





import argparse
import os
import re


__UpperCAmelCase	:       Union[str, Any]    	=							"src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__UpperCAmelCase	:       Dict    	=							re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
__UpperCAmelCase	:       Tuple    	=							re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = False)       ->   Dict:

		with open(a__ , """r""" , encoding="""utf-8""") as f:
				__snake_case:						Tuple			  =						f.read()

		__snake_case:						List[Any]			  =						content.split("""\n""")
		__snake_case:						Optional[Any]			  =						[]
		__snake_case:						int			  =						0
		while line_idx < len(a__):
				if _re_intro_mapping.search(lines[line_idx]) is not None:
						__snake_case:						List[Any]			  =						len(re.search(r"""^(\s*)\S""" , lines[line_idx]).groups()[0]) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(""" """ * indent + """("""):
								new_lines.append(lines[line_idx])
								line_idx += 1

						__snake_case:						Dict			  =						[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										__snake_case:						str			  =						line_idx
										while not lines[line_idx].startswith(""" """ * indent + """)"""):
												line_idx += 1
										blocks.append("""\n""".join(lines[start_idx : line_idx + 1]))
								else:
										blocks.append(lines[line_idx])
								line_idx += 1

						# Sort blocks by their identifiers
						__snake_case:						int			  =						sorted(a__ , key=lambda SCREAMING_SNAKE_CASE__: _re_identifier.search(a__).groups()[0])
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx])
						line_idx += 1

		if overwrite:
				with open(a__ , """w""" , encoding="""utf-8""") as f:
						f.write("""\n""".join(a__))
		elif "\n".join(a__) != content:
				return True







def       A__							(   SCREAMING_SNAKE_CASE__ = False)       ->   Dict:
		__snake_case:						List[str]			  =						[os.path.join(a__ , a__) for f in os.listdir(a__) if f.endswith(""".py""")]
		__snake_case:						str			  =						[sort_auto_mapping(a__ , overwrite=a__) for fname in fnames]

		if not overwrite and any(a__):
				__snake_case:						str			  =						[f for f, d in zip(a__ , a__) if d]
				raise ValueError(
				    F'''The following files have auto mappings that need sorting: {', '.join(a__)}. Run `make style` to fix'''
				    """ this.""")


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

						sort_all_auto_mappings(not args.check_only)













import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







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





       '''simple docstring'''







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




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






