






"""simple docstring"""

import datasets

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


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

lowercase    			=       '''\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality 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,
the 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
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU\'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
representing 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
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
'''

lowercase    			=       '''
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    \'bleu\': bleu score,
    \'precisions\': geometric mean of n-gram precisions,
    \'brevity_penalty\': brevity penalty,
    \'length_ratio\': ratio of lengths,
    \'translation_length\': translation_length,
    \'reference_length\': reference_length
Examples:

    >>> predictions = [
    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample
    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)
    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric("bleu")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results["bleu"])
    1.0
'''

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







				def     _snake_case					(							self							:						int	)				->					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     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						str=4						,    __lowerCamelCase							:						Union[str, Any]=False	)				->					Optional[Any]:
										__magic_name__            =     compute_bleu(
										    reference_corpus=__lowerCamelCase						,    translation_corpus=__lowerCamelCase						,    max_order=__lowerCamelCase						,    smooth=__lowerCamelCase	)
										((__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__))            =     score
										return {
										    "bleu": bleu,
										    "precisions": precisions,
										    "brevity_penalty": bp,
										    "length_ratio": ratio,
										    "translation_length": translation_length,
										    "reference_length": reference_length,
										}











"""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
			lowercase    			=       '''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 _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:int ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:List[str]=None ,						__lowerCamelCase:Optional[int]=None ,						__lowerCamelCase:Optional[int]=None ,						):

						'''simple docstring'''






						if attention_mask is None:
												__magic_name__            =     np.where(input_ids != config.pad_token_id ,						1 ,						0   )
						if decoder_attention_mask is None:
												__magic_name__            =     np.where(decoder_input_ids != config.pad_token_id ,						1 ,						0   )
						if head_mask is None:
												__magic_name__            =     np.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												__magic_name__            =     np.ones((config.decoder_layers, config.decoder_attention_heads)   )
						if cross_attn_head_mask is None:
												__magic_name__            =     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							:						Tuple						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Dict=False						,    __lowerCamelCase							:						Optional[int]=9_9						,    __lowerCamelCase							:						Tuple=1_6						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						int=4						,    __lowerCamelCase							:						List[Any]=4						,    __lowerCamelCase							:						List[str]="gelu"						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[int]=0.1						,    __lowerCamelCase							:						List[str]=3_2						,    __lowerCamelCase							:						int=2						,    __lowerCamelCase							:						Any=1						,    __lowerCamelCase							:						int=0						,    __lowerCamelCase							:						Tuple=0.02						,    )				->					List[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     eos_token_id
										__magic_name__            =     pad_token_id
										__magic_name__            =     bos_token_id
										__magic_name__            =     initializer_range







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

										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    1						,    2	)

										__magic_name__            =     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						,    )
										__magic_name__            =     prepare_blenderbot_inputs_dict(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
										return config, inputs_dict







				def     _snake_case					(							self							:						Optional[Any]	)				->					Any:
										__magic_name__      ,    __magic_name__            =     self.prepare_config_and_inputs()
										return config, inputs_dict







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[Any]	)				->					Union[str, Any]:
										__magic_name__            =     2_0
										__magic_name__            =     model_class_name(__lowerCamelCase	)

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

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

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

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

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

										__magic_name__            =     model.decode(__lowerCamelCase						,    __lowerCamelCase	)

										__magic_name__            =     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     _snake_case					(							self							:						List[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Tuple	)				->					int:
										__magic_name__            =     2_0
										__magic_name__            =     model_class_name(__lowerCamelCase	)

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

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

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

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

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

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

										__magic_name__            =     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  ):
				UpperCAmelCase__        =				9_9







				def     _snake_case					(							self							:						Dict	)				->					Dict:
										__magic_name__            =     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						,    )

										__magic_name__            =     input_ids.shape[0]
										__magic_name__            =     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     _snake_case					(							self							:						Optional[Any]	)				->					Optional[int]:
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     self._get_config_and_data()
										__magic_name__            =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
										__magic_name__            =     lm_model(input_ids=__lowerCamelCase	)
										__magic_name__            =     (batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["logits"].shape						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										__magic_name__            =     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						,    )
										__magic_name__            =     FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
										__magic_name__            =     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	)
										__magic_name__            =     np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]						,    dtype=np.intaa	)
										__magic_name__            =     lm_model(input_ids=__lowerCamelCase						,    decoder_input_ids=__lowerCamelCase	)
										__magic_name__            =     (*summary.shape, config.vocab_size)
										self.assertEqual(outputs["logits"].shape						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										__magic_name__            =     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	)
										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    1						,    2	)
										__magic_name__            =     np.equal(__lowerCamelCase						,    1	).astype(np.floataa	).sum()
										__magic_name__            =     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 						A_							(      snake_case_					,							unittest.TestCase					,							snake_case_  ):
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				(
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				UpperCAmelCase__        =				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										__magic_name__            =     FlaxBlenderbotModelTester(self	)







				def     _snake_case					(							self							:						int	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__            =     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     _snake_case					(							self							:						Optional[int]	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__            =     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     _snake_case					(							self							:						Tuple	)				->					Dict:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

																						with self.subTest("JIT Enabled"	):
																												__magic_name__            =     encode_jitted(**__lowerCamelCase	).to_tuple()

																						with self.subTest("JIT Disabled"	):
																												with jax.disable_jit():
																																		__magic_name__            =     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     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

																						__magic_name__            =     {
																						    "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							:						Tuple						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Dict	):
																												return model.decode(
																												    decoder_input_ids=__lowerCamelCase						,    decoder_attention_mask=__lowerCamelCase						,    encoder_outputs=__lowerCamelCase						,    )

																						with self.subTest("JIT Enabled"	):
																												__magic_name__            =     decode_jitted(**__lowerCamelCase	).to_tuple()

																						with self.subTest("JIT Disabled"	):
																												with jax.disable_jit():
																																		__magic_name__            =     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     _snake_case					(							self							:						int	)				->					int:
										for model_class_name in self.all_model_classes:
																__magic_name__            =     model_class_name.from_pretrained("facebook/blenderbot-400M-distill"	)
																# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																__magic_name__            =     np.ones((1, 1)	) * model.config.eos_token_id
																__magic_name__            =     model(__lowerCamelCase	)
																self.assertIsNotNone(__lowerCamelCase	)







				@unittest.skipUnless(jax_device != "cpu"						,    "3B test too slow on CPU."	)
				@slow
				def     _snake_case					(							self							:						int	)				->					List[Any]:
										__magic_name__            =     {"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
										__magic_name__            =     {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

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

										__magic_name__            =     ["Sam"]
										__magic_name__            =     tokenizer(__lowerCamelCase						,    return_tensors="jax"	)

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

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



