



"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       @property
       def 	lowercase__							(				self     :    Dict       )					->		Tuple:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				UNetaDModel(
                 block_out_channels=(32, 64)						,							layers_per_block=2						,							sample_size=32						,							in_channels=3						,							out_channels=3						,							down_block_types=("DownBlock2D", "AttnDownBlock2D")						,							up_block_types=("AttnUpBlock2D", "UpBlock2D")						,							)
             return model







       def 	lowercase__							(				self     :    List[str]       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.dummy_uncond_unet
             UpperCAmelCase_     					=				KarrasVeScheduler()

             UpperCAmelCase_     					=				KarrasVePipeline(unet=__lowerCamelCase						,							scheduler=__lowerCamelCase       )
             pipe.to(__lowerCamelCase       )
             pipe.set_progress_bar_config(disable=__lowerCamelCase       )

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=2						,							generator=__lowerCamelCase						,							output_type="numpy"       ).images

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=2						,							generator=__lowerCamelCase						,							output_type="numpy"						,							return_dict=__lowerCamelCase       )[0]

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             UpperCAmelCase_     					=				image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



@slow
@require_torch
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    List[Any]       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				"google/ncsnpp-celebahq-256"
             UpperCAmelCase_     					=				UNetaDModel.from_pretrained(__lowerCamelCase       )
             UpperCAmelCase_     					=				KarrasVeScheduler()

             UpperCAmelCase_     					=				KarrasVePipeline(unet=__lowerCamelCase						,							scheduler=__lowerCamelCase       )
             pipe.to(__lowerCamelCase       )
             pipe.set_progress_bar_config(disable=__lowerCamelCase       )

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=20						,							generator=__lowerCamelCase						,							output_type="numpy"       ).images

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             assert image.shape == (1, 256, 256, 3)
             UpperCAmelCase_     					=				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   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(lowerCAmelCase_ ):
      os.makedirs(lowerCAmelCase_ )

   UpperCAmelCase      					=  model.state_dict()

   def to_tf_var_name(lowerCAmelCase_ ):
      for patt, repl in iter(lowerCAmelCase_ ):
         UpperCAmelCase      					=  name.replace(lowerCAmelCase_	,    lowerCAmelCase_ )
      return F"""bert/{name}"""

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

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         UpperCAmelCase      					=  to_tf_var_name(lowerCAmelCase_ )
         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=lowerCAmelCase_	,    name=lowerCAmelCase_	,    session=lowerCAmelCase_ )
         tf.keras.backend.set_value(lowerCAmelCase_	,    lowerCAmelCase_ )
         UpperCAmelCase      					=  session.run(lowerCAmelCase_ )
         print(F"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase_	,    lowerCAmelCase_ )}""" )

      UpperCAmelCase      					=  tf.train.Saver(tf.trainable_variables() )
      saver.save(lowerCAmelCase_	,    os.path.join(lowerCAmelCase_	,    model_name.replace("""-"""	,    """_""" ) + """.ckpt""" ) )
def 	_UpperCamelCase					(    lowerCAmelCase_=None )    ->Union[str, Any]:
   UpperCAmelCase      					=  argparse.ArgumentParser()
   parser.add_argument("""--model_name"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""model name e.g. bert-base-uncased""" )
   parser.add_argument(
       """--cache_dir"""	,    type=lowerCAmelCase_	,    default=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""Directory containing pytorch model""" )
   parser.add_argument("""--pytorch_model_path"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""/path/to/<pytorch-model-name>.bin""" )
   parser.add_argument("""--tf_cache_dir"""	,    type=lowerCAmelCase_	,    required=lowerCAmelCase_	,    help="""Directory in which to save tensorflow model""" )
   UpperCAmelCase      					=  parser.parse_args(lowerCAmelCase_ )

   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=lowerCAmelCase_	,    ckpt_dir=args.tf_cache_dir	,    model_name=args.model_name )


if __name__ == "__main__":
 main()



