



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 SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    pass






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




    '''simple docstring'''






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






    @property
    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       ort.SessionOptions()
        A			:	Tuple										=       False
        return options






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

        A			:	Union[str, Any]										=       """A red cat sitting on a park bench"""

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

        assert images.shape == (1, 512, 512, 3)
        A			:	Tuple										=       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       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
        A			:	Optional[int]										=       load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
        A			:	Union[str, Any]										=       LMSDiscreteScheduler.from_pretrained(
            """runwayml/stable-diffusion-inpainting""",		subfolder="""scheduler""",		revision="""onnx"""		)
        A			:	Optional[int]										=       OnnxStableDiffusionInpaintPipeline.from_pretrained(
            """runwayml/stable-diffusion-inpainting""",		revision="""onnx""",		scheduler=lowerCamelCase__,		safety_checker=lowerCamelCase__,		feature_extractor=lowerCamelCase__,		provider=self.gpu_provider,		sess_options=self.gpu_options,		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)

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

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

        assert images.shape == (1, 512, 512, 3)
        A			:	Dict										=       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







import requests

SCREAMING_SNAKE_CASE_:List[str]					=						""""""  # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE_:Dict					=						"""https://api.openweathermap.org/data/2.5/"""




def      __UpperCamelCase     (  _lowerCAmelCase = "Chicago"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """weather"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = "Kolkata, India"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """forecast"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = 55.68				,					_lowerCAmelCase = 12.57				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """onecall"""				,					params=locals()		).json()


if __name__ == "__main__":
     from pprint import pprint

     while True:
          SCREAMING_SNAKE_CASE_:int					=						input("""Enter a location:""").strip()
          if location:
               pprint(current_weather(location))
          else:
               break


