"""simple docstring"""





import argparse
import os
import re

import packaging.version


lowerCAmelCase_     =	'''examples/'''
lowerCAmelCase_     =	{
    '''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'''),
}
lowerCAmelCase_     =	{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
lowerCAmelCase_     =	'''README.md'''



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							with open(__SCREAMING_SNAKE_CASE     ,	"""r"""     ,	encoding="""utf-8"""     ,	newline="""\n"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    f.read()
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Tuple			  =    REPLACE_PATTERNS[pattern]
							_SCREAMING_SNAKE_CASE		:						str			  =    replace.replace("""VERSION"""     ,	__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[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      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[Any]:
							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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    str:
							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      lowerCamelCase_()->    Any:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    """🤗 Transformers currently provides the following architectures"""
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    """1. Want to contribute a new model?"""
							with open(__SCREAMING_SNAKE_CASE     ,	"""r"""     ,	encoding="""utf-8"""     ,	newline="""\n"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    f.readlines()

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

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt  ):
														if lines[index].startswith("""1."""  ):
																					_SCREAMING_SNAKE_CASE		:						Union[str, 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      lowerCamelCase_()->    int:
							with open(REPLACE_FILES["""init"""]     ,	"""r"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    f.read()
							_SCREAMING_SNAKE_CASE		:						Any			  =    REPLACE_PATTERNS["""init"""][0].search(__SCREAMING_SNAKE_CASE  ).groups()[0]
							return packaging.version.parse(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE=False  )->    Any:
							_SCREAMING_SNAKE_CASE		:						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:
														_SCREAMING_SNAKE_CASE		:						str			  =    default_version.base_version
							elif patch:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
							else:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    F"""{default_version.major}.{default_version.minor + 1}.0"""

							# Now let's ask nicely if that's the right one.
							_SCREAMING_SNAKE_CASE		:						Dict			  =    input(F"""Which version are you releasing? [{default_version}]"""  )
							if len(__SCREAMING_SNAKE_CASE  ) == 0:
														_SCREAMING_SNAKE_CASE		:						int			  =    default_version

							print(F"""Updating version to {version}."""  )
							global_version_update(__SCREAMING_SNAKE_CASE     ,	patch=__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    get_version()
							_SCREAMING_SNAKE_CASE		:						str			  =    F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    current_version.base_version

							# Check with the user we got that right.
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    input(F"""Which version are we developing now? [{dev_version}]"""  )
							if len(__SCREAMING_SNAKE_CASE  ) == 0:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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__":
			lowerCAmelCase_     =	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.''')
			lowerCAmelCase_     =	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()



"""simple docstring"""





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							# set absolute/relative position embeddings parameter
							_SCREAMING_SNAKE_CASE		:						Dict			  =    reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														_SCREAMING_SNAKE_CASE		:						str			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WTQ":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    0.66_46_94
														_SCREAMING_SNAKE_CASE		:						str			  =    0.20_79_51
														_SCREAMING_SNAKE_CASE		:						str			  =    0.12_11_94
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0.0_35_25_13

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						int			  =    4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    36.45_19
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.90_34_21
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_22.0_88
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True
														_SCREAMING_SNAKE_CASE		:						Dict			  =    0.76_31_41

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "TABFACT":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
							elif task == "MLM":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
							elif task == "INTERMEDIATE_PRETRAINING":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasModel(config=__SCREAMING_SNAKE_CASE  )
							else:
														raise ValueError(F"""Task {task} not supported."""  )

							print(F"""Building PyTorch model from configuration: {config}"""  )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
							model.save_pretrained(__SCREAMING_SNAKE_CASE  )

							# Save tokenizer files
							print(F"""Save tokenizer files to {pytorch_dump_path}"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""     ,	model_max_length=512  )
							tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE  )

							print("""Used relative position embeddings:"""     ,	model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
			)
			parser.add_argument(
			    '''--reset_position_index_per_cell''',
			    default=False,
			    action='''store_true''',
			    help='''Whether to use relative position embeddings or not. Defaults to True.''',
			)
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--tapas_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			lowerCAmelCase_     =	parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.task,
			    args.reset_position_index_per_cell,
			    args.tf_checkpoint_path,
			    args.tapas_config_file,
			    args.pytorch_dump_path,
			)


