


'''simple docstring'''







import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (UnCLIPScheduler,)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       {
                'num_train_timesteps': 1000,
                'variance_type': 'fixed_small_log',
                'clip_sample': True,
                'clip_sample_range': 1.0,
                'prediction_type': 'epsilon',
            }

            config.update(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            for variance in ["fixed_small_log", "learned_range"]:
                   self.check_over_configs(variance_type=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            for clip_sample_range in [1, 5, 10, 20]:
                   self.check_over_configs(clip_sample_range=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            for time_step in [0, 500, 999]:
                   for prev_timestep in [None, 5, 100, 250, 500, 750]:
                          if prev_timestep is not None and prev_timestep >= time_step:
                                 continue

                          self.check_over_forward(time_step=snake_case__      ,	prev_timestep=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[Any]   			=       self.get_scheduler_config(variance_type='fixed_small_log'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.054_9625   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.999_4987   )   ) < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config(variance_type='learned_range'   )
            _lowerCAmelCase    :							int   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							int   			=       0.5

            assert scheduler._get_variance(1      ,	predicted_variance=snake_case__   ) - -10.171_2790 < 1E-5
            assert scheduler._get_variance(487      ,	predicted_variance=snake_case__   ) - -5.799_8052 < 1E-5
            assert scheduler._get_variance(999      ,	predicted_variance=snake_case__   ) - -0.001_0011 < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							str   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       scheduler.timesteps

            _lowerCAmelCase    :							List[str]   			=       self.dummy_model()
            _lowerCAmelCase    :							int   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Dict   			=       torch.manual_seed(0   )

            for i, t in enumerate(snake_case__   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							List[str]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							List[str]   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   _lowerCAmelCase    :							int   			=       pred_prev_sample

            _lowerCAmelCase    :							List[str]   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 252.268_2495   ) < 1E-2
            assert abs(result_mean.item() - 0.328_4743   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[str]   			=       scheduler_class(**snake_case__   )

            scheduler.set_timesteps(25   )

            _lowerCAmelCase    :							List[str]   			=       scheduler.timesteps

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

            for i, t in enumerate(snake_case__   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							Optional[Any]   			=       model(snake_case__      ,	snake_case__   )

                   if i + 1 == timesteps.shape[0]:
                          _lowerCAmelCase    :							List[Any]   			=       None
                   else:
                          _lowerCAmelCase    :							str   			=       timesteps[i + 1]

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

                   _lowerCAmelCase    :							Any   			=       pred_prev_sample

            _lowerCAmelCase    :							Union[str, Any]   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							str   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 258.204_4983   ) < 1E-2
            assert abs(result_mean.item() - 0.336_2038   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            pass





'''simple docstring'''







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 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None   ):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       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}

