
import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer


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

						'''simple docstring'''



						def __init__(  self  , A  , A  , A = None  , A = None  , A = None  , A = "longest"  , A = True  , A = 5_12  , A = None  , A = True  , A = True  , A = True  , **A  , )  ->str:
									super().__init__()
									if use_fast_bert_tokenizer:
												UpperCAmelCase__		:List[str] 		=    FastBertTokenizer(
												    A  , token_out_type=tf.intaa  , lower_case_nfd_strip_accents=A  , **A	)
									else:
												UpperCAmelCase__		:str 		=    tf.lookup.StaticVocabularyTable(
												    tf.lookup.KeyValueTensorInitializer(
												        keys=A  , key_dtype=tf.string  , values=tf.range(tf.size(A  , out_type=tf.intaa	)  , dtype=tf.intaa	)  , value_dtype=tf.intaa  , )  , num_oov_buckets=1  , )
												UpperCAmelCase__		:Optional[int] 		=    BertTokenizerLayer(
												    A  , token_out_type=tf.intaa  , lower_case=A  , **A	)

									UpperCAmelCase__		:Union[str, Any] 		=    vocab_list
									UpperCAmelCase__		:List[str] 		=    do_lower_case
									UpperCAmelCase__		:Dict 		=    cls_token_id or vocab_list.index('[CLS]'	)
									UpperCAmelCase__		:List[str] 		=    sep_token_id or vocab_list.index('[SEP]'	)
									UpperCAmelCase__		:Union[str, Any] 		=    pad_token_id or vocab_list.index('[PAD]'	)
									UpperCAmelCase__		:Union[str, Any] 		=    ShrinkLongestTrimmer(max_length - 3  , axis=1	)  # Allow room for special tokens
									UpperCAmelCase__		:Union[str, Any] 		=    max_length
									UpperCAmelCase__		:List[str] 		=    padding
									UpperCAmelCase__		:Tuple 		=    truncation
									UpperCAmelCase__		:str 		=    pad_to_multiple_of
									UpperCAmelCase__		:Optional[Any] 		=    return_token_type_ids
									UpperCAmelCase__		:Union[str, Any] 		=    return_attention_mask


						@classmethod
						def    A__       (  cls  , A  , **A	)  ->Union[str, Any]:  # noqa: F821
									UpperCAmelCase__		:List[Any] 		=    kwargs.pop('do_lower_case'  , A	)
									UpperCAmelCase__		:str 		=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
									UpperCAmelCase__		:Dict 		=    kwargs.pop('cls_token_id'  , A	)
									UpperCAmelCase__		:List[str] 		=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
									UpperCAmelCase__		:int 		=    kwargs.pop('sep_token_id'  , A	)
									UpperCAmelCase__		:Optional[Any] 		=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
									UpperCAmelCase__		:List[str] 		=    kwargs.pop('pad_token_id'  , A	)
									UpperCAmelCase__		:List[Any] 		=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

									UpperCAmelCase__		:Union[str, Any] 		=    tokenizer.get_vocab()
									UpperCAmelCase__		:List[str] 		=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]  , key=lambda A	: x[1]	)
									UpperCAmelCase__		:Optional[Any] 		=    [entry[0] for entry in vocab]
									return cls(
									    vocab_list=A  , do_lower_case=A  , cls_token_id=A  , sep_token_id=A  , pad_token_id=A  , **A  , )


						@classmethod
						def    A__       (  cls  , A  , *A  , **A	)  ->Dict:
									try:
												UpperCAmelCase__		:List[Any] 		=    BertTokenizer.from_pretrained(A  , *A  , **A	)
									except:  # noqa: E722
												from .tokenization_bert_fast import BertTokenizerFast

												UpperCAmelCase__		:Tuple 		=    BertTokenizerFast.from_pretrained(A  , *A  , **A	)
									return cls.from_tokenizer(A  , **A	)


						def    A__       (  self  , A	)  ->Tuple:
									if self.do_lower_case:
												UpperCAmelCase__		:Optional[Any] 		=    case_fold_utfa(A	)
									UpperCAmelCase__		:Tuple 		=    self.tf_tokenizer.tokenize(A	)
									return tokens.merge_dims(1  , -1	)


