




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       (PNDMScheduler,)
						UpperCamelCase      =       (("""num_inference_steps""", 50),)

						def      A__    (		self :str     ,			**__snake_case :Tuple			):
										'''simple docstring'''

										__magic_name__						:						int										={
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**__snake_case			)
										return config

						def      A__    (		self :List[Any]     ,			__snake_case :Union[str, Any]=0     ,			**__snake_case :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Any										=dict(self.forward_default_kwargs			)
										__magic_name__						:						str										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						int										=self.dummy_sample
										__magic_name__						:						Dict										=0.1 * sample
										__magic_name__						:						Tuple										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Optional[int]										=self.get_scheduler_config(**__snake_case			)
														__magic_name__						:						Dict										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)
														# copy over dummy past residuals
														__magic_name__						:						Tuple										=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case			)
																		__magic_name__						:						int										=scheduler_class.from_pretrained(__snake_case			)
																		new_scheduler.set_timesteps(__snake_case			)
																		# copy over dummy past residuals
																		__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

														__magic_name__						:						int										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Tuple										=new_scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Tuple										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						str										=new_scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										pass

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]=0     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=dict(self.forward_default_kwargs			)
										__magic_name__						:						Optional[int]										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)
										__magic_name__						:						List[Any]										=self.dummy_sample
										__magic_name__						:						Optional[Any]										=0.1 * sample
										__magic_name__						:						Optional[int]										=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						List[str]										=self.get_scheduler_config()
														__magic_name__						:						int										=scheduler_class(**__snake_case			)
														scheduler.set_timesteps(__snake_case			)

														# copy over dummy past residuals (must be after setting timesteps)
														__magic_name__						:						Union[str, Any]										=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case			)
																		__magic_name__						:						Optional[int]										=scheduler_class.from_pretrained(__snake_case			)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(__snake_case			)

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

														__magic_name__						:						List[str]										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						Tuple										=new_scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						Any										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						List[Any]										=new_scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Any     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.scheduler_classes[0]
										__magic_name__						:						Dict										=self.get_scheduler_config(**__snake_case			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)

										__magic_name__						:						int										=10
										__magic_name__						:						Tuple										=self.dummy_model()
										__magic_name__						:						Dict										=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case			)

										for i, t in enumerate(scheduler.prk_timesteps			):
														__magic_name__						:						Optional[int]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						List[str]										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps			):
														__magic_name__						:						Optional[Any]										=model(__snake_case     ,			__snake_case			)
														__magic_name__						:						Union[str, Any]										=scheduler.step_plms(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

										return sample

						def      A__    (		self :str			):
										'''simple docstring'''

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

										__magic_name__						:						Tuple										=kwargs.pop("""num_inference_steps"""     ,			__snake_case			)

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						str										=self.get_scheduler_config()
														__magic_name__						:						Dict										=scheduler_class(**__snake_case			)

														__magic_name__						:						Optional[int]										=self.dummy_sample
														__magic_name__						:						int										=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case     ,			"""set_timesteps"""			):
																		scheduler.set_timesteps(__snake_case			)
														elif num_inference_steps is not None and not hasattr(__snake_case     ,			"""set_timesteps"""			):
																		__magic_name__						:						List[str]										=num_inference_steps

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

														__magic_name__						:						Union[str, Any]										=scheduler.step_prk(__snake_case     ,			0     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						int										=scheduler.step_prk(__snake_case     ,			1     ,			__snake_case     ,			**__snake_case			).prev_sample

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

														__magic_name__						:						List[Any]										=scheduler.step_plms(__snake_case     ,			0     ,			__snake_case     ,			**__snake_case			).prev_sample
														__magic_name__						:						str										=scheduler.step_plms(__snake_case     ,			1     ,			__snake_case     ,			**__snake_case			).prev_sample

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

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case			)

						def      A__    (		self :int			):
										'''simple docstring'''

										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=__snake_case			)

										__magic_name__						:						str										=self.scheduler_classes[0]
										__magic_name__						:						Tuple										=self.get_scheduler_config(steps_offset=1			)
										__magic_name__						:						Tuple										=scheduler_class(**__snake_case			)
										scheduler.set_timesteps(10			)
										assert torch.equal(
										    scheduler.timesteps     ,			torch.LongTensor(
										        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]			)     ,			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.0001, 0.001]     ,			[0.002, 0.02]			):
														self.check_over_configs(beta_start=__snake_case     ,			beta_end=__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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

						def      A__    (		self :str			):
										'''simple docstring'''

