


from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


SCREAMING_SNAKE_CASE       :      Optional[int]												= "\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

SCREAMING_SNAKE_CASE       :      List[str]												= "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

SCREAMING_SNAKE_CASE       :      Optional[int]												= "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						int:
    return float((preds == labels).mean() )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Tuple:
    _lowercase       :							Optional[int]      		=		simple_accuracy(lowerCamelCase_							, lowerCamelCase_ )
    _lowercase       :							Any      		=		float(fa_score(y_true=lowerCamelCase_							, y_pred=lowerCamelCase_ ) )
    return {
        "accuracy": acc,
        "f1": fa,
    }



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    _lowercase       :							Tuple      		=		float(pearsonr(lowerCamelCase_							, lowerCamelCase_ )[0] )
    _lowercase       :							Optional[int]      		=		float(spearmanr(lowerCamelCase_							, lowerCamelCase_ )[0] )
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
    }






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION   )
class _lowerCamelCase(						datasets.Metric   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           if self.config_name not in [
               "sst2",
               "mnli",
               "mnli_mismatched",
               "mnli_matched",
               "cola",
               "stsb",
               "mrpc",
               "qqp",
               "qnli",
               "rte",
               "wnli",
               "hans",
           ]:
               raise KeyError(
                   'You should supply a configuration name selected in '
                   '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                   '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]')
           return datasets.MetricInfo(
               description=_DESCRIPTION,      citation=_CITATION,      inputs_description=_KWARGS_DESCRIPTION,      features=datasets.Features(
                   {
                       'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'),
                       'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'),
                   }),      codebase_urls=[],      reference_urls=[],      format='numpy',      )




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   Any:




           """simple docstring"""

           if self.config_name == "cola":
               return {"matthews_correlation": matthews_corrcoef(lowerCamelCase,      lowerCamelCase)}
           elif self.config_name == "stsb":
               return pearson_and_spearman(lowerCamelCase,      lowerCamelCase)
           elif self.config_name in ["mrpc", "qqp"]:
               return acc_and_fa(lowerCamelCase,      lowerCamelCase)
           elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
               return {"accuracy": simple_accuracy(lowerCamelCase,      lowerCamelCase)}
           else:
               raise KeyError(
                   'You should supply a configuration name selected in '
                   '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                   '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]')







"""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 : List[Any]      						=						"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				:    int							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Optional[Any]=None							,		__lowerCamelCase				:    str=None							,		__lowerCamelCase				:    Tuple=None							,		__lowerCamelCase				:    List[Any]=None							,		__lowerCamelCase				:    Tuple=None							,		__lowerCamelCase				:    str=None							,		):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__		:		Any			=np.where(input_ids != config.pad_token_id							,		1							,		0	)
			if decoder_attention_mask is None:
						lowerCamelCase__		:		str			=np.where(decoder_input_ids != config.pad_token_id							,		1							,		0	)
			if head_mask is None:
						lowerCamelCase__		:		Dict			=np.ones((config.encoder_layers, config.encoder_attention_heads)	)
			if decoder_head_mask is None:
						lowerCamelCase__		:		str			=np.ones((config.decoder_layers, config.decoder_attention_heads)	)
			if cross_attn_head_mask is None:
						lowerCamelCase__		:		Dict			=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        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  str,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  List[str]=13,  lowerCamelCase							:  Dict=7,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  int=99,  lowerCamelCase							:  Union[str, Any]=16,  lowerCamelCase							:  List[str]=2,  lowerCamelCase							:  int=4,  lowerCamelCase							:  Tuple=4,  lowerCamelCase							:  Optional[Any]="gelu",  lowerCamelCase							:  List[str]=0.1,  lowerCamelCase							:  str=0.1,  lowerCamelCase							:  Optional[int]=32,  lowerCamelCase							:  List[str]=2,  lowerCamelCase							:  Tuple=1,  lowerCamelCase							:  Optional[int]=0,  lowerCamelCase							:  int=0.02,  )->					Optional[Any]:
								lowerCamelCase__		:		List[str]			=parent
								lowerCamelCase__		:		Dict			=batch_size
								lowerCamelCase__		:		Optional[int]			=seq_length
								lowerCamelCase__		:		Any			=is_training
								lowerCamelCase__		:		Optional[int]			=use_labels
								lowerCamelCase__		:		List[str]			=vocab_size
								lowerCamelCase__		:		List[Any]			=hidden_size
								lowerCamelCase__		:		List[Any]			=num_hidden_layers
								lowerCamelCase__		:		Tuple			=num_attention_heads
								lowerCamelCase__		:		List[Any]			=intermediate_size
								lowerCamelCase__		:		Union[str, Any]			=hidden_act
								lowerCamelCase__		:		Optional[Any]			=hidden_dropout_prob
								lowerCamelCase__		:		Tuple			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[Any]			=max_position_embeddings
								lowerCamelCase__		:		List[Any]			=eos_token_id
								lowerCamelCase__		:		Tuple			=pad_token_id
								lowerCamelCase__		:		Union[str, Any]			=bos_token_id
								lowerCamelCase__		:		List[Any]			=initializer_range



