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    snake_case       (   UpperCamelCase_    ):




  def __init__(   self  :						Optional[int]  ,			a_  :						TransformeraDModel  ,			a_  :						AutoencoderKL  ,			a_  :						KarrasDiffusionSchedulers  ,			a_  :						Optional[Dict[int, str]] = None  ,			)->							Union[str, Any]:




       """simple docstring"""
       super().__init__()
       self.register_modules(transformer=a_  ,			vae=a_  ,			scheduler=a_					)

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




  def 						__lowercase(   self  :						Dict  ,			a_  :						Union[str, List[str]]					)->							List[int]:




       """simple docstring"""

       if not isinstance(a_  ,			a_					):
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		list(a_					)

       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  :						Optional[Any]  ,			a_  :						List[int]  ,			a_  :						float = 4.0  ,			a_  :						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,			a_  :						int = 50  ,			a_  :						Optional[str] = "pil"  ,			a_  :						bool = True  ,			)->							Union[ImagePipelineOutput, Tuple]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		len(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.transformer.config.sample_size
       SCREAMING_SNAKE_CASE__					:					int      =		self.transformer.config.in_channels

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

       SCREAMING_SNAKE_CASE__					:					int      =		torch.tensor(a_  ,			device=self.device					).reshape(-1					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.tensor([1000] * batch_size  ,			device=self.device					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.cat([class_labels, class_null]  ,			0					) if guidance_scale > 1 else class_labels

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

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

            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		t
            if not torch.is_tensor(a_					):
                 # 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+)
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		latent_model_input.device.type == 'mps'
                 if isinstance(a_  ,			a_					):
                      SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.floataa if is_mps else torch.floataa
                 else:
                      SCREAMING_SNAKE_CASE__					:					int      =		torch.intaa if is_mps else torch.intaa
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.tensor([timesteps]  ,			dtype=a_  ,			device=latent_model_input.device					)
            elif len(timesteps.shape					) == 0:
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		timesteps[None].to(latent_model_input.device					)
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            SCREAMING_SNAKE_CASE__					:					List[str]      =		timesteps.expand(latent_model_input.shape[0]					)
            # predict noise model_output
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self.transformer(
                a_  ,			timestep=a_  ,			class_labels=a_					).sample

            # perform guidance
            if guidance_scale > 1:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.split(a_  ,			len(a_					) // 2  ,			dim=0					)

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

                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.cat([eps, rest]  ,			dim=1					)

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					str      =		torch.split(a_  ,			a_  ,			dim=1					)
            else:
                 SCREAMING_SNAKE_CASE__					:					int      =		noise_pred

            # compute previous image: x_t -> x_t-1
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self.scheduler.step(a_  ,			a_  ,			a_					).prev_sample

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

       SCREAMING_SNAKE_CASE__					:					List[str]      =		1 / self.vae.config.scaling_factor * latents
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.vae.decode(a_					).sample

       SCREAMING_SNAKE_CASE__					:					List[str]      =		(samples / 2 + 0.5).clamp(0  ,			1					)

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

       if output_type == "pil":
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.numpy_to_pil(a_					)

       if not return_dict:
            return (samples,)

       return ImagePipelineOutput(images=a_					)






'''simple docstring'''

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				data
											SCREAMING_SNAKE_CASE_				=				[0x6_7_4_5_2_3_0_1, 0xe_f_c_d_a_b_8_9, 0x9_8_b_a_d_c_f_e, 0x1_0_3_2_5_4_7_6, 0xc_3_d_2_e_1_f_0]



				@staticmethod
				def   a__					(    _lowercase,     _lowercase       )     ->							List[str]:
											return ((n << b) | (n >> (32 - b))) & 0xf_f_f_f_f_f_f_f



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				B'\x80' + B'\x00' * (63 - (len(self.data       ) + 8) % 64)
											SCREAMING_SNAKE_CASE_				=				self.data + padding + struct.pack('>Q',     8 * len(self.data       )       )
											return padded_data



				def   a__					(    self       )     ->							Optional[Any]:
											return [
											    self.padded_data[i : i + 64] for i in range(0,     len(self.padded_data       ),     64       )
											]



				def   a__					(    self,     _lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				list(struct.unpack('>16L',     _lowercase       )       ) + [0] * 64
											for i in range(16,     80       ):
																		SCREAMING_SNAKE_CASE_				=				self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),     1       )
											return w





				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.padding()
											SCREAMING_SNAKE_CASE_				=				self.split_blocks()
											for block in self.blocks:
																		SCREAMING_SNAKE_CASE_				=				self.expand_block(_lowercase       )
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.h
																		for i in range(0,     80       ):
																									if 0 <= i < 20:
																																SCREAMING_SNAKE_CASE_				=				(b & c) | ((~b) & d)
																																SCREAMING_SNAKE_CASE_				=				0x5_a_8_2_7_9_9_9
																									elif 20 <= i < 40:
																																SCREAMING_SNAKE_CASE_				=				b ^ c ^ d
																																SCREAMING_SNAKE_CASE_				=				0x6_e_d_9_e_b_a_1
																									elif 40 <= i < 60:
																																SCREAMING_SNAKE_CASE_				=				(b & c) | (b & d) | (c & d)
																																SCREAMING_SNAKE_CASE_				=				0x8_f_1_b_b_c_d_c
																									elif 60 <= i < 80:
																																SCREAMING_SNAKE_CASE_				=				b ^ c ^ d
																																SCREAMING_SNAKE_CASE_				=				0xc_a_6_2_c_1_d_6
																									SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				(
																									    self.rotate(_lowercase,     5       ) + f + e + k + expanded_block[i] & 0xf_f_f_f_f_f_f_f,
																									    a,
																									    self.rotate(_lowercase,     30       ),
																									    c,
																									    d,
																									)
																		SCREAMING_SNAKE_CASE_				=				(
																		    self.h[0] + a & 0xf_f_f_f_f_f_f_f,
																		    self.h[1] + b & 0xf_f_f_f_f_f_f_f,
																		    self.h[2] + c & 0xf_f_f_f_f_f_f_f,
																		    self.h[3] + d & 0xf_f_f_f_f_f_f_f,
																		    self.h[4] + e & 0xf_f_f_f_f_f_f_f,
																		)
											return ("{:08x}" * 5).format(*self.h       )




def 							_UpperCamelCase				(   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				B'Test String'
							assert SHAaHash(lowerCAmelCase__   ).final_hash() == hashlib.shaa(lowerCAmelCase__   ).hexdigest()  # noqa: S324




def 							_UpperCamelCase				(   )      -> Any:
							SCREAMING_SNAKE_CASE_				=				argparse.ArgumentParser(description='Process some strings or files'   )
							parser.add_argument(
							    '--string'		,dest='input_string'		,default='Hello World!! Welcome to Cryptography'		,help='Hash the string'		,)
							parser.add_argument('--file'		,dest='input_file'		,help='Hash contents of a file'   )
							SCREAMING_SNAKE_CASE_				=				parser.parse_args()
							SCREAMING_SNAKE_CASE_				=				args.input_string
							# In any case hash input should be a bytestring
							if args.input_file:
														with open(args.input_file		,'rb'   ) as f:
																					SCREAMING_SNAKE_CASE_				=				f.read()
							else:
														SCREAMING_SNAKE_CASE_				=				bytes(lowerCAmelCase__		,'utf-8'   )
							print(SHAaHash(lowerCAmelCase__   ).final_hash()   )


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod()





