






from typing import TYPE_CHECKING

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


_a      	=  {
    '''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
    '''tokenization_ctrl''': ['''CTRLTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CTRLForSequenceClassification''',
		    '''CTRLLMHeadModel''',
		    '''CTRLModel''',
		    '''CTRLPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCTRLForSequenceClassification''',
		    '''TFCTRLLMHeadModel''',
		    '''TFCTRLModel''',
		    '''TFCTRLPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
		from .tokenization_ctrl import CTRLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ctrl import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				    CTRLPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_ctrl import (
				    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCTRLForSequenceClassification,
				    TFCTRLLMHeadModel,
				    TFCTRLModel,
				    TFCTRLPreTrainedModel,
				)

else:
		import sys

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





'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')