





from typing import List, Optional, Tuple, Union

import torch

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


class   A			(UpperCAmelCase_    ):
			'''simple docstring'''




			__lowerCamelCase	:       UNetaDModel
			__lowerCamelCase	:       KarrasVeScheduler






			def __init__(			self   :    Union[str, Any]							,	__lowerCAmelCase   :    UNetaDModel							,	__lowerCAmelCase   :    KarrasVeScheduler   )	->	Optional[int]:







					"""simple docstring"""






					super().__init__()
					self.register_modules(unet=lowerCAmelCase__							,	scheduler=lowerCAmelCase__   )







			@torch.no_grad()
			def __call__(			self   :    Union[str, Any]							,	__lowerCAmelCase   :    int = 1							,	__lowerCAmelCase   :    int = 50							,	__lowerCAmelCase   :    Optional[Union[torch.Generator, List[torch.Generator]]] = None							,	__lowerCAmelCase   :    Optional[str] = "pil"							,	__lowerCAmelCase   :    bool = True							,	**__lowerCAmelCase   :    Optional[int]							,	)	->	List[Any]:







					"""simple docstring"""






					A__        =   self.unet.config.sample_size
					A__        =   (batch_size, 3, img_size, img_size)

					A__        =   self.unet

					# sample x_0 ~ N(0, sigma_0^2 * I)
					A__        =   randn_tensor(lowerCAmelCase__							,	generator=lowerCAmelCase__							,	device=self.device   ) * self.scheduler.init_noise_sigma

					self.scheduler.set_timesteps(lowerCAmelCase__   )

					for t in self.progress_bar(self.scheduler.timesteps   ):
							# here sigma_t == t_i from the paper
							A__        =   self.scheduler.schedule[t]
							A__        =   self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							A__        =   self.scheduler.add_noise_to_input(lowerCAmelCase__							,	lowerCAmelCase__							,	generator=lowerCAmelCase__   )

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							A__        =   (sigma_hat / 2) * model((sample_hat + 1) / 2							,	sigma_hat / 2   ).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							A__        =   self.scheduler.step(lowerCAmelCase__							,	lowerCAmelCase__							,	lowerCAmelCase__							,	lowerCAmelCase__   )

							if sigma_prev != 0:
									# 6. Apply 2nd order correction
									# The model inputs and output are adjusted by following eq. (213) in [1].
									A__        =   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,	sigma_prev / 2   ).sample
									A__        =   self.scheduler.step_correct(
									    lowerCAmelCase__							,	lowerCAmelCase__							,	lowerCAmelCase__							,	lowerCAmelCase__							,	step_output.prev_sample							,	step_output["""derivative"""]							,	)
							A__        =   step_output.prev_sample

					A__        =   (sample / 2 + 0.5).clamp(0							,	1   )
					A__        =   sample.cpu().permute(0							,	2							,	3							,	1   ).numpy()
					if output_type == "pil":
							A__        =   self.numpy_to_pil(lowerCAmelCase__   )

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCAmelCase__   )






from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase							:  Any   	=						TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  int   	=						[
						    """MT5EncoderModel""",
						    """MT5ForConditionalGeneration""",
						    """MT5ForQuestionAnswering""",
						    """MT5Model""",
						    """MT5PreTrainedModel""",
						    """MT5Stack""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Tuple   	=						["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase							:  Optional[Any]   	=						_LazyModule(
						    __name__,
						    globals()["""__file__"""],
						    _import_structure,
						    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
						    module_spec=__spec__,
						)






