

"""simple docstring"""


import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin

@skip_mps
class      __a							(UpperCamelCase_   ,	UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = IFPipeline
     _SCREAMING_SNAKE_CASE						:str       = TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
     _SCREAMING_SNAKE_CASE						:Optional[int]       = TEXT_TO_IMAGE_BATCH_PARAMS
     _SCREAMING_SNAKE_CASE						:Any       = PipelineTesterMixin.required_optional_params - {"""latents"""}

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


           """simple docstring"""


           return self._get_dummy_components()

     def 					_a			(						self	,    _a	,    _a=0  )  ->      int:


           """simple docstring"""


           if str(_a  ).startswith("""mps"""  ):
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.manual_seed(_a  )
           else:
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.Generator(device=_a  ).manual_seed(_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	{
               """prompt""": """A painting of a squirrel eating a burger""",
               """generator""": generator,
               """num_inference_steps""": 2,
               """output_type""": """numpy""",
           }

           return inputs

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           self._test_save_load_optional_components()

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )

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


           """simple docstring"""


           self._test_save_load_local()

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


           """simple docstring"""


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





     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()	,    reason="""XFormers attention is only available with CUDA and `xformers` installed"""	,    )
     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )





@slow
@require_torch_gpu
class      __a							(unittest.TestCase):






     '''simple docstring'''

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""	,    variant="""fp16"""	,    torch_dtype=torch.floataa  )

           SCREAMING_SNAKE_CASE__   :      str      		=	IFSuperResolutionPipeline.from_pretrained(
               """DeepFloyd/IF-II-L-v1.0"""	,    variant="""fp16"""	,    torch_dtype=torch.floataa	,    text_encoder=_a	,    tokenizer=_a  )

           # pre compute text embeddings and remove T5 to save memory

           pipe_a.text_encoder.to("""cuda"""  )

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	pipe_a.encode_prompt("""anime turtle"""	,    device="""cuda"""  )

           del pipe_a.tokenizer
           del pipe_a.text_encoder
           gc.collect()

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	None
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	None

           pipe_a.enable_model_cpu_offload()
           pipe_a.enable_model_cpu_offload()

           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

           self._test_if(_a	,    _a	,    _a	,    _a  )

           pipe_a.remove_all_hooks()
           pipe_a.remove_all_hooks()

           # img2img

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	IFImgaImgPipeline(**pipe_a.components  )
           SCREAMING_SNAKE_CASE__   :      int      		=	IFImgaImgSuperResolutionPipeline(**pipe_a.components  )

           pipe_a.enable_model_cpu_offload()
           pipe_a.enable_model_cpu_offload()

           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

           self._test_if_imgaimg(_a	,    _a	,    _a	,    _a  )

           pipe_a.remove_all_hooks()
           pipe_a.remove_all_hooks()

           # inpainting

           SCREAMING_SNAKE_CASE__   :      Dict      		=	IFInpaintingPipeline(**pipe_a.components  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	IFInpaintingSuperResolutionPipeline(**pipe_a.components  )

           pipe_a.enable_model_cpu_offload()
           pipe_a.enable_model_cpu_offload()

           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
           pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

           self._test_if_inpainting(_a	,    _a	,    _a	,    _a  )

     def 					_a			(						self	,    _a	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.Generator(device="""cpu"""  ).manual_seed(0  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pipe_a(
               prompt_embeds=_a	,    negative_prompt_embeds=_a	,    num_inference_steps=2	,    generator=_a	,    output_type="""np"""	,    )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	output.images[0]

           assert image.shape == (64, 64, 3)

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.cuda.max_memory_allocated()
           assert mem_bytes < 13 * 10**9

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""  )
           assert_mean_pixel_difference(_a	,    _a  )

           # pipeline 2

           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.Generator(device="""cpu"""  ).manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0  )  ).to(_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pipe_a(
               prompt_embeds=_a	,    negative_prompt_embeds=_a	,    image=_a	,    generator=_a	,    num_inference_steps=2	,    output_type="""np"""	,    )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	output.images[0]

           assert image.shape == (256, 256, 3)

