



'''simple docstring'''





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     UpperCAmelCase_       (       _a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     List[str]=0						):
          snake_case__  :    List[Any]    						=      floats_tensor((1, 3, 128, 128) ,     rng=random.Random(snake_case_						)						)
          snake_case__  :    Tuple    						=      np.random.RandomState(snake_case_						)
          snake_case__  :    List[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 lowerCamelCase       (    self					:     str						):
          snake_case__  :    Dict    						=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""						)
          pipe.set_progress_bar_config(disable=snake_case_						)

          snake_case__  :    List[Any]    						=      self.get_dummy_inputs()
          snake_case__  :    int    						=      pipe(**snake_case_						).images
          snake_case__  :    Dict    						=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 128, 128, 3)
          snake_case__  :    Optional[Any]    						=      np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]						)
          assert np.abs(image_slice - expected_slice						).max() < 1E-1




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Optional[int]    						=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""						)
          snake_case__  :    Optional[Any]    						=      PNDMScheduler.from_config(pipe.scheduler.config ,     skip_prk_steps=snake_case_						)
          pipe.set_progress_bar_config(disable=snake_case_						)

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

          assert image.shape == (1, 128, 128, 3)
          snake_case__  :    Dict    						=      np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]						)

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




    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    Optional[int]    						=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""						)
          snake_case__  :    int    						=      LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
          pipe.set_progress_bar_config(disable=snake_case_						)

          # warmup pass to apply optimizations
          snake_case__  :    List[Any]    						=      pipe(**self.get_dummy_inputs()						)

          snake_case__  :    Union[str, Any]    						=      self.get_dummy_inputs()
          snake_case__  :    str    						=      pipe(**snake_case_						).images
          snake_case__  :    Union[str, Any]    						=      image[0, -3:, -3:, -1]

          assert image.shape == (1, 128, 128, 3)
          snake_case__  :    Optional[int]    						=      np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]						)

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




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    List[str]    						=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""						)
          snake_case__  :    Tuple    						=      EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
          pipe.set_progress_bar_config(disable=snake_case_						)

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

          assert image.shape == (1, 128, 128, 3)
          snake_case__  :    int    						=      np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

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




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    List[Any]    						=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""						)
          snake_case__  :    Any    						=      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
          pipe.set_progress_bar_config(disable=snake_case_						)

          snake_case__  :    Tuple    						=      self.get_dummy_inputs()
          snake_case__  :    List[str]    						=      pipe(**snake_case_						).images
          snake_case__  :    Optional[Any]    						=      image[0, -3:, -3:, -1]

          assert image.shape == (1, 128, 128, 3)
          snake_case__  :    Tuple    						=      np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

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




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Dict    						=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""						)
          snake_case__  :    Union[str, Any]    						=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
          pipe.set_progress_bar_config(disable=snake_case_						)

          snake_case__  :    Any    						=      self.get_dummy_inputs()
          snake_case__  :    Optional[int]    						=      pipe(**snake_case_						).images
          snake_case__  :    Tuple    						=      image[0, -3:, -3:, -1]

          assert image.shape == (1, 128, 128, 3)
          snake_case__  :    Any    						=      np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]						)

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







@nightly
@require_onnxruntime
@require_torch_gpu
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    @property
    def lowerCamelCase       (    self					:     int						):
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )




    @property
    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    Union[str, Any]    						=      ort.SessionOptions()
          snake_case__  :    List[str]    						=      False
          return options




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    Dict    						=      load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/img2img/sketch-mountains-input.jpg"""						)
          snake_case__  :    List[str]    						=      init_image.resize((768, 512)						)
          # using the PNDM scheduler by default
          snake_case__  :    int    						=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
              """CompVis/stable-diffusion-v1-4""" ,     revision="""onnx""" ,     safety_checker=snake_case_ ,     feature_extractor=snake_case_ ,     provider=self.gpu_provider ,     sess_options=self.gpu_options ,     )
          pipe.set_progress_bar_config(disable=snake_case_						)

          snake_case__  :    Optional[Any]    						=      """A fantasy landscape, trending on artstation"""

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

          assert images.shape == (1, 512, 768, 3)
          snake_case__  :    Tuple    						=      np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]						)
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




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

          snake_case__  :    Union[str, Any]    						=      """A fantasy landscape, trending on artstation"""

          snake_case__  :    Any    						=      np.random.RandomState(0						)
          snake_case__  :    List[Any]    						=      pipe(
              prompt=snake_case_ ,     image=snake_case_ ,     strength=0.75 ,     guidance_scale=7.5 ,     num_inference_steps=20 ,     generator=snake_case_ ,     output_type="""np""" ,     )
          snake_case__  :    str    						=      output.images
          snake_case__  :    Dict    						=      images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 768, 3)
          snake_case__  :    Union[str, Any]    						=      np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]						)
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





