



'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Dict					=					(UniPCMultistepScheduler,)
	__SCREAMING_SNAKE_CASE   :		Dict					=					(('num_inference_steps', 25),)


	def     _a	(self       ,      **lowercase							):
				A_	:						Dict	=     {
				    """num_train_timesteps""": 1000,
				    """beta_start""": 0.00_01,
				    """beta_end""": 0.02,
				    """beta_schedule""": """linear""",
				    """solver_order""": 2,
				    """solver_type""": """bh2""",
				}

				config.update(**lowercase							)
				return config


	def     _a	(self       ,      lowercase=0       ,      **lowercase							):
				A_	:						Dict	=     dict(self.forward_default_kwargs							)
				A_	:						Tuple	=     kwargs.pop("""num_inference_steps"""       ,      lowercase							)
				A_	:						Tuple	=     self.dummy_sample
				A_	:						str	=     0.1 * sample
				A_	:						int	=     [residual + 0.2, residual + 0.15, residual + 0.10]

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

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowercase							)
										A_	:						List[Any]	=     scheduler_class.from_pretrained(lowercase							)
										new_scheduler.set_timesteps(lowercase							)
										# copy over dummy past residuals
										A_	:						List[Any]	=     dummy_past_residuals[: new_scheduler.config.solver_order]

							A_,    A_	:						Optional[int]	=     sample, sample
							for t in range(lowercase       ,      time_step + scheduler.config.solver_order + 1							):
										A_	:						List[Any]	=     scheduler.step(lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							).prev_sample
										A_	:						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     _a	(self       ,      lowercase=0       ,      **lowercase							):
				A_	:						List[Any]	=     dict(self.forward_default_kwargs							)
				A_	:						Optional[Any]	=     kwargs.pop("""num_inference_steps"""       ,      lowercase							)
				A_	:						int	=     self.dummy_sample
				A_	:						int	=     0.1 * sample
				A_	:						Dict	=     [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
							A_	:						str	=     self.get_scheduler_config()
							A_	:						str	=     scheduler_class(**lowercase							)
							scheduler.set_timesteps(lowercase							)

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

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowercase							)
										A_	:						Optional[int]	=     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)
										A_	:						List[Any]	=     dummy_past_residuals[: new_scheduler.config.solver_order]

							A_	:						str	=     scheduler.step(lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							).prev_sample
							A_	:						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     _a	(self       ,      lowercase=None       ,      **lowercase							):
				if scheduler is None:
							A_	:						Optional[int]	=     self.scheduler_classes[0]
							A_	:						Tuple	=     self.get_scheduler_config(**lowercase							)
							A_	:						Union[str, Any]	=     scheduler_class(**lowercase							)

				A_	:						Optional[Any]	=     self.scheduler_classes[0]
				A_	:						Union[str, Any]	=     self.get_scheduler_config(**lowercase							)
				A_	:						List[Any]	=     scheduler_class(**lowercase							)

				A_	:						Optional[int]	=     10
				A_	:						Optional[Any]	=     self.dummy_model()
				A_	:						Dict	=     self.dummy_sample_deter
				scheduler.set_timesteps(lowercase							)

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

				return sample


	def     _a	(self							):
				A_	:						int	=     dict(self.forward_default_kwargs							)

				A_	:						Optional[Any]	=     kwargs.pop("""num_inference_steps"""       ,      lowercase							)

				for scheduler_class in self.scheduler_classes:
							A_	:						Optional[int]	=     self.get_scheduler_config()
							A_	:						Optional[int]	=     scheduler_class(**lowercase							)

							A_	:						Tuple	=     self.dummy_sample
							A_	:						Dict	=     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"""							):
										A_	:						List[Any]	=     num_inference_steps

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

							A_	:						int	=     scheduler.timesteps[5]
							A_	:						int	=     scheduler.timesteps[6]

							A_	:						str	=     scheduler.step(lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							).prev_sample
							A_	:						Optional[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     _a	(self							):
				# make sure that iterating over schedulers with same config names gives same results
				# for defaults
				A_	:						Union[str, Any]	=     UniPCMultistepScheduler(**self.get_scheduler_config()							)
				A_	:						Optional[Any]	=     self.full_loop(scheduler=lowercase							)
				A_	:						Union[str, Any]	=     torch.mean(torch.abs(lowercase							)							)

				assert abs(result_mean.item() - 0.24_64							) < 1E-3

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

				A_	:						Dict	=     self.full_loop(scheduler=lowercase							)
				A_	:						str	=     torch.mean(torch.abs(lowercase							)							)

				assert abs(result_mean.item() - 0.24_64							) < 1E-3


	def     _a	(self							):
				for timesteps in [25, 50, 100, 999, 1000]:
							self.check_over_configs(num_train_timesteps=lowercase							)


