
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowercase   =	'''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowercase   =	'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

lowercase   =	'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''





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

					def  lowercase__   (						self       :  List[Any] ):
												if version.parse(scb.__version__ ) < version.parse("""1.4.12""" ):
																			raise ImportWarning(
																			    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
																			    """You can install it with `pip install \"sacrebleu>=1.4.12\"`.""" )
												return datasets.MetricInfo(
												    description=_DESCRIPTION ,       citation=_CITATION ,       homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf""" ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
												        {
												            """predictions""": datasets.Value("""string""" ,       id="""sequence""" ),
												            """references""": datasets.Sequence(datasets.Value("""string""" ,       id="""sequence""" ) ,       id="""references""" ),
												        } ) ,       codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""] ,       reference_urls=[
												        """https://github.com/m-popovic/chrF""",
												    ] ,       )



					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  int = CHRF.CHAR_ORDER ,       __UpperCamelCase       :  int = CHRF.WORD_ORDER ,       __UpperCamelCase       :  int = CHRF.BETA ,       __UpperCamelCase       :  bool = False ,       __UpperCamelCase       :  bool = False ,       __UpperCamelCase       :  bool = False ,       ):
												lowerCamelCase_					=       len(references[0] )
												if any(len(__UpperCamelCase ) != references_per_prediction for refs in references ):
																			raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
												lowerCamelCase_					=       [[refs[i] for refs in references] for i in range(__UpperCamelCase )]

												lowerCamelCase_					=       CHRF(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       sb_chrf.corpus_score(__UpperCamelCase ,       __UpperCamelCase )

												return {
												    "score": output.score,
												    "char_order": output.char_order,
												    "word_order": output.word_order,
												    "beta": output.beta,
												}







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
		lowercase   =	'''platform'''

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Any=None      ,					UpperCAmelCase__					:   Optional[Any]=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Optional[int]=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Dict=None      ,					)	->     List[Any]:
							if attention_mask is None:
														lowerCamelCase_					=       np.where(input_ids != config.pad_token_id      ,					1      ,					0							)
							if decoder_attention_mask is None:
														lowerCamelCase_					=       np.where(decoder_input_ids != config.pad_token_id      ,					1      ,					0							)
							if head_mask is None:
														lowerCamelCase_					=       np.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														lowerCamelCase_					=       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														lowerCamelCase_					=       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    __A:

					def __init__(						self       :  str ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  str=1_3 ,       __UpperCamelCase       :  List[str]=7 ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  List[Any]=False ,       __UpperCamelCase       :  Dict=9_9 ,       __UpperCamelCase       :  Tuple=1_6 ,       __UpperCamelCase       :  Any=2 ,       __UpperCamelCase       :  Dict=4 ,       __UpperCamelCase       :  List[str]=4 ,       __UpperCamelCase       :  Optional[Any]="gelu" ,       __UpperCamelCase       :  List[str]=0.1 ,       __UpperCamelCase       :  str=0.1 ,       __UpperCamelCase       :  Union[str, Any]=3_2 ,       __UpperCamelCase       :  str=2 ,       __UpperCamelCase       :  Tuple=1 ,       __UpperCamelCase       :  Optional[Any]=0 ,       __UpperCamelCase       :  str=0.02 ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       seq_length
												lowerCamelCase_					=       is_training
												lowerCamelCase_					=       use_labels
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       eos_token_id
												lowerCamelCase_					=       pad_token_id
												lowerCamelCase_					=       bos_token_id
												lowerCamelCase_					=       initializer_range

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size ) ,       3 ,       self.vocab_size )
												lowerCamelCase_					=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,       dtype=np.intaa )) ,       -1 )

												lowerCamelCase_					=       shift_tokens_right(__UpperCamelCase ,       1 ,       2 )

												lowerCamelCase_					=       BlenderbotSmallConfig(
												    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=__UpperCamelCase ,       )
												lowerCamelCase_					=       prepare_blenderbot_inputs_dict(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
												return config, inputs_dict

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.prepare_config_and_inputs()
												return config, inputs_dict

