

import os
from collections import deque

import torch
from torch.utils.data import Dataset

class 						__UpperCamelCase    (					lowercase						):




	def __init__(		self	:						List[Any]		,	lowerCAmelCase	:						Optional[int]=""		,	lowerCAmelCase	:						List[str]="train"							):


		'''simple docstring'''







		assert os.path.isdir(lowerCAmelCase							)

		UpperCAmelCase_       				=			[]
		UpperCAmelCase_       				=			os.listdir(lowerCAmelCase							)
		for story_filename in story_filenames_list:
			if "summary" in story_filename:
				continue
			UpperCAmelCase_       				=			os.path.join(lowerCAmelCase		,	lowerCAmelCase							)
			if not os.path.isfile(lowerCAmelCase							):
				continue
			self.documents.append(lowerCAmelCase							)




	def __len__(		self	:						Optional[int]							):


		'''simple docstring'''







		return len(self.documents							)




	def __getitem__(		self	:						Dict		,	lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.documents[idx]
		UpperCAmelCase_       				=			document_path.split("/"							)[-1]
		with open(lowerCAmelCase		,	encoding="utf-8"							) as source:
			UpperCAmelCase_       				=			source.read()
			UpperCAmelCase_				,   UpperCAmelCase_       				=			process_story(lowerCAmelCase							)
		return document_name, story_lines, summary_lines



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			list(filter(lambda A    : len(A    ) != 0       ,     [line.strip() for line in raw_story.split("\n"    )]    )    )

	# for some unknown reason some lines miss a period, add it
	UpperCAmelCase_       				=			[_add_missing_period(A    ) for line in nonempty_lines]

	# gather article lines
	UpperCAmelCase_       				=			[]
	UpperCAmelCase_       				=			deque(A    )
	while True:
		try:
			UpperCAmelCase_       				=			lines.popleft()
			if element.startswith("@highlight"    ):
				break
			story_lines.append(A    )
		except IndexError:
			# if "@highlight" is absent from the file we pop
			# all elements until there is None, raising an exception.
			return story_lines, []

    # gather summary lines
	UpperCAmelCase_       				=			list(filter(lambda A    : not t.startswith("@highlight"    )       ,     A    )    )

	return story_lines, summary_lines



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			[".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
	if line.startswith("@highlight"    ):
		return line
	if line[-1] in END_TOKENS:
		return line
	return line + "."



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	if len(A    ) > block_size:
		return sequence[:block_size]
	else:
		sequence.extend([pad_token_id] * (block_size - len(A    ))    )
		return sequence



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			torch.ones_like(A    )
	UpperCAmelCase_       				=			sequence == pad_token_id
	UpperCAmelCase_       				=			0
	return mask



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[tokenizer.encode(A    ) for line in story_lines]
	UpperCAmelCase_       				=			[token for sentence in story_lines_token_ids for token in sentence]
	UpperCAmelCase_       				=			[tokenizer.encode(A    ) for line in summary_lines]
	UpperCAmelCase_       				=			[token for sentence in summary_lines_token_ids for token in sentence]

	return story_token_ids, summary_token_ids




def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			[]
	for sequence in batch:
		UpperCAmelCase_       				=			-1
		UpperCAmelCase_       				=			[]
		for s in sequence:
			if s == separator_token_id:
				sentence_num += 1
			embeddings.append(sentence_num % 2    )
		batch_embeddings.append(A    )
	return torch.tensor(A    )


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     (KDPMaDiscreteScheduler,)
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Union[str, Any]		,	**lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{
		    "num_train_timesteps": 1_100,
		    "beta_start": 0.0_001,
		    "beta_end": 0.02,
		    "beta_schedule": "linear",
		}

		config.update(**lowerCAmelCase							)
		return config




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		for timesteps in [10, 50, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,	[0.0_002, 0.002, 0.02]							):
			self.check_over_configs(beta_start=lowerCAmelCase		,	beta_end=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config(prediction_type="v_prediction"							)
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 4.6_934e-07							) < 1e-2
			assert abs(result_mean.item() - 6.1_112e-10							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 4.693_428_650_170_972e-07							) < 1e-2
			assert abs(result_mean.item() - 0.0_002							) < 1e-3




	def       __A					(		self	:						int							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps		,	device=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter.to(lowerCAmelCase							) * scheduler.init_noise_sigma

		for t in scheduler.timesteps:
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if str(lowerCAmelCase							).startswith("cpu"							):
			# The following sum varies between 148 and 156 on mps. Why?
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3