


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


UpperCAmelCase_		   =				getLogger(__name__)


UpperCAmelCase_		   =				4
UpperCAmelCase_		   =				7

UpperCAmelCase_		   =				{
    'float16': torch.floataa,
    'float32': torch.floataa,
}

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[int]							)     ->    Dict:



   if isinstance(__A  ,      torch.Tensor							):
      return image
   elif isinstance(__A  ,      PIL.Image.Image							):
      _A						     =					[image]

   if isinstance(image[0]  ,      PIL.Image.Image							):
      _A     ,						_A						     =					image[0].size
      _A     ,						_A						     =					(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

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

   return image


class   lowerCamelCase__  (					UpperCAmelCase_):



 """simple docstring"""

 def __init__(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				Optional[int] ,	__lowerCAmelCase				:				Tuple ,	__lowerCAmelCase				:				List[Any] ,	__lowerCAmelCase				:				str ,	__lowerCAmelCase				:				int ,	__lowerCAmelCase				:				Optional[Any] ,	__lowerCAmelCase				:				List[Any] = 3_50 ,	)      ->  Any:
    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				:				List[str] ,	__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				str ,	__lowerCAmelCase				:				Dict = 75 ,	__lowerCAmelCase				:				Any = 9.0 ,	__lowerCAmelCase				:				List[str] = 20 ,	__lowerCAmelCase				:				Any = None ,	__lowerCAmelCase				:				List[str] = 1 ,	__lowerCAmelCase				:				int = 0.0 ,	__lowerCAmelCase				:				Dict = None ,	__lowerCAmelCase				:				int = None ,	__lowerCAmelCase				:				Any = None ,	__lowerCAmelCase				:				Tuple = None ,	__lowerCAmelCase				:				List[Any] = "pil" ,	__lowerCAmelCase				:				int = True ,	__lowerCAmelCase				:				Union[str, Any] = None ,	__lowerCAmelCase				:				Any = 1 ,	)      ->  Dict:
    self.check_inputs(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			)

    # 2. Define call parameters
    if prompt is not None and isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
       _A						     =					1
    elif prompt is not None and isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
       _A						     =					len(__lowerCAmelCase			)
    else:
       _A						     =					prompt_embeds.shape[0]

    _A						     =					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.
    _A						     =					guidance_scale > 1.0

    # 3. Encode input prompt
    _A						     =					self._encode_prompt(
        __lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	prompt_embeds=__lowerCAmelCase ,	negative_prompt_embeds=__lowerCAmelCase ,	)

    _A						     =					ORT_TO_PT_TYPE[str(text_embeddings.dtype			)]

    # 4. Preprocess image
    _A						     =					preprocess(__lowerCAmelCase			)
    _A						     =					image.cpu()

    # 5. set timesteps
    self.scheduler.set_timesteps(__lowerCAmelCase ,	device=__lowerCAmelCase			)
    _A						     =					self.scheduler.timesteps

    # 5. Add noise to image
    _A						     =					torch.tensor([noise_level] ,	dtype=torch.long ,	device=__lowerCAmelCase			)
    _A						     =					torch.randn(image.shape ,	generator=__lowerCAmelCase ,	device=__lowerCAmelCase ,	dtype=__lowerCAmelCase			)
    _A						     =					self.low_res_scheduler.add_noise(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			)

    _A						     =					2 if do_classifier_free_guidance else 1
    _A						     =					np.concatenate([image] * batch_multiplier * num_images_per_prompt			)
    _A						     =					np.concatenate([noise_level] * image.shape[0]			)

    # 6. Prepare latent variables
    _A     ,						_A						     =					image.shape[2:]
    _A						     =					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
    _A						     =					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
    _A						     =					self.prepare_extra_step_kwargs(__lowerCAmelCase ,	__lowerCAmelCase			)

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

    # 9. Denoising loop
    _A						     =					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
          _A						     =					np.concatenate([latents] * 2			) if do_classifier_free_guidance else latents

          # concat latents, mask, masked_image_latents in the channel dimension
          _A						     =					self.scheduler.scale_model_input(__lowerCAmelCase ,	__lowerCAmelCase			)
          _A						     =					np.concatenate([latent_model_input, image] ,	axis=1			)

          # timestep to tensor
          _A						     =					np.array([t] ,	dtype=__lowerCAmelCase			)

          # predict the noise residual
          _A						     =					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:
             _A     ,						_A						     =					np.split(__lowerCAmelCase ,	2			)
          _A						     =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # compute the previous noisy sample x_t -> x_t-1
          _A						     =					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
    _A						     =					self.decode_latents(latents.float()			)

