import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[int]	      = (DEISMultistepScheduler,)
   lowerCAmelCase   :     str	      = (('''num_inference_steps''', 2_5),)



   def 	lowerCAmelCase    (						self       :	Any		,	**_lowercase       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			{
        '''num_train_timesteps''': 1_000,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
        '''solver_order''': 2,
    }

    config.update(**_lowercase				)
    return config



   def 	lowerCAmelCase    (						self       :	int		,	_lowercase       :	Any=0		,	**_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			dict(self.forward_default_kwargs				)
    _UpperCamelCase:  List[Any]    						=			kwargs.pop('''num_inference_steps'''		,	_lowercase				)
    _UpperCamelCase:  List[Any]    						=			self.dummy_sample
    _UpperCamelCase:  int    						=			0.1 * sample
    _UpperCamelCase:  Tuple    						=			[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
     _UpperCamelCase:  Optional[Any]    						=			self.get_scheduler_config(**_lowercase				)
     _UpperCamelCase:  Tuple    						=			scheduler_class(**_lowercase				)
     scheduler.set_timesteps(_lowercase				)
     # copy over dummy past residuals
     _UpperCamelCase:  List[Any]    						=			dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(_lowercase				)
      _UpperCamelCase:  Optional[int]    						=			scheduler_class.from_pretrained(_lowercase				)
      new_scheduler.set_timesteps(_lowercase				)
      # copy over dummy past residuals
      _UpperCamelCase:  Tuple    						=			dummy_past_residuals[: new_scheduler.config.solver_order]

     _UpperCamelCase						,				_UpperCamelCase:  Dict    						=			sample, sample
     for t in range(_lowercase		,	time_step + scheduler.config.solver_order + 1				):
      _UpperCamelCase:  str    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample
      _UpperCamelCase:  Tuple    						=			new_scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample

      assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	int		,	_lowercase       :	List[Any]=0		,	**_lowercase       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			dict(self.forward_default_kwargs				)
    _UpperCamelCase:  int    						=			kwargs.pop('''num_inference_steps'''		,	_lowercase				)
    _UpperCamelCase:  Union[str, Any]    						=			self.dummy_sample
    _UpperCamelCase:  Union[str, Any]    						=			0.1 * sample
    _UpperCamelCase:  Optional[Any]    						=			[residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
     _UpperCamelCase:  str    						=			self.get_scheduler_config()
     _UpperCamelCase:  str    						=			scheduler_class(**_lowercase				)
     scheduler.set_timesteps(_lowercase				)

     # copy over dummy past residuals (must be after setting timesteps)
     _UpperCamelCase:  str    						=			dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(_lowercase				)
      _UpperCamelCase:  Tuple    						=			scheduler_class.from_pretrained(_lowercase				)
      # copy over dummy past residuals
      new_scheduler.set_timesteps(_lowercase				)

      # copy over dummy past residual (must be after setting timesteps)
      _UpperCamelCase:  List[Any]    						=			dummy_past_residuals[: new_scheduler.config.solver_order]

     _UpperCamelCase:  List[str]    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample
     _UpperCamelCase:  Union[str, Any]    						=			new_scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample

     assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	List[str]=None		,	**_lowercase       :	List[Any]				):



    """simple docstring"""



    if scheduler is None:
     _UpperCamelCase:  Optional[int]    						=			self.scheduler_classes[0]
     _UpperCamelCase:  Optional[int]    						=			self.get_scheduler_config(**_lowercase				)
     _UpperCamelCase:  List[str]    						=			scheduler_class(**_lowercase				)

    _UpperCamelCase:  List[str]    						=			self.scheduler_classes[0]
    _UpperCamelCase:  Tuple    						=			self.get_scheduler_config(**_lowercase				)
    _UpperCamelCase:  str    						=			scheduler_class(**_lowercase				)

    _UpperCamelCase:  Any    						=			10
    _UpperCamelCase:  Any    						=			self.dummy_model()
    _UpperCamelCase:  int    						=			self.dummy_sample_deter
    scheduler.set_timesteps(_lowercase				)

    for i, t in enumerate(scheduler.timesteps				):
     _UpperCamelCase:  Any    						=			model(_lowercase		,	_lowercase				)
     _UpperCamelCase:  Optional[Any]    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase				).prev_sample

    return sample



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			dict(self.forward_default_kwargs				)

