






from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


A_	:	Tuple				=				getLogger(__name__)


A_	:	Optional[Any]				=				4
A_	:	Optional[Any]				=				7

A_	:	Tuple				=				{
    'float16': torch.floataa,
    'float32': torch.floataa,
}

def 						snake_case      (UpperCAmelCase__						) ->       List[str]:
		if isinstance(UpperCAmelCase__			,					torch.Tensor						):
				return image
		elif isinstance(UpperCAmelCase__			,					PIL.Image.Image						):
				UpperCamelCase_:      List[str]					   =       [image]

		if isinstance(image[0]			,					PIL.Image.Image						):
				UpperCamelCase_:      Any					   =       image[0].size
				UpperCamelCase_:      Optional[int]					   =       (x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

				UpperCamelCase_:      List[str]					   =       [np.array(i.resize((w, h)						)						)[None, :] for i in image]
				UpperCamelCase_:      int					   =       np.concatenate(UpperCAmelCase__			,					axis=0						)
				UpperCamelCase_:      Any					   =       np.array(UpperCAmelCase__						).astype(np.floataa						) / 255.0
				UpperCamelCase_:      int					   =       image.transpose(0			,					3			,					1			,					2						)
				UpperCamelCase_:      Union[str, Any]					   =       2.0 * image - 1.0
				UpperCamelCase_:      Dict					   =       torch.from_numpy(UpperCAmelCase__						)
		elif isinstance(image[0]			,					torch.Tensor						):
				UpperCamelCase_:      Union[str, Any]					   =       torch.cat(UpperCAmelCase__			,					dim=0						)

		return image




class        _lowerCAmelCase( UpperCAmelCase_      ):





							"""simple docstring"""







							def __init__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = 3_5_0						,    ):
									super().__init__(
									    vae=_lowerCamelCase						,    text_encoder=_lowerCamelCase						,    tokenizer=_lowerCamelCase						,    unet=_lowerCamelCase						,    low_res_scheduler=_lowerCamelCase						,    scheduler=_lowerCamelCase						,    safety_checker=_lowerCamelCase						,    feature_extractor=_lowerCamelCase						,    watermarker=_lowerCamelCase						,    max_noise_level=_lowerCamelCase						,    )





							def __call__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = 7_5						,    _lowerCamelCase = 9.0						,    _lowerCamelCase = 2_0						,    _lowerCamelCase = None						,    _lowerCamelCase = 1						,    _lowerCamelCase = 0.0						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    _lowerCamelCase = "pil"						,    _lowerCamelCase = True						,    _lowerCamelCase = None						,    _lowerCamelCase = 1						,    ):
									self.check_inputs(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)

									# 2. Define call parameters
									if prompt is not None and isinstance(_lowerCamelCase						,    _lowerCamelCase							):
											UpperCamelCase_:      List[Any]					   =       1
									elif prompt is not None and isinstance(_lowerCamelCase						,    _lowerCamelCase							):
											UpperCamelCase_:      Optional[Any]					   =       len(_lowerCamelCase							)
									else:
											UpperCamelCase_:      Optional[Any]					   =       prompt_embeds.shape[0]

									UpperCamelCase_:      Any					   =       self._execution_device
									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									UpperCamelCase_:      List[Any]					   =       guidance_scale > 1.0

									# 3. Encode input prompt
									UpperCamelCase_:      Dict					   =       self._encode_prompt(
									    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    prompt_embeds=_lowerCamelCase						,    negative_prompt_embeds=_lowerCamelCase						,    )

									UpperCamelCase_:      Any					   =       ORT_TO_PT_TYPE[str(text_embeddings.dtype							)]

									# 4. Preprocess image
									UpperCamelCase_:      Optional[Any]					   =       preprocess(_lowerCamelCase							)
									UpperCamelCase_:      int					   =       image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(_lowerCamelCase						,    device=_lowerCamelCase							)
									UpperCamelCase_:      Dict					   =       self.scheduler.timesteps

									# 5. Add noise to image
									UpperCamelCase_:      Union[str, Any]					   =       torch.tensor([noise_level]						,    dtype=torch.long						,    device=_lowerCamelCase							)
									UpperCamelCase_:      List[str]					   =       torch.randn(image.shape						,    generator=_lowerCamelCase						,    device=_lowerCamelCase						,    dtype=_lowerCamelCase							)
									UpperCamelCase_:      str					   =       self.low_res_scheduler.add_noise(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)

