





from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


_A :   List[Any]								=  logging.get_logger(__name__)  # pylint: disable=invalid-name

_A :   Optional[Any]								=  '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=8					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							h // scale_factor**2
   if h % scale_factor**2 != 0:
      new_h += 1
   lowerCamelCase__       :						Optional[Any]        =							w // scale_factor**2
   if w % scale_factor**2 != 0:
      new_w += 1
   return new_h * scale_factor, new_w * scale_factor




class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):







       def __init__(				self					:     Optional[Any]		, A					:     MultilingualCLIP		, A					:     XLMRobertaTokenizer		, A					:     UNetaDConditionModel		, A					:     Union[DDIMScheduler, DDPMScheduler]		, A					:     VQModel		, )       ->Dict:
          super().__init__()

          self.register_modules(
              text_encoder=A		, tokenizer=A		, unet=A		, scheduler=A		, movq=A		, )
          lowerCamelCase__       :						int        =							2 ** (len(self.movq.config.block_out_channels      ) - 1)







       def 						__lowerCamelCase   (				self					:     int		, A					:     List[str]		, A					:     Any		, A					:     str		, A					:     Dict		, A					:     Tuple		, A					:     List[Any]      )       ->Optional[Any]:
          if latents is None:
             lowerCamelCase__       :						int        =							randn_tensor(A		, generator=A		, device=A		, dtype=A      )
          else:
             if latents.shape != shape:
                raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"      )
             lowerCamelCase__       :						Optional[int]        =							latents.to(A      )

          lowerCamelCase__       :						Optional[int]        =							latents * scheduler.init_noise_sigma
          return latents







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     Optional[int]		, A					:     Tuple		, A					:     Optional[Any]		, A					:     Optional[Any]		, A					:     Dict=None		, )       ->Tuple:
          lowerCamelCase__       :						List[str]        =							len(A      ) if isinstance(A		, A      ) else 1
          # get prompt text embeddings
          lowerCamelCase__       :						Tuple        =							self.tokenizer(
              A		, padding='''max_length'''		, truncation=A		, max_length=7_7		, return_attention_mask=A		, add_special_tokens=A		, return_tensors='''pt'''		, )

          lowerCamelCase__       :						int        =							text_inputs.input_ids
          lowerCamelCase__       :						Union[str, Any]        =							self.tokenizer(A		, padding='''longest'''		, return_tensors='''pt'''      ).input_ids

          if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A		, A      ):
             lowerCamelCase__       :						int        =							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}"      )

          lowerCamelCase__       :						int        =							text_input_ids.to(A      )
          lowerCamelCase__       :						Dict        =							text_inputs.attention_mask.to(A      )

          lowerCamelCase__       :						List[Any]        =							self.text_encoder(
              input_ids=A		, attention_mask=A      )

          lowerCamelCase__       :						str        =							prompt_embeds.repeat_interleave(A		, dim=0      )
          lowerCamelCase__       :						Union[str, Any]        =							text_encoder_hidden_states.repeat_interleave(A		, dim=0      )
          lowerCamelCase__       :						Dict        =							text_mask.repeat_interleave(A		, dim=0      )

          if do_classifier_free_guidance:
             lowerCamelCase__       :						List[str]
             if negative_prompt is None:
                lowerCamelCase__       :						Dict        =							[''''''] * batch_size
             elif type(A      ) is not type(A      ):
                raise TypeError(
                    F"`negative_prompt` should be the same type to `prompt`, but got {type(A      )} !="
                    F" {type(A      )}."      )
             elif isinstance(A		, A      ):
                lowerCamelCase__       :						List[str]        =							[negative_prompt]
             elif batch_size != len(A      ):
                raise ValueError(
                    F"`negative_prompt`: {negative_prompt} has batch size {len(A      )}, but `prompt`:"
                    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    ''' the batch size of `prompt`.'''      )
             else:
                lowerCamelCase__       :						Union[str, Any]        =							negative_prompt

             lowerCamelCase__       :						Optional[Any]        =							self.tokenizer(
                 A		, padding='''max_length'''		, max_length=7_7		, truncation=A		, return_attention_mask=A		, add_special_tokens=A		, return_tensors='''pt'''		, )
             lowerCamelCase__       :						Optional[int]        =							uncond_input.input_ids.to(A      )
             lowerCamelCase__       :						List[Any]        =							uncond_input.attention_mask.to(A      )

             lowerCamelCase__       :						int        =							self.text_encoder(
                 input_ids=A		, attention_mask=A      )

