






'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__											=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     pass

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__											=     [
         'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'RoCBertForCausalLM',
         'RoCBertForMaskedLM',
         'RoCBertForMultipleChoice',
         'RoCBertForPreTraining',
         'RoCBertForQuestionAnswering',
         'RoCBertForSequenceClassification',
         'RoCBertForTokenClassification',
         'RoCBertLayer',
         'RoCBertModel',
         'RoCBertPreTrainedModel',
         'load_tf_weights_in_roc_bert',
     ]

if TYPE_CHECKING:
     from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
     from .tokenization_roc_bert import RoCBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          raise OptionalDependencyNotAvailable()

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roc_bert import (
              ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RoCBertForCausalLM,
              RoCBertForMaskedLM,
              RoCBertForMultipleChoice,
              RoCBertForPreTraining,
              RoCBertForQuestionAnswering,
              RoCBertForSequenceClassification,
              RoCBertForTokenClassification,
              RoCBertLayer,
              RoCBertModel,
              RoCBertPreTrainedModel,
              load_tf_weights_in_roc_bert,
          )


else:
     import sys

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








'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     SCREAMING_SNAKE_CASE__											=     CLIPImageProcessor()
     SCREAMING_SNAKE_CASE__											=     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     SCREAMING_SNAKE_CASE__											=     UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)
