





'''simple docstring'''

import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


__lowerCAmelCase							:     Tuple		     =trt.Logger(trt.Logger.WARNING)
__lowerCAmelCase							:     Optional[Any]		     =absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__lowerCAmelCase							:     List[Any]		     =logging.getLogger(__name__)

__lowerCAmelCase							:     Optional[Any]		     =argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=384,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=128,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=20,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=30,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

__lowerCAmelCase							:     Tuple		     =parser.parse_args()

if args.tokenizer_name:
		__lowerCAmelCase							:     int		     =AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
		raise ValueError(
		    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
		    "You can do it from another script, save it, and load it from here, using --tokenizer_name."
		)

logger.info("Training/evaluation parameters %s", args)

__lowerCAmelCase							:     Union[str, Any]		     =args.per_device_eval_batch_size

__lowerCAmelCase							:     List[Any]		     =(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__lowerCAmelCase							:     Tuple		     =True

__lowerCAmelCase							:     int		     ="temp_engine/bert-fp32.engine"
if args.fpaa:
		__lowerCAmelCase							:     Tuple		     ="temp_engine/bert-fp16.engine"
if args.inta:
		__lowerCAmelCase							:     Optional[int]		     ="temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
		os.makedirs("temp_engine")

__lowerCAmelCase							:     Tuple		     =1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
		with open(args.onnx_model_path, "rb") as model:
				if not parser.parse(model.read()):
						for error in range(parser.num_errors):
								print(parser.get_error(error))

				# Query input names and shapes from parsed TensorRT network
		__lowerCAmelCase							:     Optional[Any]		     =[network.get_input(i) for i in range(network.num_inputs)]
		__lowerCAmelCase							:     Any		     =[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

		with builder.create_builder_config() as config:
				__lowerCAmelCase							:     Optional[Any]		     =1 << 50
				if STRICT_TYPES:
						config.set_flag(trt.BuilderFlag.STRICT_TYPES)
				if args.fpaa:
						config.set_flag(trt.BuilderFlag.FPaa)
				if args.inta:
						config.set_flag(trt.BuilderFlag.INTa)
				__lowerCAmelCase							:     int		     =builder.create_optimization_profile()
				config.add_optimization_profile(profile)
				for i in range(len(input_names)):
						profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
				__lowerCAmelCase							:     Dict		     =builder.build_engine(network, config)

				# serialize_engine and store in file (can be directly loaded and deserialized):
				with open(engine_name, "wb") as f:
						f.write(engine.serialize())





def 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : Dict	):
				A__    =      np.asarray(inputs["input_ids"]						,	dtype=np.intaa	)
				A__    =      np.asarray(inputs["attention_mask"]						,	dtype=np.intaa	)
				A__    =      np.asarray(inputs["token_type_ids"]						,	dtype=np.intaa	)

				# Copy inputs
				cuda.memcpy_htod_async(d_inputs[0]						,	input_ids.ravel()						,	_lowerCamelCase	)
				cuda.memcpy_htod_async(d_inputs[1]						,	attention_mask.ravel()						,	_lowerCamelCase	)
				cuda.memcpy_htod_async(d_inputs[2]						,	token_type_ids.ravel()						,	_lowerCamelCase	)
				# start time
				A__    =      time.time()
				# Run inference
				context.execute_async(
				    bindings=[int(_lowerCamelCase	) for d_inp in d_inputs] + [int(_lowerCamelCase	), int(_lowerCamelCase	)]						,	stream_handle=stream.handle	)
				# Transfer predictions back from GPU
				cuda.memcpy_dtoh_async(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
				cuda.memcpy_dtoh_async(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
				# Synchronize the stream and take time
				stream.synchronize()
				# end time
				A__    =      time.time()
				A__    =      end_time - start_time
				A__    =      (h_outputa, h_outputa)
				# print(outputs)
				return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__lowerCAmelCase							:     str		     =Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_info()
else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
		set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		__lowerCAmelCase							:     List[Any]		     =load_dataset(args.dataset_name, args.dataset_config_name)
else:
		raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__lowerCAmelCase							:     Optional[Any]		     =raw_datasets["validation"].column_names

