"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__  		=						{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						["""BertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """BertForMaskedLM""",
        """BertForMultipleChoice""",
        """BertForNextSentencePrediction""",
        """BertForPreTraining""",
        """BertForQuestionAnswering""",
        """BertForSequenceClassification""",
        """BertForTokenClassification""",
        """BertLayer""",
        """BertLMHeadModel""",
        """BertModel""",
        """BertPreTrainedModel""",
        """load_tf_weights_in_bert""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFBertEmbeddings""",
        """TFBertForMaskedLM""",
        """TFBertForMultipleChoice""",
        """TFBertForNextSentencePrediction""",
        """TFBertForPreTraining""",
        """TFBertForQuestionAnswering""",
        """TFBertForSequenceClassification""",
        """TFBertForTokenClassification""",
        """TFBertLMHeadModel""",
        """TFBertMainLayer""",
        """TFBertModel""",
        """TFBertPreTrainedModel""",
    ]
try:
    if not is_tensorflow_text_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						["""TFBertTokenizer"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxBertForCausalLM""",
        """FlaxBertForMaskedLM""",
        """FlaxBertForMultipleChoice""",
        """FlaxBertForNextSentencePrediction""",
        """FlaxBertForPreTraining""",
        """FlaxBertForQuestionAnswering""",
        """FlaxBertForSequenceClassification""",
        """FlaxBertForTokenClassification""",
        """FlaxBertModel""",
        """FlaxBertPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
    from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_bert_fast import BertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bert import (
            BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BertForMaskedLM,
            BertForMultipleChoice,
            BertForNextSentencePrediction,
            BertForPreTraining,
            BertForQuestionAnswering,
            BertForSequenceClassification,
            BertForTokenClassification,
            BertLayer,
            BertLMHeadModel,
            BertModel,
            BertPreTrainedModel,
            load_tf_weights_in_bert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_bert import (
            TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFBertEmbeddings,
            TFBertForMaskedLM,
            TFBertForMultipleChoice,
            TFBertForNextSentencePrediction,
            TFBertForPreTraining,
            TFBertForQuestionAnswering,
            TFBertForSequenceClassification,
            TFBertForTokenClassification,
            TFBertLMHeadModel,
            TFBertMainLayer,
            TFBertModel,
            TFBertPreTrainedModel,
        )

    try:
        if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_bert_tf import TFBertTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_bert import (
            FlaxBertForCausalLM,
            FlaxBertForMaskedLM,
            FlaxBertForMultipleChoice,
            FlaxBertForNextSentencePrediction,
            FlaxBertForPreTraining,
            FlaxBertForQuestionAnswering,
            FlaxBertForSequenceClassification,
            FlaxBertForTokenClassification,
            FlaxBertModel,
            FlaxBertPreTrainedModel,
        )

else:
    import sys

    UpperCAmelCase__  		=						_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__":
    UpperCAmelCase__  		=						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.""",
    )

    UpperCAmelCase__  		=						parser.parse_args()

    UpperCAmelCase__  		=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    UpperCAmelCase__  		=						CLIPImageProcessor()
    UpperCAmelCase__  		=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

    UpperCAmelCase__  		=						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)