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

             lowerCamelCase__       :						Tuple        =							negative_prompt_embeds.shape[1]
             lowerCamelCase__       :						Dict        =							negative_prompt_embeds.repeat(1		, A      )
             lowerCamelCase__       :						str        =							negative_prompt_embeds.view(batch_size * num_images_per_prompt		, A      )

             lowerCamelCase__       :						List[str]        =							uncond_text_encoder_hidden_states.shape[1]
             lowerCamelCase__       :						int        =							uncond_text_encoder_hidden_states.repeat(1		, A		, 1      )
             lowerCamelCase__       :						Optional[Any]        =							uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt		, A		, -1      )
             lowerCamelCase__       :						Tuple        =							uncond_text_mask.repeat_interleave(A		, dim=0      )

             # done duplicates

             # 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
             lowerCamelCase__       :						Any        =							torch.cat([negative_prompt_embeds, prompt_embeds]      )
             lowerCamelCase__       :						Tuple        =							torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]      )

             lowerCamelCase__       :						Dict        =							torch.cat([uncond_text_mask, text_mask]      )

          return prompt_embeds, text_encoder_hidden_states, text_mask







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     Optional[Any]=0      )       ->Optional[Any]:
          if is_accelerate_available():
             from accelerate import cpu_offload
          else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

          lowerCamelCase__       :						str        =							torch.device(F"cuda:{gpu_id}"      )

          lowerCamelCase__       :						Tuple        =							[
              self.unet,
              self.text_encoder,
              self.movq,
          ]
          for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                cpu_offload(A		, A      )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     str=0      )       ->Optional[Any]:
          if is_accelerate_available() and is_accelerate_version('''>='''		, '''0.17.0.dev0'''      ):
             from accelerate import cpu_offload_with_hook
          else:
             raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''      )

          lowerCamelCase__       :						Any        =							torch.device(F"cuda:{gpu_id}"      )

          if self.device.type != "cpu":
             self.to('''cpu'''		, silence_dtype_warnings=A      )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          lowerCamelCase__       :						Union[str, Any]        =							None
          for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
             lowerCamelCase__       :						Tuple        =							cpu_offload_with_hook(A		, A		, prev_module_hook=A      )

          if self.safety_checker is not None:
             lowerCamelCase__       :						Tuple        =							cpu_offload_with_hook(self.safety_checker		, A		, prev_module_hook=A      )

          # We'll offload the last model manually.
          lowerCamelCase__       :						Union[str, Any]        =							hook







       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          if not hasattr(self.unet		, '''_hf_hook'''      ):
             return self.device
          for module in self.unet.modules():
             if (
                 hasattr(A		, '''_hf_hook'''      )
                 and hasattr(module._hf_hook		, '''execution_device'''      )
                 and module._hf_hook.execution_device is not None
             ):
                return torch.device(module._hf_hook.execution_device      )
          return self.device







       @torch.no_grad()
       @replace_example_docstring(A      )
       def __call__(				self					:     str		, A					:     Union[str, List[str]]		, A					:     Union[torch.FloatTensor, List[torch.FloatTensor]]		, A					:     Union[torch.FloatTensor, List[torch.FloatTensor]]		, A					:     Optional[Union[str, List[str]]] = None		, A					:     int = 5_1_2		, A					:     int = 5_1_2		, A					:     int = 1_0_0		, A					:     float = 4.0		, A					:     int = 1		, A					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		, A					:     Optional[torch.FloatTensor] = None		, A					:     Optional[str] = "pil"		, A					:     bool = True		, )       ->int:

          if isinstance(A		, A      ):
             lowerCamelCase__       :						Dict        =							1
          elif isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[Any]        =							len(A      )
          else:
             raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(A      )}"      )

          lowerCamelCase__       :						Dict        =							self._execution_device

          lowerCamelCase__       :						Dict        =							batch_size * num_images_per_prompt
          lowerCamelCase__       :						int        =							guidance_scale > 1.0