    _UpperCamelCase:  Dict    						=			kwargs.pop('''num_inference_steps'''		,	_lowercase				)

    for scheduler_class in self.scheduler_classes:
     _UpperCamelCase:  List[Any]    						=			self.get_scheduler_config()
     _UpperCamelCase:  str    						=			scheduler_class(**_lowercase				)

     _UpperCamelCase:  Optional[Any]    						=			self.dummy_sample
     _UpperCamelCase:  Any    						=			0.1 * sample

     if num_inference_steps is not None and hasattr(_lowercase		,	'''set_timesteps'''				):
      scheduler.set_timesteps(_lowercase				)
     elif num_inference_steps is not None and not hasattr(_lowercase		,	'''set_timesteps'''				):
      _UpperCamelCase:  Any    						=			num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _UpperCamelCase:  Dict    						=			[residual + 0.2, residual + 0.15, residual + 0.10]
     _UpperCamelCase:  Tuple    						=			dummy_past_residuals[: scheduler.config.solver_order]

     _UpperCamelCase:  List[Any]    						=			scheduler.timesteps[5]
     _UpperCamelCase:  Optional[int]    						=			scheduler.timesteps[6]

     _UpperCamelCase:  List[Any]    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample
     _UpperCamelCase:  List[Any]    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample

     self.assertEqual(output_a.shape		,	sample.shape				)
     self.assertEqual(output_a.shape		,	output_a.shape				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			DEISMultistepScheduler(**self.get_scheduler_config()				)
    _UpperCamelCase:  Dict    						=			self.full_loop(scheduler=_lowercase				)
    _UpperCamelCase:  List[str]    						=			torch.mean(torch.abs(_lowercase				)				)

    assert abs(result_mean.item() - 0.23916				) < 1E-3

    _UpperCamelCase:  Dict    						=			DPMSolverSinglestepScheduler.from_config(scheduler.config				)
    _UpperCamelCase:  int    						=			DPMSolverMultistepScheduler.from_config(scheduler.config				)
    _UpperCamelCase:  List[Any]    						=			UniPCMultistepScheduler.from_config(scheduler.config				)
    _UpperCamelCase:  Optional[int]    						=			DEISMultistepScheduler.from_config(scheduler.config				)

    _UpperCamelCase:  int    						=			self.full_loop(scheduler=_lowercase				)
    _UpperCamelCase:  Dict    						=			torch.mean(torch.abs(_lowercase				)				)

    assert abs(result_mean.item() - 0.23916				) < 1E-3



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    for timesteps in [25, 50, 100, 999, 1_000]:
     self.check_over_configs(num_train_timesteps=_lowercase				)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    self.check_over_configs(thresholding=_lowercase				)
    for order in [1, 2, 3]:
     for solver_type in ["logrho"]:
      for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(
            thresholding=_lowercase		,	prediction_type=_lowercase		,	sample_max_value=_lowercase		,	algorithm_type='''deis'''		,	solver_order=_lowercase		,	solver_type=_lowercase		,	)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    for algorithm_type in ["deis"]:
     for solver_type in ["logrho"]:
      for order in [1, 2, 3]:
       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(
            solver_order=_lowercase		,	solver_type=_lowercase		,	prediction_type=_lowercase		,	algorithm_type=_lowercase		,	)
        _UpperCamelCase:  int    						=			self.full_loop(
            solver_order=_lowercase		,	solver_type=_lowercase		,	prediction_type=_lowercase		,	algorithm_type=_lowercase		,	)
        assert not torch.isnan(_lowercase				).any(), "Samples have nan numbers"



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    self.check_over_configs(lower_order_final=_lowercase				)
    self.check_over_configs(lower_order_final=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
     self.check_over_forward(num_inference_steps=_lowercase		,	time_step=0				)



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			self.full_loop()
    _UpperCamelCase:  int    						=			torch.mean(torch.abs(_lowercase				)				)

    assert abs(result_mean.item() - 0.23916				) < 1E-3



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.full_loop(prediction_type='''v_prediction'''				)
    _UpperCamelCase:  List[Any]    						=			torch.mean(torch.abs(_lowercase				)				)

    assert abs(result_mean.item() - 0.091				) < 1E-3



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			self.scheduler_classes[0]
    _UpperCamelCase:  int    						=			self.get_scheduler_config(thresholding=_lowercase		,	dynamic_thresholding_ratio=0				)
    _UpperCamelCase:  str    						=			scheduler_class(**_lowercase				)

