





'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_trajectory_transformer''': [
        '''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TrajectoryTransformerConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TrajectoryTransformerModel''',
	    '''TrajectoryTransformerPreTrainedModel''',
	    '''load_tf_weights_in_trajectory_transformer''',
	]


if TYPE_CHECKING:
	from .configuration_trajectory_transformer import (
	    TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TrajectoryTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_trajectory_transformer import (
		    TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TrajectoryTransformerModel,
		    TrajectoryTransformerPreTrainedModel,
		    load_tf_weights_in_trajectory_transformer,
		)


else:
	import sys

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












'''simple docstring'''



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[List[PIL.Image.Image], np.ndarray]
	a      :			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