					def 		snake_case       (       self							:  Optional[Any]     )->					str:
								lowerCamelCase__		:		Dict			=np.clip(ids_tensor([self.batch_size, self.seq_length - 1],  self.vocab_size     ),  3,  self.vocab_size     )
								lowerCamelCase__		:		Union[str, Any]			=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),  dtype=np.intaa     )),  -1     )

								lowerCamelCase__		:		Dict			=shift_tokens_right(lowerCamelCase,  1,  2     )

								lowerCamelCase__		:		Optional[Any]			=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,  )
								lowerCamelCase__		:		List[str]			=prepare_blenderbot_inputs_dict(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     )
								return config, inputs_dict



					def 		snake_case       (       self							:  str     )->					Optional[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=self.prepare_config_and_inputs()
								return config, inputs_dict



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Tuple     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=20
								lowerCamelCase__		:		Optional[int]			=model_class_name(lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=model.encode(inputs_dict['''input_ids''']     )

								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								lowerCamelCase__		:		Any			=model.init_cache(decoder_input_ids.shape[0],  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Dict			=jnp.ones((decoder_input_ids.shape[0], max_decoder_length),  dtype='''i4'''     )

								lowerCamelCase__		:		List[Any]			=jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :],  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),  )
								lowerCamelCase__		:		int			=model.decode(
								    decoder_input_ids[:, :-1],  lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase,  decoder_position_ids=lowerCamelCase,  )

								lowerCamelCase__		:		Optional[int]			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype='''i4'''     )
								lowerCamelCase__		:		Union[str, Any]			=model.decode(
								    decoder_input_ids[:, -1:],  lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_position_ids=lowerCamelCase,  )

								lowerCamelCase__		:		int			=model.decode(lowerCamelCase,  lowerCamelCase     )

								lowerCamelCase__		:		Dict			=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							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  str     )->					List[str]:
								lowerCamelCase__		:		List[Any]			=20
								lowerCamelCase__		:		List[Any]			=model_class_name(lowerCamelCase     )

								lowerCamelCase__		:		Union[str, Any]			=model.encode(inputs_dict['''input_ids''']     )

								lowerCamelCase__	,   lowerCamelCase__		:		str			=(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

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

								lowerCamelCase__		:		List[str]			=model.init_cache(decoder_input_ids.shape[0],  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :],  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),  )

								lowerCamelCase__		:		List[Any]			=model.decode(
								    decoder_input_ids[:, :-1],  lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase,  decoder_position_ids=lowerCamelCase,  )
								lowerCamelCase__		:		List[str]			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype='''i4'''     )
								lowerCamelCase__		:		Optional[Any]			=model.decode(
								    decoder_input_ids[:, -1:],  lowerCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_attention_mask=lowerCamelCase,  decoder_position_ids=lowerCamelCase,  )

								lowerCamelCase__		:		Optional[Any]			=model.decode(lowerCamelCase,  lowerCamelCase,  decoder_attention_mask=lowerCamelCase     )

								lowerCamelCase__		:		List[Any]			=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        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							9_9



					def 		snake_case       (       self							:  Optional[int]     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=np.array(
								    [
								        [71, 82, 18, 33, 46, 91, 2],
								        [68, 34, 26, 58, 30, 82, 2],
								        [5, 97, 17, 39, 94, 40, 2],
								        [76, 83, 94, 25, 70, 78, 2],
								        [87, 59, 41, 35, 48, 66, 2],
								        [55, 13, 16, 58, 5, 2, 1],  # note padding
								        [64, 27, 31, 51, 12, 75, 2],
								        [52, 64, 86, 17, 83, 39, 2],
								        [48, 61, 9, 24, 71, 82, 2],
								        [26, 1, 60, 48, 22, 13, 2],
								        [21, 5, 62, 28, 14, 76, 2],
								        [45, 98, 37, 86, 59, 48, 2],
								        [70, 70, 50, 9, 28, 0, 2],
								    ],  dtype=np.intaa,  )

								lowerCamelCase__		:		Any			=input_ids.shape[0]
								lowerCamelCase__		:		Any			=BlenderbotConfig(
								    vocab_size=self.vocab_size,  d_model=24,  encoder_layers=2,  decoder_layers=2,  encoder_attention_heads=2,  decoder_attention_heads=2,  encoder_ffn_dim=32,  decoder_ffn_dim=32,  max_position_embeddings=48,  eos_token_id=2,  pad_token_id=1,  bos_token_id=0,  )
								return config, input_ids, batch_size



