





'''simple docstring'''




import requests

lowercase__    :      int						=    ''''''  # <-- Put your OpenWeatherMap appid here!
lowercase__    :      List[str]						=    '''https://api.openweathermap.org/data/2.5/'''






def        _lowerCAmelCase			(  __snake_case  : str = "Chicago" ,							__snake_case  : str = APPID			)     ->     dict:
     return requests.get(URL_BASE + 'weather' ,							params=locals()			).json()






def        _lowerCAmelCase			(  __snake_case  : str = "Kolkata, India" ,							__snake_case  : str = APPID			)     ->     dict:
     return requests.get(URL_BASE + 'forecast' ,							params=locals()			).json()






def        _lowerCAmelCase			(  __snake_case  : float = 55.68 ,							__snake_case  : float = 12.57 ,							__snake_case  : str = APPID			)     ->     dict:
     return requests.get(URL_BASE + 'onecall' ,							params=locals()			).json()


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

  while True:
    lowercase__    :      Tuple						=    input('''Enter a location:''').strip()
    if location:
      pprint(current_weather(location))
    else:
      break






'''simple docstring'''




import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard

  from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline


@require_flax
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with tempfile.TemporaryDirectory() as tmpdirname:
               # pipeline has Flax weights
               __A						: Union[str, Any]      =       FlaxDiffusionPipeline.from_pretrained(
                   'hf-internal-testing/tiny-stable-diffusion-pipe'						,	safety_checker=_UpperCAmelCase						,	cache_dir=_UpperCAmelCase)

               __A						: Optional[Any]      =       [t[-1] for t in os.walk(os.path.join(_UpperCAmelCase						,	os.listdir(_UpperCAmelCase)[0]						,	'snapshots'))]
               __A						: int      =       [item for sublist in all_root_files for item in sublist]

               # None of the downloaded files should be a PyTorch file even if we have some here:
               # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
               assert not any(f.endswith('.bin') for f in files)




@slow
@require_flax
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: Optional[int]      =       FlaxStableDiffusionPipeline.from_pretrained(
              'hf-internal-testing/tiny-stable-diffusion-pipe'						,	safety_checker=_UpperCAmelCase)

          __A						: Dict      =       (
              'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
              ' field, close up, split lighting, cinematic'
          )

          __A						: Optional[Any]      =       jax.random.PRNGKey(0)
          __A						: int      =       4

          __A						: Tuple      =       jax.device_count()
          __A						: Union[str, Any]      =       num_samples * [prompt]
          __A						: Tuple      =       pipeline.prepare_inputs(_UpperCAmelCase)

          # shard inputs and rng
          __A						: str      =       replicate(_UpperCAmelCase)
          __A						: Tuple      =       jax.random.split(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Union[str, Any]      =       shard(_UpperCAmelCase)

          __A						: Union[str, Any]      =       pipeline(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	jit=_UpperCAmelCase).images

          assert images.shape == (num_samples, 1, 64, 64, 3)
          if jax.device_count() == 8:
               assert np.abs(np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa).sum() - 4.1514745) < 1e-3
               assert np.abs(np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 49947.875) < 5e-1

          __A						: List[str]      =       pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
          assert len(_UpperCAmelCase) == num_samples


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: str      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='flax'						,	safety_checker=_UpperCAmelCase)

          __A						: List[Any]      =       (
              'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
              ' field, close up, split lighting, cinematic'
          )

          __A						: Tuple      =       jax.random.PRNGKey(0)
          __A						: Any      =       50

          __A						: str      =       jax.device_count()
          __A						: Union[str, Any]      =       num_samples * [prompt]
          __A						: List[str]      =       pipeline.prepare_inputs(_UpperCAmelCase)

          # shard inputs and rng
          __A						: Dict      =       replicate(_UpperCAmelCase)
          __A						: Optional[Any]      =       jax.random.split(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: int      =       shard(_UpperCAmelCase)

          __A						: Tuple      =       pipeline(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	jit=_UpperCAmelCase).images

          assert images.shape == (num_samples, 1, 512, 512, 3)
          if jax.device_count() == 8:
               assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa).sum() - 0.05652401)) < 1e-3
               assert np.abs((np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 2383808.2)) < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: List[str]      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa						,	safety_checker=_UpperCAmelCase)

          __A						: List[Any]      =       (
              'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
              ' field, close up, split lighting, cinematic'
          )

          __A						: str      =       jax.random.PRNGKey(0)
          __A						: Any      =       50

          __A						: Optional[int]      =       jax.device_count()
          __A						: int      =       num_samples * [prompt]
          __A						: Optional[int]      =       pipeline.prepare_inputs(_UpperCAmelCase)

