
"""simple docstring"""






from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase 					=    TypeVar('T')




class      _SCREAMING_SNAKE_CASE   (     A__				):
			pass




class      _SCREAMING_SNAKE_CASE   (     A__				):
			pass




class      _SCREAMING_SNAKE_CASE   (     Generic[T]				):






			def __init__(   self			,  __A = 10			)     ->	Optional[int]:
						lowerCAmelCase_       :list[T]     	=    []
						lowerCAmelCase_       :int     	=    limit






			def __bool__(   self			)     ->	bool:
						return bool(self.stack			)






			def __str__(   self			)     ->	str:
						return str(self.stack			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	None:
						if len(self.stack			) >= self.limit:
									raise StackOverflowError
						self.stack.append(__A			)






			def 					__lowerCAmelCase					(   self			)     ->	T:
						if not self.stack:
									raise StackUnderflowError
						return self.stack.pop()






			def 					__lowerCAmelCase					(   self			)     ->	T:
						if not self.stack:
									raise StackUnderflowError
						return self.stack[-1]






			def 					__lowerCAmelCase					(   self			)     ->	bool:
						return not bool(self.stack			)






			def 					__lowerCAmelCase					(   self			)     ->	bool:
						return self.size() == self.limit






			def 					__lowerCAmelCase					(   self			)     ->	int:
						return len(self.stack			)






			def __contains__(   self			,  __A			)     ->	bool:
						return item in self.stack






def 		_snake_case			(      )    ->							None:


			'''simple docstring'''





			lowerCAmelCase_       :Stack[int]     	=    Stack(1_0						)
			assert bool(lowercase__						) is False
			assert stack.is_empty() is True
			assert stack.is_full() is False
			assert str(lowercase__						) == "[]"

			try:
						lowerCAmelCase_       :Optional[Any]     	=    stack.pop()
						raise AssertionError  # This should not happen
			except StackUnderflowError:
						assert True  # This should happen

			try:
						lowerCAmelCase_       :Optional[int]     	=    stack.peek()
						raise AssertionError  # This should not happen
			except StackUnderflowError:
						assert True  # This should happen

			for i in range(1_0						):
						assert stack.size() == i
						stack.push(lowercase__						)

			assert bool(lowercase__						)
			assert not stack.is_empty()
			assert stack.is_full()
			assert str(lowercase__						) == str(list(range(1_0						)						)						)
			assert stack.pop() == 9
			assert stack.peek() == 8

			stack.push(1_0_0						)
			assert str(lowercase__						) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 1_0_0]						)

			try:
						stack.push(2_0_0						)
						raise AssertionError  # This should not happen
			except StackOverflowError:
						assert True  # This should happen

			assert not stack.is_empty()
			assert stack.size() == 1_0

			assert 5 in stack
			assert 5_5 not in stack


if __name__ == "__main__":
				test_stack()








"""simple docstring"""






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
				import torch

__UpperCAmelCase 					=    logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
				from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






				def 		_snake_case			(      cls						:    Tuple						)    ->							List[Any]:


							'''simple docstring'''





							return cls






def 		_snake_case			(      lowercase__						:    Union[Tuple, List] ,	lowercase__						:    Optional[Union[List["torch.Generator"], "torch.Generator"]] = None ,	lowercase__						:    Optional["torch.device"] = None ,	lowercase__						:    Optional["torch.dtype"] = None ,	lowercase__						:    Optional["torch.layout"] = None ,	)    ->							Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    device
			lowerCAmelCase_       :Any     	=    shape[0]

			lowerCAmelCase_       :Dict     	=    layout or torch.strided
			lowerCAmelCase_       :List[str]     	=    device or torch.device("""cpu"""						)

			if generator is not None:
						lowerCAmelCase_       :Optional[Any]     	=    generator.device.type if not isinstance(lowercase__ ,	lowercase__						) else generator[0].device.type
						if gen_device_type != device.type and gen_device_type == "cpu":
									lowerCAmelCase_       :Optional[int]     	=    """cpu"""
									if device != "mps":
												logger.info(
												    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
												    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
												    f""" slighly speed up this function by passing a generator that was created on the {device} device."""						)
						elif gen_device_type != device.type and gen_device_type == "cuda":
									raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""						)

			if isinstance(lowercase__ ,	lowercase__						):
						lowerCAmelCase_       :List[str]     	=    (1,) + shape[1:]
						lowerCAmelCase_       :Optional[Any]     	=    [
						    torch.randn(lowercase__ ,	generator=generator[i] ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						)
						    for i in range(lowercase__						)
						]
						lowerCAmelCase_       :str     	=    torch.cat(lowercase__ ,	dim=0						).to(lowercase__						)
			else:
						lowerCAmelCase_       :Dict     	=    torch.randn(lowercase__ ,	generator=lowercase__ ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						).to(lowercase__						)

			return latents






def 		_snake_case			(      lowercase__						:    Union[str, Any]						)    ->							int:


			'''simple docstring'''





			if is_torch_version("""<""" ,	"""2.0.0"""						) or not hasattr(lowercase__ ,	"""_dynamo"""						):
						return False
			return isinstance(lowercase__ ,	torch._dynamo.eval_frame.OptimizedModule						)






