


'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						_snake_case   (       a__		):
					lowerCAmelCase :Union[str, Any]						=   (DDPMScheduler,)




					def 			snake_case__  ( self							,							**_lowerCamelCase) ->    List[str]:
								UpperCAmelCase__					:   Union[str, Any]       				=       {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """variance_type""": """fixed_small""",
								    """clip_sample""": True,
								}

								config.update(**__lowerCAmelCase)
								return config




					def 			snake_case__  ( self) ->    List[str]:



								for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__lowerCAmelCase)




					def 			snake_case__  ( self) ->    List[Any]:



								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=__lowerCAmelCase							,							beta_end=__lowerCAmelCase)




					def 			snake_case__  ( self) ->    int:



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




					def 			snake_case__  ( self) ->    Any:



								for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=__lowerCAmelCase)




					def 			snake_case__  ( self) ->    Optional[int]:



								for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=__lowerCAmelCase)




					def 			snake_case__  ( self) ->    Tuple:
								self.check_over_configs(thresholding=__lowerCAmelCase)
								for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(
														    thresholding=__lowerCAmelCase							,							prediction_type=__lowerCAmelCase							,							sample_max_value=__lowerCAmelCase							,							)




					def 			snake_case__  ( self) ->    Any:



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




					def 			snake_case__  ( self) ->    Optional[int]:



								for t in [0, 500, 999]:
											self.check_over_forward(time_step=__lowerCAmelCase)




					def 			snake_case__  ( self) ->    Optional[Any]:
								UpperCAmelCase__					:   Optional[Any]       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   Optional[Any]       				=       self.get_scheduler_config()
								UpperCAmelCase__					:   List[Any]       				=       scheduler_class(**__lowerCAmelCase)

								assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5




					def 			snake_case__  ( self) ->    List[str]:
								UpperCAmelCase__					:   Dict       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   List[Any]       				=       self.get_scheduler_config()
								UpperCAmelCase__					:   Optional[int]       				=       scheduler_class(**__lowerCAmelCase)

								UpperCAmelCase__					:   Dict       				=       len(__lowerCAmelCase)

								UpperCAmelCase__					:   Dict       				=       self.dummy_model()
								UpperCAmelCase__					:   Optional[Any]       				=       self.dummy_sample_deter
								UpperCAmelCase__					:   int       				=       torch.manual_seed(0)

								for t in reversed(range(__lowerCAmelCase)):
											# 1. predict noise residual
											UpperCAmelCase__					:   int       				=       model(__lowerCAmelCase							,							__lowerCAmelCase)

											# 2. predict previous mean of sample x_t-1
											UpperCAmelCase__					:   Optional[int]       				=       scheduler.step(__lowerCAmelCase							,							__lowerCAmelCase							,							__lowerCAmelCase							,							generator=__lowerCAmelCase).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											UpperCAmelCase__					:   List[Any]       				=       pred_prev_sample

								UpperCAmelCase__					:   Any       				=       torch.sum(torch.abs(__lowerCAmelCase))
								UpperCAmelCase__					:   Optional[int]       				=       torch.mean(torch.abs(__lowerCAmelCase))

								assert abs(result_sum.item() - 258.9606) < 1e-2
								assert abs(result_mean.item() - 0.3372) < 1e-3




					def 			snake_case__  ( self) ->    Union[str, Any]:
								UpperCAmelCase__					:   Union[str, Any]       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   Tuple       				=       self.get_scheduler_config(prediction_type="""v_prediction""")
								UpperCAmelCase__					:   Union[str, Any]       				=       scheduler_class(**__lowerCAmelCase)

								UpperCAmelCase__					:   str       				=       len(__lowerCAmelCase)

								UpperCAmelCase__					:   Optional[Any]       				=       self.dummy_model()
								UpperCAmelCase__					:   Dict       				=       self.dummy_sample_deter
								UpperCAmelCase__					:   List[str]       				=       torch.manual_seed(0)

								for t in reversed(range(__lowerCAmelCase)):
											# 1. predict noise residual
											UpperCAmelCase__					:   Tuple       				=       model(__lowerCAmelCase							,							__lowerCAmelCase)

