




import argparse
import os
import re


SCREAMING_SNAKE_CASE__       :      Any  =   'src/transformers'

# Pattern that looks at the indentation in a line.
SCREAMING_SNAKE_CASE__       :      Any  =   re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
SCREAMING_SNAKE_CASE__       :      List[str]  =   re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
SCREAMING_SNAKE_CASE__       :      str  =   re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
SCREAMING_SNAKE_CASE__       :      int  =   re.compile(r'\[([^\]]+)\]')

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[int]:
       lowerCamelCase   :      List[str]			 =					_re_indent.search(_SCREAMING_SNAKE_CASE    )
       return "" if search is None else search.groups()[0]

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE="" ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None    )	-> Tuple:
       lowerCamelCase   :      Optional[Any]			 =					0
       lowerCamelCase   :      Any			 =					code.split("\n"    )
       if start_prompt is not None:
              while not lines[index].startswith(_SCREAMING_SNAKE_CASE    ):
                     index += 1
              lowerCamelCase   :      List[Any]			 =					["\n".join(lines[:index]    )]
       else:
              lowerCamelCase   :      str			 =					[]

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       lowerCamelCase   :      Union[str, Any]			 =					[lines[index]]
       index += 1
       while index < len(_SCREAMING_SNAKE_CASE    ) and (end_prompt is None or not lines[index].startswith(_SCREAMING_SNAKE_CASE    )):
              if len(lines[index]    ) > 0 and get_indent(lines[index]    ) == indent_level:
                     if len(_SCREAMING_SNAKE_CASE    ) > 0 and get_indent(current_block[-1]    ).startswith(indent_level + " "    ):
                            current_block.append(lines[index]    )
                            blocks.append("\n".join(_SCREAMING_SNAKE_CASE    )    )
                            if index < len(_SCREAMING_SNAKE_CASE    ) - 1:
                                   lowerCamelCase   :      Any			 =					[lines[index + 1]]
                                   index += 1
                            else:
                                   lowerCamelCase   :      Any			 =					[]
                     else:
                            blocks.append("\n".join(_SCREAMING_SNAKE_CASE    )    )
                            lowerCamelCase   :      Tuple			 =					[lines[index]]
              else:
                     current_block.append(lines[index]    )
              index += 1

       # Adds current block if it's nonempty.
       if len(_SCREAMING_SNAKE_CASE    ) > 0:
              blocks.append("\n".join(_SCREAMING_SNAKE_CASE    )    )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(_SCREAMING_SNAKE_CASE    ):
              blocks.append("\n".join(lines[index:]    )    )

       return blocks

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:

       def _inner(_SCREAMING_SNAKE_CASE    ):
              return key(_SCREAMING_SNAKE_CASE    ).lower().replace("_" ,""    )

       return _inner

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None    )	-> Union[str, Any]:

       # If no key is provided, we use a noop.
       def noop(_SCREAMING_SNAKE_CASE    ):
              return x

       if key is None:
              lowerCamelCase   :      List[str]			 =					noop
       # Constants are all uppercase, they go first.
       lowerCamelCase   :      int			 =					[obj for obj in objects if key(_SCREAMING_SNAKE_CASE    ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       lowerCamelCase   :      Union[str, Any]			 =					[obj for obj in objects if key(_SCREAMING_SNAKE_CASE    )[0].isupper() and not key(_SCREAMING_SNAKE_CASE    ).isupper()]
       # Functions begin with a lowercase, they go last.
       lowerCamelCase   :      Optional[int]			 =					[obj for obj in objects if not key(_SCREAMING_SNAKE_CASE    )[0].isupper()]

       lowerCamelCase   :      Union[str, Any]			 =					ignore_underscore(_SCREAMING_SNAKE_CASE    )
       return sorted(_SCREAMING_SNAKE_CASE ,key=_SCREAMING_SNAKE_CASE    ) + sorted(_SCREAMING_SNAKE_CASE ,key=_SCREAMING_SNAKE_CASE    ) + sorted(_SCREAMING_SNAKE_CASE ,key=_SCREAMING_SNAKE_CASE    )

def      A      (      _SCREAMING_SNAKE_CASE    )	-> List[Any]:

       # This inner function sort imports between [ ].
       def _replace(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      Optional[Any]			 =					match.groups()[0]
              if "," not in imports:
                     return f'''[{imports}]'''
              lowerCamelCase   :      int			 =					[part.strip().replace("\"" ,""    ) for part in imports.split(","    )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]    ) == 0:
                     lowerCamelCase   :      Optional[int]			 =					keys[:-1]
              return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(_SCREAMING_SNAKE_CASE    )]    ) + "]"

