import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Tuple	      = (UniPCMultistepScheduler,)
   lowerCAmelCase   :     Union[str, Any]	      = (('''num_inference_steps''', 2_5),)



   def 	lowerCAmelCase    (						self       :	Tuple		,	**_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			{
        '''num_train_timesteps''': 1_000,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
        '''solver_order''': 2,
        '''solver_type''': '''bh2''',
    }

    config.update(**_lowercase				)
    return config



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



    """simple docstring"""



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

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

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(_lowercase				)
      _UpperCamelCase:  Union[str, Any]    						=			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:  Tuple    						=			sample, sample
     for t in range(_lowercase		,	time_step + scheduler.config.solver_order + 1				):
      _UpperCamelCase:  Any    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample
      _UpperCamelCase:  List[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       :	Dict		,	_lowercase       :	List[str]=0		,	**_lowercase       :	Union[str, Any]				):



    """simple docstring"""



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

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

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

     _UpperCamelCase:  Tuple    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample
     _UpperCamelCase:  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       :	Union[str, Any]		,	_lowercase       :	Dict=None		,	**_lowercase       :	Optional[int]				):



    """simple docstring"""



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

    _UpperCamelCase:  Any    						=			self.scheduler_classes[0]
    _UpperCamelCase:  Dict    						=			self.get_scheduler_config(**_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			scheduler_class(**_lowercase				)

    _UpperCamelCase:  str    						=			10
    _UpperCamelCase:  Dict    						=			self.dummy_model()
    _UpperCamelCase:  Optional[Any]    						=			self.dummy_sample_deter
    scheduler.set_timesteps(_lowercase				)

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

    return sample



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			dict(self.forward_default_kwargs				)

    _UpperCamelCase:  Optional[Any]    						=			kwargs.pop('''num_inference_steps'''		,	_lowercase				)

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

     _UpperCamelCase:  Optional[int]    						=			self.dummy_sample
     _UpperCamelCase:  Union[str, 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:  Optional[Any]    						=			num_inference_steps

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

     _UpperCamelCase:  Tuple    						=			scheduler.timesteps[5]
     _UpperCamelCase:  Dict    						=			scheduler.timesteps[6]

     _UpperCamelCase:  str    						=			scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase				).prev_sample
     _UpperCamelCase:  Dict    						=			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       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			UniPCMultistepScheduler(**self.get_scheduler_config()				)
    _UpperCamelCase:  int    						=			self.full_loop(scheduler=_lowercase				)
    _UpperCamelCase:  Tuple    						=			torch.mean(torch.abs(_lowercase				)				)

    assert abs(result_mean.item() - 0.2464				) < 1E-3

    _UpperCamelCase:  Optional[Any]    						=			DPMSolverSinglestepScheduler.from_config(scheduler.config				)
    _UpperCamelCase:  Union[str, Any]    						=			DEISMultistepScheduler.from_config(scheduler.config				)
    _UpperCamelCase:  Optional[Any]    						=			DPMSolverMultistepScheduler.from_config(scheduler.config				)
    _UpperCamelCase:  Tuple    						=			UniPCMultistepScheduler.from_config(scheduler.config				)

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

    assert abs(result_mean.item() - 0.2464				) < 1E-3



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



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



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



    """simple docstring"""



    self.check_over_configs(thresholding=_lowercase				)
    for order in [1, 2, 3]:
     for solver_type in ["bh1", "bh2"]:
      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		,	solver_order=_lowercase		,	solver_type=_lowercase		,	)



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



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    for solver_type in ["bh1", "bh2"]:
     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		,	)
       _UpperCamelCase:  Tuple    						=			self.full_loop(
           solver_order=_lowercase		,	solver_type=_lowercase		,	prediction_type=_lowercase		,	)
       assert not torch.isnan(_lowercase				).any(), "Samples have nan numbers"



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Optional[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       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.full_loop()
    _UpperCamelCase:  Any    						=			torch.mean(torch.abs(_lowercase				)				)

    assert abs(result_mean.item() - 0.2464				) < 1E-3



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



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

    assert abs(result_mean.item() - 0.1014				) < 1E-3



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



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			self.scheduler_classes[0]
    _UpperCamelCase:  Dict    						=			self.get_scheduler_config(thresholding=_lowercase		,	dynamic_thresholding_ratio=0				)
    _UpperCamelCase:  Optional[Any]    						=			scheduler_class(**_lowercase				)

