






import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class        snake_case_				(							lowercase_   ,      lowercase_   ,      unittest.TestCase   ):







   """simple docstring"""



   __lowerCAmelCase  =    IFImgaImgPipeline
   __lowerCAmelCase  =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
   __lowerCAmelCase  =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __lowerCAmelCase  =    PipelineTesterMixin.required_optional_params - {"""latents"""}

   def       SCREAMING_SNAKE_CASE__					(     self:						List[Any]						)	->		List[str]:


       """simple docstring"""



       return self._get_dummy_components()

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						Tuple ,							snake_case__:						List[Any]=0						)	->		str:


       """simple docstring"""



       if str(snake_case__						).startswith('mps'						):
           _lowercase     			=						torch.manual_seed(snake_case__						)
       else:
           _lowercase     			=						torch.Generator(device=snake_case__						).manual_seed(snake_case__						)

       _lowercase     			=						floats_tensor((1, 3, 32, 32) ,							rng=random.Random(snake_case__						)						).to(snake_case__						)

       _lowercase     			=						{
           'prompt': 'A painting of a squirrel eating a burger',
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'output_type': 'numpy',
       }

       return inputs

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Optional[int]:


       """simple docstring"""



       self._test_save_load_optional_components()

   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available() ,							reason='XFormers attention is only available with CUDA and `xformers` installed' ,							)
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Any:


       """simple docstring"""



       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)

   @unittest.skipIf(torch_device != 'cuda' ,							reason='float16 requires CUDA'						)
   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Union[str, Any]:


       """simple docstring"""



       super().test_save_load_floataa(expected_max_diff=1E-1						)

   @unittest.skipIf(torch_device != 'cuda' ,							reason='float16 requires CUDA'						)
   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		str:


       """simple docstring"""



       super().test_floataa_inference(expected_max_diff=1E-1						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		Optional[int]:


       """simple docstring"""



       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		Union[str, Any]:


       """simple docstring"""



       self._test_save_load_local()

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Tuple:


       """simple docstring"""



       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2 ,							)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCamelCase					    =	{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50Tokenizer''']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50TokenizerFast''']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa import MBartaaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
   import sys

   lowerCamelCase					    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
