



'''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        UpperCAmelCase__						(		UpperCamelCase__				,							UpperCamelCase__				,							unittest.TestCase			):
					a   :  int   =				IFImgaImgPipeline
					a   :  Optional[int]   =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
					a   :  int   =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					a   :  Tuple   =				PipelineTesterMixin.required_optional_params - {"""latents"""}



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						return self._get_dummy_components()



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase=0  )  ->      List[Any]:
						if str(a_  ).startswith("mps"  ):
							__lowerCAmelCase       				=	torch.manual_seed(a_  )
						else:
							__lowerCAmelCase       				=	torch.Generator(device=a_  ).manual_seed(a_  )

						__lowerCAmelCase       				=	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(a_  )  ).to(a_  )

						__lowerCAmelCase       				=	{
						    "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  )  ->      Tuple:
						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  )  ->      List[Any]:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )



					@unittest.skipIf(torch_device != "cuda"      ,					reason="float16 requires CUDA"  )
					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1E-1  )



					@unittest.skipIf(torch_device != "cuda"      ,					reason="float16 requires CUDA"  )
					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						super().test_floataa_inference(expected_max_diff=1E-1  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						self._test_save_load_local()



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2      ,					)




from collections import defaultdict

def       A_   (						__a  :					str		):

				"""simple docstring"""
				a__					   =		defaultdict(__a		)
				# Creating a dictionary containing count of each word
				for word in sentence.split():
								occurrence[word] += 1
				return occurrence


if __name__ == "__main__":
	for word, count in word_occurrence("""INPUT STRING""").items():
		print(f"""{word}: {count}""")

