




'''simple docstring'''

import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort







class __a (   UpperCamelCase__,   unittest.TestCase			):
   __UpperCamelCase   : Optional[Any]	      =	'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





   def      UpperCAmelCase__     (					self				:			Any				,lowerCamelCase				:			str=0    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       np.random.RandomState(_a    )
      __SCREAMING_SNAKE_CASE      							=       {
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 7.5,
          """output_type""": """numpy""",
      }
      return inputs





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="""CPUExecutionProvider"""    )
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    ).images
      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="""CPUExecutionProvider"""    )
      __SCREAMING_SNAKE_CASE      							=       PNDMScheduler.from_config(pipe.scheduler.config				,skip_prk_steps=_a    )
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    ).images
      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="""CPUExecutionProvider"""    )
      __SCREAMING_SNAKE_CASE      							=       LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    ).images
      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="""CPUExecutionProvider"""    )
      __SCREAMING_SNAKE_CASE      							=       EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    ).images
      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="""CPUExecutionProvider"""    )
      __SCREAMING_SNAKE_CASE      							=       EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    ).images
      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="""CPUExecutionProvider"""    )
      __SCREAMING_SNAKE_CASE      							=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    ).images
      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="""CPUExecutionProvider"""    )
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       3 * [inputs["""prompt"""]]

      # forward
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    )
      __SCREAMING_SNAKE_CASE      							=       output.images[0, -3:, -3:, -1]

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       3 * [inputs.pop("""prompt"""    )]

      __SCREAMING_SNAKE_CASE      							=       pipe.tokenizer(
          _a				,padding="""max_length"""				,max_length=pipe.tokenizer.model_max_length				,truncation=_a				,return_tensors="""np"""				,)
      __SCREAMING_SNAKE_CASE      							=       text_inputs["""input_ids"""]

      __SCREAMING_SNAKE_CASE      							=       pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

      __SCREAMING_SNAKE_CASE      							=       prompt_embeds

      # forward
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    )
      __SCREAMING_SNAKE_CASE      							=       output.images[0, -3:, -3:, -1]

      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="""CPUExecutionProvider"""    )
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       3 * ["""this is a negative prompt"""]
      __SCREAMING_SNAKE_CASE      							=       negative_prompt
      __SCREAMING_SNAKE_CASE      							=       3 * [inputs["""prompt"""]]

      # forward
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    )
      __SCREAMING_SNAKE_CASE      							=       output.images[0, -3:, -3:, -1]

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE      							=       3 * [inputs.pop("""prompt"""    )]

      __SCREAMING_SNAKE_CASE      							=       []
      for p in [prompt, negative_prompt]:
         __SCREAMING_SNAKE_CASE      							=       pipe.tokenizer(
             _a				,padding="""max_length"""				,max_length=pipe.tokenizer.model_max_length				,truncation=_a				,return_tensors="""np"""				,)
         __SCREAMING_SNAKE_CASE      							=       text_inputs["""input_ids"""]

         embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]    )

      __SCREAMING_SNAKE_CASE      							=       embeds

      # forward
      __SCREAMING_SNAKE_CASE      							=       pipe(**_a    )
      __SCREAMING_SNAKE_CASE      							=       output.images[0, -3:, -3:, -1]

      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4







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





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       ort.SessionOptions()
      __SCREAMING_SNAKE_CASE      							=       False
      return options





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(
          """CompVis/stable-diffusion-v1-4"""				,revision="""onnx"""				,safety_checker=_a				,feature_extractor=_a				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
      sd_pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       """A painting of a squirrel eating a burger"""
      np.random.seed(0    )
      __SCREAMING_SNAKE_CASE      							=       sd_pipe([prompt]				,guidance_scale=6.0				,num_inference_steps=10				,output_type="""np"""    )
      __SCREAMING_SNAKE_CASE      							=       output.images

      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720]    )

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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       DDIMScheduler.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""				,subfolder="""scheduler"""				,revision="""onnx"""    )
      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""				,revision="""onnx"""				,scheduler=_a				,safety_checker=_a				,feature_extractor=_a				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
      sd_pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       """open neural network exchange"""
      __SCREAMING_SNAKE_CASE      							=       np.random.RandomState(0    )
      __SCREAMING_SNAKE_CASE      							=       sd_pipe([prompt]				,guidance_scale=7.5				,num_inference_steps=10				,generator=_a				,output_type="""np"""    )
      __SCREAMING_SNAKE_CASE      							=       output.images
      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486]    )

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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       LMSDiscreteScheduler.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""				,subfolder="""scheduler"""				,revision="""onnx"""    )
      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""				,revision="""onnx"""				,scheduler=_a				,safety_checker=_a				,feature_extractor=_a				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
      sd_pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       """open neural network exchange"""
      __SCREAMING_SNAKE_CASE      							=       np.random.RandomState(0    )
      __SCREAMING_SNAKE_CASE      							=       sd_pipe([prompt]				,guidance_scale=7.5				,num_inference_steps=10				,generator=_a				,output_type="""np"""    )
      __SCREAMING_SNAKE_CASE      							=       output.images
      __SCREAMING_SNAKE_CASE      							=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161]    )

