# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   lowercase_    (    UpperCAmelCase__		):



	"""simple docstring"""





	UpperCAmelCase_    :   torch.FloatTensor
	UpperCAmelCase_    :   Optional[torch.FloatTensor]  =							None

def   SCREAMING_SNAKE_CASE_	(		snake_case__  ,    snake_case__=0.9_99  ,    snake_case__="cosine"  ,    )	->      Any:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(snake_case__				):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(snake_case__				):
																					return math.exp(t * -12.0				)

							else:
														raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"				)

							lowerCAmelCase      						=						[]
							for i in range(snake_case__				):
														lowerCAmelCase      						=						i / num_diffusion_timesteps
														lowerCAmelCase      						=						(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(snake_case__				) / alpha_bar_fn(snake_case__				)  ,    snake_case__				)				)
							return torch.tensor(snake_case__  ,    dtype=torch.floataa				)






class   lowercase_    (    UpperCAmelCase__ ,	UpperCAmelCase__		):



	"""simple docstring"""





	UpperCAmelCase_    :   str  =							1


	@register_to_config
	def __init__(				self						,     __SCREAMING_SNAKE_CASE = 1000						,     __SCREAMING_SNAKE_CASE = 0.0_0_0_1						,     __SCREAMING_SNAKE_CASE = 0.0_2						,     __SCREAMING_SNAKE_CASE = "linear"						,     __SCREAMING_SNAKE_CASE = None						,     __SCREAMING_SNAKE_CASE = True						,     __SCREAMING_SNAKE_CASE = True						,     __SCREAMING_SNAKE_CASE = 0						,     __SCREAMING_SNAKE_CASE = "epsilon"						,     __SCREAMING_SNAKE_CASE = 1.0						,     **__SCREAMING_SNAKE_CASE						,     )		->Optional[Any]:
								if kwargs.get('''set_alpha_to_one'''						,     lowerCamelCase__							) is not None:
															lowerCAmelCase      						=						(
															    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
															)
															deprecate('''set_alpha_to_one'''						,     '''1.0.0'''						,     lowerCamelCase__						,     standard_warn=lowerCamelCase__							)
															lowerCAmelCase      						=						kwargs["set_alpha_to_one"]
								if trained_betas is not None:
															lowerCAmelCase      						=						torch.tensor(lowerCamelCase__						,     dtype=torch.floataa							)
								elif beta_schedule == "linear":
															lowerCAmelCase      						=						torch.linspace(lowerCamelCase__						,     lowerCamelCase__						,     lowerCamelCase__						,     dtype=torch.floataa							)
								elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															lowerCAmelCase      						=						(
															    torch.linspace(beta_start**0.5						,     beta_end**0.5						,     lowerCamelCase__						,     dtype=torch.floataa							) ** 2
															)
								elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															lowerCAmelCase      						=						betas_for_alpha_bar(lowerCamelCase__							)
								else:
															raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"							)

								lowerCAmelCase      						=						1.0 - self.betas
								lowerCAmelCase      						=						torch.cumprod(self.alphas						,     dim=0							)

								# At every step in inverted ddim, we are looking into the next alphas_cumprod
								# For the final step, there is no next alphas_cumprod, and the index is out of bounds
								# `set_alpha_to_zero` decides whether we set this parameter simply to zero
								# in this case, self.step() just output the predicted noise
								# or whether we use the final alpha of the "non-previous" one.
								lowerCAmelCase      						=						torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

								# standard deviation of the initial noise distribution
								lowerCAmelCase      						=						1.0

								# setable values
								lowerCAmelCase      						=						None
								lowerCAmelCase      						=						torch.from_numpy(np.arange(0						,     lowerCamelCase__							).copy().astype(np.intaa							)							)


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE = None							)		->Union[str, Any]:
								return sample


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE = None							)		->str:

								if num_inference_steps > self.config.num_train_timesteps:
															raise ValueError(
															    F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
															    F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
															    F" maximal {self.config.num_train_timesteps} timesteps."							)

								lowerCAmelCase      						=						num_inference_steps
								lowerCAmelCase      						=						self.config.num_train_timesteps // self.num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# casting to int to avoid issues when num_inference_step is power of 3
								lowerCAmelCase      						=						(np.arange(0						,     lowerCamelCase__							) * step_ratio).round().copy().astype(np.intaa							)
								lowerCAmelCase      						=						torch.from_numpy(lowerCamelCase__							).to(lowerCamelCase__							)
								self.timesteps += self.config.steps_offset