					def 		snake_case       (       self							:  Any     )->					Union[str, Any]:
								lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		List[Any]			=self._get_config_and_data()
								lowerCamelCase__		:		int			=FlaxBlenderbotForConditionalGeneration(lowerCamelCase     )
								lowerCamelCase__		:		str			=lm_model(input_ids=lowerCamelCase     )
								lowerCamelCase__		:		List[Any]			=(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,  lowerCamelCase     )



					def 		snake_case       (       self							:  Tuple     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=BlenderbotConfig(
								    vocab_size=self.vocab_size,  d_model=14,  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=48,  )
								lowerCamelCase__		:		Union[str, Any]			=FlaxBlenderbotForConditionalGeneration(lowerCamelCase     )
								lowerCamelCase__		:		List[Any]			=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],  dtype=np.intaa     )
								lowerCamelCase__		:		Optional[Any]			=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],  dtype=np.intaa     )
								lowerCamelCase__		:		Optional[int]			=lm_model(input_ids=lowerCamelCase,  decoder_input_ids=lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,  lowerCamelCase     )




					def 		snake_case       (       self							:  Union[str, Any]     )->					Union[str, Any]:
								lowerCamelCase__		:		Optional[int]			=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],  dtype=np.intaa     )
								lowerCamelCase__		:		Optional[Any]			=shift_tokens_right(lowerCamelCase,  1,  2     )
								lowerCamelCase__		:		str			=np.equal(lowerCamelCase,  1     ).astype(np.floataa     ).sum()
								lowerCamelCase__		:		List[str]			=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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   unittest.TestCase		,   lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							True
					_a   			=							(
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					_a   			=							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								lowerCamelCase__		:		str			=FlaxBlenderbotModelTester(self     )



					def 		snake_case       (       self							:  Optional[int]     )->					int:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=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							:  List[str]     )->					Union[str, Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Union[str, Any]			=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							:  List[Any]     )->					Tuple:
								lowerCamelCase__	,   lowerCamelCase__		:		int			=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__     ):
														lowerCamelCase__		:		List[Any]			=self._prepare_for_class(lowerCamelCase,  lowerCamelCase     )
														lowerCamelCase__		:		int			=model_class(lowerCamelCase     )

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

														with self.subTest('''JIT Enabled'''     ):
																	lowerCamelCase__		:		Any			=encode_jitted(**lowerCamelCase     ).to_tuple()

														with self.subTest('''JIT Disabled'''     ):
																	with jax.disable_jit():
																				lowerCamelCase__		:		Dict			=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							:  List[str]     )->					Dict:
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__     ):
														lowerCamelCase__		:		Optional[Any]			=model_class(lowerCamelCase     )
														lowerCamelCase__		:		List[Any]			=model.encode(inputs_dict['''input_ids'''],  inputs_dict['''attention_mask''']     )

														lowerCamelCase__		:		Optional[int]			={
														    '''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							:  Union[str, Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  Tuple     ):
																	return model.decode(
																	    decoder_input_ids=lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  encoder_outputs=lowerCamelCase,  )

														with self.subTest('''JIT Enabled'''     ):
																	lowerCamelCase__		:		Union[str, Any]			=decode_jitted(**lowerCamelCase     ).to_tuple()

														with self.subTest('''JIT Disabled'''     ):
																	with jax.disable_jit():
																				lowerCamelCase__		:		Optional[Any]			=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							:  Tuple     )->					Tuple:
								for model_class_name in self.all_model_classes:
											lowerCamelCase__		:		int			=model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''     )
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											lowerCamelCase__		:		Union[str, Any]			=np.ones((1, 1)     ) * model.config.eos_token_id
											lowerCamelCase__		:		Optional[Any]			=model(lowerCamelCase     )
											self.assertIsNotNone(lowerCamelCase     )




					@unittest.skipUnless(jax_device != '''cpu''',  '''3B test too slow on CPU.'''     )
					@slow
					def 		snake_case       (       self							:  Optional[int]     )->					Tuple:
								lowerCamelCase__		:		List[Any]			={'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
								lowerCamelCase__		:		Optional[int]			={'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

								lowerCamelCase__		:		Tuple			=FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''',  from_pt=lowerCamelCase     )
								lowerCamelCase__		:		int			=BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''     )

								lowerCamelCase__		:		str			=['''Sam''']
								lowerCamelCase__		:		Union[str, Any]			=tokenizer(lowerCamelCase,  return_tensors='''jax'''     )

								lowerCamelCase__		:		Tuple			=model.generate(**lowerCamelCase,  **lowerCamelCase     )
								lowerCamelCase__		:		Tuple			='''Sam is a great name. It means "sun" in Gaelic.'''

								lowerCamelCase__		:		Union[str, Any]			=tokenizer.batch_decode(lowerCamelCase,  **lowerCamelCase     )
								assert generated_txt[0].strip() == tgt_text




