


'''simple docstring'''

import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
 lowerCamelCase							:       Tuple  						=     []

 lowerCamelCase							:       str  						=     os.path.join('tests', 'models', '**', 'test_modeling_*.py')
 lowerCamelCase							:       Tuple  						=     glob.glob(pattern)
 # TODO: deal with TF/Flax too
 lowerCamelCase							:       List[Any]  						=     [
     x for x in test_files if not (x.startswith('test_modeling_tf_') or x.startswith('test_modeling_flax_'))
 ]

 for test_file in test_files:
  lowerCamelCase							:       str  						=     get_tester_classes(test_file)
  for tester_class in tester_classes:
   # A few tester classes don't have `parent` parameter in `__init__`.
   # TODO: deal this better
   try:
    lowerCamelCase							:       Any  						=     tester_class(parent=None)
   except Exception:
    continue
   if hasattr(tester, 'get_config'):
    lowerCamelCase							:       int  						=     tester.get_config()
    for k, v in config.to_dict().items():
     if isinstance(v, int):
      lowerCamelCase							:       Dict  						=     None
      if k in ["vocab_size"]:
       lowerCamelCase							:       List[Any]  						=     100
      elif k in ["max_position_embeddings"]:
       lowerCamelCase							:       Dict  						=     128
      elif k in ["hidden_size", "d_model"]:
       lowerCamelCase							:       List[Any]  						=     40
      elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
       lowerCamelCase							:       Tuple  						=     5
      if target is not None and v > target:
       failures.append(
           f"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
           f""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
           f""" than {target}."""
       )

 if len(failures) > 0:
  raise Exception(f"""There were {len(failures)} failures:\n""" + '\n'.join(failures))








'''simple docstring'''

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   __lowerCAmelCase						(lowercase_  ,					lowercase_  ,					unittest.TestCase					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							IFImgaImgPipeline
      lowerCAmelCase__ :					int   =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      lowerCAmelCase__ :					Optional[int]   =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__ :					str   =							PipelineTesterMixin.required_optional_params - {"""latents"""}



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             return self._get_dummy_components()



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]=0			):

             '''simple docstring'''







             if str(UpperCamelCase			).startswith('''mps'''			):
                    lowercase__				      =			torch.manual_seed(UpperCamelCase			)
             else:
                    lowercase__				      =			torch.Generator(device=UpperCamelCase			).manual_seed(UpperCamelCase			)

             lowercase__				      =			floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(UpperCamelCase			)			).to(UpperCamelCase			)

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

             return inputs



      def 				UpperCamelCase__		(self   :   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 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Any			):

             '''simple docstring'''







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



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

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             self._test_save_load_local()






      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







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