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE = 0.0						,     __SCREAMING_SNAKE_CASE = False						,     __SCREAMING_SNAKE_CASE = None						,     __SCREAMING_SNAKE_CASE = True						,     )		->List[Any]:
								# 1. get previous step value (=t+1)
								lowerCAmelCase      						=						timestep + self.config.num_train_timesteps // self.num_inference_steps

								# 2. compute alphas, betas
								# change original implementation to exactly match noise levels for analogous forward process
								lowerCAmelCase      						=						self.alphas_cumprod[timestep]
								lowerCAmelCase      						=						(
								    self.alphas_cumprod[prev_timestep]
								    if prev_timestep < self.config.num_train_timesteps
								    else self.final_alpha_cumprod
								)

								lowerCAmelCase      						=						1 - alpha_prod_t

								# 3. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								if self.config.prediction_type == "epsilon":
															lowerCAmelCase      						=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
															lowerCAmelCase      						=						model_output
								elif self.config.prediction_type == "sample":
															lowerCAmelCase      						=						model_output
															lowerCAmelCase      						=						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
								elif self.config.prediction_type == "v_prediction":
															lowerCAmelCase      						=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
															lowerCAmelCase      						=						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
								else:
															raise ValueError(
															    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
															    ''' `v_prediction`'''							)

								# 4. Clip or threshold "predicted x_0"
								if self.config.clip_sample:
															lowerCAmelCase      						=						pred_original_sample.clamp(
															    -self.config.clip_sample_range						,     self.config.clip_sample_range							)

								# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								lowerCAmelCase      						=						(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

								# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								lowerCAmelCase      						=						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

								if not return_dict:
															return (prev_sample, pred_original_sample)
								return DDIMSchedulerOutput(prev_sample=lowerCamelCase__						,     pred_original_sample=lowerCamelCase__							)




	def __len__(				self							)		->Tuple:
								return self.config.num_train_timesteps




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 a  :      Tuple            =    """platform"""

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				lowercase=None							,				):
   if attention_mask is None:
      lowerCAmelCase_:							Optional[int]       =	np.where(input_ids != config.pad_token_id							,				1							,				0					)
   if decoder_attention_mask is None:
      lowerCAmelCase_:							Union[str, Any]       =	np.where(decoder_input_ids != config.pad_token_id							,				1							,				0					)
   if head_mask is None:
      lowerCAmelCase_:							Optional[int]       =	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_:							Any       =	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 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=7       ,						lowerCamelCase__=True       ,						lowerCamelCase__=False       ,						lowerCamelCase__=99       ,						lowerCamelCase__=16       ,						lowerCamelCase__=2       ,						lowerCamelCase__=4       ,						lowerCamelCase__=4       ,						lowerCamelCase__="gelu"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=32       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1       ,						lowerCamelCase__=0       ,						lowerCamelCase__=0.0_2       ,						):
       lowerCAmelCase_:							Union[str, Any]       =	parent
       lowerCAmelCase_:							Tuple       =	batch_size
       lowerCAmelCase_:							Any       =	seq_length
       lowerCAmelCase_:							Tuple       =	is_training
       lowerCAmelCase_:							Optional[int]       =	use_labels
       lowerCAmelCase_:							List[Any]       =	vocab_size
       lowerCAmelCase_:							str       =	hidden_size
       lowerCAmelCase_:							Union[str, Any]       =	num_hidden_layers
       lowerCAmelCase_:							List[str]       =	num_attention_heads
       lowerCAmelCase_:							Dict       =	intermediate_size
       lowerCAmelCase_:							int       =	hidden_act
       lowerCAmelCase_:							Any       =	hidden_dropout_prob
       lowerCAmelCase_:							str       =	attention_probs_dropout_prob
       lowerCAmelCase_:							Union[str, Any]       =	max_position_embeddings
       lowerCAmelCase_:							Any       =	eos_token_id
       lowerCAmelCase_:							Union[str, Any]       =	pad_token_id
       lowerCAmelCase_:							Union[str, Any]       =	bos_token_id
       lowerCAmelCase_:							Optional[int]       =	initializer_range




