import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class 		_lowerCamelCase		:
    @property
    def     UpperCamelCase_					(	self )							->       Tuple:
     return self.get_dummy_input()
    @property
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     if self.block_type == "down":
      return (4, 32, 16, 16)
     elif self.block_type == "mid":
      return (4, 32, 32, 32)
     elif self.block_type == "up":
      return (4, 32, 64, 64)

     raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.' )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase=True			,			lowerCAmelCase=False			,			lowerCAmelCase=False			,			lowerCAmelCase=False			,			)							->       str:
     SCREAMING_SNAKE_CASE__: str=   4
     SCREAMING_SNAKE_CASE__: List[str]=   32
     SCREAMING_SNAKE_CASE__: Any=   (32, 32)

     SCREAMING_SNAKE_CASE__: int=   torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: List[Any]=   torch.device(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[str]=   (batch_size, num_channels) + sizes
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   randn_tensor(lowerCAmelCase			,			generator=lowerCAmelCase			,			device=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[Any]=   {'''hidden_states''': hidden_states}

     if include_temb:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   128
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   randn_tensor((batch_size, temb_channels)			,			generator=lowerCAmelCase			,			device=lowerCAmelCase )

     if include_res_hidden_states_tuple:
      SCREAMING_SNAKE_CASE__: List[Any]=   torch.manual_seed(1 )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   (randn_tensor(lowerCAmelCase			,			generator=lowerCAmelCase			,			device=lowerCAmelCase ),)

     if include_encoder_hidden_states:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   floats_tensor((batch_size, 32, 32) ).to(lowerCAmelCase )

     if include_skip_sample:
      SCREAMING_SNAKE_CASE__: int=   randn_tensor(((batch_size, 3) + sizes)			,			generator=lowerCAmelCase			,			device=lowerCAmelCase )

     return dummy_input
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: int=   {
         '''in_channels''': 32,
         '''out_channels''': 32,
         '''temb_channels''': 128,
     }
     if self.block_type == "up":
      SCREAMING_SNAKE_CASE__: Optional[Any]=   32

     if self.block_type == "mid":
      init_dict.pop('''out_channels''' )

     SCREAMING_SNAKE_CASE__: List[str]=   self.dummy_input
     return init_dict, inputs_dict
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       List[str]:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[Any]=   self.prepare_init_args_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__: Any=   self.block_class(**lowerCAmelCase )
     unet_block.to(lowerCAmelCase )
     unet_block.eval()

     with torch.no_grad():
      SCREAMING_SNAKE_CASE__: Dict=   unet_block(**lowerCAmelCase )

     if isinstance(lowerCAmelCase			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Tuple=   output[0]

     self.assertEqual(output.shape			,			self.output_shape )

     SCREAMING_SNAKE_CASE__: List[str]=   output[0, -1, -3:, -3:]
     SCREAMING_SNAKE_CASE__: Tuple=   torch.tensor(lowerCAmelCase ).to(lowerCAmelCase )
     assert torch_all_close(output_slice.flatten()			,			lowerCAmelCase			,			atol=5e-3 )



    @unittest.skipIf(torch_device == '''mps'''			,			'''Training is not supported in mps''' )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: str=   self.prepare_init_args_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__: Tuple=   self.block_class(**lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.train()
     SCREAMING_SNAKE_CASE__: Dict=   model(**lowerCAmelCase )

     if isinstance(lowerCAmelCase			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Any=   output[0]

     SCREAMING_SNAKE_CASE__: List[Any]=   torch.device(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Any=   randn_tensor(output.shape			,			device=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Any=   torch.nn.functional.mse_loss(lowerCAmelCase			,			lowerCAmelCase )
     loss.backward()








'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):

		def __init__(   self					:   int  ,      *__magic_name__					:   Optional[Any]  ,      **__magic_name__					:   Any			)					->    None:


					"""simple docstring"""

					warnings.warn(
					    """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use OwlViTImageProcessor instead."""  ,      __magic_name__  ,      )
					super().__init__(*__magic_name__  ,      **__magic_name__			)


