


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    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__=13					, a__=7					, a__=True					, a__=True					, a__=True					, a__=True					, a__=99					, a__=32					, a__=5					, a__=4					, a__=37					, a__="gelu"					, a__=0.1					, a__=0.1					, a__=512					, a__=16					, a__=2					, a__=0.02					, a__=4					, )						->					Any:
					A             =					parent
					A             =					batch_size
					A             =					seq_length
					A             =					is_training
					A             =					use_attention_mask
					A             =					use_token_type_ids
					A             =					use_labels
					A             =					vocab_size
					A             =					hidden_size
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					intermediate_size
					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					attention_probs_dropout_prob
					A             =					max_position_embeddings
					A             =					type_vocab_size
					A             =					type_sequence_label_size
					A             =					initializer_range
					A             =					num_choices

				def   _UpperCAmelCase       (    self )						->					Dict:
					A             =					ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size )

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

					A             =					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_=a__					, )

					return config, input_ids, attention_mask

				def   _UpperCAmelCase       (    self )						->					str:
					A             =					self.prepare_config_and_inputs()
					A       ,      A       ,      A             =					config_and_inputs
					A             =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict






@require_flax
class    _UpperCamelCase     (       __snake_case     ,		unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					FlaxDistilBertModelTester(self )

				@slow
				def   _UpperCAmelCase       (    self )						->					Tuple:
					for model_class_name in self.all_model_classes:
						A             =					model_class_name.from_pretrained("""distilbert-base-uncased""" )
						A             =					model(np.ones((1, 1) ) )
						self.assertIsNotNone(a__ )






@require_flax
class    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				@slow
				def   _UpperCAmelCase       (    self )						->					List[str]:
					A             =					FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
					A             =					np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
					A             =					np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
					A             =					model(a__					, attention_mask=a__ )[0]
					A             =					(1, 11, 768)
					self.assertEqual(output.shape					, a__ )
					A             =					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]					, a__					, atol=1e-4 ) )







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			(IPNDMScheduler,)
				lowerCAmelCase					=			(('num_inference_steps', 5_0),)

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[str]:
					A             =					{"""num_train_timesteps""": 1000}
					config.update(**a__ )
					return config

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Tuple:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config(**a__ )
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )
						# copy over dummy past residuals
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							new_scheduler.set_timesteps(a__ )
							# copy over dummy past residuals
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Any:
					pass

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					int:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )

						# copy over dummy past residuals (must be after setting timesteps)
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

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

							# copy over dummy past residual (must be after setting timesteps)
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[Any]:
					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(**a__ )
					A             =					scheduler_class(**a__ )

					A             =					10
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter
					scheduler.set_timesteps(a__ )

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					return sample

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					dict(self.forward_default_kwargs )

					A             =					kwargs.pop("""num_inference_steps"""					, a__ )

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )

						A             =					self.dummy_sample
						A             =					0.1 * sample

						if num_inference_steps is not None and hasattr(a__					, """set_timesteps""" ):
							scheduler.set_timesteps(a__ )
						elif num_inference_steps is not None and not hasattr(a__					, """set_timesteps""" ):
							A             =					num_inference_steps

						# copy over dummy past residuals (must be done after set_timesteps)
						A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
						A             =					dummy_past_residuals[:]

						A             =					scheduler.timesteps[5]
						A             =					scheduler.timesteps[6]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Tuple:
					for timesteps in [100, 1000]:
						self.check_over_configs(num_train_timesteps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					for t, num_inference_steps in zip([1, 5, 10]					, [10, 50, 100] ):
						self.check_over_forward(num_inference_steps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.full_loop()
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 254_0529 ) < 10



