

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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							UNetaDModel
			_UpperCAmelCase   :							KarrasVeScheduler



			def __init__( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     UNetaDModel   ,						lowerCAmelCase__  :     KarrasVeScheduler):
									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]   ,						):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.unet.config.sample_size
									SCREAMING_SNAKE_CASE_: str  		=       (batch_size, 3, img_size, img_size)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.unet

									# sample x_0 ~ N(0, sigma_0^2 * I)
									SCREAMING_SNAKE_CASE_: str  		=       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
															SCREAMING_SNAKE_CASE_: List[str]  		=       self.scheduler.schedule[t]
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       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
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       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].
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       (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
															SCREAMING_SNAKE_CASE_: Any  		=       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].
																					SCREAMING_SNAKE_CASE_: Any  		=       (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2   ,						sigma_prev / 2).sample
																					SCREAMING_SNAKE_CASE_: List[str]  		=       self.scheduler.step_correct(
																					    lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						step_output.prev_sample   ,						step_output["derivative"]   ,						)
															SCREAMING_SNAKE_CASE_: Tuple  		=       step_output.prev_sample

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       (sample / 2 + 0.5).clamp(0   ,						1)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       sample.cpu().permute(0   ,						2   ,						3   ,						1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_: List[str]  		=       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__,
						)






