self.hidden_size							,       encoder_layers=self.num_hidden_layers							,       decoder_layers=self.num_hidden_layers							,       encoder_attention_heads=self.num_attention_heads							,       decoder_attention_heads=self.num_attention_heads							,       encoder_ffn_dim=self.intermediate_size							,       decoder_ffn_dim=self.intermediate_size							,       dropout=self.hidden_dropout_prob							,       attention_dropout=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       eos_token_id=self.eos_token_id							,       bos_token_id=self.bos_token_id							,       pad_token_id=self.pad_token_id							,       initializer_range=self.initializer_range							,       use_cache=__lowerCamelCase							,       )
     _snake_case   	=      prepare_blenderbot_inputs_dict(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.prepare_config_and_inputs()
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _snake_case   	=      model.init_cache(decoder_input_ids.shape[0]							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,       dtype='''i4'''				)

     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )
     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase				)

     _snake_case   	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
     self.parent.assertTrue(diff < 1E-3							,       msg=f"""Max diff is {diff}"""				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _snake_case   	=      jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
         ]							,       axis=-1							,       )

     _snake_case   	=      model.init_cache(decoder_input_ids.shape[0]							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )

     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )
     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_attention_mask=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase				)

     _snake_case   	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
     self.parent.assertTrue(diff < 1E-3							,       msg=f"""Max diff is {diff}"""				)



@require_flax
class     UpperCAmelCase    (	unittest.TestCase  ):
   A__		:	Dict           =  99



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      np.array(
         [
             [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
             [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
             [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
             [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
             [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
             [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
             [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
             [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
             [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
             [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
             [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
             [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
             [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
         ]							,       dtype=np.intaa							,       )

     _snake_case   	=      input_ids.shape[0]
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=2_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=3_2							,       decoder_ffn_dim=3_2							,       max_position_embeddings=4_8							,       eos_token_id=2							,       pad_token_id=1							,       bos_token_id=0							,       )
     return config, input_ids, batch_size



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case	,     _snake_case   	=      self._get_config_and_data()
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase				)
     _snake_case   	=      (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=1_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=8							,       decoder_ffn_dim=8							,       max_position_embeddings=4_8							,       )
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase							,       decoder_input_ids=__lowerCamelCase				)
     _snake_case   	=      (*summary.shape, config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,       dtype=np.intaa				)
     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     self.assertEqual(shifted.shape							,       input_ids.shape				)
     self.assertEqual(__lowerCamelCase							,       n_pad_before - 1				)
     self.assertTrue(np.equal(shifted[:, 0]							,       2				).all()				)



@require_flax
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase,__SCREAMING_SNAKE_CASE  ):
   A__		:	Union[str, Any]           =  True
   A__		:	int           =  (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   A__		:	List[Any]           =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      FlaxBlenderbotModelTester(self				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)
         _snake_case   	=      model_class(__lowerCamelCase				)

         @jax.jit
         def encode_jitted(__lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[int]=None							,       **__lowerCamelCase    :	Union[str, Any]				):
           return model.encode(input_ids=__lowerCamelCase							,       attention_mask=__lowerCamelCase				)

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      model_class(__lowerCamelCase				)
         _snake_case   	=      model.encode(inputs_dict['''input_ids''']							,       inputs_dict['''attention_mask''']				)

         _snake_case   	=      {
             '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
             '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
             '''encoder_outputs''': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(__lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):
           return model.decode(
               decoder_input_ids=__lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       encoder_outputs=__lowerCamelCase							,       )

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   @slow
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     for model_class_name in self.all_model_classes:
       _snake_case   	=      model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       _snake_case   	=      np.ones((1, 1)				) * model.config.eos_token_id
       _snake_case   	=      model(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)





   @unittest.skipUnless(jax_device != '''cpu'''							,       '''3B test too slow on CPU.'''				)
   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
     _snake_case   	=      {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

     _snake_case   	=      FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''							,       from_pt=__lowerCamelCase				)
     _snake_case   	=      BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

     _snake_case   	=      ['''Sam''']
     _snake_case   	=      tokenizer(__lowerCamelCase							,       return_tensors='''jax'''				)

     _snake_case   	=      model.generate(**__lowerCamelCase							,       **__lowerCamelCase				)
     _snake_case   	=      '''Sam is a great name. It means "sun" in Gaelic.'''

     _snake_case   	=      tokenizer.batch_decode(__lowerCamelCase							,       **__lowerCamelCase				)
     assert generated_txt[0].strip() == tgt_text





