





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCamelCase 					=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCamelCase 					=      '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'




class 	UpperCamelCase_							(  unittest.TestCase     ):


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   tempfile.mkdtemp()
									os.makedirs(os.path.join(self.diffusers_dir				,					"""schedulers/"""		)		)
									SCREAMING_SNAKE_CASE__								=   self.diffusers_dir
									shutil.copy(
									    os.path.join(__A				,					"""src/diffusers/schedulers/scheduling_ddpm.py"""		)				,					os.path.join(self.diffusers_dir				,					"""schedulers/scheduling_ddpm.py"""		)				,					)


							def   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   """src/diffusers"""
									shutil.rmtree(self.diffusers_dir		)


							def   _snake_case	(				self     :List[Any]				,					__A     :Optional[int]				,					__A     :List[str]				,					__A     :Dict				,					__A     :int=None		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
									if overwrite_result is not None:
											SCREAMING_SNAKE_CASE__								=   comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
									SCREAMING_SNAKE_CASE__								=   black.Mode(target_versions={black.TargetVersion.PYaa}				,					line_length=119		)
									SCREAMING_SNAKE_CASE__								=   black.format_str(__A				,					mode=__A		)
									SCREAMING_SNAKE_CASE__								=   os.path.join(self.diffusers_dir				,					"""new_code.py"""		)
									with open(__A				,					"""w"""				,					newline="""\n"""		) as f:
											f.write(__A		)
									if overwrite_result is None:
											self.assertTrue(len(check_copies.is_copy_consistent(__A		)		) == 0		)
									else:
											check_copies.is_copy_consistent(f.name				,					overwrite=__A		)
											with open(__A				,					"""r"""		) as f:
													self.assertTrue(f.read()				,					__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput"""		)
									self.assertEqual(__A				,					__A		)


							def   _snake_case	(				self     :List[str]		) -> Any:



									"""simple docstring"""



									self.check_copy_consistency(
									    """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"""				,					"""DDPMSchedulerOutput"""				,					REFERENCE_CODE + """\n"""				,					)

									# With no empty line at the end
									self.check_copy_consistency(
									    """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"""				,					"""DDPMSchedulerOutput"""				,					__A				,					)

									# Copy consistency with rename
									self.check_copy_consistency(
									    """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"""				,					"""TestSchedulerOutput"""				,					re.sub("""DDPM"""				,					"""Test"""				,					__A		)				,					)

									# Copy consistency with a really long name
									SCREAMING_SNAKE_CASE__								=   """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
									self.check_copy_consistency(
									    f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'''				,					f'''{long_class_name}SchedulerOutput'''				,					re.sub("""Bert"""				,					__A				,					__A		)				,					)

									# Copy consistency with overwrite
									self.check_copy_consistency(
									    """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"""				,					"""TestSchedulerOutput"""				,					__A				,					overwrite_result=re.sub("""DDPM"""				,					"""Test"""				,					__A		)				,					)



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()