									UpperCamelCase_:      Dict					   =       2 if do_classifier_free_guidance else 1
									UpperCamelCase_:      List[Any]					   =       np.concatenate([image] * batch_multiplier * num_images_per_prompt							)
									UpperCamelCase_:      str					   =       np.concatenate([noise_level] * image.shape[0]							)

									# 6. Prepare latent variables
									UpperCamelCase_:      Optional[int]					   =       image.shape[2:]
									UpperCamelCase_:      Dict					   =       self.prepare_latents(
									    batch_size * num_images_per_prompt						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    )

									# 7. Check that sizes of image and latents match
									UpperCamelCase_:      Any					   =       image.shape[1]
									if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
											raise ValueError(
											    'Incorrect configuration settings! The config of `pipeline.unet` expects'
											    f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
											    f''' `num_channels_image`: {num_channels_image} '''
											    f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
											    ' `pipeline.unet` or your `image` input.'							)

									# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
									UpperCamelCase_:      str					   =       self.prepare_extra_step_kwargs(_lowerCamelCase						,    _lowerCamelCase							)

									UpperCamelCase_:      List[Any]					   =       next(
									    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')						,    'tensor(float)'							)
									UpperCamelCase_:      List[Any]					   =       ORT_TO_NP_TYPE[timestep_dtype]

									# 9. Denoising loop
									UpperCamelCase_:      Optional[int]					   =       len(_lowerCamelCase							) - num_inference_steps * self.scheduler.order
									with self.progress_bar(total=_lowerCamelCase							) as progress_bar:
											for i, t in enumerate(_lowerCamelCase							):
													# expand the latents if we are doing classifier free guidance
													UpperCamelCase_:      Optional[int]					   =       np.concatenate([latents] * 2							) if do_classifier_free_guidance else latents

													# concat latents, mask, masked_image_latents in the channel dimension
													UpperCamelCase_:      Tuple					   =       self.scheduler.scale_model_input(_lowerCamelCase						,    _lowerCamelCase							)
													UpperCamelCase_:      Optional[int]					   =       np.concatenate([latent_model_input, image]						,    axis=1							)

													# timestep to tensor
													UpperCamelCase_:      Any					   =       np.array([t]						,    dtype=_lowerCamelCase							)

													# predict the noise residual
													UpperCamelCase_:      int					   =       self.unet(
													    sample=_lowerCamelCase						,    timestep=_lowerCamelCase						,    encoder_hidden_states=_lowerCamelCase						,    class_labels=noise_level.astype(np.intaa							)						,    )[0]

													# perform guidance
													if do_classifier_free_guidance:
															UpperCamelCase_:      Optional[int]					   =       np.split(_lowerCamelCase						,    2							)
													UpperCamelCase_:      int					   =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													UpperCamelCase_:      Optional[Any]					   =       self.scheduler.step(
													    torch.from_numpy(_lowerCamelCase							)						,    _lowerCamelCase						,    _lowerCamelCase						,    **_lowerCamelCase							).prev_sample

													# call the callback, if provided
													if i == len(_lowerCamelCase							) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
															progress_bar.update()
															if callback is not None and i % callback_steps == 0:
																	callback(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)

        # 10. Post-processing
									UpperCamelCase_:      Any					   =       self.decode_latents(latents.float()							)

									# 11. Convert to PIL
									if output_type == "pil":
											UpperCamelCase_:      Union[str, Any]					   =       self.numpy_to_pil(_lowerCamelCase							)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=_lowerCamelCase							)





							def     _a					(      self						,    _lowerCamelCase							):
									UpperCamelCase_:      Dict					   =       1 / 0.0_8_3_3_3 * latents
									UpperCamelCase_:      Tuple					   =       self.vae(latent_sample=_lowerCamelCase							)[0]
									UpperCamelCase_:      List[str]					   =       np.clip(image / 2 + 0.5						,    0						,    1							)
									UpperCamelCase_:      Optional[int]					   =       image.transpose((0, 2, 3, 1)							)
									return image





							def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = None						,    _lowerCamelCase = None						,    ):
									if prompt is not None and isinstance(_lowerCamelCase						,    _lowerCamelCase							):
											UpperCamelCase_:      str					   =       1
									elif prompt is not None and isinstance(_lowerCamelCase						,    _lowerCamelCase							):
											UpperCamelCase_:      Optional[int]					   =       len(_lowerCamelCase							)
									else:
											UpperCamelCase_:      Dict					   =       prompt_embeds.shape[0]