											# 2. predict previous mean of sample x_t-1
											UpperCAmelCase__					:   int       				=       scheduler.step(__lowerCAmelCase							,							__lowerCAmelCase							,							__lowerCAmelCase							,							generator=__lowerCAmelCase).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											UpperCAmelCase__					:   Any       				=       pred_prev_sample

								UpperCAmelCase__					:   List[str]       				=       torch.sum(torch.abs(__lowerCAmelCase))
								UpperCAmelCase__					:   Any       				=       torch.mean(torch.abs(__lowerCAmelCase))

								assert abs(result_sum.item() - 202.0296) < 1e-2
								assert abs(result_mean.item() - 0.2631) < 1e-3




					def 			snake_case__  ( self) ->    str:
								UpperCAmelCase__					:   Optional[Any]       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   str       				=       self.get_scheduler_config()
								UpperCAmelCase__					:   Optional[Any]       				=       scheduler_class(**__lowerCAmelCase)

								UpperCAmelCase__					:   str       				=       [100, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=__lowerCAmelCase)

								UpperCAmelCase__					:   Dict       				=       scheduler.timesteps

								for i, timestep in enumerate(__lowerCAmelCase):
											if i == len(__lowerCAmelCase) - 1:
														UpperCAmelCase__					:   List[str]       				=       -1
											else:
														UpperCAmelCase__					:   Any       				=       timesteps[i + 1]

											UpperCAmelCase__					:   Any       				=       scheduler.previous_timestep(__lowerCAmelCase)
											UpperCAmelCase__					:   Union[str, Any]       				=       prev_t.item()

											self.assertEqual(__lowerCAmelCase							,							__lowerCAmelCase)




					def 			snake_case__  ( self) ->    Any:
								UpperCAmelCase__					:   Optional[Any]       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   int       				=       self.get_scheduler_config()
								UpperCAmelCase__					:   Tuple       				=       scheduler_class(**__lowerCAmelCase)

								UpperCAmelCase__					:   Optional[int]       				=       [100, 87, 50, 51, 0]

								with self.assertRaises(__lowerCAmelCase							,							msg="""`custom_timesteps` must be in descending order."""):
											scheduler.set_timesteps(timesteps=__lowerCAmelCase)




					def 			snake_case__  ( self) ->    Dict:
								UpperCAmelCase__					:   str       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   Tuple       				=       self.get_scheduler_config()
								UpperCAmelCase__					:   List[str]       				=       scheduler_class(**__lowerCAmelCase)

								UpperCAmelCase__					:   List[str]       				=       [100, 87, 50, 1, 0]
								UpperCAmelCase__					:   Optional[Any]       				=       len(__lowerCAmelCase)

								with self.assertRaises(__lowerCAmelCase							,							msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
											scheduler.set_timesteps(num_inference_steps=__lowerCAmelCase							,							timesteps=__lowerCAmelCase)




					def 			snake_case__  ( self) ->    Union[str, Any]:
								UpperCAmelCase__					:   Any       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   Optional[int]       				=       self.get_scheduler_config()
								UpperCAmelCase__					:   Any       				=       scheduler_class(**__lowerCAmelCase)

								UpperCAmelCase__					:   str       				=       [scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    __lowerCAmelCase							,							msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""							,							):
											scheduler.set_timesteps(timesteps=__lowerCAmelCase)



'''simple docstring'''



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 tensorflow as tf

		from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM


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




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""")
								UpperCAmelCase__					:   str       				=       AutoTokenizer.from_pretrained("""google/mt5-small""")

								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer("""Hello there"""							,							return_tensors="""tf""").input_ids
								UpperCAmelCase__					:   Tuple       				=       tokenizer("""Hi I am"""							,							return_tensors="""tf""").input_ids

								UpperCAmelCase__					:   List[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase).loss
								UpperCAmelCase__					:   Tuple       				=       -tf.math.reduce_mean(_lowerCamelCase).numpy()

								UpperCAmelCase__					:   Optional[int]       				=       -21.228168
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)