





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


snake_case__					:							Union[str, Any]													=     'scheduler_config.json'





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = 1
   lowerCAmelCase__        = 2
   lowerCAmelCase__        = 3
   lowerCAmelCase__        = 4
   lowerCAmelCase__        = 5





@dataclass
class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = 42





class A_					:
   lowerCAmelCase__        = SCHEDULER_CONFIG_NAME
   lowerCAmelCase__        = ["""dtype"""]
   lowerCAmelCase__        = []
   lowerCAmelCase__        = True




   @classmethod
   def _lowerCAmelCase      (cls	:Optional[int]    ,   _UpperCamelCase	:Dict[str, Any] = None    ,   _UpperCamelCase	:Optional[str] = None    ,   _UpperCamelCase	:Dict=False    ,   **_UpperCamelCase	:List[Any]    ,   )->					str:
         __A   ,						__A          =				cls.load_config(
             pretrained_model_name_or_path=_UpperCamelCase    ,   subfolder=_UpperCamelCase    ,   return_unused_kwargs=_UpperCamelCase    ,   **_UpperCamelCase    ,   )
         __A   ,						__A          =				cls.from_config(_UpperCamelCase    ,   return_unused_kwargs=_UpperCamelCase    ,   **_UpperCamelCase )

         if hasattr(_UpperCamelCase    ,   '''create_state''' ) and getattr(_UpperCamelCase    ,   '''has_state'''    ,   _UpperCamelCase ):
               __A          =				scheduler.create_state()

         if return_unused_kwargs:
               return scheduler, state, unused_kwargs

         return scheduler, state




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Union[str, os.PathLike]    ,   _UpperCamelCase	:bool = False    ,   **_UpperCamelCase	:Optional[Any] )->					str:
         self.save_config(save_directory=_UpperCamelCase    ,   push_to_hub=_UpperCamelCase    ,   **_UpperCamelCase )




   @property
   def _lowerCAmelCase      (self	:int )->					List[Any]:
         return self._get_compatibles()






   @classmethod
   def _lowerCAmelCase      (cls	:Any )->					str:
         __A          =				list(set([cls.__name__] + cls._compatibles ) )
         __A          =				importlib.import_module(__name__.split('''.''' )[0] )
         __A          =				[
             getattr(_UpperCamelCase    ,   _UpperCamelCase ) for c in compatible_classes_str if hasattr(_UpperCamelCase    ,   _UpperCamelCase )
         ]
         return compatible_classes





def _a				(	lowerCamelCase:   jnp.ndarray					,				lowerCamelCase:   Tuple[int] )	->						jnp.ndarray:







      '''simple docstring'''






      assert len(lowerCamelCase ) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase ) - x.ndim) )					,				lowerCamelCase )



def _a				(	lowerCamelCase:   int					,				lowerCamelCase:   int=0.999					,				lowerCamelCase:   Dict=jnp.floataa )	->						jnp.ndarray:







      '''simple docstring'''






      def alpha_bar(lowerCamelCase:   Dict ):
            return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

      __A          =				[]
      for i in range(lowerCamelCase ):
            __A          =				i / num_diffusion_timesteps
            __A          =				(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar(lowerCamelCase ) / alpha_bar(lowerCamelCase )					,				lowerCamelCase ) )
      return jnp.array(lowerCamelCase					,				dtype=lowerCamelCase )





@flax.struct.dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42




   @classmethod
   def _lowerCAmelCase      (cls	:Union[str, Any]    ,   _UpperCamelCase	:Optional[int] )->					Any:
         __A          =				scheduler.config

         if config.trained_betas is not None:
               __A          =				jnp.asarray(config.trained_betas    ,   dtype=scheduler.dtype )
         elif config.beta_schedule == "linear":
               __A          =				jnp.linspace(config.beta_start    ,   config.beta_end    ,   config.num_train_timesteps    ,   dtype=scheduler.dtype )
         elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               __A          =				(
                   jnp.linspace(
                       config.beta_start**0.5    ,   config.beta_end**0.5    ,   config.num_train_timesteps    ,   dtype=scheduler.dtype )
                   ** 2
               )
         elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               __A          =				betas_for_alpha_bar(config.num_train_timesteps    ,   dtype=scheduler.dtype )
         else:
               raise NotImplementedError(
                   f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}""" )

         __A          =				1.0 - betas

         __A          =				jnp.cumprod(_UpperCamelCase    ,   axis=0 )

         return cls(
             alphas=_UpperCamelCase    ,   betas=_UpperCamelCase    ,   alphas_cumprod=_UpperCamelCase    ,   )





def _a				(	lowerCamelCase:   CommonSchedulerState					,				lowerCamelCase:   jnp.ndarray					,				lowerCamelCase:   jnp.ndarray					,				lowerCamelCase:   jnp.ndarray )	->						Dict:







      '''simple docstring'''






      __A          =				state.alphas_cumprod

      __A          =				alphas_cumprod[timesteps] ** 0.5
      __A          =				sqrt_alpha_prod.flatten()
      __A          =				broadcast_to_shape_from_left(lowerCamelCase					,				original_samples.shape )

