
"""simple docstring"""





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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





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

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




		def 	snake_case_     (     self							,  lowerCAmelCase__):

				if not isinstance(lowerCAmelCase__							,  lowerCAmelCase__):
						__SCREAMING_SNAKE_CASE            = list(lowerCAmelCase__)

				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							,  lowerCAmelCase__							,  lowerCAmelCase__ = 4.0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 5_0							,  lowerCAmelCase__ = "pil"							,  lowerCAmelCase__ = True							,  ):
				__SCREAMING_SNAKE_CASE            = len(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.transformer.config.sample_size
				__SCREAMING_SNAKE_CASE            = self.transformer.config.in_channels

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

				__SCREAMING_SNAKE_CASE            = torch.tensor(lowerCAmelCase__							,  device=self.device).reshape(-1)
				__SCREAMING_SNAKE_CASE            = torch.tensor([1_0_0_0] * batch_size							,  device=self.device)
				__SCREAMING_SNAKE_CASE            = torch.cat([class_labels, class_null]							,  0) if guidance_scale > 1 else class_labels

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

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

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

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

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

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

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

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

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

				__SCREAMING_SNAKE_CASE            = 1 / self.vae.config.scaling_factor * latents
				__SCREAMING_SNAKE_CASE            = self.vae.decode(lowerCAmelCase__).sample

				__SCREAMING_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
				__SCREAMING_SNAKE_CASE            = samples.cpu().permute(0							,  2							,  3							,  1).float().numpy()

				if output_type == "pil":
						__SCREAMING_SNAKE_CASE            = self.numpy_to_pil(lowerCAmelCase__)

				if not return_dict:
						return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase__)








from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase           =		{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		['''GPTNeoXTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTNeoXForCausalLM''',
	    '''GPTNeoXForQuestionAnswering''',
	    '''GPTNeoXForSequenceClassification''',
	    '''GPTNeoXForTokenClassification''',
	    '''GPTNeoXLayer''',
	    '''GPTNeoXModel''',
	    '''GPTNeoXPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase           =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