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       2_0
												lowerCamelCase_					=       model_class_name(__UpperCamelCase )

												lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] )

												lowerCamelCase_	, lowerCamelCase_					=       (
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												lowerCamelCase_					=       model.init_cache(decoder_input_ids.shape[0] ,       __UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,       dtype="""i4""" )

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

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

												lowerCamelCase_					=       model.decode(__UpperCamelCase ,       __UpperCamelCase )

												lowerCamelCase_					=       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  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       2_0
												lowerCamelCase_					=       model_class_name(__UpperCamelCase )

												lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] )

												lowerCamelCase_	, lowerCamelCase_					=       (
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

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

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

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

												lowerCamelCase_					=       model.decode(__UpperCamelCase ,       __UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase )

												lowerCamelCase_					=       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    __A(					unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     9_9

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       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 ,       )

												lowerCamelCase_					=       input_ids.shape[0]
												lowerCamelCase_					=       BlenderbotSmallConfig(
												    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  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       self._get_config_and_data()
												lowerCamelCase_					=       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase )
												lowerCamelCase_					=       lm_model(input_ids=__UpperCamelCase )
												lowerCamelCase_					=       (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       BlenderbotSmallConfig(
												    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 ,       )
												lowerCamelCase_					=       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase )
												lowerCamelCase_					=       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 )
												lowerCamelCase_					=       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] ,       dtype=np.intaa )
												lowerCamelCase_					=       lm_model(input_ids=__UpperCamelCase ,       decoder_input_ids=__UpperCamelCase )
												lowerCamelCase_					=       (*summary.shape, config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape ,       __UpperCamelCase )



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       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 )
												lowerCamelCase_					=       shift_tokens_right(__UpperCamelCase ,       1 ,       2 )
												lowerCamelCase_					=       np.equal(__UpperCamelCase ,       1 ).astype(np.floataa ).sum()
												lowerCamelCase_					=       np.equal(__UpperCamelCase ,       1 ).astype(np.floataa ).sum()
												self.assertEqual(shifted.shape ,       input_ids.shape )
												self.assertEqual(__UpperCamelCase ,       n_pad_before - 1 )
												self.assertTrue(np.equal(shifted[:, 0] ,       2 ).all() )





@require_flax
class    __A(					UpperCAmelCase  ,		unittest.TestCase  ,		UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     True
					SCREAMING_SNAKE_CASE           =     (
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE           =     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       FlaxBlenderbotSmallModelTester(self )

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_	, lowerCamelCase_					=       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(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																										lowerCamelCase_					=       self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase )
																										lowerCamelCase_					=       model_class(__UpperCamelCase )

																										@jax.jit
																										def encode_jitted(__UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict=None ,       **__UpperCamelCase       :  Tuple ):
																																	return model.encode(input_ids=__UpperCamelCase ,       attention_mask=__UpperCamelCase )

																										with self.subTest("""JIT Enabled""" ):
																																	lowerCamelCase_					=       encode_jitted(**__UpperCamelCase ).to_tuple()

																										with self.subTest("""JIT Disabled""" ):
																																	with jax.disable_jit():
																																								lowerCamelCase_					=       encode_jitted(**__UpperCamelCase ).to_tuple()

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

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																										lowerCamelCase_					=       model_class(__UpperCamelCase )
																										lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] ,       inputs_dict["""attention_mask"""] )

																										lowerCamelCase_					=       {
																										    """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(__UpperCamelCase       :  int ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Union[str, Any] ):
																																	return model.decode(
																																	    decoder_input_ids=__UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase ,       encoder_outputs=__UpperCamelCase ,       )

																										with self.subTest("""JIT Enabled""" ):
																																	lowerCamelCase_					=       decode_jitted(**__UpperCamelCase ).to_tuple()

																										with self.subTest("""JIT Disabled""" ):
																																	with jax.disable_jit():
																																								lowerCamelCase_					=       decode_jitted(**__UpperCamelCase ).to_tuple()

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



					@slow
					def  lowercase__   (						self       :  str ):
												for model_class_name in self.all_model_classes:
																			lowerCamelCase_					=       model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
																			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																			lowerCamelCase_					=       np.ones((1, 1) ) * model.config.eos_token_id
																			lowerCamelCase_					=       model(__UpperCamelCase )
																			self.assertIsNotNone(__UpperCamelCase )