						def    A__       (  self  , A  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , A=None  , )  ->List[Any]:
									if padding is None:
												UpperCAmelCase__		:List[str] 		=    self.padding
									if padding not in ("longest", "max_length"):
												raise ValueError('Padding must be either \'longest\' or \'max_length\'!'	)
									if max_length is not None and text_pair is not None:
												# Because we have to instantiate a Trimmer to do it properly
												raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'	)
									if max_length is None:
												UpperCAmelCase__		:Optional[int] 		=    self.max_length
									if truncation is None:
												UpperCAmelCase__		:str 		=    self.truncation
									if pad_to_multiple_of is None:
												UpperCAmelCase__		:List[Any] 		=    self.pad_to_multiple_of
									if return_token_type_ids is None:
												UpperCAmelCase__		:List[Any] 		=    self.return_token_type_ids
									if return_attention_mask is None:
												UpperCAmelCase__		:Any 		=    self.return_attention_mask
									if not isinstance(A  , tf.Tensor	):
												UpperCAmelCase__		:List[Any] 		=    tf.convert_to_tensor(A	)
									if text_pair is not None and not isinstance(A  , tf.Tensor	):
												UpperCAmelCase__		:List[Any] 		=    tf.convert_to_tensor(A	)
									if text_pair is not None:
												if text.shape.rank > 1:
															raise ValueError('text argument should not be multidimensional when a text pair is supplied!'	)
												if text_pair.shape.rank > 1:
															raise ValueError('text_pair should not be multidimensional!'	)
									if text.shape.rank == 2:
												UpperCAmelCase__		,       UpperCAmelCase__		:Union[str, Any] 		=    text[:, 0], text[:, 1]
									UpperCAmelCase__		:Optional[Any] 		=    self.unpaired_tokenize(A	)
									if text_pair is None:  # Unpaired text
												if truncation:
															UpperCAmelCase__		:str 		=    text[:, : max_length - 2]  # Allow room for special tokens
												UpperCAmelCase__		,       UpperCAmelCase__		:str 		=    combine_segments(
												    (text,)  , start_of_sequence_id=self.cls_token_id  , end_of_segment_id=self.sep_token_id	)
									else:  # Paired text
												UpperCAmelCase__		:Union[str, Any] 		=    self.unpaired_tokenize(A	)
												if truncation:
															UpperCAmelCase__		,       UpperCAmelCase__		:List[Any] 		=    self.paired_trimmer.trim([text, text_pair]	)
												UpperCAmelCase__		,       UpperCAmelCase__		:List[str] 		=    combine_segments(
												    (text, text_pair)  , start_of_sequence_id=self.cls_token_id  , end_of_segment_id=self.sep_token_id	)
									if padding == "longest":
												UpperCAmelCase__		:Union[str, Any] 		=    input_ids.bounding_shape(axis=1	)
												if pad_to_multiple_of is not None:
															# No ceiling division in tensorflow, so we negate floordiv instead
															UpperCAmelCase__		:List[Any] 		=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length  , A	))
									else:
												UpperCAmelCase__		:Optional[Any] 		=    max_length

									UpperCAmelCase__		,       UpperCAmelCase__		:List[Any] 		=    pad_model_inputs(A  , max_seq_length=A  , pad_value=self.pad_token_id	)
									UpperCAmelCase__		:Optional[Any] 		=    {'input_ids': input_ids}
									if return_attention_mask:
												UpperCAmelCase__		:Optional[Any] 		=    attention_mask
									if return_token_type_ids:
												UpperCAmelCase__		,       UpperCAmelCase__		:str 		=    pad_model_inputs(
												    A  , max_seq_length=A  , pad_value=self.pad_token_id	)
												UpperCAmelCase__		:Dict 		=    token_type_ids
									return output


						def    A__       (  self	)  ->int:
									return {
									    "vocab_list": self.vocab_list,
									    "do_lower_case": self.do_lower_case,
									    "cls_token_id": self.cls_token_id,
									    "sep_token_id": self.sep_token_id,
									    "pad_token_id": self.pad_token_id,
									}







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    List[Any]												=			(DPMSolverMultistepScheduler,)
						__a     :    Dict												=			(("""num_inference_steps""", 2_5),)


						def    A__       (  self  , **A	)  ->List[Any]:
									UpperCAmelCase__		:int 		=    {
									    'num_train_timesteps': 10_00,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lower_order_final': False,
									    'lambda_min_clipped': -float('inf'	),
									    'variance_type': None,
									}

									config.update(**A	)
									return config


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:int 		=    self.dummy_sample
									UpperCAmelCase__		:Optional[int] 		=    0.1 * sample
									UpperCAmelCase__		:Any 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:List[Any] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)
												# copy over dummy past residuals
												UpperCAmelCase__		:Optional[Any] 		=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:List[Any] 		=    scheduler_class.from_pretrained(A	)
															new_scheduler.set_timesteps(A	)
															# copy over dummy past residuals
															UpperCAmelCase__		:Union[str, Any] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    sample, sample
												for t in range(A  , time_step + scheduler.config.solver_order + 1	):
															UpperCAmelCase__		:Dict 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
															UpperCAmelCase__		:Optional[Any] 		=    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    A__       (  self	)  ->List[Any]:
									pass


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Dict 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:str 		=    self.dummy_sample
									UpperCAmelCase__		:Union[str, Any] 		=    0.1 * sample
									UpperCAmelCase__		:Union[str, Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:List[Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:List[str] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)

