






from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any





class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				("_front", "_back", "_len")





	@dataclass
	class __lowerCamelCase			:





		"""simple docstring"""






		UpperCamelCase__								=				None
		UpperCamelCase__								=				None
		UpperCamelCase__								=				None





	class __lowerCamelCase			:





		"""simple docstring"""






		UpperCamelCase__								=				("_cur",)

		def __init__(							self	,   UpperCAmelCase   ):
								"""simple docstring"""







								_UpperCAmelCase     =      cur

		def __iter__(							self   ):
								"""simple docstring"""







								return self

		def 	UpperCamelCase   (							self   ):
								"""simple docstring"""







								if self._cur is None:
														# finished iterating
														raise StopIteration
								_UpperCAmelCase     =      self._cur.val
								_UpperCAmelCase     =      self._cur.next_node

								return val



	def __init__(							self	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      0

							if iterable is not None:
													# append every value to the deque
													for val in iterable:
																			self.append(UpperCAmelCase   )

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self._Node(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )
							if self.is_empty():
													# front = back
													_UpperCAmelCase     =      _UpperCAmelCase     =      node
													_UpperCAmelCase     =      1
							else:
													# connect nodes
													_UpperCAmelCase     =      node
													_UpperCAmelCase     =      self._back
													_UpperCAmelCase     =      node  # assign new back to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self._Node(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )
							if self.is_empty():
													# front = back
													_UpperCAmelCase     =      _UpperCAmelCase     =      node
													_UpperCAmelCase     =      1
							else:
													# connect nodes
													_UpperCAmelCase     =      self._front
													_UpperCAmelCase     =      node
													_UpperCAmelCase     =      node  # assign new front to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							for val in iterable:
													self.append(UpperCAmelCase   )

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							for val in iterable:
													self.appendleft(UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							assert not self.is_empty(), "Deque is empty."

							_UpperCAmelCase     =      self._back
							_UpperCAmelCase     =      self._back.prev_node  # set new back
							# drop the last node - python will deallocate memory automatically
							_UpperCAmelCase     =      None

							self._len -= 1

							return topop.val

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							assert not self.is_empty(), "Deque is empty."

							_UpperCAmelCase     =      self._front
							_UpperCAmelCase     =      self._front.next_node  # set new front and drop the first node
							_UpperCAmelCase     =      None

							self._len -= 1

							return topop.val

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return self._front is None

	def __len__(							self   ):
							"""simple docstring"""







							return self._len

	def __eq__(							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							if not isinstance(UpperCAmelCase	,   UpperCAmelCase   ):
													return NotImplemented

							_UpperCAmelCase     =      self._front
							_UpperCAmelCase     =      other._front

							# if the length of the dequeues are not the same, they are not equal
							if len(self   ) != len(UpperCAmelCase   ):
													return False

							while me is not None and oth is not None:
													# compare every value
													if me.val != oth.val:
																			return False
													_UpperCAmelCase     =      me.next_node
													_UpperCAmelCase     =      oth.next_node

							return True

	def __iter__(							self   ):
							"""simple docstring"""







							return Deque._Iterator(self._front   )

	def __repr__(							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      []
							_UpperCAmelCase     =      self._front
							while aux is not None:
													# append the values in a list to display
													values_list.append(aux.val   )
													_UpperCAmelCase     =      aux.next_node

							return F"""[{", ".join(repr(UpperCAmelCase   ) for val in values_list   )}]"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()








import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference





@slow
@require_torch_gpu
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'runwayml/stable-diffusion-v1-5'
							_UpperCAmelCase     =      TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase	,   torch_dtype=torch.floataa   ).to('cuda'   )
							_UpperCAmelCase     =      DDIMScheduler.from_config(pipe.scheduler.config   )
							_UpperCAmelCase     =      torch.Generator(device='cuda'   ).manual_seed(0   )

							_UpperCAmelCase     =      'A bear is playing a guitar on Times Square'
							_UpperCAmelCase     =      pipe(prompt=UpperCAmelCase	,   generator=UpperCAmelCase   ).images

							_UpperCAmelCase     =      load_pt(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'   )

							assert_mean_pixel_difference(UpperCAmelCase	,   UpperCAmelCase   )
