


from typing import TYPE_CHECKING

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


_A		:				Tuple									=       {
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A		:				Optional[Any]									=       ['''RemBertTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A		:				List[Any]									=       ['''RemBertTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A		:				Optional[int]									=       [
			    '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RemBertForCausalLM''',
			    '''RemBertForMaskedLM''',
			    '''RemBertForMultipleChoice''',
			    '''RemBertForQuestionAnswering''',
			    '''RemBertForSequenceClassification''',
			    '''RemBertForTokenClassification''',
			    '''RemBertLayer''',
			    '''RemBertModel''',
			    '''RemBertPreTrainedModel''',
			    '''load_tf_weights_in_rembert''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A		:				Tuple									=       [
			    '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFRemBertForCausalLM''',
			    '''TFRemBertForMaskedLM''',
			    '''TFRemBertForMultipleChoice''',
			    '''TFRemBertForQuestionAnswering''',
			    '''TFRemBertForSequenceClassification''',
			    '''TFRemBertForTokenClassification''',
			    '''TFRemBertLayer''',
			    '''TFRemBertModel''',
			    '''TFRemBertPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_rembert import RemBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_rembert_fast import RemBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rembert import (
						    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RemBertForCausalLM,
						    RemBertForMaskedLM,
						    RemBertForMultipleChoice,
						    RemBertForQuestionAnswering,
						    RemBertForSequenceClassification,
						    RemBertForTokenClassification,
						    RemBertLayer,
						    RemBertModel,
						    RemBertPreTrainedModel,
						    load_tf_weights_in_rembert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rembert import (
						    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRemBertForCausalLM,
						    TFRemBertForMaskedLM,
						    TFRemBertForMultipleChoice,
						    TFRemBertForQuestionAnswering,
						    TFRemBertForSequenceClassification,
						    TFRemBertForTokenClassification,
						    TFRemBertLayer,
						    TFRemBertModel,
						    TFRemBertPreTrainedModel,
						)


else:
			import sys

			_A		:				List[str]									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
   _A		:				str									=       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.''',
   )

   _A		:				Optional[Any]									=       parser.parse_args()

   _A		:				str									=       UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

   _A		:				Optional[Any]									=       CLIPImageProcessor()
   _A		:				List[Any]									=       CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

   _A		:				str									=       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)