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

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for t in [1, 5, 10]:
														self.check_over_forward(time_step=__snake_case			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]     ,			[10, 50, 1_00]			):
														self.check_over_forward(num_inference_steps=__snake_case			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						str										=27

										for scheduler_class in self.scheduler_classes:
														__magic_name__						:						Union[str, Any]										=self.dummy_sample
														__magic_name__						:						str										=0.1 * sample

														__magic_name__						:						List[str]										=self.get_scheduler_config()
														__magic_name__						:						Optional[int]										=scheduler_class(**__snake_case			)

														scheduler.set_timesteps(__snake_case			)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]			):
																		__magic_name__						:						Dict										=scheduler.step_prk(__snake_case     ,			__snake_case     ,			__snake_case			).prev_sample

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										with self.assertRaises(__snake_case			):
														__magic_name__						:						List[Any]										=self.scheduler_classes[0]
														__magic_name__						:						Optional[Any]										=self.get_scheduler_config()
														__magic_name__						:						List[str]										=scheduler_class(**__snake_case			)

														scheduler.step_plms(self.dummy_sample     ,			1     ,			self.dummy_sample			).prev_sample

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.full_loop()
										__magic_name__						:						int										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Optional[Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 198.1318			) < 1E-2
										assert abs(result_mean.item() - 0.2580			) < 1E-3

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.full_loop(prediction_type="""v_prediction"""			)
										__magic_name__						:						List[str]										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						List[str]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 67.3986			) < 1E-2
										assert abs(result_mean.item() - 0.0878			) < 1E-3

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.full_loop(set_alpha_to_one=__snake_case     ,			beta_start=0.01			)
										__magic_name__						:						Tuple										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						List[Any]										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 230.0399			) < 1E-2
										assert abs(result_mean.item() - 0.2995			) < 1E-3


						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.full_loop(set_alpha_to_one=__snake_case     ,			beta_start=0.01			)
										__magic_name__						:						Union[str, Any]										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Any										=torch.mean(torch.abs(__snake_case			)			)

										assert abs(result_sum.item() - 186.9482			) < 1E-2
										assert abs(result_mean.item() - 0.2434			) < 1E-3










import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class 		__A   (      tf.keras.layers.Layer      ):

						def __init__(		self :Optional[int]     ,			__snake_case :Dict[str, int]     ,			__snake_case :List[str]     ,			__snake_case :int = None     ,			__snake_case :int = None			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Optional[int]										=pad_token_id
										__magic_name__						:						List[Any]										=max_length
										__magic_name__						:						Dict										=vocab
										__magic_name__						:						int										=merges
										__magic_name__						:						Optional[int]										=BytePairTokenizer(__snake_case     ,			__snake_case     ,			sequence_length=__snake_case			)

						@classmethod
						def      A__    (		cls :List[Any]     ,			__snake_case :GPTaTokenizer     ,			*__snake_case :int     ,			**__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=[""" """.join(__snake_case			) for m in tokenizer.bpe_ranks.keys()]
										__magic_name__						:						str										=tokenizer.get_vocab()
										return cls(__snake_case     ,			__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Dict     ,			__snake_case :Union[str, os.PathLike]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=GPTaTokenizer.from_pretrained(__snake_case     ,			*__snake_case     ,			**__snake_case			)
										return cls.from_tokenizer(__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Optional[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										return cls(**__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return {
										    "vocab": self.vocab,
										    "merges": self.merges,
										    "max_length": self.max_length,
										    "pad_token_id": self.pad_token_id,
										}


						def      A__    (		self :List[Any]     ,			__snake_case :Dict     ,			__snake_case :int = None			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.tf_tokenizer(__snake_case			)
										__magic_name__						:						Tuple										=tf.ones_like(__snake_case			)

										if self.pad_token_id is not None:
														# pad the tokens up to max length
														__magic_name__						:						Tuple										=max_length if max_length is not None else self.max_length

														if max_length is not None:
																		__magic_name__					,			__magic_name__						:						Tuple										=pad_model_inputs(
																		    __snake_case     ,			max_seq_length=__snake_case     ,			pad_value=self.pad_token_id			)

										return {"attention_mask": attention_mask, "input_ids": input_ids}