       lowerCamelCase   :      List[str]			 =					import_statement.split("\n"    )
       if len(_SCREAMING_SNAKE_CASE    ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              lowerCamelCase   :      List[str]			 =					2 if lines[1].strip() == "[" else 1
              lowerCamelCase   :      Union[str, Any]			 =					[(i, _re_strip_line.search(_SCREAMING_SNAKE_CASE    ).groups()[0]) for i, line in enumerate(lines[idx:-idx]    )]
              lowerCamelCase   :      str			 =					sort_objects(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE    : x[1]    )
              lowerCamelCase   :      str			 =					[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]    )
       elif len(_SCREAMING_SNAKE_CASE    ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]    ) is not None:
                     lowerCamelCase   :      Optional[int]			 =					_re_bracket_content.sub(_replace ,lines[1]    )
              else:
                     lowerCamelCase   :      List[str]			 =					[part.strip().replace("\"" ,""    ) for part in lines[1].split(","    )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]    ) == 0:
                            lowerCamelCase   :      Any			 =					keys[:-1]
                     lowerCamelCase   :      Any			 =					get_indent(lines[1]    ) + ", ".join([f'''"{k}"''' for k in sort_objects(_SCREAMING_SNAKE_CASE    )]    )
              return "\n".join(_SCREAMING_SNAKE_CASE    )
       else:
              # Finally we have to deal with imports fitting on one line
              lowerCamelCase   :      Dict			 =					_re_bracket_content.sub(_replace ,_SCREAMING_SNAKE_CASE    )
              return import_statement

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=True    )	-> Optional[Any]:
       with open(_SCREAMING_SNAKE_CASE ,encoding="utf-8"    ) as f:
              lowerCamelCase   :      Union[str, Any]			 =					f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       lowerCamelCase   :      Optional[int]			 =					split_code_in_indented_blocks(
           _SCREAMING_SNAKE_CASE ,start_prompt="_import_structure = {" ,end_prompt="if TYPE_CHECKING:"    )

       # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1 ,len(_SCREAMING_SNAKE_CASE    ) - 1    ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              lowerCamelCase   :      int			 =					main_blocks[block_idx]
              lowerCamelCase   :      Union[str, Any]			 =					block.split("\n"    )

              # Get to the start of the imports.
              lowerCamelCase   :      List[str]			 =					0
              while line_idx < len(_SCREAMING_SNAKE_CASE    ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            lowerCamelCase   :      int			 =					len(_SCREAMING_SNAKE_CASE    )
                     else:
                            line_idx += 1
              if line_idx >= len(_SCREAMING_SNAKE_CASE    ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              lowerCamelCase   :      Optional[int]			 =					"\n".join(block_lines[line_idx:-1]    )
              lowerCamelCase   :      Optional[int]			 =					get_indent(block_lines[1]    )
              # Slit the internal block into blocks of indent level 1.
              lowerCamelCase   :      Dict			 =					split_code_in_indented_blocks(_SCREAMING_SNAKE_CASE ,indent_level=_SCREAMING_SNAKE_CASE    )
              # We have two categories of import key: list or _import_structure[key].append/extend
              lowerCamelCase   :      Union[str, Any]			 =					_re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              lowerCamelCase   :      Optional[Any]			 =					[(pattern.search(_SCREAMING_SNAKE_CASE    ).groups()[0] if pattern.search(_SCREAMING_SNAKE_CASE    ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              lowerCamelCase   :      Optional[Any]			 =					[(i, key) for i, key in enumerate(_SCREAMING_SNAKE_CASE    ) if key is not None]
              lowerCamelCase   :      str			 =					[x[0] for x in sorted(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE    : x[1]    )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              lowerCamelCase   :      List[Any]			 =					0
              lowerCamelCase   :      Optional[Any]			 =					[]
              for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
                     if keys[i] is None:
                            reorderded_blocks.append(internal_blocks[i]    )
                     else:
                            lowerCamelCase   :      Union[str, Any]			 =					sort_objects_in_import(internal_blocks[sorted_indices[count]]    )
                            reorderded_blocks.append(_SCREAMING_SNAKE_CASE    )
                            count += 1

        # And we put our main block back together with its first and last line.
              lowerCamelCase   :      Dict			 =					"\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]    )

       if code != "\n".join(_SCREAMING_SNAKE_CASE    ):
              if check_only:
                     return True
              else:
                     print(f'''Overwriting {file}.'''    )
                     with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
                            f.write("\n".join(_SCREAMING_SNAKE_CASE    )    )

def      A      (      _SCREAMING_SNAKE_CASE=True    )	-> Any:
       lowerCamelCase   :      Optional[Any]			 =					[]
       for root, _, files in os.walk(_SCREAMING_SNAKE_CASE    ):
              if "__init__.py" in files:
                     lowerCamelCase   :      Any			 =					sort_imports(os.path.join(_SCREAMING_SNAKE_CASE ,"__init__.py"    ) ,check_only=_SCREAMING_SNAKE_CASE    )
                     if result:
                            lowerCamelCase   :      Optional[Any]			 =					[os.path.join(_SCREAMING_SNAKE_CASE ,"__init__.py"    )]
       if len(_SCREAMING_SNAKE_CASE    ) > 0:
              raise ValueError(f'''Would overwrite {len(_SCREAMING_SNAKE_CASE    )} files, run `make style`.'''    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Tuple  =   argparse.ArgumentParser()
       parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
       SCREAMING_SNAKE_CASE__       :      List[Any]  =   parser.parse_args()

