
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__A				=[
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
__A				=[
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

__A				=(
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

__A				=(
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

__A				=[
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for tf_name, hf_name in patterns:
								lowerCamelCase_						=			k.replace(lowerCamelCase__	, lowerCamelCase__    )
				return k





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			BigBirdPegasusConfig(**lowerCamelCase__    )
				lowerCamelCase_						=			BigBirdPegasusForConditionalGeneration(lowerCamelCase__    )
				lowerCamelCase_						=			torch_model.state_dict()
				lowerCamelCase_						=			{}

				# separating decoder weights
				lowerCamelCase_						=			{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"    )}
				lowerCamelCase_						=			{k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"    )}

				for k, v in tqdm(decoder_weights.items()	, "tf -> hf conversion"    ):
								lowerCamelCase_						=			[k.endswith(lowerCamelCase__    ) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase__    ):
												continue
								lowerCamelCase_						=			DECODER_PATTERNS
								lowerCamelCase_						=			rename_state_dict_key(lowerCamelCase__	, lowerCamelCase__    )
								if new_k not in state_dict:
												raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'    )
								if any(True if i in k else False for i in ["dense", "query", "key", "value"]    ):
												lowerCamelCase_						=			v.T
								lowerCamelCase_						=			torch.from_numpy(lowerCamelCase__    )
								assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

				for k, v in tqdm(remaining_weights.items()	, "tf -> hf conversion"    ):
								lowerCamelCase_						=			[k.endswith(lowerCamelCase__    ) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase__    ):
												continue
								lowerCamelCase_						=			REMAINING_PATTERNS
								lowerCamelCase_						=			rename_state_dict_key(lowerCamelCase__	, lowerCamelCase__    )
								if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
												raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'    )
								if any(True if i in k else False for i in ["dense", "query", "key", "value"]    ):
												lowerCamelCase_						=			v.T
								lowerCamelCase_						=			torch.from_numpy(lowerCamelCase__    )
								if k != "pegasus/embeddings/position_embeddings":
												assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

				lowerCamelCase_						=			mapping["model.embed_positions.weight"]
				lowerCamelCase_						=			mapping.pop("model.embed_positions.weight"    )
				lowerCamelCase_	,    lowerCamelCase_						=			torch_model.load_state_dict(lowerCamelCase__	, strict=lowerCamelCase__    )
				lowerCamelCase_						=			[
				    k
				    for k in missing
				    if k
				    not in [
				        "final_logits_bias",
				        "model.encoder.embed_tokens.weight",
				        "model.decoder.embed_tokens.weight",
				        "lm_head.weight",
				    ]
				]
				assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
				assert extra == [], F'no matches found for the following tf keys {extra}'
				return torch_model





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			tf.train.list_variables(lowerCamelCase__    )
				lowerCamelCase_						=			{}
				lowerCamelCase_						=			["global_step"]
				for name, shape in tqdm(lowerCamelCase__	, desc="converting tf checkpoint to dict"    ):
								lowerCamelCase_						=			any(pat in name for pat in ignore_name    )
								if skip_key:
												continue
								lowerCamelCase_						=			tf.train.load_variable(lowerCamelCase__	, lowerCamelCase__    )
								lowerCamelCase_						=			array
				return tf_weights





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			get_tf_weights_as_numpy(lowerCamelCase__    )
				lowerCamelCase_						=			convert_bigbird_pegasus(lowerCamelCase__	, lowerCamelCase__    )
				torch_model.save_pretrained(lowerCamelCase__    )


if __name__ == "__main__":
			__A				=argparse.ArgumentParser()
			parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
			parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
			__A				=parser.parse_args()
			__A				={}
			convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)










"""simple docstring"""



import argparse
import os
import re

import packaging.version


__A		:       List[str]  				=		'''examples/'''
__A		:       int  				=		{
    '''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'''),
}
__A		:       Dict  				=		{
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
__A		:       Optional[int]  				=		'''README.md'''



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



def 						lowercase    (				__snake_case				: int	):
		for folder, directories, fnames in os.walk(__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(__snake_case		,					__snake_case	)		,					__snake_case		,					pattern='''examples'''	)



def 						lowercase    (				__snake_case				: Optional[Any]		,					__snake_case				: Optional[Any]=False	):
		for pattern, fname in REPLACE_FILES.items():
				update_version_in_file(__snake_case		,					__snake_case		,					__snake_case	)
		if not patch:
				update_version_in_examples(__snake_case	)



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

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

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

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



def 						lowercase    (				):
		with open(REPLACE_FILES['''init''']		,					'''r'''	) as f:
				lowercase_ : List[Any]							       =   f.read()
		lowercase_ : List[str]							       =   REPLACE_PATTERNS['''init'''][0].search(__snake_case	).groups()[0]
		return packaging.version.parse(__snake_case	)



def 						lowercase    (				__snake_case				: Optional[Any]=False	):
		lowercase_ : 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_ : Optional[Any]							       =   default_version.base_version
		elif patch:
				lowercase_ : Optional[int]							       =   F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
		else:
				lowercase_ : Optional[int]							       =   F'''{default_version.major}.{default_version.minor + 1}.0'''

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

		print(F'''Updating version to {version}.'''	)
		global_version_update(__snake_case		,					patch=__snake_case	)
		if not patch:
				print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''	)
				clean_main_ref_in_model_list()



def 						lowercase    (				):
		lowercase_ : List[Any]							       =   get_version()
		lowercase_ : List[str]							       =   F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
		lowercase_ : Any							       =   current_version.base_version

		# Check with the user we got that right.
		lowercase_ : Tuple							       =   input(F'''Which version are we developing now? [{dev_version}]'''	)
		if len(__snake_case	) == 0:
				lowercase_ : str							       =   dev_version

		print(F'''Updating version to {version}.'''	)
		global_version_update(__snake_case	)
		print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''	)
		clean_main_ref_in_model_list()


if __name__ == "__main__":
						__A		:       int  				=		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.''')
						__A		:       Any  				=		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()