    _UpperCamelCase:  List[Any]    						=			10
    _UpperCamelCase:  List[Any]    						=			self.dummy_model()
    _UpperCamelCase:  int    						=			self.dummy_sample_deter.half()
    scheduler.set_timesteps(_lowercase				)

    for i, t in enumerate(scheduler.timesteps				):
     _UpperCamelCase:  int    						=			model(_lowercase		,	_lowercase				)
     _UpperCamelCase:  Dict    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase				).prev_sample

    assert sample.dtype == torch.floataa
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
  UpperCAmelCase_           =				'''platform'''

  import jax
  import jax.numpy as jnp

  from transformers import BlenderbotTokenizer
  from transformers.models.blenderbot.modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      shift_tokens_right,
  )




def    lowerCAmelCase_  (		lowercase:				Any	,       lowercase:				Optional[int]	,       lowercase:				Union[str, Any]=None	,       lowercase:				int=None	,       lowercase:				Any=None	,       lowercase:				Optional[int]=None	,       lowercase:				int=None	,       lowercase:				int=None	,       )     ->					int:







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





   """simple docstring"""




   def __init__(						self       :	Optional[int]		,	_lowercase       :	Tuple		,	_lowercase       :	int=13		,	_lowercase       :	Any=7		,	_lowercase       :	List[str]=True		,	_lowercase       :	int=False		,	_lowercase       :	Union[str, Any]=99		,	_lowercase       :	Optional[Any]=16		,	_lowercase       :	int=2		,	_lowercase       :	Any=4		,	_lowercase       :	Any=4		,	_lowercase       :	Optional[Any]="gelu"		,	_lowercase       :	Optional[Any]=0.1		,	_lowercase       :	Any=0.1		,	_lowercase       :	List[Any]=32		,	_lowercase       :	str=2		,	_lowercase       :	List[Any]=1		,	_lowercase       :	int=0		,	_lowercase       :	int=0.02		,	):



    """simple docstring"""



    _UpperCamelCase:  int    						=			parent
    _UpperCamelCase:  Any    						=			batch_size
    _UpperCamelCase:  Union[str, Any]    						=			seq_length
    _UpperCamelCase:  List[Any]    						=			is_training
    _UpperCamelCase:  Any    						=			use_labels
    _UpperCamelCase:  Any    						=			vocab_size
    _UpperCamelCase:  List[Any]    						=			hidden_size
    _UpperCamelCase:  str    						=			num_hidden_layers
    _UpperCamelCase:  List[Any]    						=			num_attention_heads
    _UpperCamelCase:  Dict    						=			intermediate_size
    _UpperCamelCase:  str    						=			hidden_act
    _UpperCamelCase:  int    						=			hidden_dropout_prob
    _UpperCamelCase:  Dict    						=			attention_probs_dropout_prob
    _UpperCamelCase:  Dict    						=			max_position_embeddings
    _UpperCamelCase:  List[str]    						=			eos_token_id
    _UpperCamelCase:  str    						=			pad_token_id
    _UpperCamelCase:  List[Any]    						=			bos_token_id
    _UpperCamelCase:  List[Any]    						=			initializer_range



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



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

    _UpperCamelCase:  Tuple    						=			shift_tokens_right(_lowercase		,	1		,	2				)

    _UpperCamelCase:  List[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=_lowercase		,	)
    _UpperCamelCase:  int    						=			prepare_blenderbot_inputs_dict(_lowercase		,	_lowercase		,	_lowercase				)
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Dict    						=			self.prepare_config_and_inputs()
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	Tuple		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			20
    _UpperCamelCase:  Union[str, Any]    						=			model_class_name(_lowercase				)

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

    _UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			(
        inputs_dict['''decoder_input_ids'''],
        inputs_dict['''decoder_attention_mask'''],
    )

    _UpperCamelCase:  Tuple    						=			model.init_cache(decoder_input_ids.shape[0]		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,	dtype='''i4'''				)

    _UpperCamelCase:  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)		,	)
    _UpperCamelCase:  List[str]    						=			model.decode(
        decoder_input_ids[:, :-1]		,	_lowercase		,	decoder_attention_mask=_lowercase		,	past_key_values=_lowercase		,	decoder_position_ids=_lowercase		,	)

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

    _UpperCamelCase:  List[Any]    						=			model.decode(_lowercase		,	_lowercase				)