          # shard inputs and rng
          __A						: Optional[int]      =       replicate(_UpperCAmelCase)
          __A						: List[str]      =       jax.random.split(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Dict      =       shard(_UpperCAmelCase)

          __A						: str      =       pipeline(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	jit=_UpperCAmelCase).images

          assert images.shape == (num_samples, 1, 512, 512, 3)
          if jax.device_count() == 8:
               assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa).sum() - 0.04003906)) < 1e-3
               assert np.abs((np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 2373516.75)) < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A						: Optional[Any]      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa)

          __A						: Union[str, Any]      =       (
              'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
              ' field, close up, split lighting, cinematic'
          )

          __A						: Any      =       jax.random.PRNGKey(0)
          __A						: List[str]      =       50

          __A						: Optional[int]      =       jax.device_count()
          __A						: List[Any]      =       num_samples * [prompt]
          __A						: List[Any]      =       pipeline.prepare_inputs(_UpperCAmelCase)

          # shard inputs and rng
          __A						: Union[str, Any]      =       replicate(_UpperCAmelCase)
          __A						: Optional[Any]      =       jax.random.split(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: List[str]      =       shard(_UpperCAmelCase)

          __A						: int      =       pipeline(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	jit=_UpperCAmelCase).images

          assert images.shape == (num_samples, 1, 512, 512, 3)
          if jax.device_count() == 8:
               assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa).sum() - 0.04003906)) < 1e-3
               assert np.abs((np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 2373516.75)) < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       FlaxDDIMScheduler(
              beta_start=0.00085						,	beta_end=0.012						,	beta_schedule='scaled_linear'						,	set_alpha_to_one=_UpperCAmelCase						,	steps_offset=1						,	)

          __A ,__A						: Any      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa						,	scheduler=_UpperCAmelCase						,	safety_checker=_UpperCAmelCase						,	)
          __A						: Optional[Any]      =       scheduler.create_state()

          __A						: Any      =       scheduler_state

          __A						: List[str]      =       (
              'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
              ' field, close up, split lighting, cinematic'
          )

          __A						: Union[str, Any]      =       jax.random.PRNGKey(0)
          __A						: Optional[int]      =       50

          __A						: Optional[Any]      =       jax.device_count()
          __A						: Any      =       num_samples * [prompt]
          __A						: Optional[Any]      =       pipeline.prepare_inputs(_UpperCAmelCase)

          # shard inputs and rng
          __A						: int      =       replicate(_UpperCAmelCase)
          __A						: Any      =       jax.random.split(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Tuple      =       shard(_UpperCAmelCase)

          __A						: Union[str, Any]      =       pipeline(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	jit=_UpperCAmelCase).images

          assert images.shape == (num_samples, 1, 512, 512, 3)
          if jax.device_count() == 8:
               assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,	dtype=np.floataa).sum() - 0.045043945)) < 1e-3
               assert np.abs((np.abs(_UpperCAmelCase						,	dtype=np.floataa).sum() - 2347693.5)) < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       (
              'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
              ' field, close up, split lighting, cinematic'
          )

          __A						: int      =       jax.device_count()
          __A						: List[Any]      =       num_samples * [prompt]
          __A						: List[Any]      =       jax.random.split(jax.random.PRNGKey(0)						,	_UpperCAmelCase)

          __A ,__A						: Tuple      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa						,	safety_checker=_UpperCAmelCase						,	)

          __A						: str      =       replicate(_UpperCAmelCase)
          __A						: str      =       pipeline.prepare_inputs(_UpperCAmelCase)
          __A						: str      =       shard(_UpperCAmelCase)
          __A						: int      =       pipeline(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	jit=_UpperCAmelCase).images
          assert images.shape == (num_samples, 1, 512, 512, 3)
          __A						: Any      =       images[2, 0, 256, 10:17, 1]

          # With memory efficient attention
          __A ,__A						: str      =       FlaxStableDiffusionPipeline.from_pretrained(
              'CompVis/stable-diffusion-v1-4'						,	revision='bf16'						,	dtype=jnp.bfloataa						,	safety_checker=_UpperCAmelCase						,	use_memory_efficient_attention=_UpperCAmelCase						,	)

          __A						: Any      =       replicate(_UpperCAmelCase)
          __A						: List[Any]      =       pipeline.prepare_inputs(_UpperCAmelCase)
          __A						: Optional[Any]      =       shard(_UpperCAmelCase)
          __A						: List[Any]      =       pipeline(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	jit=_UpperCAmelCase).images
          assert images_eff.shape == (num_samples, 1, 512, 512, 3)
          __A						: List[Any]      =       images[2, 0, 256, 10:17, 1]

          # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
          # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
          assert abs(slice_eff - slice).max() < 1e-2