import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 	_lowercase		(      snake_case_		,   unittest.TestCase ):
 lowercase         =     KandinskyPipeline
 lowercase         =     [
     'prompt',
     'image_embeds',
     'negative_image_embeds',
 ]
 lowercase         =     ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
 lowercase         =     [
     'generator',
     'height',
     'width',
     'latents',
     'guidance_scale',
     'negative_prompt',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 lowercase         =     False

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		int:

  """simple docstring"""





  return 3_2

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		List[str]:

  """simple docstring"""





  return 3_2

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Any:

  """simple docstring"""





  return self.time_input_dim

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Union[str, Any]:

  """simple docstring"""





  return self.time_input_dim * 4

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		int:

  """simple docstring"""





  return 1_0_0

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple    )     ->		Optional[Any]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'    )
  return tokenizer

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		int:

  """simple docstring"""





  torch.manual_seed(0    )
  UpperCamelCase_      :		List[Any]				=      MCLIPConfig(
      numDims=self.cross_attention_dim     ,   transformerDimensions=self.text_embedder_hidden_size     ,   hidden_size=self.text_embedder_hidden_size     ,   intermediate_size=3_7     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   vocab_size=1_0_0_5     ,   )

  UpperCamelCase_      :		List[Any]				=      MultilingualCLIP(snake_case    )
  UpperCamelCase_      :		Tuple				=      text_encoder.eval()

  return text_encoder

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		Optional[Any]:

  """simple docstring"""





  torch.manual_seed(0    )

  UpperCamelCase_      :		Any				=      {
      'in_channels': 4,
      # Out channels is double in channels because predicts mean and variance
      'out_channels': 8,
      'addition_embed_type': 'text_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': 'text_image_proj',
      'cross_attention_dim': self.cross_attention_dim,
      'attention_head_dim': 4,
      'resnet_time_scale_shift': 'scale_shift',
      'class_embed_type': None,
  }

  UpperCamelCase_      :		str				=      UNetaDConditionModel(**snake_case    )
  return model

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Union[str, Any]:

  """simple docstring"""





  return {
      "block_out_channels": [3_2, 6_4],
      "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": 1_2,
      "out_channels": 3,
      "up_block_types": [
          "AttnUpDecoderBlock2D",
          "UpDecoderBlock2D",
      ],
      "vq_embed_dim": 4,
  }

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		Optional[int]:

  """simple docstring"""





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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      self.dummy_text_encoder
  UpperCamelCase_      :		Tuple				=      self.dummy_tokenizer
  UpperCamelCase_      :		int				=      self.dummy_unet
  UpperCamelCase_      :		Any				=      self.dummy_movq

  UpperCamelCase_      :		Any				=      DDIMScheduler(
      num_train_timesteps=1_0_0_0     ,   beta_schedule='linear'     ,   beta_start=0.00085     ,   beta_end=0.012     ,   clip_sample=snake_case     ,   set_alpha_to_one=snake_case     ,   steps_offset=1     ,   prediction_type='epsilon'     ,   thresholding=snake_case     ,   )

  UpperCamelCase_      :		Tuple				=      {
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'unet': unet,
      'scheduler': scheduler,
      'movq': movq,
  }
  return components

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							int     ,   snake_case			:							Tuple=0    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(snake_case    )    ).to(snake_case    )
  UpperCamelCase_      :		Dict				=      floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(seed + 1    )    ).to(snake_case    )
  if str(snake_case    ).startswith('mps'    ):
   UpperCamelCase_      :		str				=      torch.manual_seed(snake_case    )
  else:
   UpperCamelCase_      :		Optional[int]				=      torch.Generator(device=snake_case    ).manual_seed(snake_case    )
  UpperCamelCase_      :		Tuple				=      {
      'prompt': 'horse',
      'image_embeds': image_embeds,
      'negative_image_embeds': negative_image_embeds,
      'generator': generator,
      'height': 6_4,
      'width': 6_4,
      'guidance_scale': 4.0,
      'num_inference_steps': 2,
      'output_type': 'np',
  }
  return inputs


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Optional[Any]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      'cpu'

  UpperCamelCase_      :		List[str]				=      self.get_dummy_components()

  UpperCamelCase_      :		List[str]				=      self.pipeline_class(**snake_case    )
  UpperCamelCase_      :		List[Any]				=      pipe.to(snake_case    )

  pipe.set_progress_bar_config(disable=snake_case    )

  UpperCamelCase_      :		str				=      pipe(**self.get_dummy_inputs(snake_case    )    )
  UpperCamelCase_      :		List[Any]				=      output.images

  UpperCamelCase_      :		List[Any]				=      pipe(
      **self.get_dummy_inputs(snake_case    )     ,   return_dict=snake_case     ,   )[0]

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

  assert image.shape == (1, 6_4, 6_4, 3)

  UpperCamelCase_      :		List[Any]				=      np.array(
      [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953]    )

  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()}"





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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Any:

  """simple docstring"""





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


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		str				=      load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/kandinsky/kandinsky_text2img_cat_fp16.npy'    )

  UpperCamelCase_      :		int				=      KandinskyPriorPipeline.from_pretrained(
      'kandinsky-community/kandinsky-2-1-prior'     ,   torch_dtype=torch.floataa    )
  pipe_prior.to(snake_case    )

  UpperCamelCase_      :		str				=      KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'     ,   torch_dtype=torch.floataa    )
  UpperCamelCase_      :		Any				=      pipeline.to(snake_case    )
  pipeline.set_progress_bar_config(disable=snake_case    )

  UpperCamelCase_      :		List[str]				=      'red cat, 4k photo'

  UpperCamelCase_      :		Tuple				=      torch.Generator(device='cuda'    ).manual_seed(0    )
  UpperCamelCase_,				UpperCamelCase_      :		Union[str, Any]				=      pipe_prior(
      snake_case     ,   generator=snake_case     ,   num_inference_steps=5     ,   negative_prompt=''     ,   ).to_tuple()

  UpperCamelCase_      :		Any				=      torch.Generator(device='cuda'    ).manual_seed(0    )
  UpperCamelCase_      :		Optional[Any]				=      pipeline(
      snake_case     ,   image_embeds=snake_case     ,   negative_image_embeds=snake_case     ,   generator=snake_case     ,   num_inference_steps=1_0_0     ,   output_type='np'     ,   )

  UpperCamelCase_      :		Optional[int]				=      output.images[0]

  assert image.shape == (5_1_2, 5_1_2, 3)

  assert_mean_pixel_difference(snake_case     ,   snake_case    )




from datetime import date

import requests

a_					      = 'https://api.carbonintensity.org.uk/intensity'





def      __lowercase  (		):
 UpperCamelCase_      :		Dict				=      requests.get(lowerCamelCase			).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def      __lowercase  (		lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  Union[str, Any]			):
 return requests.get(F"{BASE_URL}/{start}/{end}"			).json()["data"]


if __name__ == "__main__":
   for entry in fetch_from_to(start=date(2_020, 10, 1), end=date(2_020, 10, 3)):
      print('from {from} to {to}: {intensity[actual]}'.format(**entry))
   print(F"""{fetch_last_half_hour() = }""")