__lowerCAmelCase							:     Optional[Any]		     ="question" if "question" in column_names else column_names[0]
__lowerCAmelCase							:     str		     ="context" if "context" in column_names else column_names[1]
__lowerCAmelCase							:     Optional[Any]		     ="answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__lowerCAmelCase							:     Any		     =tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
		logger.warning(
		    f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
		    f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
		)

__lowerCAmelCase							:     Any		     =min(args.max_seq_length, tokenizer.model_max_length)





def 						UpperCamelCase    (							_lowerCamelCase : Optional[int]	):
				# Some of the questions have lots of whitespace on the left, which is not useful and will make the
				# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
				# left whitespace
				A__    =      [q.lstrip() for q in examples[question_column_name]]

				# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
				# in one example possible giving several features when a context is long, each of those features having a
				# context that overlaps a bit the context of the previous feature.
				A__    =      tokenizer(
				    examples[question_column_name if pad_on_right else context_column_name]						,	examples[context_column_name if pad_on_right else question_column_name]						,	truncation="only_second" if pad_on_right else "only_first"						,	max_length=_lowerCamelCase						,	stride=args.doc_stride						,	return_overflowing_tokens=_lowerCamelCase						,	return_offsets_mapping=_lowerCamelCase						,	padding="max_length"						,	)

				# Since one example might give us several features if it has a long context, we need a map from a feature to
				# its corresponding example. This key gives us just that.
				A__    =      tokenized_examples.pop("overflow_to_sample_mapping"	)

				# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
				# corresponding example_id and we will store the offset mappings.
				A__    =      []

				for i in range(len(tokenized_examples["input_ids"]	)	):
								# Grab the sequence corresponding to that example (to know what is the context and what is the question).
								A__    =      tokenized_examples.sequence_ids(_lowerCamelCase	)
								A__    =      1 if pad_on_right else 0

								# One example can give several spans, this is the index of the example containing this span of text.
								A__    =      sample_mapping[i]
								tokenized_examples["example_id"].append(examples["id"][sample_index]	)

								# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
								# position is part of the context or not.
								A__    =      [
								    (o if sequence_ids[k] == context_index else None)
								    for k, o in enumerate(tokenized_examples["offset_mapping"][i]	)
								]

				return tokenized_examples


__lowerCAmelCase							:     str		     =raw_datasets["validation"]
# Validation Feature Creation
__lowerCAmelCase							:     Union[str, Any]		     =eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

__lowerCAmelCase							:     List[Any]		     =default_data_collator

__lowerCAmelCase							:     List[Any]		     =eval_dataset.remove_columns(["example_id", "offset_mapping"])
__lowerCAmelCase							:     List[str]		     =DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, Any]="eval"	):
				# Post-processing: we match the start logits and end logits to answers in the original context.
				A__    =      postprocess_qa_predictions(
				    examples=_lowerCamelCase						,	features=_lowerCamelCase						,	predictions=_lowerCamelCase						,	version_2_with_negative=args.version_2_with_negative						,	n_best_size=args.n_best_size						,	max_answer_length=args.max_answer_length						,	null_score_diff_threshold=args.null_score_diff_threshold						,	output_dir=args.output_dir						,	prefix=_lowerCamelCase						,	)
				# Format the result to the format the metric expects.
				if args.version_2_with_negative:
								A__    =      [
								    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
								]
				else:
								A__    =      [{"id": k, "prediction_text": v} for k, v in predictions.items()]

				A__    =      [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
				return EvalPrediction(predictions=_lowerCamelCase						,	label_ids=_lowerCamelCase	)


__lowerCAmelCase							:     Tuple		     =load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
		# setup for TRT inferrence
		for i in range(len(input_names)):
				context.set_binding_shape(i, INPUT_SHAPE)
		assert context.all_binding_shapes_specified





		def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]	):
						return trt.volume(engine.get_binding_shape(_lowerCamelCase	)	) * engine.get_binding_dtype(_lowerCamelCase	).itemsize

		# Allocate device memory for inputs and outputs.
		__lowerCAmelCase							:     Any		     =[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

