

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			["FunnelTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE							:				str     	=			[
							    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "FunnelBaseModel",
							    "FunnelForMaskedLM",
							    "FunnelForMultipleChoice",
							    "FunnelForPreTraining",
							    "FunnelForQuestionAnswering",
							    "FunnelForSequenceClassification",
							    "FunnelForTokenClassification",
							    "FunnelModel",
							    "FunnelPreTrainedModel",
							    "load_tf_weights_in_funnel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			[
							    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFFunnelBaseModel",
							    "TFFunnelForMaskedLM",
							    "TFFunnelForMultipleChoice",
							    "TFFunnelForPreTraining",
							    "TFFunnelForQuestionAnswering",
							    "TFFunnelForSequenceClassification",
							    "TFFunnelForTokenClassification",
							    "TFFunnelModel",
							    "TFFunnelPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
							from .tokenization_funnel import FunnelTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_funnel_fast import FunnelTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_funnel import (
														    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FunnelBaseModel,
														    FunnelForMaskedLM,
														    FunnelForMultipleChoice,
														    FunnelForPreTraining,
														    FunnelForQuestionAnswering,
														    FunnelForSequenceClassification,
														    FunnelForTokenClassification,
														    FunnelModel,
														    FunnelPreTrainedModel,
														    load_tf_weights_in_funnel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_funnel import (
														    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFFunnelBaseModel,
														    TFFunnelForMaskedLM,
														    TFFunnelForMultipleChoice,
														    TFFunnelForPreTraining,
														    TFFunnelForQuestionAnswering,
														    TFFunnelForSequenceClassification,
														    TFFunnelForTokenClassification,
														    TFFunnelModel,
														    TFFunnelPreTrainedModel,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE							:				Dict     	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''

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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





