






"""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

lowercase_								=	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    lowerCAmelCase							(       cls					):




         """simple docstring"""







         return cls



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase = None						,			__UpperCamelCase = None						,			__UpperCamelCase = None						,			__UpperCamelCase = None						,			):




  """simple docstring"""







  __A		     =					device
  __A		     =					shape[0]

  __A		     =					layout or torch.strided
  __A		     =					device or torch.device('''cpu'''					)

  if generator is not None:
    __A		     =					generator.device.type if not isinstance(__UpperCamelCase						,			__UpperCamelCase					) else generator[0].device.type
    if gen_device_type != device.type and gen_device_type == "cpu":
      __A		     =					'''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(__UpperCamelCase						,			__UpperCamelCase					):
    __A		     =					(1,) + shape[1:]
    __A		     =					[
        torch.randn(__UpperCamelCase						,			generator=generator[i]						,			device=__UpperCamelCase						,			dtype=__UpperCamelCase						,			layout=__UpperCamelCase					)
        for i in range(__UpperCamelCase					)
    ]
    __A		     =					torch.cat(__UpperCamelCase						,			dim=0					).to(__UpperCamelCase					)
  else:
    __A		     =					torch.randn(__UpperCamelCase						,			generator=__UpperCamelCase						,			device=__UpperCamelCase						,			dtype=__UpperCamelCase						,			layout=__UpperCamelCase					).to(__UpperCamelCase					)

  return latents





def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







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









import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__lowerCamelCase								=      [(1,)]







def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"







def  _snake_case			(							)  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 	snake_case_			(unittest.TestCase	):




       """simple docstring"""






       @slow
       @require_torch_gpu
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @require_torch_gpu
       @slow
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @parameterized.expand(lowercase)
       @slow
       def 			A_ (  self  ,lowercase=None):

             """simple docstring"""





             check_slow()







@slow
@require_torch_gpu
def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      check_slow_torch_cuda()







@require_torch_gpu
@slow
def  _snake_case			(							)  ->  Union[str, Any]:






      '''simple docstring'''







      check_slow_torch_cuda()







@slow
@pytest.mark.parametrize("param"       ,	[1]     )
def  _snake_case			(							__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      check_slow()







@pytest.mark.parametrize("param"       ,	[1]     )
@slow
def  _snake_case			(							__snake_case     )  ->  Any:






      '''simple docstring'''







      check_slow()