    _UpperCamelCase:  List[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 	lowerCAmelCase    (						self       :	List[str]		,	_lowercase       :	List[str]		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			20
    _UpperCamelCase:  int    						=			model_class_name(_lowercase				)

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

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

    _UpperCamelCase:  List[Any]    						=			jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
        ]		,	axis=-1		,	)

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

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

    _UpperCamelCase:  Any    						=			model.decode(_lowercase		,	_lowercase		,	decoder_attention_mask=_lowercase				)

    _UpperCamelCase:  Optional[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 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     List[str]	      = 9_9



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  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		,	)

    _UpperCamelCase:  Dict    						=			input_ids.shape[0]
    _UpperCamelCase:  int    						=			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       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			self._get_config_and_data()
    _UpperCamelCase:  Dict    						=			FlaxBlenderbotForConditionalGeneration(_lowercase				)
    _UpperCamelCase:  Any    						=			lm_model(input_ids=_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs['''logits'''].shape		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,	dtype=np.intaa				)
    _UpperCamelCase:  Dict    						=			shift_tokens_right(_lowercase		,	1		,	2				)
    _UpperCamelCase:  Tuple    						=			np.equal(_lowercase		,	1				).astype(np.floataa				).sum()
    _UpperCamelCase:  Optional[Any]    						=			np.equal(_lowercase		,	1				).astype(np.floataa				).sum()
    self.assertEqual(shifted.shape		,	input_ids.shape				)
    self.assertEqual(_lowercase		,	n_pad_before - 1				)
    self.assertTrue(np.equal(shifted[:, 0]		,	2				).all()				)




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase   ,						__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = True
   lowerCAmelCase   :     Optional[int]	      = (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   lowerCAmelCase   :     Optional[int]	      = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			FlaxBlenderbotModelTester(self				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(_lowercase		,	_lowercase		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			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(_lowercase		,	_lowercase		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__				):
      _UpperCamelCase:  Union[str, Any]    						=			self._prepare_for_class(_lowercase		,	_lowercase				)
      _UpperCamelCase:  Any    						=			model_class(_lowercase				)

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

      with self.subTest('''JIT Enabled'''				):
       _UpperCamelCase:  List[Any]    						=			encode_jitted(**_lowercase				).to_tuple()

      with self.subTest('''JIT Disabled'''				):
       with jax.disable_jit():
        _UpperCamelCase:  str    						=			encode_jitted(**_lowercase				).to_tuple()

      self.assertEqual(len(_lowercase				)		,	len(_lowercase				)				)
      for jitted_output, output in zip(_lowercase		,	_lowercase				):
       self.assertEqual(jitted_output.shape		,	output.shape				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			self.model_tester.prepare_config_and_inputs_for_common()

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

      _UpperCamelCase:  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(_lowercase       :	Any		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	str				):
       return model.decode(
           decoder_input_ids=_lowercase		,	decoder_attention_mask=_lowercase		,	encoder_outputs=_lowercase		,	)

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

      with self.subTest('''JIT Disabled'''				):
       with jax.disable_jit():
        _UpperCamelCase:  Optional[Any]    						=			decode_jitted(**_lowercase				).to_tuple()

      self.assertEqual(len(_lowercase				)		,	len(_lowercase				)				)
      for jitted_output, output in zip(_lowercase		,	_lowercase				):
       self.assertEqual(jitted_output.shape		,	output.shape				)



   @slow
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    for model_class_name in self.all_model_classes:
     _UpperCamelCase:  Optional[int]    						=			model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     _UpperCamelCase:  List[str]    						=			np.ones((1, 1)				) * model.config.eos_token_id
     _UpperCamelCase:  List[Any]    						=			model(_lowercase				)
     self.assertIsNotNone(_lowercase				)



   @unittest.skipUnless(jax_device != '''cpu'''		,	'''3B test too slow on CPU.'''				)
   @slow
   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
    _UpperCamelCase:  Union[str, Any]    						=			{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

    _UpperCamelCase:  Union[str, Any]    						=			FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''		,	from_pt=_lowercase				)
    _UpperCamelCase:  Any    						=			BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

    _UpperCamelCase:  Optional[Any]    						=			['''Sam''']
    _UpperCamelCase:  List[str]    						=			tokenizer(_lowercase		,	return_tensors='''jax'''				)

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

    _UpperCamelCase:  List[Any]    						=			tokenizer.batch_decode(_lowercase		,	**_lowercase				)
    assert generated_txt[0].strip() == tgt_text