
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class     lowercase		( _UpperCAmelCase		):
       lowercase_	:	Optional[Any]	 =(DDIMParallelScheduler,)
       lowercase_	:	Any	 =(('eta', 0.0), ('num_inference_steps', 50))




       def 			A__				(    self       ,**A__):
            lowercase						     =						{
                """num_train_timesteps""": 1_0_0_0,
                """beta_start""": 0.0001,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
                """clip_sample""": True,
            }

            config.update(**SCREAMING_SNAKE_CASE_)
            return config




       def 			A__				(    self       ,**A__):
            lowercase						     =						self.scheduler_classes[0]
            lowercase						     =						self.get_scheduler_config(**SCREAMING_SNAKE_CASE_)
            lowercase						     =						scheduler_class(**SCREAMING_SNAKE_CASE_)

            lowercase						     =						1_0, 0.0

            lowercase						     =						self.dummy_model()
            lowercase						     =						self.dummy_sample_deter

            scheduler.set_timesteps(SCREAMING_SNAKE_CASE_)

            for t in scheduler.timesteps:
                 lowercase						     =						model(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_)
                 lowercase						     =						scheduler.step(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_).prev_sample

            return sample




       def 			A__				(    self):
            for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)




       def 			A__				(    self):
            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_)

            lowercase						     =						self.scheduler_classes[0]
            lowercase						     =						self.get_scheduler_config(steps_offset=1)
            lowercase						     =						scheduler_class(**SCREAMING_SNAKE_CASE_)
            scheduler.set_timesteps(5)
            assert torch.equal(scheduler.timesteps       ,torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]))




       def 			A__				(    self):
            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,[0.002, 0.02, 0.2, 2]):
                 self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,beta_end=SCREAMING_SNAKE_CASE_)




       def 			A__				(    self):
            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)




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




       def 			A__				(    self):
            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_)




       def 			A__				(    self):
            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_)




       def 			A__				(    self):
            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_)




       def 			A__				(    self):
            self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_)
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=SCREAMING_SNAKE_CASE_       ,prediction_type=SCREAMING_SNAKE_CASE_       ,sample_max_value=SCREAMING_SNAKE_CASE_       ,)




       def 			A__				(    self):
            for t in [1, 1_0, 4_9]:
                 self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)




       def 			A__				(    self):
            for t, num_inference_steps in zip([1, 1_0, 5_0]       ,[1_0, 5_0, 5_0_0]):
                 self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,num_inference_steps=SCREAMING_SNAKE_CASE_)




       def 			A__				(    self):
            for t, eta in zip([1, 1_0, 4_9]       ,[0.0, 0.5, 1.0]):
                 self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,eta=SCREAMING_SNAKE_CASE_)




       def 			A__				(    self):
            lowercase						     =						self.scheduler_classes[0]
            lowercase						     =						self.get_scheduler_config()
            lowercase						     =						scheduler_class(**SCREAMING_SNAKE_CASE_)

            assert torch.sum(torch.abs(scheduler._get_variance(0       ,0) - 0.0)) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_2_0       ,4_0_0) - 0.14771)) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_8_0       ,9_6_0) - 0.32460)) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0       ,0) - 0.0)) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ,4_8_6) - 0.00979)) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ,9_9_8) - 0.02)) < 1E-5




       def 			A__				(    self):
            lowercase						     =						self.scheduler_classes[0]
            lowercase						     =						self.get_scheduler_config()
            lowercase						     =						scheduler_class(**SCREAMING_SNAKE_CASE_)

            lowercase						     =						1_0, 0.0
            scheduler.set_timesteps(SCREAMING_SNAKE_CASE_)

            lowercase						     =						self.dummy_model()
            lowercase						     =						self.dummy_sample_deter
            lowercase						     =						self.dummy_sample_deter + 0.1
            lowercase						     =						self.dummy_sample_deter - 0.1

            lowercase						     =						samplea.shape[0]
            lowercase						     =						torch.stack([samplea, samplea, samplea]       ,dim=0)
            lowercase						     =						torch.arange(SCREAMING_SNAKE_CASE_)[0:3, None].repeat(1       ,SCREAMING_SNAKE_CASE_)

            lowercase						     =						model(samples.flatten(0       ,1)       ,timesteps.flatten(0       ,1))
            lowercase						     =						scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_       ,timesteps.flatten(0       ,1)       ,samples.flatten(0       ,1)       ,SCREAMING_SNAKE_CASE_)

            lowercase						     =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
            lowercase						     =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

            assert abs(result_sum.item() - 1147.7904) < 1E-2
            assert abs(result_mean.item() - 0.4982) < 1E-3




       def 			A__				(    self):
            lowercase						     =						self.full_loop()

            lowercase						     =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
            lowercase						     =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

            assert abs(result_sum.item() - 172.0067) < 1E-2
            assert abs(result_mean.item() - 0.223967) < 1E-3




       def 			A__				(    self):
            lowercase						     =						self.full_loop(prediction_type='''v_prediction''')

            lowercase						     =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
            lowercase						     =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

            assert abs(result_sum.item() - 52.5302) < 1E-2
            assert abs(result_mean.item() - 0.0684) < 1E-3




       def 			A__				(    self):
            # We specify different beta, so that the first alpha is 0.99
            lowercase						     =						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,beta_start=0.01)
            lowercase						     =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
            lowercase						     =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

            assert abs(result_sum.item() - 149.8295) < 1E-2
            assert abs(result_mean.item() - 0.1951) < 1E-3





       def 			A__				(    self):
            # We specify different beta, so that the first alpha is 0.99
            lowercase						     =						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,beta_start=0.01)
            lowercase						     =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
            lowercase						     =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

            assert abs(result_sum.item() - 149.0784) < 1E-2
            assert abs(result_mean.item() - 0.1941) < 1E-3






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')

												lowercase						     =						{
												    '''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]       ,dtype=tf.intaa),  # "My dog is cute"
												    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]       ,dtype=tf.intaa),
												}

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												# compare the actual values for a slice.
												lowercase						     =						tf.convert_to_tensor(
												    [
												        [
												            [0.0681762, 0.10894451, 0.06772504],
												            [-0.06423668, 0.02366615, 0.04329344],
												            [-0.06057295, 0.09974135, -0.00070584],
												        ]
												    ]       ,dtype=tf.floataa       ,)

												self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




