


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 					lowerCAmelCase_ (       _a):




						def __init__(	self						:   List[Any]   ,     __A						:   int   ,     __A						:   Any						)      ->Optional[Any]:






									"""simple docstring"""






									super().__init__()

									# make sure scheduler can always be converted to DDIM
									a__ :List[str]						  =    DDIMScheduler.from_config(scheduler.config						)

									self.register_modules(unet=__A   ,     scheduler=__A						)






						@torch.no_grad()
						def __call__(	self						:   Optional[Any]   ,     __A						:   int = 1   ,     __A						:   Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,     __A						:   float = 0.0   ,     __A						:   int = 50   ,     __A						:   Optional[bool] = None   ,     __A						:   Optional[str] = "pil"   ,     __A						:   bool = True   ,     )      ->Union[ImagePipelineOutput, Tuple]:






									"""simple docstring"""






									if isinstance(self.unet.config.sample_size   ,     __A						):
												a__ :str						  =    (
												    batch_size,
												    self.unet.config.in_channels,
												    self.unet.config.sample_size,
												    self.unet.config.sample_size,
												)
									else:
												a__ :str						  =    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(__A   ,     __A						) and len(__A						) != batch_size:
												raise ValueError(
												    F'''You have passed a list of generators of length {len(__A						)}, but requested an effective batch'''
												    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

									a__ :Optional[int]						  =    randn_tensor(__A   ,     generator=__A   ,     device=self.device   ,     dtype=self.unet.dtype						)

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

									for t in self.progress_bar(self.scheduler.timesteps						):
												# 1. predict noise model_output
												a__ :Optional[Any]						  =    self.unet(__A   ,     __A						).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												a__ :Union[str, Any]						  =    self.scheduler.step(
												    __A   ,     __A   ,     __A   ,     eta=__A   ,     use_clipped_model_output=__A   ,     generator=__A						).prev_sample

									a__ :List[str]						  =    (image / 2 + 0.5).clamp(0   ,     1						)
									a__ :Optional[Any]						  =    image.cpu().permute(0   ,     2   ,     3   ,     1						).numpy()
									if output_type == "pil":
												a__ :List[Any]						  =    self.numpy_to_pil(__A						)

									if not return_dict:
												return (image,)

									return ImagePipelineOutput(images=__A						)






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__			 =						{
    '''configuration_mgp_str''': ['''MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MgpstrConfig'''],
    '''processing_mgp_str''': ['''MgpstrProcessor'''],
    '''tokenization_mgp_str''': ['''MgpstrTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case__			 =						[
		    '''MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MgpstrModel''',
		    '''MgpstrPreTrainedModel''',
		    '''MgpstrForSceneTextRecognition''',
		]

if TYPE_CHECKING:
		from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
		from .processing_mgp_str import MgpstrProcessor
		from .tokenization_mgp_str import MgpstrTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mgp_str import (
				    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MgpstrForSceneTextRecognition,
				    MgpstrModel,
				    MgpstrPreTrainedModel,
				)
else:
		import sys

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