    # 11. Convert to PIL
    if output_type == "pil":
       _A						     =					self.numpy_to_pil(__lowerCAmelCase			)

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=__lowerCAmelCase			)

 def 						snake_case_			(						self				:				List[str] ,	__lowerCAmelCase				:				Dict			)      ->  Any:
    _A						     =					1 / 0.0_8333 * latents
    _A						     =					self.vae(latent_sample=__lowerCAmelCase			)[0]
    _A						     =					np.clip(image / 2 + 0.5 ,	0 ,	1			)
    _A						     =					image.transpose((0, 2, 3, 1)			)
    return image

 def 						snake_case_			(						self				:				str ,	__lowerCAmelCase				:				Optional[Any] ,	__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				Optional[int] ,	__lowerCAmelCase				:				Union[str, Any] ,	__lowerCAmelCase				:				int ,	__lowerCAmelCase				:				List[Any] = None ,	__lowerCAmelCase				:				Optional[int] = None ,	)      ->  Dict:
    if prompt is not None and isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
       _A						     =					1
    elif prompt is not None and isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
       _A						     =					len(__lowerCAmelCase			)
    else:
       _A						     =					prompt_embeds.shape[0]

    if prompt_embeds is None:
       _A						     =					self.tokenizer(
           __lowerCAmelCase ,	padding='''max_length''' ,	max_length=self.tokenizer.model_max_length ,	truncation=__lowerCAmelCase ,	return_tensors='''pt''' ,	)
       _A						     =					text_inputs.input_ids
       _A						     =					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			):
          _A						     =					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
       _A						     =					self.text_encoder(
           input_ids=text_input_ids.int().to(__lowerCAmelCase			) ,	)
       _A						     =					prompt_embeds[0]

    _A     ,						_A     ,						_A						     =					prompt_embeds.shape
    # duplicate text embeddings for each generation per prompt, using mps friendly method
    _A						     =					prompt_embeds.repeat(1 ,	__lowerCAmelCase			)
    _A						     =					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:
       _A						     =					42
       if negative_prompt is None:
          _A						     =					[''''''] * 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			):
          _A						     =					[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:
          _A						     =					negative_prompt

       _A						     =					text_input_ids.shape[-1]
       _A						     =					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

       _A						     =					self.text_encoder(
           input_ids=uncond_input.input_ids.int().to(__lowerCAmelCase			) ,	)
       _A						     =					uncond_embeddings[0]

    if do_classifier_free_guidance:
       _A						     =					uncond_embeddings.shape[1]
       # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
       _A						     =					uncond_embeddings.repeat(1 ,	__lowerCAmelCase			)
       _A						     =					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
       _A						     =					np.concatenate([uncond_embeddings, prompt_embeds]			)

    return prompt_embeds








import numpy as np
import requests

def   SCREAMING_SNAKE_CASE_       (  )     ->    Any:
   _A						     =					requests.get(
       '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
       '''master/Week1/ADRvsRating.csv'''							)
   _A						     =					response.text.splitlines()
   _A						     =					[]
   for item in lines:
      _A						     =					item.split(''','''							)
      data.append(_snake_case							)
   data.pop(0							)  # This is for removing the labels from the list
   _A						     =					np.matrix(_snake_case							)
   return dataset

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :Dict  ,      _snake_case  :Tuple  ,      _snake_case  :Tuple  ,      _snake_case  :str							)     ->    int:
   _A						     =					len_data

   _A						     =					np.dot(_snake_case  ,      data_x.transpose()							)
   prod -= data_y.transpose()
   _A						     =					np.dot(_snake_case  ,      _snake_case							)
   _A						     =					theta - (alpha / n) * sum_grad
   return theta

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Any  ,      _snake_case  :Optional[Any]  ,      _snake_case  :List[Any]							)     ->    Tuple:
   _A						     =					np.dot(_snake_case  ,      data_x.transpose()							)
   prod -= data_y.transpose()
   _A						     =					np.sum(np.square(_snake_case							)							)
   _A						     =					sum_elem / (2 * len_data)
   return error

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Union[str, Any]							)     ->    List[str]:
   _A						     =					100_000
   _A						     =					0.000_1550

   _A						     =					data_x.shape[1]
   _A						     =					data_x.shape[0] - 1

   _A						     =					np.zeros((1, no_features)							)

   for i in range(0  ,      _snake_case							):
      _A						     =					run_steep_gradient_descent(_snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case							)
      _A						     =					sum_of_square_error(_snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case							)
      print(F'''At Iteration {i + 1} - Error is {error:.5f}'''							)

   return theta

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :Optional[int]							)     ->    Tuple:
   _A						     =					sum(abs(y - predicted_y[i]							) for i, y in enumerate(_snake_case							)							)
   return total / len(_snake_case							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Tuple:
   _A						     =					collect_dataset()

   _A						     =					data.shape[0]
   _A						     =					np.c_[np.ones(_snake_case							), data[:, :-1]].astype(_snake_case							)
   _A						     =					data[:, -1].astype(_snake_case							)

   _A						     =					run_linear_regression(_snake_case  ,      _snake_case							)
   _A						     =					theta.shape[1]
   print('''Resultant Feature vector : '''							)
   for i in range(0  ,      _snake_case							):
      print(F'''{theta[0, i]:.5f}'''							)


if __name__ == "__main__":
   main()