'''simple docstring'''





import argparse
import os
import re


__a          =		"src/transformers"

# Pattern that looks at the indentation in a line.
__a          =		re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__a          =		re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__a          =		re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
__a          =		re.compile(R"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__a          =		re.compile(R"\[([^\]]+)\]")





def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    int    						=      _re_indent.search(_lowerCAmelCase		)
      return "" if search is None else search.groups()[0]





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

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

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

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

      return blocks





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:

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

      return _inner





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=None		)		-> List[Any]:

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

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

      snake_case__  :    List[str]    						=      ignore_underscore(_lowerCAmelCase		)
      return sorted(_lowerCAmelCase     ,   key=_lowerCAmelCase		) + sorted(_lowerCAmelCase     ,   key=_lowerCAmelCase		) + sorted(_lowerCAmelCase     ,   key=_lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase		)		-> int:

      # This inner function sort imports between [ ].
      def _replace(_lowerCAmelCase		):
            snake_case__  :    Union[str, Any]    						=      match.groups()[0]
            if "," not in imports:
                  return f"[{imports}]"
            snake_case__  :    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:
                  snake_case__  :    List[str]    						=      keys[:-1]
            return "[" + ", ".join([f"\"{k}\"" for k in sort_objects(_lowerCAmelCase		)]		) + "]"

      snake_case__  :    str    						=      import_statement.split("""\n"""		)
      if len(_lowerCAmelCase		) > 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.
            snake_case__  :    Dict    						=      2 if lines[1].strip() == """[""" else 1
            snake_case__  :    str    						=      [(i, _re_strip_line.search(_lowerCAmelCase		).groups()[0]) for i, line in enumerate(lines[idx:-idx]		)]
            snake_case__  :    str    						=      sort_objects(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x[1]		)
            snake_case__  :    Union[str, Any]    						=      [lines[x[0] + idx] for x in sorted_indices]
            return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]		)
      elif len(_lowerCAmelCase		) == 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:
                  snake_case__  :    Union[str, Any]    						=      _re_bracket_content.sub(_replace     ,   lines[1]		)
            else:
                  snake_case__  :    List[Any]    						=      [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:
                        snake_case__  :    List[str]    						=      keys[:-1]
                  snake_case__  :    int    						=      get_indent(lines[1]		) + """, """.join([f"\"{k}\"" for k in sort_objects(_lowerCAmelCase		)]		)
            return "\n".join(_lowerCAmelCase		)
      else:
            # Finally we have to deal with imports fitting on one line
            snake_case__  :    Optional[Any]    						=      _re_bracket_content.sub(_replace     ,   _lowerCAmelCase		)
            return import_statement





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=True		)		-> Dict:
      with open(_lowerCAmelCase     ,   encoding="""utf-8"""		) as f:
            snake_case__  :    Optional[int]    						=      f.read()

      if "_import_structure" not in code:
            return

      # Blocks of indent level 0
      snake_case__  :    Optional[int]    						=      split_code_in_indented_blocks(
          _lowerCAmelCase     ,   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(_lowerCAmelCase		) - 1		):
            # Check if the block contains some `_import_structure`s thingy to sort.
            snake_case__  :    Optional[Any]    						=      main_blocks[block_idx]
            snake_case__  :    Dict    						=      block.split("""\n"""		)

            # Get to the start of the imports.
            snake_case__  :    Dict    						=      0
            while line_idx < len(_lowerCAmelCase		) and "_import_structure" not in block_lines[line_idx]:
                  # Skip dummy import blocks
                  if "import dummy" in block_lines[line_idx]:
                        snake_case__  :    Union[str, Any]    						=      len(_lowerCAmelCase		)
                  else:
                        line_idx += 1
            if line_idx >= len(_lowerCAmelCase		):
                  continue

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

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

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

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





def 						__snake_case(				_lowerCAmelCase=True		)		-> Tuple:
      snake_case__  :    str    						=      []
      for root, _, files in os.walk(_lowerCAmelCase		):
            if "__init__.py" in files:
                  snake_case__  :    Union[str, Any]    						=      sort_imports(os.path.join(_lowerCAmelCase     ,   """__init__.py"""		)     ,   check_only=_lowerCAmelCase		)
                  if result:
                        snake_case__  :    Union[str, Any]    						=      [os.path.join(_lowerCAmelCase     ,   """__init__.py"""		)]
      if len(_lowerCAmelCase		) > 0:
            raise ValueError(f"Would overwrite {len(_lowerCAmelCase		)} files, run `make style`."		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
       __a          =		parser.parse_args()

       sort_imports_in_all_inits(check_only=args.check_only)
