

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 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     ):
											super().__init__()
											self.register_modules(transformer=snake_case   ,     vae=snake_case   ,     scheduler=snake_case )

											# create a imagenet -> id dictionary for easier use
											snake_case_           =					{}
											if idalabel is not None:
																		for key, value in idalabel.items():
																									for label in value.split(',' ):
																																snake_case_           =					int(snake_case )
																		snake_case_           =					dict(sorted(self.labels.items() ) )




				def 					a							(      self   ,     snake_case ):

											if not isinstance(snake_case   ,     snake_case ):
																		snake_case_           =					list(snake_case )

											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   ,     snake_case   ,     snake_case = 4.0   ,     snake_case = None   ,     snake_case = 50   ,     snake_case = "pil"   ,     snake_case = True   ,     ):
											snake_case_           =					len(snake_case )
											snake_case_           =					self.transformer.config.sample_size
											snake_case_           =					self.transformer.config.in_channels

											snake_case_           =					randn_tensor(
											    shape=(batch_size, latent_channels, latent_size, latent_size)   ,     generator=snake_case   ,     device=self.device   ,     dtype=self.transformer.dtype   ,     )
											snake_case_           =					torch.cat([latents] * 2 ) if guidance_scale > 1 else latents

											snake_case_           =					torch.tensor(snake_case   ,     device=self.device ).reshape(-1 )
											snake_case_           =					torch.tensor([1000] * batch_size   ,     device=self.device )
											snake_case_           =					torch.cat([class_labels, class_null]   ,     0 ) if guidance_scale > 1 else class_labels

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

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

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

																		# perform guidance
																		if guidance_scale > 1:
																									snake_case_       ,					snake_case_           =					noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																									snake_case_       ,					snake_case_           =					torch.split(snake_case   ,     len(snake_case ) // 2   ,     dim=0 )

																									snake_case_           =					uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																									snake_case_           =					torch.cat([half_eps, half_eps]   ,     dim=0 )

																									snake_case_           =					torch.cat([eps, rest]   ,     dim=1 )

																		# learned sigma
																		if self.transformer.config.out_channels // 2 == latent_channels:
																									snake_case_       ,					snake_case_           =					torch.split(snake_case   ,     snake_case   ,     dim=1 )
																		else:
																									snake_case_           =					noise_pred

																		# compute previous image: x_t -> x_t-1
																		snake_case_           =					self.scheduler.step(snake_case   ,     snake_case   ,     snake_case ).prev_sample

											if guidance_scale > 1:
																		snake_case_       ,					snake_case_           =					latent_model_input.chunk(2   ,     dim=0 )
											else:
																		snake_case_           =					latent_model_input

											snake_case_           =					1 / self.vae.config.scaling_factor * latents
											snake_case_           =					self.vae.decode(snake_case ).sample

											snake_case_           =					(samples / 2 + 0.5).clamp(0   ,     1 )

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

											if output_type == "pil":
																		snake_case_           =					self.numpy_to_pil(snake_case )

											if not return_dict:
																		return (samples,)

											return ImagePipelineOutput(images=snake_case )







from __future__ import annotations







class 				lowercase       :




				def __init__(      self   ,     snake_case ):
											snake_case_           =					TypeError(
											    'Matrices must be formed from a list of zero or more lists containing at '
											    'least one and the same number of values, each of which must be of type '
											    'int or float.' )
											if len(snake_case ) != 0:
																		snake_case_           =					len(rows[0] )
																		if cols == 0:
																									raise error
																		for row in rows:
																									if len(snake_case ) != cols:
																																raise error
																									for value in row:
																																if not isinstance(snake_case   ,     (int, float) ):
																																							raise error
																		snake_case_           =					rows
											else:
																		snake_case_           =					[]




				def 					a							(      self ):
											return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]




				@property
				def 					a							(      self ):
											return len(self.rows )




				@property
				def 					a							(      self ):
											return len(self.rows[0] )




				@property
				def 					a							(      self ):
											return (self.num_rows, self.num_columns)




				@property
				def 					a							(      self ):
											return self.order[0] == self.order[1]




				def 					a							(      self ):
											snake_case_           =					[
											    [0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
											    for row_num in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											if not self.is_square:
																		return 0
											if self.order == (0, 0):
																		return 1
											if self.order == (1, 1):
																		return int(self.rows[0][0] )
											if self.order == (2, 2):
																		return int(
																		    (self.rows[0][0] * self.rows[1][1])
																		    - (self.rows[0][1] * self.rows[1][0]) )
											else:
																		return sum(
																		    self.rows[0][column] * self.cofactors().rows[0][column]
																		    for column in range(self.num_columns ) )




				def 					a							(      self ):
											return bool(self.determinant() )




