





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     :			Any     						=	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      SCREAMING_SNAKE_CASE      (   cls       ):
              return cls


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase = None     ,							lowerCAmelCase = None     ,							lowerCAmelCase = None     ,							lowerCAmelCase = None     ,							):
       _UpperCamelCase														=    device
       _UpperCamelCase														=    shape[0]

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

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

       return latents


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













import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
       from PIL import Image

class 			__A(    unittest.TestCase    ,      __UpperCAmelCase			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    load_tool('''image-question-answering'''   )
            self.tool.setup()
            _UpperCamelCase														=    load_tool('''image-question-answering''',							remote=A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )