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


	def     _a	(self							):
				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       ,      )
													A_	:						Optional[int]	=     self.full_loop(
													    solver_order=lowercase       ,      solver_type=lowercase       ,      prediction_type=lowercase       ,      )
													assert not torch.isnan(lowercase							).any(), "Samples have nan numbers"


	def     _a	(self							):
				self.check_over_configs(lower_order_final=lowercase							)
				self.check_over_configs(lower_order_final=lowercase							)


	def     _a	(self							):
				for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
							self.check_over_forward(num_inference_steps=lowercase       ,      time_step=0							)


	def     _a	(self							):
				A_	:						List[str]	=     self.full_loop()
				A_	:						Dict	=     torch.mean(torch.abs(lowercase							)							)

				assert abs(result_mean.item() - 0.24_64							) < 1E-3


	def     _a	(self							):
				A_	:						Tuple	=     self.full_loop(prediction_type="""v_prediction"""							)
				A_	:						Optional[Any]	=     torch.mean(torch.abs(lowercase							)							)

				assert abs(result_mean.item() - 0.10_14							) < 1E-3


	def     _a	(self							):
				A_	:						Any	=     self.scheduler_classes[0]
				A_	:						Union[str, Any]	=     self.get_scheduler_config(thresholding=lowercase       ,      dynamic_thresholding_ratio=0							)
				A_	:						str	=     scheduler_class(**lowercase							)

				A_	:						Tuple	=     10
				A_	:						Dict	=     self.dummy_model()
				A_	:						Tuple	=     self.dummy_sample_deter.half()
				scheduler.set_timesteps(lowercase							)

				for i, t in enumerate(scheduler.timesteps							):
							A_	:						Any	=     model(lowercase       ,      lowercase							)
							A_	:						Any	=     scheduler.step(lowercase       ,      lowercase       ,      lowercase							).prev_sample

				assert sample.dtype == torch.floataa


	def     _a	(self       ,      **lowercase							):
				for scheduler_class in self.scheduler_classes:
							A_	:						Union[str, Any]	=     self.get_scheduler_config(**lowercase							)
							A_	:						Tuple	=     scheduler_class(**lowercase							)

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




'''simple docstring'''




import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)







class 	_lowerCAmelCase							(			unittest.TestCase ):


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=7       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=99       ,      lowercase=32       ,      lowercase=5       ,      lowercase=4       ,      lowercase=37       ,      lowercase="gelu"       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=512       ,      lowercase=16       ,      lowercase=2       ,      lowercase=0.02       ,      lowercase=4       ,      ):
				A_	:						List[Any]	=     parent
				A_	:						str	=     batch_size
				A_	:						List[Any]	=     seq_length
				A_	:						Dict	=     is_training
				A_	:						List[Any]	=     use_attention_mask
				A_	:						Any	=     use_token_type_ids
				A_	:						Optional[int]	=     use_labels
				A_	:						Tuple	=     vocab_size
				A_	:						List[str]	=     hidden_size
				A_	:						List[str]	=     num_hidden_layers
				A_	:						Optional[Any]	=     num_attention_heads
				A_	:						int	=     intermediate_size
				A_	:						Optional[Any]	=     hidden_act
				A_	:						List[Any]	=     hidden_dropout_prob
				A_	:						Optional[Any]	=     attention_probs_dropout_prob
				A_	:						Any	=     max_position_embeddings
				A_	:						Union[str, Any]	=     type_vocab_size
				A_	:						int	=     type_sequence_label_size
				A_	:						Any	=     initializer_range
				A_	:						List[str]	=     num_choices


	def     _a	(self							):
				A_	:						List[str]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

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

				A_	:						Union[str, Any]	=     DistilBertConfig(
				    vocab_size=self.vocab_size       ,      dim=self.hidden_size       ,      n_layers=self.num_hidden_layers       ,      n_heads=self.num_attention_heads       ,      hidden_dim=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      dropout=self.hidden_dropout_prob       ,      attention_dropout=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      initializer_range=self.initializer_range       ,      tie_weights_=lowercase       ,      )

				return config, input_ids, attention_mask


	def     _a	(self							):
				A_	:						List[str]	=     self.prepare_config_and_inputs()
				A_,    A_,    A_	:						str	=     config_and_inputs
				A_	:						Any	=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict







@require_flax
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)


	def     _a	(self							):
				A_	:						Tuple	=     FlaxDistilBertModelTester(self							)


	@slow
	def     _a	(self							):
				for model_class_name in self.all_model_classes:
							A_	:						Union[str, Any]	=     model_class_name.from_pretrained("""distilbert-base-uncased"""							)
							A_	:						Any	=     model(np.ones((1, 1)							)							)
							self.assertIsNotNone(lowercase							)







@require_flax
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						List[str]	=     FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
				A_	:						Optional[Any]	=     np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
				A_	:						int	=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
				A_	:						Optional[int]	=     model(lowercase       ,      attention_mask=lowercase							)[0]
				A_	:						Optional[Any]	=     (1, 11, 768)
				self.assertEqual(output.shape       ,      lowercase							)
				A_	:						Union[str, Any]	=     np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

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