



'''simple docstring'''

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 		lowerCamelCase__      (						_lowerCamelCase     ):




	'''simple docstring'''



	A_						    =						42
	A_						    =						42




	def __init__(  self				:	int     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       List[str]:



		'''simple docstring'''





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




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



		'''simple docstring'''





		_lowercase					:       Union[str, Any]       =		self.unet.config.sample_size
		_lowercase					:       Union[str, Any]       =		(batch_size, 3, img_size, img_size)

		_lowercase					:       Tuple       =		self.unet

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

		self.scheduler.set_timesteps(A__					)

		for t in self.progress_bar(self.scheduler.timesteps					):
			# here sigma_t == t_i from the paper
			_lowercase					:       str       =		self.scheduler.schedule[t]
			_lowercase					:       str       =		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
			_lowercase  ,    _lowercase					:       Union[str, Any]       =		self.scheduler.add_noise_to_input(A__     ,  A__     ,  generator=A__					)

			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
			# The model inputs and output are adjusted by following eq. (213) in [1].
			_lowercase					:       List[str]       =		(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
			_lowercase					:       int       =		self.scheduler.step(A__     ,  A__     ,  A__     ,  A__					)

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

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

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=A__					)





'''simple docstring'''

from typing import TYPE_CHECKING

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


_A				:    Union[str, Any]     						={'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Dict     						=['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Union[str, Any]     						=['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		_A				:    Union[str, Any]     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
