




import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


lowerCamelCase__			:       str         =							logging.get_logger(__name__)

@dataclass
class 	lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       =   field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys() )} )
 lowercase_       =   field(
     metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."} )
 lowercase_       =   field(
     default=128	,       metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }	,       )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Overwrite the cached training and evaluation sets"} )



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.task_name.lower()


class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   "train"
 lowercase_       =   "dev"
 lowercase_       =   "test"


class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   42
 lowercase_       =   42
 lowercase_       =   42



 def __init__(							self  :		List[Any]    ,    _lowerCAmelCase  :		GlueDataTrainingArguments    ,    _lowerCAmelCase  :		PreTrainedTokenizerBase    ,    _lowerCAmelCase  :		Optional[int] = None    ,    _lowerCAmelCase  :		Union[str, Split] = Split.train    ,    _lowerCAmelCase  :		Optional[str] = None    ,    ):
       warnings.warn(
           'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
           'library. You can have a look at this example script for pointers: '
           'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'    ,    _lowerCAmelCase    ,    )
       SCREAMING_SNAKE_CASE_     						=      args
       SCREAMING_SNAKE_CASE_     						=      glue_processors[args.task_name]()
       SCREAMING_SNAKE_CASE_     						=      glue_output_modes[args.task_name]
       if isinstance(_lowerCAmelCase    ,    _lowerCAmelCase						):
             try:
                   SCREAMING_SNAKE_CASE_     						=      Split[mode]
             except KeyError:
                   raise KeyError('mode is not a valid split name'						)
        # Load data features from cache or dataset file
       SCREAMING_SNAKE_CASE_     						=      os.path.join(
           cache_dir if cache_dir is not None else args.data_dir    ,    F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"    ,    )
       SCREAMING_SNAKE_CASE_     						=      self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      label_list[2], label_list[1]
       SCREAMING_SNAKE_CASE_     						=      label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       SCREAMING_SNAKE_CASE_     						=      cached_features_file + '.lock'
       with FileLock(_lowerCAmelCase						):
             if os.path.exists(_lowerCAmelCase						) and not args.overwrite_cache:
                   SCREAMING_SNAKE_CASE_     						=      time.time()
                   SCREAMING_SNAKE_CASE_     						=      torch.load(_lowerCAmelCase						)
                   logger.info(
                       F"Loading features from cached file {cached_features_file} [took %.3f s]"    ,    time.time() - start						)
             else:
                   logger.info(F"Creating features from dataset file at {args.data_dir}"						)

                   if mode == Split.dev:
                         SCREAMING_SNAKE_CASE_     						=      self.processor.get_dev_examples(args.data_dir						)
                   elif mode == Split.test:
                         SCREAMING_SNAKE_CASE_     						=      self.processor.get_test_examples(args.data_dir						)
                   else:
                         SCREAMING_SNAKE_CASE_     						=      self.processor.get_train_examples(args.data_dir						)
                   if limit_length is not None:
                         SCREAMING_SNAKE_CASE_     						=      examples[:limit_length]
                   SCREAMING_SNAKE_CASE_     						=      glue_convert_examples_to_features(
                       _lowerCAmelCase    ,    _lowerCAmelCase    ,    max_length=args.max_seq_length    ,    label_list=_lowerCAmelCase    ,    output_mode=self.output_mode    ,    )
                   SCREAMING_SNAKE_CASE_     						=      time.time()
                   torch.save(self.features    ,    _lowerCAmelCase						)
                   # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                   logger.info(
                       F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"						)



 def __len__(							self  :		Tuple						):
       return len(self.features						)



 def __getitem__(							self  :		List[str]    ,    _lowerCAmelCase  :		Dict						):
       return self.features[i]




 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       return self.label_list





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
   lowerCamelCase__			:       Union[str, 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  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     Dict=None      ,      __UpperCAmelCase						:     List[str]=None      ,      __UpperCAmelCase						:     Optional[Any]=None      ,      __UpperCAmelCase						:     Optional[Any]=None      ,      __UpperCAmelCase						:     Any=None      ,      __UpperCAmelCase						:     Union[str, Any]=None      ,      ) ->			List[Any]:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_     						=      np.where(input_ids != config.pad_token_id      ,      1      ,      0							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_     						=      np.where(decoder_input_ids != config.pad_token_id      ,      1      ,      0							)
      if head_mask is None:
            SCREAMING_SNAKE_CASE_     						=      np.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_     						=      np.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            SCREAMING_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 	lowerCamelCase_							:


 '''simple docstring'''







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



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

       SCREAMING_SNAKE_CASE_     						=      shift_tokens_right(_lowerCAmelCase    ,    1    ,    2						)