												# copy over dummy past residuals (must be after setting timesteps)
												UpperCAmelCase__		:Tuple 		=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:int 		=    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)
															UpperCAmelCase__		:Optional[int] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		:Any 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:Dict 		=    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    A__       (  self  , A=None  , **A	)  ->List[str]:
									if scheduler is None:
												UpperCAmelCase__		:Optional[Any] 		=    self.scheduler_classes[0]
												UpperCAmelCase__		:Optional[Any] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler_class(**A	)

									UpperCAmelCase__		:List[Any] 		=    10
									UpperCAmelCase__		:int 		=    self.dummy_model()
									UpperCAmelCase__		:int 		=    self.dummy_sample_deter
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:Union[str, Any] 		=    model(A  , A	)
												UpperCAmelCase__		:str 		=    scheduler.step(A  , A  , A	).prev_sample

									return sample


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Tuple 		=    dict(self.forward_default_kwargs	)

									UpperCAmelCase__		:Optional[Any] 		=    kwargs.pop('num_inference_steps'  , A	)

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Union[str, Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:Optional[Any] 		=    scheduler_class(**A	)

												UpperCAmelCase__		:Dict 		=    self.dummy_sample
												UpperCAmelCase__		:Dict 		=    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'	):
															UpperCAmelCase__		:List[Any] 		=    num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												UpperCAmelCase__		:List[Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]
												UpperCAmelCase__		:Any 		=    dummy_past_residuals[: scheduler.config.solver_order]

												UpperCAmelCase__		:str 		=    scheduler.timesteps[5]
												UpperCAmelCase__		:str 		=    scheduler.timesteps[6]

												UpperCAmelCase__		:List[str] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:List[Any] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->Optional[Any]:
									for timesteps in [25, 50, 1_00, 9_99, 10_00]:
												self.check_over_configs(num_train_timesteps=A	)


						def    A__       (  self	)  ->str:
									self.check_over_configs(thresholding=A	)
									for order in [1, 2, 3]:
												for solver_type in ["midpoint", "heun"]:
															for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    thresholding=A  , prediction_type=A  , sample_max_value=A  , algorithm_type='dpmsolver++'  , solver_order=A  , solver_type=A  , )


						def    A__       (  self	)  ->Optional[int]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=A	)


						def    A__       (  self	)  ->List[str]:
									for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
												for solver_type in ["midpoint", "heun"]:
															for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																					if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																								if order == 3:
																											continue
																					else:
																								self.check_over_configs(
																								    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					UpperCAmelCase__		:Optional[int] 		=    self.full_loop(
																					    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					assert not torch.isnan(A	).any(), "Samples have nan numbers"


						def    A__       (  self	)  ->List[Any]:
									self.check_over_configs(lower_order_final=A	)
									self.check_over_configs(lower_order_final=A	)


						def    A__       (  self	)  ->Optional[Any]:
									self.check_over_configs(lambda_min_clipped=-float('inf'	)	)
									self.check_over_configs(lambda_min_clipped=-5.1	)


						def    A__       (  self	)  ->Union[str, Any]:
									self.check_over_configs(variance_type=A	)
									self.check_over_configs(variance_type='learned_range'	)


						def    A__       (  self	)  ->List[str]:
									for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
												self.check_over_forward(num_inference_steps=A  , time_step=0	)


						def    A__       (  self	)  ->int:
									UpperCAmelCase__		:int 		=    self.full_loop()
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(thresholding=A  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5	)
									UpperCAmelCase__		:Any 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 1.1364	) < 1e-3


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.full_loop(prediction_type='v_prediction'	)
									UpperCAmelCase__		:int 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2251	) < 1e-3


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(prediction_type='v_prediction'  , use_karras_sigmas=A	)
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2096	) < 1e-3


						def    A__       (  self	)  ->List[Any]:
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									UpperCAmelCase__		:Tuple 		=    DPMSolverMultistepScheduler(**self.get_scheduler_config()	)
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:List[Any] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3

									UpperCAmelCase__		:Dict 		=    DPMSolverSinglestepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    UniPCMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:Union[str, Any] 		=    DEISMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    DPMSolverMultistepScheduler.from_config(scheduler.config	)

									UpperCAmelCase__		:Dict 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:Dict 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Tuple 		=    self.scheduler_classes[0]
									UpperCAmelCase__		:List[str] 		=    self.get_scheduler_config(thresholding=A  , dynamic_thresholding_ratio=0	)
									UpperCAmelCase__		:str 		=    scheduler_class(**A	)

									UpperCAmelCase__		:int 		=    10
									UpperCAmelCase__		:Optional[int] 		=    self.dummy_model()
									UpperCAmelCase__		:List[str] 		=    self.dummy_sample_deter.half()
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:str 		=    model(A  , A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler.step(A  , A  , A	).prev_sample

									assert sample.dtype == torch.floataa


						def    A__       (  self  , **A	)  ->Any:
									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:int 		=    scheduler_class(**A	)

												scheduler.set_timesteps(scheduler.config.num_train_timesteps	)
												assert len(scheduler.timesteps.unique()	) == scheduler.num_inference_steps