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

       lowerCAmelCase_:							Optional[Any]       =	shift_tokens_right(lowerCamelCase__       ,						1       ,						2 )

       lowerCAmelCase_:							Optional[int]       =	BlenderbotSmallConfig(
           vocab_size=self.vocab_size       ,						d_model=self.hidden_size       ,						encoder_layers=self.num_hidden_layers       ,						decoder_layers=self.num_hidden_layers       ,						encoder_attention_heads=self.num_attention_heads       ,						decoder_attention_heads=self.num_attention_heads       ,						encoder_ffn_dim=self.intermediate_size       ,						decoder_ffn_dim=self.intermediate_size       ,						dropout=self.hidden_dropout_prob       ,						attention_dropout=self.attention_probs_dropout_prob       ,						max_position_embeddings=self.max_position_embeddings       ,						eos_token_id=self.eos_token_id       ,						bos_token_id=self.bos_token_id       ,						pad_token_id=self.pad_token_id       ,						initializer_range=self.initializer_range       ,						use_cache=lowerCamelCase__       ,						)
       lowerCAmelCase_:							Optional[Any]       =	prepare_blenderbot_inputs_dict(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       return config, inputs_dict




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	self.prepare_config_and_inputs()
       return config, inputs_dict




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	20
       lowerCAmelCase_:							int       =	model_class_name(lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	model.encode(inputs_dict["input_ids"] )

       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	(
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       lowerCAmelCase_:							List[str]       =	model.init_cache(decoder_input_ids.shape[0]       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,						dtype="i4" )

       lowerCAmelCase_:							Optional[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_:							Optional[Any]       =	model.decode(
           decoder_input_ids[:, :-1]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)

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

       lowerCAmelCase_:							Any       =	model.decode(lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							str       =	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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	20
       lowerCAmelCase_:							Union[str, Any]       =	model_class_name(lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	model.encode(inputs_dict["input_ids"] )

       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

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

       lowerCAmelCase_:							Any       =	model.init_cache(decoder_input_ids.shape[0]       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       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_:							Tuple       =	model.decode(
           decoder_input_ids[:, :-1]       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						past_key_values=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)
       lowerCAmelCase_:							str       =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,						dtype="i4" )
       lowerCAmelCase_:							Tuple       =	model.decode(
           decoder_input_ids[:, -1:]       ,						lowerCamelCase__       ,						past_key_values=outputs_cache.past_key_values       ,						decoder_attention_mask=lowerCamelCase__       ,						decoder_position_ids=lowerCamelCase__       ,						)

       lowerCAmelCase_:							Union[str, Any]       =	model.decode(lowerCamelCase__       ,						lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[int]       =	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 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    99




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	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_:							Optional[int]       =	input_ids.shape[0]
       lowerCAmelCase_:							str       =	BlenderbotSmallConfig(
           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      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	self._get_config_and_data()
       lowerCAmelCase_:							Union[str, Any]       =	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	lm_model(input_ids=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["logits"].shape       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	BlenderbotSmallConfig(
           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]       =	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__ )
       lowerCAmelCase_:							str       =	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							str       =	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,						dtype=np.intaa )
       lowerCAmelCase_:							Any       =	lm_model(input_ids=lowerCamelCase__       ,						decoder_input_ids=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	(*summary.shape, config.vocab_size)
       self.assertEqual(outputs["logits"].shape       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	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_:							List[Any]       =	np.equal(lowerCamelCase__       ,						1 ).astype(np.floataa ).sum()
       lowerCAmelCase_:							Any       =	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 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase ,  UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[Any]          =    True
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	FlaxBlenderbotSmallModelTester(self )




    def      _a    (    self ):
       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(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[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      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[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_:							Dict       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ )
             lowerCAmelCase_:							Union[str, Any]       =	model_class(lowerCamelCase__ )

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

             with self.subTest("JIT Enabled" ):
                lowerCAmelCase_:							Optional[int]       =	encode_jitted(**lowerCamelCase__ ).to_tuple()

             with self.subTest("JIT Disabled" ):
                with jax.disable_jit():
                   lowerCAmelCase_:							int       =	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      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[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_:							Dict       =	model_class(lowerCamelCase__ )
             lowerCAmelCase_:							Optional[int]       =	model.encode(inputs_dict["input_ids"]       ,						inputs_dict["attention_mask"] )

             lowerCAmelCase_:							Any       =	{
                 "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__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
                return model.decode(
                    decoder_input_ids=lowerCamelCase__       ,						decoder_attention_mask=lowerCamelCase__       ,						encoder_outputs=lowerCamelCase__       ,						)

             with self.subTest("JIT Enabled" ):
                lowerCAmelCase_:							Dict       =	decode_jitted(**lowerCamelCase__ ).to_tuple()

             with self.subTest("JIT Disabled" ):
                with jax.disable_jit():
                   lowerCAmelCase_:							Union[str, 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      _a    (    self ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_:							Any       =	model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
          # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
          lowerCAmelCase_:							str       =	np.ones((1, 1) ) * model.config.eos_token_id
          lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )