

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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     lowerCAmelCase				(    __UpperCamelCase   ,				__UpperCamelCase   ,				unittest.TestCase				):


       '''simple docstring'''







       snake_case    							=	IFSuperResolutionPipeline
       snake_case    							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
       snake_case    							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       snake_case    							=	PipelineTesterMixin.required_optional_params - {'latents'}

       def   lowerCamelCase__						(	self					:  List[Any]     )   -> Dict:







        '''simple docstring'''

        return self._get_superresolution_dummy_components()

       def   lowerCamelCase__						(	self					:  int						,	__snake_case					:  List[str]						,	__snake_case					:  Optional[Any]=0     )   -> Tuple:







        '''simple docstring'''

        if str(__snake_case     ).startswith('mps'     ):
         lowerCamelCase      						=    torch.manual_seed(__snake_case     )
        else:
         lowerCamelCase      						=    torch.Generator(device=__snake_case     ).manual_seed(__snake_case     )

        lowerCamelCase      						=    floats_tensor((1, 3, 32, 32)						,	rng=random.Random(__snake_case     )     ).to(__snake_case     )

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

        return inputs

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







        '''simple docstring'''

        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )

       def   lowerCamelCase__						(	self					:  List[str]     )   -> Dict:







        '''simple docstring'''

        self._test_save_load_optional_components()

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







        '''simple docstring'''

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

       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Any:







        '''simple docstring'''

        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )

       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Optional[int]:







        '''simple docstring'''

        self._test_save_load_local()





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







        '''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_torch_available


_lowerCAmelCase :	Optional[Any]     				=    {
    'configuration_xlm_prophetnet': ['XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMProphetNetConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Optional[Any]     				=    ['XLMProphetNetTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	List[str]     				=    [
         'XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XLMProphetNetDecoder',
         'XLMProphetNetEncoder',
         'XLMProphetNetForCausalLM',
         'XLMProphetNetForConditionalGeneration',
         'XLMProphetNetModel',
         'XLMProphetNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_prophetnet import (
              XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMProphetNetDecoder,
              XLMProphetNetEncoder,
              XLMProphetNetForCausalLM,
              XLMProphetNetForConditionalGeneration,
              XLMProphetNetModel,
              XLMProphetNetPreTrainedModel,
          )

else:
     import sys

     _lowerCAmelCase :	List[Any]     				=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




