


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase			=							{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							['''CLIPTokenizerFast''']

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							['''CLIPFeatureExtractor''']
 __lowerCAmelCase			=							['''CLIPImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							[
     '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CLIPModel''',
     '''CLIPPreTrainedModel''',
     '''CLIPTextModel''',
     '''CLIPTextModelWithProjection''',
     '''CLIPVisionModel''',
     '''CLIPVisionModelWithProjection''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							[
     '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFCLIPModel''',
     '''TFCLIPPreTrainedModel''',
     '''TFCLIPTextModel''',
     '''TFCLIPVisionModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							[
     '''FlaxCLIPModel''',
     '''FlaxCLIPPreTrainedModel''',
     '''FlaxCLIPTextModel''',
     '''FlaxCLIPTextPreTrainedModel''',
     '''FlaxCLIPVisionModel''',
     '''FlaxCLIPVisionPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_clip import (
     CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     CLIPConfig,
     CLIPOnnxConfig,
     CLIPTextConfig,
     CLIPVisionConfig,
 )
 from .processing_clip import CLIPProcessor
 from .tokenization_clip import CLIPTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_clip_fast import CLIPTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clip import CLIPFeatureExtractor
  from .image_processing_clip import CLIPImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_clip import (
      CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      CLIPModel,
      CLIPPreTrainedModel,
      CLIPTextModel,
      CLIPTextModelWithProjection,
      CLIPVisionModel,
      CLIPVisionModelWithProjection,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_clip import (
      TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCLIPModel,
      TFCLIPPreTrainedModel,
      TFCLIPTextModel,
      TFCLIPVisionModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_clip import (
      FlaxCLIPModel,
      FlaxCLIPPreTrainedModel,
      FlaxCLIPTextModel,
      FlaxCLIPTextPreTrainedModel,
      FlaxCLIPVisionModel,
      FlaxCLIPVisionPreTrainedModel,
  )


else:
 import sys

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






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase			=							'''path-to-your-trained-model'''
__lowerCAmelCase			=							StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

__lowerCAmelCase			=							'''A photo of sks dog in a bucket'''
__lowerCAmelCase			=							pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

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