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					[
											    [
											        self.rows[other_row][other_column]
											        for other_column in range(self.num_columns )
											        if other_column != column
											    ]
											    for other_row in range(self.num_rows )
											    if other_row != row
											]
											return Matrix(snake_case ).determinant()




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											if (row + column) % 2 == 0:
																		return self.get_minor(snake_case   ,     snake_case )
											return -1 * self.get_minor(snake_case   ,     snake_case )




				def 					a							(      self ):
											return Matrix(
											    [
											        [self.get_minor(snake_case   ,     snake_case ) for column in range(self.num_columns )]
											        for row in range(self.num_rows )
											    ] )




				def 					a							(      self ):
											return Matrix(
											    [
											        [
											            self.minors().rows[row][column]
											            if (row + column) % 2 == 0
											            else self.minors().rows[row][column] * -1
											            for column in range(self.minors().num_columns )
											        ]
											        for row in range(self.minors().num_rows )
											    ] )




				def 					a							(      self ):
											snake_case_           =					[
											    [self.cofactors().rows[column][row] for column in range(self.num_columns )]
											    for row in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											snake_case_           =					self.determinant()
											if not determinant:
																		raise TypeError('Only matrices with a non-zero determinant have an inverse' )
											return self.adjugate() * (1 / determinant)




				def __repr__(      self ):
											return str(self.rows )




				def __str__(      self ):
											if self.num_rows == 0:
																		return "[]"
											if self.num_rows == 1:
																		return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
											return (
											    "["
											    + "\n ".join(
											        [
											            '[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
											            for row in self.rows
											        ] )
											    + "]"
											)




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError('Row must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in row:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != self.num_columns:
																		raise ValueError(
																		    'Row must be equal in length to the other rows in the matrix' )
											if position is None:
																		self.rows.append(snake_case )
											else:
																		snake_case_           =					self.rows[0:position] + [row] + self.rows[position:]




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError(
											    'Column must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in column:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != self.num_rows:
																		raise ValueError(
																		    'Column must be equal in length to the other columns in the matrix' )
											if position is None:
																		snake_case_           =					[self.rows[i] + [column[i]] for i in range(self.num_rows )]
											else:
																		snake_case_           =					[
																		    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																		    for i in range(self.num_rows )
																		]




				def __eq__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		return NotImplemented
											return self.rows == other.rows




				def __ne__(      self   ,     snake_case ):
											return not self == other




				def __neg__(      self ):
											return self * -1




				def __add__(      self   ,     snake_case ):
											if self.order != other.order:
																		raise ValueError('Addition requires matrices of the same order' )
											return Matrix(
											    [
											        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
											        for i in range(self.num_rows )
											    ] )




				def __sub__(      self   ,     snake_case ):
											if self.order != other.order:
																		raise ValueError('Subtraction requires matrices of the same order' )
											return Matrix(
											    [
											        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
											        for i in range(self.num_rows )
											    ] )




				def __mul__(      self   ,     snake_case ):
											if isinstance(snake_case   ,     (int, float) ):
																		return Matrix(
																		    [[int(element * other ) for element in row] for row in self.rows] )
											elif isinstance(snake_case   ,     snake_case ):
																		if self.num_columns != other.num_rows:
																									raise ValueError(
																									    'The number of columns in the first matrix must '
																									    'be equal to the number of rows in the second' )
																		return Matrix(
																		    [
																		        [Matrix.dot_product(snake_case   ,     snake_case ) for column in other.columns()]
																		        for row in self.rows
																		    ] )
											else:
																		raise TypeError(
																		    'A Matrix can only be multiplied by an int, float, or another matrix' )




				def __pow__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		raise TypeError('A Matrix can only be raised to the power of an int' )
											if not self.is_square:
																		raise ValueError('Only square matrices can be raised to a power' )
											if other == 0:
																		return self.identity()
											if other < 0:
																		if self.is_invertable():
																									return self.inverse() ** (-other)
																		raise ValueError(
																		    'Only invertable matrices can be raised to a negative power' )
											snake_case_           =					self
											for _ in range(other - 1 ):
																		result *= self
											return result




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     snake_case ):
											return sum(row[i] * column[i] for i in range(len(snake_case ) ) )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




