





"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     KandinskyVaaInpaintPipeline
 _lowerCamelCase        =     ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 _lowerCamelCase        =     [
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 _lowerCamelCase        =     [
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCamelCase        =     False

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 32

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 32

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.time_input_dim

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.time_input_dim * 4

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 100

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )

     __A    :						Any						 =     {
         '''in_channels''': 9,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 8,
         '''addition_embed_type''': '''image''',
         '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
         '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
         '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
         '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
         '''layers_per_block''': 1,
         '''encoder_hid_dim''': self.text_embedder_hidden_size,
         '''encoder_hid_dim_type''': '''image_proj''',
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': None,
     }

     __A    :						List[str]						 =     UNetaDConditionModel(**__lowerCamelCase  )
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						List[str]						 =     VQModel(**self.dummy_movq_kwargs  )
     return model

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     self.dummy_unet
     __A    :						List[str]						 =     self.dummy_movq

     __A    :						Optional[Any]						 =     DDIMScheduler(
         num_train_timesteps=1000					,						beta_schedule='''linear'''					,						beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						clip_sample=__lowerCamelCase					,						set_alpha_to_one=__lowerCamelCase					,						steps_offset=1					,						prediction_type='''epsilon'''					,						thresholding=__lowerCamelCase					,						)

     __A    :						Tuple						 =     {
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''movq''': movq,
     }

     return components

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=0  ):



     '''simple docstring'''




     __A    :						Any						 =     floats_tensor((1, self.text_embedder_hidden_size)					,						rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )
     __A    :						Tuple						 =     floats_tensor((1, self.text_embedder_hidden_size)					,						rng=random.Random(seed + 1  )  ).to(
         __lowerCamelCase  )
     # create init_image
     __A    :						Any						 =     floats_tensor((1, 3, 64, 64)					,						rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )
     __A    :						Union[str, Any]						 =     image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						Any						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((256, 256)  )
     # create mask
     __A    :						int						 =     np.ones((64, 64)					,						dtype=np.floataa  )
     __A    :						Optional[Any]						 =     0

     if str(__lowerCamelCase  ).startswith('''mps'''  ):
         __A    :						str						 =     torch.manual_seed(__lowerCamelCase  )
     else:
         __A    :						List[Any]						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(__lowerCamelCase  )
     __A    :						Any						 =     {
         '''image''': init_image,
         '''mask_image''': mask,
         '''image_embeds''': image_embeds,
         '''negative_image_embeds''': negative_image_embeds,
         '''generator''': generator,
         '''height''': 64,
         '''width''': 64,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 4.0,
         '''output_type''': '''np''',
     }
     return inputs

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Any						 =     '''cpu'''

     __A    :						Any						 =     self.get_dummy_components()

     __A    :						List[Any]						 =     self.pipeline_class(**__lowerCamelCase  )
     __A    :						str						 =     pipe.to(__lowerCamelCase  )

     pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						str						 =     pipe(**self.get_dummy_inputs(__lowerCamelCase  )  )
     __A    :						Optional[Any]						 =     output.images

     __A    :						Optional[Any]						 =     pipe(
         **self.get_dummy_inputs(__lowerCamelCase  )					,						return_dict=__lowerCamelCase					,						)[0]

     __A    :						Optional[Any]						 =     image[0, -3:, -3:, -1]
     __A    :						str						 =     image_from_tuple[0, -3:, -3:, -1]

     print(F"""image.shape {image.shape}"""  )

     assert image.shape == (1, 64, 64, 3)

     __A    :						str						 =     np.array(
         [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]  )

     assert (
         np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
     ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
     ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().test_inference_batch_single_identical(expected_max_diff=3e-3  )






@slow
@require_torch_gpu
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''  )

     __A    :						Optional[Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
     __A    :						Optional[int]						 =     np.ones((768, 768)					,						dtype=np.floataa  )
     __A    :						str						 =     0

     __A    :						int						 =     '''a hat'''

     __A    :						List[Any]						 =     KandinskyVaaPriorPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-prior'''					,						torch_dtype=torch.floataa  )
     pipe_prior.to(__lowerCamelCase  )

     __A    :						Tuple						 =     KandinskyVaaInpaintPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''					,						torch_dtype=torch.floataa  )
     __A    :						Union[str, Any]						 =     pipeline.to(__lowerCamelCase  )
     pipeline.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						List[Any]						 =     torch.Generator(device='''cpu'''  ).manual_seed(0  )
     __A ,       __A    :						int						 =     pipe_prior(
         __lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=5					,						negative_prompt=''''''					,						).to_tuple()

     __A    :						Optional[Any]						 =     pipeline(
         image=__lowerCamelCase					,						mask_image=__lowerCamelCase					,						image_embeds=__lowerCamelCase					,						negative_image_embeds=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=100					,						height=768					,						width=768					,						output_type='''np'''					,						)

     __A    :						str						 =     output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(__lowerCamelCase					,						__lowerCamelCase  )











"""simple docstring"""



import requests

a_ =			""""""  # <-- Put your OpenWeatherMap appid here!
a_ =			"""https://api.openweathermap.org/data/2.5/"""







def 		__lowercase     (   snake_case_  :  str = "Chicago"				,snake_case_  :  str = APPID						)   ->dict:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  str = "Kolkata, India"				,snake_case_  :  str = APPID						)   ->dict:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  float = 55.68				,snake_case_  :  float = 12.57				,snake_case_  :  str = APPID						)   ->dict:



    '''simple docstring'''







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


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

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