		# Allocate output buffer
		__lowerCAmelCase							:     List[Any]		     =cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
		__lowerCAmelCase							:     List[str]		     =cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
		__lowerCAmelCase							:     List[str]		     =cuda.mem_alloc(h_outputa.nbytes)
		__lowerCAmelCase							:     int		     =cuda.mem_alloc(h_outputa.nbytes)

		# Create a stream in which to copy inputs/outputs and run inference.
		__lowerCAmelCase							:     Optional[Any]		     =cuda.Stream()

		# Evaluation
		logger.info("***** Running Evaluation *****")
		logger.info(f"""  Num examples = {len(eval_dataset)}""")
		logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

		__lowerCAmelCase							:     str		     =0.0
		__lowerCAmelCase							:     Tuple		     =0
		__lowerCAmelCase							:     List[str]		     =timeit.default_timer()

		__lowerCAmelCase							:     Union[str, Any]		     =None
		for step, batch in enumerate(eval_dataloader):
				__lowerCAmelCase							:     Dict		     =model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
				total_time += infer_time
				niter += 1

				__lowerCAmelCase							:     List[Any]		     =outputs
				__lowerCAmelCase							:     Tuple		     =torch.tensor(start_logits)
				__lowerCAmelCase							:     Tuple		     =torch.tensor(end_logits)

				# necessary to pad predictions and labels for being gathered
				__lowerCAmelCase							:     Tuple		     =accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
				__lowerCAmelCase							:     Union[str, Any]		     =accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

				__lowerCAmelCase							:     int		     =(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
				__lowerCAmelCase							:     List[Any]		     =logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

		if all_preds is not None:
				__lowerCAmelCase							:     Dict		     =nested_truncate(all_preds, len(eval_dataset))

		__lowerCAmelCase							:     Optional[int]		     =timeit.default_timer() - start_time
		logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
		# Inference time from TRT
		logger.info("Average Inference Time = {:.3f} ms".format(total_time * 1000 / niter))
		logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1000))



		logger.info("Total Number of Inference =  %d", niter)

__lowerCAmelCase							:     Optional[Any]		     =post_processing_function(eval_examples, eval_dataset, all_preds)
__lowerCAmelCase							:     Optional[Any]		     =metric.compute(predictions=prediction.predictions, references=prediction.label_ids)



logger.info(f"""Evaluation metrics: {eval_metric}""")










'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (PNDMScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 50),)
 def 					UpperCAmelCase_							(    self				:List[str]						,	**lowercase_				:Optional[int]							)->							List[str]:
     A__    =      {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_							)
     return config
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[int]=0						,	**lowercase_				:Any							)->							int:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config(**lowercase_							)
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)
         # copy over dummy past residuals
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             new_scheduler.set_timesteps(lowercase_							)
             # copy over dummy past residuals
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Optional[Any]=0						,	**lowercase_				:List[str]							)->							str:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)

         # copy over dummy past residuals (must be after setting timesteps)
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(lowercase_							)

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

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Dict							)->							Optional[int]:
     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(**lowercase_							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.prk_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
     A__    =      dict(self.forward_default_kwargs							)

     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         if num_inference_steps is not None and hasattr(lowercase_						,	"set_timesteps"							):
             scheduler.set_timesteps(lowercase_							)
         elif num_inference_steps is not None and not hasattr(lowercase_						,	"set_timesteps"							):
             A__    =      num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     for timesteps in [1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase_							)

     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(steps_offset=1							)
     A__    =      scheduler_class(**lowercase_							)
     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 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,	[0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
     for t in [1, 5, 10]:
         self.check_over_forward(time_step=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
     for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 1_00]							):
         self.check_over_forward(num_inference_steps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     A__    =      27

     for scheduler_class in self.scheduler_classes:
         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.set_timesteps(lowercase_							)

         # 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]							):
             A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     with self.assertRaises(lowercase_							):
         A__    =      self.scheduler_classes[0]
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample							).prev_sample
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     A__    =      self.full_loop()
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1E-2
     assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      self.full_loop(prediction_type="v_prediction"							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1E-2
     assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1E-2
     assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3



 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1E-2
     assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3