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           SCREAMING_SNAKE_CASE__   :      Dict      		=	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""  )
           assert_mean_pixel_difference(_a	,    _a  )

     def 					_a			(						self	,    _a	,    _a	,    _a	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0  )  ).to(_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	torch.Generator(device="""cpu"""  ).manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	pipe_a(
               prompt_embeds=_a	,    negative_prompt_embeds=_a	,    image=_a	,    num_inference_steps=2	,    generator=_a	,    output_type="""np"""	,    )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	output.images[0]

           assert image.shape == (64, 64, 3)

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.cuda.max_memory_allocated()
           assert mem_bytes < 10 * 10**9

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""  )
           assert_mean_pixel_difference(_a	,    _a  )

           # pipeline 2

           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.Generator(device="""cpu"""  ).manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	floats_tensor((1, 3, 256, 256)	,    rng=random.Random(0  )  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0  )  ).to(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	pipe_a(
               prompt_embeds=_a	,    negative_prompt_embeds=_a	,    image=_a	,    original_image=_a	,    generator=_a	,    num_inference_steps=2	,    output_type="""np"""	,    )

           SCREAMING_SNAKE_CASE__   :      Any      		=	output.images[0]

           assert image.shape == (256, 256, 3)

           SCREAMING_SNAKE_CASE__   :      int      		=	torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           SCREAMING_SNAKE_CASE__   :      int      		=	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""  )
           assert_mean_pixel_difference(_a	,    _a  )





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


           """simple docstring"""


           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0  )  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	floats_tensor((1, 3, 64, 64)	,    rng=random.Random(1  )  ).to(_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.Generator(device="""cpu"""  ).manual_seed(0  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	pipe_a(
               prompt_embeds=_a	,    negative_prompt_embeds=_a	,    image=_a	,    mask_image=_a	,    num_inference_steps=2	,    generator=_a	,    output_type="""np"""	,    )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	output.images[0]

           assert image.shape == (64, 64, 3)

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.cuda.max_memory_allocated()
           assert mem_bytes < 10 * 10**9

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""  )
           assert_mean_pixel_difference(_a	,    _a  )

           # pipeline 2

           _start_torch_memory_measurement()

           SCREAMING_SNAKE_CASE__   :      int      		=	torch.Generator(device="""cpu"""  ).manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      int      		=	floats_tensor((1, 3, 64, 64)	,    rng=random.Random(0  )  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	floats_tensor((1, 3, 256, 256)	,    rng=random.Random(0  )  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	floats_tensor((1, 3, 256, 256)	,    rng=random.Random(1  )  ).to(_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pipe_a(
               prompt_embeds=_a	,    negative_prompt_embeds=_a	,    image=_a	,    mask_image=_a	,    original_image=_a	,    generator=_a	,    num_inference_steps=2	,    output_type="""np"""	,    )

           SCREAMING_SNAKE_CASE__   :      Any      		=	output.images[0]

           assert image.shape == (256, 256, 3)

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.cuda.max_memory_allocated()
           assert mem_bytes < 4 * 10**9

           SCREAMING_SNAKE_CASE__   :      Dict      		=	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""  )
           assert_mean_pixel_difference(_a	,    _a  )





def  _lowercase					(    )       ->				Optional[int]:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()







"""simple docstring"""


import os

a					:List[str]    = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Any      		=	0

      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      while index < len(__lowerCAmelCase							) - 1:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	SYMBOLS[numerals[index]]
            SCREAMING_SNAKE_CASE__   :      Dict      		=	SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	""""""

      SCREAMING_SNAKE_CASE__   :      int      		=	num // 1000
      numerals += m_count * "M"
      num %= 1000

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	num // 100
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 100

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals




def  _lowercase					(    __lowerCAmelCase = "/p089_roman.txt"							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      int      		=	0

      with open(os.path.dirname(__lowerCAmelCase							) + roman_numerals_filename							) as filea:
            SCREAMING_SNAKE_CASE__   :      str      		=	filea.readlines()

      for line in lines:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	line.strip()
            SCREAMING_SNAKE_CASE__   :      Dict      		=	parse_roman_numerals(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      str      		=	generate_roman_numerals(__lowerCAmelCase							)
            savings += len(__lowerCAmelCase							) - len(__lowerCAmelCase							)

      return savings


if __name__ == "__main__":
  print(f'{solution() = }')