    _UpperCamelCase:  Dict    						=			10
    _UpperCamelCase:  Union[str, Any]    						=			self.dummy_model()
    _UpperCamelCase:  Union[str, Any]    						=			self.dummy_sample_deter.half()
    scheduler.set_timesteps(_lowercase				)

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

    assert sample.dtype == torch.floataa



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



    """simple docstring"""



    for scheduler_class in self.scheduler_classes:
     _UpperCamelCase:  Tuple    						=			self.get_scheduler_config(**_lowercase				)
     _UpperCamelCase:  Any    						=			scheduler_class(**_lowercase				)

     scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
     assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps
import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Any		,	_lowercase       :	Optional[int]		,	_lowercase       :	Tuple=13		,	_lowercase       :	str=7		,	_lowercase       :	List[Any]=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	str=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	Dict=99		,	_lowercase       :	List[Any]=32		,	_lowercase       :	List[str]=5		,	_lowercase       :	str=4		,	_lowercase       :	int=37		,	_lowercase       :	List[str]="gelu"		,	_lowercase       :	Any=0.1		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	Dict=512		,	_lowercase       :	int=16		,	_lowercase       :	Optional[Any]=2		,	_lowercase       :	Dict=0.02		,	_lowercase       :	List[Any]=4		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			parent
    _UpperCamelCase:  int    						=			batch_size
    _UpperCamelCase:  List[str]    						=			seq_length
    _UpperCamelCase:  Optional[int]    						=			is_training
    _UpperCamelCase:  Optional[Any]    						=			use_attention_mask
    _UpperCamelCase:  Any    						=			use_token_type_ids
    _UpperCamelCase:  List[str]    						=			use_labels
    _UpperCamelCase:  Optional[int]    						=			vocab_size
    _UpperCamelCase:  List[str]    						=			hidden_size
    _UpperCamelCase:  Union[str, Any]    						=			num_hidden_layers
    _UpperCamelCase:  Any    						=			num_attention_heads
    _UpperCamelCase:  List[str]    						=			intermediate_size
    _UpperCamelCase:  Union[str, Any]    						=			hidden_act
    _UpperCamelCase:  Dict    						=			hidden_dropout_prob
    _UpperCamelCase:  List[str]    						=			attention_probs_dropout_prob
    _UpperCamelCase:  str    						=			max_position_embeddings
    _UpperCamelCase:  Dict    						=			type_vocab_size
    _UpperCamelCase:  Tuple    						=			type_sequence_label_size
    _UpperCamelCase:  List[Any]    						=			initializer_range
    _UpperCamelCase:  str    						=			num_choices



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  Any    						=			None
    if self.use_attention_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  Optional[Any]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  Tuple    						=			AlbertConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	is_decoder=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, token_type_ids, attention_mask



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  int    						=			config_and_inputs
    _UpperCamelCase:  int    						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict




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





   """simple docstring"""




   lowerCAmelCase   :     Any	      = (
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



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



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			FlaxAlbertModelTester(self				)



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



    """simple docstring"""



    for model_class_name in self.all_model_classes:
     _UpperCamelCase:  int    						=			model_class_name.from_pretrained('''albert-base-v2'''				)
     _UpperCamelCase:  List[Any]    						=			model(np.ones((1, 1)				)				)
     self.assertIsNotNone(_lowercase				)




@require_flax
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   @slow
   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			FlaxAlbertModel.from_pretrained('''albert-base-v2'''				)
    _UpperCamelCase:  List[Any]    						=			np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]				)
    _UpperCamelCase:  Optional[int]    						=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
    _UpperCamelCase:  Optional[int]    						=			model(_lowercase		,	attention_mask=_lowercase				)[0]
    _UpperCamelCase:  Tuple    						=			(1, 11, 768)
    self.assertEqual(output.shape		,	_lowercase				)
    _UpperCamelCase:  Tuple    						=			np.array(
        [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]				)

    self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]		,	_lowercase		,	atol=1E-4				)				)