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





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       0

      def test_callback_fn(lowerCamelCase				:			Dict				,lowerCamelCase				:			Union[str, Any]				,lowerCamelCase				:			Any    ) -> None:
         __SCREAMING_SNAKE_CASE      							=       True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 0:
            assert latents.shape == (1, 4, 64, 64)
            __SCREAMING_SNAKE_CASE      							=       latents[0, -3:, -3:, -1]
            __SCREAMING_SNAKE_CASE      							=       np.array(
                [-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167]    )

            assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3
         elif step == 5:
            assert latents.shape == (1, 4, 64, 64)
            __SCREAMING_SNAKE_CASE      							=       latents[0, -3:, -3:, -1]
            __SCREAMING_SNAKE_CASE      							=       np.array(
                [-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875]    )

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

      __SCREAMING_SNAKE_CASE      							=       False

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""				,revision="""onnx"""				,safety_checker=_a				,feature_extractor=_a				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
      pipe.set_progress_bar_config(disable=_a    )

      __SCREAMING_SNAKE_CASE      							=       """Andromeda galaxy in a bottle"""

      __SCREAMING_SNAKE_CASE      							=       np.random.RandomState(0    )
      pipe(
          prompt=_a				,num_inference_steps=5				,guidance_scale=7.5				,generator=_a				,callback=_a				,callback_steps=1				,)
      assert test_callback_fn.has_been_called
      assert number_of_steps == 6





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(
          """runwayml/stable-diffusion-v1-5"""				,revision="""onnx"""				,safety_checker=_a				,feature_extractor=_a				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
      assert isinstance(_a				,_a    )
      assert pipe.safety_checker is None

      __SCREAMING_SNAKE_CASE      							=       pipe("""example prompt"""				,num_inference_steps=2    ).images[0]
      assert image is not None

      # check that there's no error when saving a pipeline with one of the models being None
      with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(_a    )
         __SCREAMING_SNAKE_CASE      							=       OnnxStableDiffusionPipeline.from_pretrained(_a    )

      # sanity check that the pipeline still works
      assert pipe.safety_checker is None
      __SCREAMING_SNAKE_CASE      							=       pipe("""example prompt"""				,num_inference_steps=2    ).images[0]
      assert image is not None












'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
    from ..tf_utils import stable_softmax

a        =						logging.get_logger(__name__)







@add_end_docstrings(_snake_case			)
class __a (   _snake_case			):





   def __init__(					self				:			Union[str, Any]				,**lowerCamelCase				:			str    ):

      '''simple docstring'''

      super().__init__(**lowerCamelCase    )

      requires_backends(self				,"""vision"""    )
      self.check_model_type(
          TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == """tf"""
          else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





   def __call__(					self				:			Dict				,lowerCamelCase				:			Union[str, List[str], "Image", List["Image"]]				,**lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

      return super().__call__(lowerCamelCase				,**lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,**lowerCamelCase				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       {}
      if "candidate_labels" in kwargs:
         __SCREAMING_SNAKE_CASE      							=       kwargs["""candidate_labels"""]
      if "hypothesis_template" in kwargs:
         __SCREAMING_SNAKE_CASE      							=       kwargs["""hypothesis_template"""]

      return preprocess_params, {}, {}





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Union[str, Any]=None				,lowerCamelCase				:			Union[str, Any]="This is a photo of {}."    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       load_image(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.image_processor(images=[image]				,return_tensors=self.framework    )
      __SCREAMING_SNAKE_CASE      							=       candidate_labels
      __SCREAMING_SNAKE_CASE      							=       [hypothesis_template.format(lowerCamelCase    ) for x in candidate_labels]
      __SCREAMING_SNAKE_CASE      							=       self.tokenizer(lowerCamelCase				,return_tensors=self.framework				,padding=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       [text_inputs]
      return inputs





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""candidate_labels"""    )
      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""text_inputs"""    )
      if isinstance(text_inputs[0]				,lowerCamelCase    ):
         __SCREAMING_SNAKE_CASE      							=       text_inputs[0]
      else:
         # Batching case.
         __SCREAMING_SNAKE_CASE      							=       text_inputs[0][0]

      __SCREAMING_SNAKE_CASE      							=       self.model(**lowerCamelCase				,**lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       {
          """candidate_labels""": candidate_labels,
          """logits""": outputs.logits_per_image,
      }
      return model_outputs





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_outputs.pop("""candidate_labels"""    )
      __SCREAMING_SNAKE_CASE      							=       model_outputs["""logits"""][0]
      if self.framework == "pt":
         __SCREAMING_SNAKE_CASE      							=       logits.softmax(dim=-1    ).squeeze(-1    )
         __SCREAMING_SNAKE_CASE      							=       probs.tolist()
         if not isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       [scores]
      elif self.framework == "tf":
         __SCREAMING_SNAKE_CASE      							=       stable_softmax(lowerCamelCase				,axis=-1    )
         __SCREAMING_SNAKE_CASE      							=       probs.numpy().tolist()
      else:
         raise ValueError(f"""Unsupported framework: {self.framework}"""    )

      __SCREAMING_SNAKE_CASE      							=       [
          {"""score""": score, """label""": candidate_label}
          for score, candidate_label in sorted(zip(lowerCamelCase				,lowerCamelCase    )				,key=lambda lowerCamelCase    : -x[0]    )
      ]
      return result





