from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = None			,			)	->     List[Any]:
        super().__init__()
        self.register_modules(transformer=__lowercase			,			vae=__lowercase			,			scheduler=__lowercase)

        # create a imagenet -> id dictionary for easier use
        __UpperCamelCase					:Tuple        =		{}
        if idalabel is not None:
            for key, value in idalabel.items():
                for label in value.split(''','''):
                    __UpperCamelCase					:List[Any]        =		int(__lowercase)
            __UpperCamelCase					:List[str]        =		dict(sorted(self.labels.items()))







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[int]:

        if not isinstance(__lowercase			,			__lowercase):
            __UpperCamelCase					:Tuple        =		list(__lowercase)

        for l in label:
            if l not in self.labels:
                raise ValueError(
                    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""")

        return [self.labels[l] for l in label]







    @torch.no_grad()
    def __call__(     self			,			__lowercase			,			__lowercase = 4.0			,			__lowercase = None			,			__lowercase = 50			,			__lowercase = "pil"			,			__lowercase = True			,			)	->     Union[ImagePipelineOutput, Tuple]:
        __UpperCamelCase					:str        =		len(__lowercase)
        __UpperCamelCase					:List[Any]        =		self.transformer.config.sample_size
        __UpperCamelCase					:Dict        =		self.transformer.config.in_channels

        __UpperCamelCase					:List[Any]        =		randn_tensor(
            shape=(batch_size, latent_channels, latent_size, latent_size)			,			generator=__lowercase			,			device=self.device			,			dtype=self.transformer.dtype			,			)
        __UpperCamelCase					:List[Any]        =		torch.cat([latents] * 2) if guidance_scale > 1 else latents

        __UpperCamelCase					:List[Any]        =		torch.tensor(__lowercase			,			device=self.device).reshape(-1)
        __UpperCamelCase					:Optional[int]        =		torch.tensor([1_000] * batch_size			,			device=self.device)
        __UpperCamelCase					:Any        =		torch.cat([class_labels, class_null]			,			0) if guidance_scale > 1 else class_labels

        # set step values
        self.scheduler.set_timesteps(__lowercase)

        for t in self.progress_bar(self.scheduler.timesteps):
            if guidance_scale > 1:
                __UpperCamelCase					:Tuple        =		latent_model_input[: len(__lowercase) // 2]
                __UpperCamelCase					:int        =		torch.cat([half, half]			,			dim=0)
            __UpperCamelCase					:List[Any]        =		self.scheduler.scale_model_input(__lowercase			,			__lowercase)

            __UpperCamelCase					:Any        =		t
            if not torch.is_tensor(__lowercase):
                # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                # This would be a good case for the `match` statement (Python 3.10+)
                __UpperCamelCase					:int        =		latent_model_input.device.type == '''mps'''
                if isinstance(__lowercase			,			__lowercase):
                    __UpperCamelCase					:int        =		torch.floataa if is_mps else torch.floataa
                else:
                    __UpperCamelCase					:Optional[int]        =		torch.intaa if is_mps else torch.intaa
                __UpperCamelCase					:Tuple        =		torch.tensor([timesteps]			,			dtype=__lowercase			,			device=latent_model_input.device)
            elif len(timesteps.shape) == 0:
                __UpperCamelCase					:str        =		timesteps[None].to(latent_model_input.device)
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            __UpperCamelCase					:Optional[Any]        =		timesteps.expand(latent_model_input.shape[0])
            # predict noise model_output
            __UpperCamelCase					:Tuple        =		self.transformer(
                __lowercase			,			timestep=__lowercase			,			class_labels=__lowercase).sample

            # perform guidance
            if guidance_scale > 1:
                __UpperCamelCase  ,	__UpperCamelCase					:Dict        =		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		torch.split(__lowercase			,			len(__lowercase) // 2			,			dim=0)

                __UpperCamelCase					:Union[str, Any]        =		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                __UpperCamelCase					:Any        =		torch.cat([half_eps, half_eps]			,			dim=0)

                __UpperCamelCase					:List[str]        =		torch.cat([eps, rest]			,			dim=1)

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		torch.split(__lowercase			,			__lowercase			,			dim=1)
            else:
                __UpperCamelCase					:str        =		noise_pred

            # compute previous image: x_t -> x_t-1
            __UpperCamelCase					:List[Any]        =		self.scheduler.step(__lowercase			,			__lowercase			,			__lowercase).prev_sample

        if guidance_scale > 1:
            __UpperCamelCase  ,	__UpperCamelCase					:Any        =		latent_model_input.chunk(2			,			dim=0)
        else:
            __UpperCamelCase					:Optional[int]        =		latent_model_input

        __UpperCamelCase					:List[str]        =		1 / self.vae.config.scaling_factor * latents
        __UpperCamelCase					:Dict        =		self.vae.decode(__lowercase).sample

        __UpperCamelCase					:Any        =		(samples / 2 + 0.5).clamp(0			,			1)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        __UpperCamelCase					:Any        =		samples.cpu().permute(0			,			2			,			3			,			1).float().numpy()

        if output_type == "pil":
            __UpperCamelCase					:Optional[Any]        =		self.numpy_to_pil(__lowercase)

        if not return_dict:
            return (samples,)

        return ImagePipelineOutput(images=__lowercase)






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    print(f"""Vertex\tShortest Distance from vertex {src}"""     )
    for i, d in enumerate(SCREAMING_SNAKE_CASE     ):
        print(f"""{i}\t\t{d}"""     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for j in range(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:int        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
            return True
    return False



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		[float('''inf'''     )] * vertex_count
    __UpperCamelCase					:str        =		0.0

    for _ in range(vertex_count - 1     ):
        for j in range(SCREAMING_SNAKE_CASE     ):
            __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Dict        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
                __UpperCamelCase					:Any        =		distance[u] + w

    __UpperCamelCase					:Tuple        =		check_negative_cycle(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''     )

    return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    __lowercase										= int(input('''Enter number of vertices: ''').strip())
    __lowercase										= int(input('''Enter number of edges: ''').strip())

    __lowercase										= [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        __lowercase			,	__lowercase			,	__lowercase										= (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        __lowercase										= {'''src''': src, '''dst''': dest, '''weight''': weight}

    __lowercase										= int(input('''\nEnter shortest path source:''').strip())
    __lowercase										= bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)