          lowerCamelCase__       :						Optional[Any]        =							self._encode_prompt(
              A		, A		, A		, A		, A      )

          if isinstance(A		, A      ):
             lowerCamelCase__       :						int        =							torch.cat(A		, dim=0      )
          if isinstance(A		, A      ):
             lowerCamelCase__       :						int        =							torch.cat(A		, dim=0      )

          if do_classifier_free_guidance:
             lowerCamelCase__       :						List[str]        =							image_embeds.repeat_interleave(A		, dim=0      )
             lowerCamelCase__       :						Any        =							negative_image_embeds.repeat_interleave(A		, dim=0      )

          lowerCamelCase__       :						Tuple        =							torch.cat([negative_image_embeds, image_embeds]		, dim=0      ).to(
              dtype=prompt_embeds.dtype		, device=A      )

          self.scheduler.set_timesteps(A		, device=A      )
          lowerCamelCase__       :						Union[str, Any]        =							self.scheduler.timesteps

          lowerCamelCase__       :						List[Any]        =							self.unet.config.in_channels

          lowerCamelCase__       :						int        =							get_new_h_w(A		, A		, self.movq_scale_factor      )

          # create initial latent
          lowerCamelCase__       :						Dict        =							self.prepare_latents(
              (batch_size, num_channels_latents, height, width)		, text_encoder_hidden_states.dtype		, A		, A		, A		, self.scheduler		, )

          for i, t in enumerate(self.progress_bar(A      )      ):
             # expand the latents if we are doing classifier free guidance
             lowerCamelCase__       :						Any        =							torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

             lowerCamelCase__       :						Tuple        =							{'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
             lowerCamelCase__       :						Optional[int]        =							self.unet(
                 sample=A		, timestep=A		, encoder_hidden_states=A		, added_cond_kwargs=A		, return_dict=A		, )[0]

             if do_classifier_free_guidance:
                lowerCamelCase__       :						Optional[Any]        =							noise_pred.split(latents.shape[1]		, dim=1      )
                lowerCamelCase__       :						Tuple        =							noise_pred.chunk(2      )
                lowerCamelCase__       :						Tuple        =							variance_pred.chunk(2      )
                lowerCamelCase__       :						Union[str, Any]        =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                lowerCamelCase__       :						Optional[int]        =							torch.cat([noise_pred, variance_pred_text]		, dim=1      )

             if not (
                 hasattr(self.scheduler.config		, '''variance_type'''      )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                lowerCamelCase__       :						Tuple        =							noise_pred.split(latents.shape[1]		, dim=1      )

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase__       :						List[Any]        =							self.scheduler.step(
                 A		, A		, A		, generator=A		, ).prev_sample
          # post-processing
          lowerCamelCase__       :						List[Any]        =							self.movq.decode(A		, force_not_quantize=A      )['''sample''']

          if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"      )

          if output_type in ["np", "pil"]:
             lowerCamelCase__       :						List[str]        =							image * 0.5 + 0.5
             lowerCamelCase__       :						Optional[int]        =							image.clamp(0		, 1      )
             lowerCamelCase__       :						Union[str, Any]        =							image.cpu().permute(0		, 2		, 3		, 1      ).float().numpy()

          if output_type == "pil":
             lowerCamelCase__       :						Tuple        =							self.numpy_to_pil(A      )

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A      )










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







   """simple docstring"""
   if deriv:
      return value * (1 - value)
   return 1 / (1 + math.exp(-value					))


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

   for _ in range(UpperCAmelCase					):
      # Forward propagation
      lowerCamelCase__       :						Optional[int]        =							sigmoid_function(INITIAL_VALUE * weight					)
      # How much did we miss?
      lowerCamelCase__       :						int        =							(expected / 100) - layer_a
      # Error delta
      lowerCamelCase__       :						Union[str, Any]        =							layer_1_error * sigmoid_function(UpperCAmelCase      ,  UpperCAmelCase					)
      # Update weight
      weight += INITIAL_VALUE * layer_1_delta

   return layer_a * 100


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _A :   Union[str, Any]  			=  int(input('Expected value: '))
       _A :   int  			=  int(input('Number of propagations: '))
       print(forward_propagation(expected, number_propagations))



