

import argparse
import os
import re

import packaging.version


snake_case_        =			'''examples/'''
snake_case_        =			{
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
snake_case_        =			{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
snake_case_        =			'''README.md'''

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE_     ,					'r'     ,					encoding='utf-8'     ,					newline='\n'      ) as f:
          lowercase__   :       List[Any]				=	f.read()
     lowercase__   :       List[str]				=	REPLACE_PATTERNS[pattern]
     lowercase__   :       int				=	replace.replace('VERSION'     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Union[str, Any]				=	re_pattern.sub(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     with open(SCREAMING_SNAKE_CASE_     ,					'w'     ,					encoding='utf-8'     ,					newline='\n'      ) as f:
          f.write(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]      ):



     '''simple docstring'''





     for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE_      ):
          # Removing some of the folders with non-actively maintained examples from the walk
          if "research_projects" in directories:
               directories.remove('research_projects'      )
          if "legacy" in directories:
               directories.remove('legacy'      )
          for fname in fnames:
               if fname.endswith('.py'      ):
                    update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )     ,					SCREAMING_SNAKE_CASE_     ,					pattern='examples'      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]=False      ):



     '''simple docstring'''





     for pattern, fname in REPLACE_FILES.items():
          update_version_in_file(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     if not patch:
          update_version_in_examples(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       Dict				=	'🤗 Transformers currently provides the following architectures'
     lowercase__   :       List[str]				=	'1. Want to contribute a new model?'
     with open(SCREAMING_SNAKE_CASE_     ,					'r'     ,					encoding='utf-8'     ,					newline='\n'      ) as f:
          lowercase__   :       Optional[Any]				=	f.readlines()

     # Find the start of the list.
     lowercase__   :       Any				=	0
     while not lines[start_index].startswith(_start_prompt      ):
          start_index += 1
     start_index += 1

     lowercase__   :       Union[str, Any]				=	start_index
     # Update the lines in the model list.
     while not lines[index].startswith(_end_prompt      ):
          if lines[index].startswith('1.'      ):
               lowercase__   :       List[Any]				=	lines[index].replace(
                   'https://huggingface.co/docs/diffusers/main/model_doc'     ,					'https://huggingface.co/docs/diffusers/model_doc'     ,					)
          index += 1

     with open(SCREAMING_SNAKE_CASE_     ,					'w'     ,					encoding='utf-8'     ,					newline='\n'      ) as f:
          f.writelines(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   ):



     '''simple docstring'''





     with open(REPLACE_FILES['init']     ,					'r'      ) as f:
          lowercase__   :       Union[str, Any]				=	f.read()
     lowercase__   :       Any				=	REPLACE_PATTERNS['init'][0].search(SCREAMING_SNAKE_CASE_      ).groups()[0]
     return packaging.version.parse(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]=False      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	get_version()
     if patch and default_version.is_devrelease:
          raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'      )
     if default_version.is_devrelease:
          lowercase__   :       Any				=	default_version.base_version
     elif patch:
          lowercase__   :       Optional[int]				=	f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
     else:
          lowercase__   :       List[str]				=	f"""{default_version.major}.{default_version.minor + 1}.0"""

     # Now let's ask nicely if that's the right one.
     lowercase__   :       List[str]				=	input(f"""Which version are you releasing? [{default_version}]"""      )
     if len(SCREAMING_SNAKE_CASE_      ) == 0:
          lowercase__   :       List[Any]				=	default_version

     print(f"""Updating version to {version}."""      )
     global_version_update(SCREAMING_SNAKE_CASE_     ,					patch=SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	get_version()
     lowercase__   :       Union[str, Any]				=	f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
     lowercase__   :       Optional[Any]				=	current_version.base_version

     # Check with the user we got that right.
     lowercase__   :       Dict				=	input(f"""Which version are we developing now? [{dev_version}]"""      )
     if len(SCREAMING_SNAKE_CASE_      ) == 0:
          lowercase__   :       str				=	dev_version

     print(f"""Updating version to {version}."""      )
     global_version_update(SCREAMING_SNAKE_CASE_      )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
       snake_case_        =			argparse.ArgumentParser()
       parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
       parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
       snake_case_        =			parser.parse_args()
       if not args.post_release:
              pre_release_work(patch=args.patch)
       elif args.patch:
              print('''Nothing to do after a patch :-)''')
       else:
              post_release_work()




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   BertModel     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

     lowercase__   :       Dict				=	(
         ('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(SCREAMING_SNAKE_CASE_      ):
          os.makedirs(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	model.state_dict()

     def to_tf_var_name(SCREAMING_SNAKE_CASE_		:   str      ):
          for patt, repl in iter(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	name.replace(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          return f"""bert/{name}"""

     def create_tf_var(SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   tf.Session      ):
          lowercase__   :       Optional[Any]				=	tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__   :       List[str]				=	tf.get_variable(dtype=SCREAMING_SNAKE_CASE_     ,					shape=tensor.shape     ,					name=SCREAMING_SNAKE_CASE_     ,					initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(SCREAMING_SNAKE_CASE_      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__   :       Tuple				=	to_tf_var_name(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       str				=	state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__   :       str				=	torch_tensor.T
               lowercase__   :       Any				=	create_tf_var(tensor=SCREAMING_SNAKE_CASE_     ,					name=SCREAMING_SNAKE_CASE_     ,					session=SCREAMING_SNAKE_CASE_      )
               tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	session.run(SCREAMING_SNAKE_CASE_      )
               print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )}"""      )

          lowercase__   :       Tuple				=	tf.train.Saver(tf.trainable_variables()      )
          saver.save(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					model_name.replace('-'     ,					'_'      ) + '.ckpt'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]=None      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	argparse.ArgumentParser()
     parser.add_argument('--model_name'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='model name e.g. bert-base-uncased'      )
     parser.add_argument(
         '--cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory containing pytorch model'      )
     parser.add_argument('--pytorch_model_path'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='/path/to/<pytorch-model-name>.bin'      )
     parser.add_argument('--tf_cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory in which to save tensorflow model'      )
     lowercase__   :       List[str]				=	parser.parse_args(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	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=SCREAMING_SNAKE_CASE_     ,					ckpt_dir=args.tf_cache_dir     ,					model_name=args.model_name      )


if __name__ == "__main__":
       main()