       SCREAMING_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    ,    )
       SCREAMING_SNAKE_CASE_     						=      prepare_blenderbot_inputs_dict(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
       return config, inputs_dict



 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
       return config, inputs_dict



 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      20
       SCREAMING_SNAKE_CASE_     						=      model_class_name(_lowerCAmelCase						)

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

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

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

       SCREAMING_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)    ,    )
       SCREAMING_SNAKE_CASE_     						=      model.decode(
           decoder_input_ids[:, :-1]    ,    _lowerCAmelCase    ,    decoder_attention_mask=_lowerCAmelCase    ,    past_key_values=_lowerCAmelCase    ,    decoder_position_ids=_lowerCAmelCase    ,    )

       SCREAMING_SNAKE_CASE_     						=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,    dtype='i4'						)
       SCREAMING_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    ,    )

       SCREAMING_SNAKE_CASE_     						=      model.decode(_lowerCAmelCase    ,    _lowerCAmelCase						)

       SCREAMING_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 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_     						=      20
       SCREAMING_SNAKE_CASE_     						=      model_class_name(_lowerCAmelCase						)

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

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

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

       SCREAMING_SNAKE_CASE_     						=      model.init_cache(decoder_input_ids.shape[0]    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
       SCREAMING_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)    ,    )

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

       SCREAMING_SNAKE_CASE_     						=      model.decode(_lowerCAmelCase    ,    _lowerCAmelCase    ,    decoder_attention_mask=_lowerCAmelCase						)

       SCREAMING_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 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   99



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      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    ,    )

       SCREAMING_SNAKE_CASE_     						=      input_ids.shape[0]
       SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self._get_config_and_data()
       SCREAMING_SNAKE_CASE_     						=      FlaxBlenderbotForConditionalGeneration(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      lm_model(input_ids=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['logits'].shape    ,    _lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      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    ,    )
       SCREAMING_SNAKE_CASE_     						=      FlaxBlenderbotForConditionalGeneration(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,    dtype=np.intaa						)
       SCREAMING_SNAKE_CASE_     						=      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,    dtype=np.intaa						)
       SCREAMING_SNAKE_CASE_     						=      lm_model(input_ids=_lowerCAmelCase    ,    decoder_input_ids=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['logits'].shape    ,    _lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,    dtype=np.intaa						)
       SCREAMING_SNAKE_CASE_     						=      shift_tokens_right(_lowerCAmelCase    ,    1    ,    2						)
       SCREAMING_SNAKE_CASE_     						=      np.equal(_lowerCAmelCase    ,    1						).astype(np.floataa						).sum()
       SCREAMING_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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase	,       _SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







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



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      FlaxBlenderbotModelTester(self						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_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 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_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 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_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__						):
                   SCREAMING_SNAKE_CASE_     						=      self._prepare_for_class(_lowerCAmelCase    ,    _lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)

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

                   with self.subTest('JIT Enabled'						):
                         SCREAMING_SNAKE_CASE_     						=      encode_jitted(**_lowerCAmelCase						).to_tuple()

                   with self.subTest('JIT Disabled'						):
                         with jax.disable_jit():
                               SCREAMING_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 				lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_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__						):
                   SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_     						=      model.encode(inputs_dict['input_ids']    ,    inputs_dict['attention_mask']						)

                   SCREAMING_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  :		Tuple    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		str						):
                         return model.decode(
                             decoder_input_ids=_lowerCAmelCase    ,    decoder_attention_mask=_lowerCAmelCase    ,    encoder_outputs=_lowerCAmelCase    ,    )

                   with self.subTest('JIT Enabled'						):
                         SCREAMING_SNAKE_CASE_     						=      decode_jitted(**_lowerCAmelCase						).to_tuple()

                   with self.subTest('JIT Disabled'						):
                         with jax.disable_jit():
                               SCREAMING_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 				lowerCAmelCase_  (							self  :		str						):
       for model_class_name in self.all_model_classes:
             SCREAMING_SNAKE_CASE_     						=      model_class_name.from_pretrained('facebook/blenderbot-400M-distill'						)
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             SCREAMING_SNAKE_CASE_     						=      np.ones((1, 1)						) * model.config.eos_token_id
             SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)




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

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

       SCREAMING_SNAKE_CASE_     						=      ['Sam']
       SCREAMING_SNAKE_CASE_     						=      tokenizer(_lowerCAmelCase    ,    return_tensors='jax'						)

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

       SCREAMING_SNAKE_CASE_     						=      tokenizer.batch_decode(_lowerCAmelCase    ,    **_lowerCAmelCase						)
       assert generated_txt[0].strip() == tgt_text