



"""simple docstring"""


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

  _UpperCAmelCase						 =      (
      ('''layer.''', '''layer_'''),
      ('''word_embeddings.weight''', '''word_embeddings'''),
      ('''position_embeddings.weight''', '''position_embeddings'''),
      ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
      ('''.''', '''/'''),
      ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
      ('''LayerNorm/bias''', '''LayerNorm/beta'''),
      ('''weight''', '''kernel'''),
  )

  if not os.path.isdir(__UpperCamelCase		):
    os.makedirs(__UpperCamelCase		)

  _UpperCAmelCase						 =      model.state_dict()

  def to_tf_var_name(lowercase		):
    for patt, repl in iter(__UpperCamelCase		):
      _UpperCAmelCase						 =      name.replace(__UpperCamelCase   ,__UpperCamelCase		)
    return f'''bert/{name}'''

  def create_tf_var(lowercase   ,lowercase   ,lowercase		):
    _UpperCAmelCase						 =      tf.dtypes.as_dtype(tensor.dtype		)
    _UpperCAmelCase						 =      tf.get_variable(dtype=__UpperCamelCase   ,shape=tensor.shape   ,name=__UpperCamelCase   ,initializer=tf.zeros_initializer()		)
    session.run(tf.variables_initializer([tf_var]		)		)
    session.run(__UpperCamelCase		)
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      _UpperCAmelCase						 =      to_tf_var_name(__UpperCamelCase		)
      _UpperCAmelCase						 =      state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose		):
        _UpperCAmelCase						 =      torch_tensor.T
      _UpperCAmelCase						 =      create_tf_var(tensor=__UpperCamelCase   ,name=__UpperCamelCase   ,session=__UpperCamelCase		)
      tf.keras.backend.set_value(__UpperCamelCase   ,__UpperCamelCase		)
      _UpperCAmelCase						 =      session.run(__UpperCamelCase		)
      print(f'''Successfully created {tf_name}: {np.allclose(__UpperCamelCase   ,__UpperCamelCase		)}'''		)

    _UpperCAmelCase						 =      tf.train.Saver(tf.trainable_variables()		)
    saver.save(__UpperCamelCase   ,os.path.join(__UpperCamelCase   ,model_name.replace("""-"""   ,"""_"""		) + """.ckpt"""		)		)






def     __UpperCAmelCase					(			lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase						 =      argparse.ArgumentParser()
  parser.add_argument("""--model_name"""   ,type=__UpperCamelCase   ,required=__UpperCamelCase   ,help="""model name e.g. bert-base-uncased"""		)
  parser.add_argument(
      """--cache_dir"""   ,type=__UpperCamelCase   ,default=__UpperCamelCase   ,required=__UpperCamelCase   ,help="""Directory containing pytorch model"""		)
  parser.add_argument("""--pytorch_model_path"""   ,type=__UpperCamelCase   ,required=__UpperCamelCase   ,help="""/path/to/<pytorch-model-name>.bin"""		)
  parser.add_argument("""--tf_cache_dir"""   ,type=__UpperCamelCase   ,required=__UpperCamelCase   ,help="""Directory in which to save tensorflow model"""		)
  _UpperCAmelCase						 =      parser.parse_args(__UpperCamelCase		)

  _UpperCAmelCase						 =      BertModel.from_pretrained(
      pretrained_model_name_or_path=args.model_name   ,state_dict=torch.load(args.pytorch_model_path		)   ,cache_dir=args.cache_dir   ,)

  convert_pytorch_checkpoint_to_tf(model=__UpperCamelCase   ,ckpt_dir=args.tf_cache_dir   ,model_name=args.model_name		)


if __name__ == "__main__":
    main()

"""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)