      __A          =				(1 - alphas_cumprod[timesteps]) ** 0.5
      __A          =				sqrt_one_minus_alpha_prod.flatten()
      __A          =				broadcast_to_shape_from_left(lowerCamelCase					,				original_samples.shape )

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def _a				(	lowerCamelCase:   CommonSchedulerState					,				lowerCamelCase:   jnp.ndarray					,				lowerCamelCase:   jnp.ndarray					,				lowerCamelCase:   jnp.ndarray )	->						str:







      '''simple docstring'''






      __A   ,						__A          =				get_sqrt_alpha_prod(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      __A          =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples







def _a				(	lowerCamelCase:   CommonSchedulerState					,				lowerCamelCase:   jnp.ndarray					,				lowerCamelCase:   jnp.ndarray					,				lowerCamelCase:   jnp.ndarray )	->						List[Any]:







      '''simple docstring'''






      __A   ,						__A          =				get_sqrt_alpha_prod(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      __A          =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity












import unittest

import numpy as np
import timeout_decorator  # noqa

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

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


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       snake_case__					:							Tuple													=     '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 _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   int=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Tuple=None					,				lowerCamelCase:   Union[str, Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				)	->						Union[str, Any]:







      '''simple docstring'''






      if attention_mask is None:
            __A          =				np.where(input_ids != config.pad_token_id					,				1					,				0 )
      if decoder_attention_mask is None:
            __A          =				np.where(decoder_input_ids != config.pad_token_id					,				1					,				0 )
      if head_mask is None:
            __A          =				np.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
            __A          =				np.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
            __A          =				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	:int    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Dict=13    ,   _UpperCamelCase	:Optional[Any]=7    ,   _UpperCamelCase	:str=True    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:int=99    ,   _UpperCamelCase	:int=16    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:int=4    ,   _UpperCamelCase	:str=4    ,   _UpperCamelCase	:Dict="gelu"    ,   _UpperCamelCase	:int=0.1    ,   _UpperCamelCase	:Tuple=0.1    ,   _UpperCamelCase	:Union[str, Any]=32    ,   _UpperCamelCase	:Any=2    ,   _UpperCamelCase	:Union[str, Any]=1    ,   _UpperCamelCase	:Tuple=0    ,   _UpperCamelCase	:List[str]=0.0_2    ,   )->					str:
         __A          =				parent
         __A          =				batch_size
         __A          =				seq_length
         __A          =				is_training
         __A          =				use_labels
         __A          =				vocab_size
         __A          =				hidden_size
         __A          =				num_hidden_layers
         __A          =				num_attention_heads
         __A          =				intermediate_size
         __A          =				hidden_act
         __A          =				hidden_dropout_prob
         __A          =				attention_probs_dropout_prob
         __A          =				max_position_embeddings
         __A          =				eos_token_id
         __A          =				pad_token_id
         __A          =				bos_token_id
         __A          =				initializer_range




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

         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )

         __A          =				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=_UpperCamelCase    ,   )
         __A          =				prepare_blenderbot_inputs_dict(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A   ,						__A          =				self.prepare_config_and_inputs()
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Optional[int] )->					str:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

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

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

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

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

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

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase )

         __A          =				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	:Optional[int]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Any )->					Dict:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

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

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

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

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

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

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase )

         __A          =				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   ):
   lowerCAmelCase__        = 99




   def _lowerCAmelCase      (self	:Dict )->					int:
         __A          =				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    ,   )

         __A          =				input_ids.shape[0]
         __A          =				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	:Any )->					List[Any]:
         __A   ,						__A   ,						__A          =				self._get_config_and_data()
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				lm_model(input_ids=_UpperCamelCase )
         __A          =				(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )




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






   def _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,   dtype=np.intaa )
         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         __A          =				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_					(			_lowerCamelCase ,      unittest.TestCase ,      _lowerCamelCase   ):
   lowerCAmelCase__        = True
   lowerCAmelCase__        = (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   lowerCAmelCase__        = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




   def _lowerCAmelCase      (self	:List[str] )->					Optional[int]:
         __A          =				FlaxBlenderbotModelTester(self )




   def _lowerCAmelCase      (self	:List[str] )->					List[str]:
         __A   ,						__A          =				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 _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A   ,						__A          =				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 _lowerCAmelCase      (self	:Union[str, Any] )->					Union[str, Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				self._prepare_for_class(_UpperCamelCase    ,   _UpperCamelCase )
                     __A          =				model_class(_UpperCamelCase )

                     @jax.jit
                     def encode_jitted(_UpperCamelCase	:int    ,   _UpperCamelCase	:int=None    ,   **_UpperCamelCase	:Dict ):
                           return model.encode(input_ids=_UpperCamelCase    ,   attention_mask=_UpperCamelCase )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

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

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

                     __A          =				{
                         '''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	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Dict ):
                           return model.decode(
                               decoder_input_ids=_UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   encoder_outputs=_UpperCamelCase    ,   )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

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






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

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

         __A          =				['''Sam''']
         __A          =				tokenizer(_UpperCamelCase    ,   return_tensors='''jax''' )

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

         __A          =				tokenizer.batch_decode(_UpperCamelCase    ,   **_UpperCamelCase )
         assert generated_txt[0].strip() == tgt_text