									if prompt_embeds is None:
											UpperCamelCase_:      str					   =       self.tokenizer(
											    _lowerCamelCase						,    padding='max_length'						,    max_length=self.tokenizer.model_max_length						,    truncation=_lowerCamelCase						,    return_tensors='pt'						,    )
											UpperCamelCase_:      Tuple					   =       text_inputs.input_ids
											UpperCamelCase_:      List[str]					   =       self.tokenizer(_lowerCamelCase						,    padding='longest'						,    return_tensors='pt'							).input_ids

											if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
											    _lowerCamelCase						,    _lowerCamelCase							):
													UpperCamelCase_:      List[str]					   =       self.tokenizer.batch_decode(
													    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]							)
													logger.warning(
													    'The following part of your input was truncated because CLIP can only handle sequences up to'
													    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)

											# no positional arguments to text_encoder
											UpperCamelCase_:      Tuple					   =       self.text_encoder(
											    input_ids=text_input_ids.int().to(_lowerCamelCase							)						,    )
											UpperCamelCase_:      Tuple					   =       prompt_embeds[0]

									UpperCamelCase_:      Dict					   =       prompt_embeds.shape
									# duplicate text embeddings for each generation per prompt, using mps friendly method
									UpperCamelCase_:      Optional[Any]					   =       prompt_embeds.repeat(1						,    _lowerCamelCase							)
									UpperCamelCase_:      List[Any]					   =       prompt_embeds.reshape(bs_embed * num_images_per_prompt						,    _lowerCamelCase						,    -1							)

									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance and negative_prompt_embeds is None:
											UpperCamelCase_:      List[str]
											if negative_prompt is None:
													UpperCamelCase_:      Dict					   =       [''] * batch_size
											elif type(_lowerCamelCase							) is not type(_lowerCamelCase							):
													raise TypeError(
													    f'''`negative_prompt` should be the same type to `prompt`, but got {type(_lowerCamelCase							)} !='''
													    f''' {type(_lowerCamelCase							)}.'''							)
											elif isinstance(_lowerCamelCase						,    _lowerCamelCase							):
													UpperCamelCase_:      Optional[int]					   =       [negative_prompt]
											elif batch_size != len(_lowerCamelCase							):
													raise ValueError(
													    f'''`negative_prompt`: {negative_prompt} has batch size {len(_lowerCamelCase							)}, but `prompt`:'''
													    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
													    ' the batch size of `prompt`.'							)
											else:
													UpperCamelCase_:      Union[str, Any]					   =       negative_prompt

											UpperCamelCase_:      Dict					   =       text_input_ids.shape[-1]
											UpperCamelCase_:      Dict					   =       self.tokenizer(
											    _lowerCamelCase						,    padding='max_length'						,    max_length=_lowerCamelCase						,    truncation=_lowerCamelCase						,    return_tensors='pt'						,    )

											# if hasattr(uncond_input, "attention_mask"):
											#     attention_mask = uncond_input.attention_mask.to(device)
											# else:
											#     attention_mask = None

											UpperCamelCase_:      Tuple					   =       self.text_encoder(
											    input_ids=uncond_input.input_ids.int().to(_lowerCamelCase							)						,    )
											UpperCamelCase_:      Optional[int]					   =       uncond_embeddings[0]

									if do_classifier_free_guidance:
											UpperCamelCase_:      List[str]					   =       uncond_embeddings.shape[1]
											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
											UpperCamelCase_:      str					   =       uncond_embeddings.repeat(1						,    _lowerCamelCase							)
											UpperCamelCase_:      int					   =       uncond_embeddings.reshape(batch_size * num_images_per_prompt						,    _lowerCamelCase						,    -1							)

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											UpperCamelCase_:      Optional[int]					   =       np.concatenate([uncond_embeddings, prompt_embeds]							)

									return prompt_embeds







from datetime import date

import requests

A_	:	Any				=				'https://api.carbonintensity.org.uk/intensity'

def 						snake_case      () ->       str:
  UpperCamelCase_:      Any					   =       requests.get(UpperCAmelCase__						).json()['data'][0]
  return last_half_hour["intensity"]["actual"]

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       list:
  return requests.get(F'''{BASE_URL}/{start}/{end}'''						).json()["data"]


if __name__ == "__main__":
   for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
      print('from {from} to {to}: {intensity[actual]}'.format(**entry))
   print(F'''{fetch_last_half_hour() = }''')