       sort_imports_in_all_inits(check_only=args.check_only)














import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
       import onnxruntime as ort
class     a    ( lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : int							       =	'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			Optional[Any]=0					)							->      Any:
        __UpperCAmelCase							:	Any           =  floats_tensor((1, 3, 128, 128)		,      rng=random.Random(__lowercase					)					)
        __UpperCAmelCase							:	int           =  np.random.RandomState(__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  {
            """prompt""": """A painting of a squirrel eating a burger""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 3,
            """strength""": 0.75,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[Any]:
        __UpperCAmelCase							:	List[Any]           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	int           =  self.get_dummy_inputs()
        __UpperCAmelCase							:	Optional[Any]           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	Union[str, Any]           =  image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	List[str]           =  np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]					)
        assert np.abs(image_slice - expected_slice					).max() < 1e-1
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      List[str]:
        __UpperCAmelCase							:	Dict           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	int           =  PNDMScheduler.from_config(pipe.scheduler.config		,      skip_prk_steps=__lowercase					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Any           =  self.get_dummy_inputs()
        __UpperCAmelCase							:	Tuple           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	Dict           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	str           =  np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1
      def 	UpperCAmelCase      (      self					:			str					)							->      Tuple:
        __UpperCAmelCase							:	Tuple           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	int           =  LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        # warmup pass to apply optimizations
        __UpperCAmelCase							:	Optional[int]           =  pipe(**self.get_dummy_inputs()					)

        __UpperCAmelCase							:	Tuple           =  self.get_dummy_inputs()
        __UpperCAmelCase							:	Any           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	List[Any]           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	Optional[int]           =  np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	int           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	Union[str, Any]           =  EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	List[str]           =  self.get_dummy_inputs()
        __UpperCAmelCase							:	int           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	Dict           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	Tuple           =  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1
      def 	UpperCAmelCase      (      self					:			int					)							->      Any:
        __UpperCAmelCase							:	Any           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	List[Any]           =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Union[str, Any]           =  self.get_dummy_inputs()
        __UpperCAmelCase							:	int           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	List[Any]           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	List[str]           =  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1




      def 	UpperCAmelCase      (      self					:			Tuple					)							->      str:
        __UpperCAmelCase							:	Dict           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	str           =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Optional[Any]           =  self.get_dummy_inputs()
        __UpperCAmelCase							:	int           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	List[Any]           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	Union[str, Any]           =  np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1


@nightly
@require_onnxruntime
@require_torch_gpu
class     a    ( unittest.TestCase   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[Any]:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )
      @property
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Tuple:
        __UpperCAmelCase							:	Optional[int]           =  ort.SessionOptions()
        __UpperCAmelCase							:	List[Any]           =  False
        return options
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Tuple:
        __UpperCAmelCase							:	List[Any]           =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""					)
        __UpperCAmelCase							:	Dict           =  init_image.resize((768, 512)					)
        # using the PNDM scheduler by default
        __UpperCAmelCase							:	Any           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            """CompVis/stable-diffusion-v1-4"""		,      revision="""onnx"""		,      safety_checker=__lowercase		,      feature_extractor=__lowercase		,      provider=self.gpu_provider		,      sess_options=self.gpu_options		,      )
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Dict           =  """A fantasy landscape, trending on artstation"""

        __UpperCAmelCase							:	str           =  np.random.RandomState(0					)
        __UpperCAmelCase							:	Optional[Any]           =  pipe(
            prompt=__lowercase		,      image=__lowercase		,      strength=0.75		,      guidance_scale=7.5		,      num_inference_steps=10		,      generator=__lowercase		,      output_type="""np"""		,      )
        __UpperCAmelCase							:	str           =  output.images
        __UpperCAmelCase							:	int           =  images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 768, 3)
        __UpperCAmelCase							:	Union[str, Any]           =  np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]					)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2




      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	Tuple           =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""					)
        __UpperCAmelCase							:	int           =  init_image.resize((768, 512)					)
        __UpperCAmelCase							:	Tuple           =  LMSDiscreteScheduler.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""		,      subfolder="""scheduler"""		,      revision="""onnx"""					)
        __UpperCAmelCase							:	Tuple           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""		,      revision="""onnx"""		,      scheduler=__lowercase		,      safety_checker=__lowercase		,      feature_extractor=__lowercase		,      provider=self.gpu_provider		,      sess_options=self.gpu_options		,      )
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Dict           =  """A fantasy landscape, trending on artstation"""

        __UpperCAmelCase							:	int           =  np.random.RandomState(0					)
        __UpperCAmelCase							:	Optional[int]           =  pipe(
            prompt=__lowercase		,      image=__lowercase		,      strength=0.75		,      guidance_scale=7.5		,      num_inference_steps=20		,      generator=__lowercase		,      output_type="""np"""		,      )
        __UpperCAmelCase							:	Union[str, Any]           =  output.images
        __UpperCAmelCase							:	Union[str, Any]           =  images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 768, 3)
        __UpperCAmelCase							:	str           =  np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]					)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2


