



'''simple docstring'''

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_A				:    int     						=(
    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				:    Any     						=(
    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				:    Optional[Any]     						=[
    '''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 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	for tf_name, hf_name in patterns:
		_lowercase					:       List[str]       =		k.replace(_lowercase,				_lowercase					)
	return k







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			BigBirdPegasusForConditionalGeneration:
	_lowercase					:       List[str]       =		BigBirdPegasusConfig(**_lowercase					)
	_lowercase					:       List[Any]       =		BigBirdPegasusForConditionalGeneration(_lowercase					)
	_lowercase					:       Any       =		torch_model.state_dict()
	_lowercase					:       List[str]       =		{}

	# separating decoder weights
	_lowercase					:       Tuple       =		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'					)}
	_lowercase					:       Dict       =		{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'					):
		_lowercase					:       Tuple       =		[k.endswith(_lowercase					) for ending in KEYS_TO_IGNORE]
		if any(_lowercase					):
			continue
		_lowercase					:       str       =		DECODER_PATTERNS
		_lowercase					:       Optional[Any]       =		rename_state_dict_key(_lowercase,				_lowercase					)
		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']					):
			_lowercase					:       Union[str, Any]       =		v.T
		_lowercase					:       str       =		torch.from_numpy(_lowercase					)
		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'					):
		_lowercase					:       str       =		[k.endswith(_lowercase					) for ending in KEYS_TO_IGNORE]
		if any(_lowercase					):
			continue
		_lowercase					:       Tuple       =		REMAINING_PATTERNS
		_lowercase					:       Optional[Any]       =		rename_state_dict_key(_lowercase,				_lowercase					)
		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']					):
			_lowercase					:       List[Any]       =		v.T
		_lowercase					:       Optional[Any]       =		torch.from_numpy(_lowercase					)
		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}'''

	_lowercase					:       Any       =		mapping['model.embed_positions.weight']
	_lowercase					:       Optional[Any]       =		mapping.pop('model.embed_positions.weight'					)
	_lowercase  ,    _lowercase					:       Any       =		torch_model.load_state_dict(_lowercase,				strict=_lowercase					)
	_lowercase					:       Any       =		[
	    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 						__UpperCamelCase				(							_lowercase					) ->			Dict:
	_lowercase					:       Tuple       =		tf.train.list_variables(_lowercase					)
	_lowercase					:       Union[str, Any]       =		{}
	_lowercase					:       Optional[Any]       =		['global_step']
	for name, shape in tqdm(_lowercase,				desc='converting tf checkpoint to dict'					):
		_lowercase					:       str       =		any(pat in name for pat in ignore_name					)
		if skip_key:
			continue
		_lowercase					:       Optional[int]       =		tf.train.load_variable(_lowercase,				_lowercase					)
		_lowercase					:       str       =		array
	return tf_weights







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Optional[Any]:
	_lowercase					:       int       =		get_tf_weights_as_numpy(_lowercase					)
	_lowercase					:       Dict       =		convert_bigbird_pegasus(_lowercase,				_lowercase					)
	torch_model.save_pretrained(_lowercase					)


if __name__ == "__main__":
		_A				:    List[Any]     						=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				:    int     						=parser.parse_args()
		_A				:    int     						={}
		convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





'''simple docstring'''

import os
from collections.abc import Iterator







def 						__UpperCamelCase				(							_lowercase = "."					) ->			Iterator[str]:
	for dir_path, dir_names, filenames in os.walk(_lowercase					):
		_lowercase					:       Optional[int]       =		[d for d in dir_names if d != 'scripts' and d[0] not in '._']
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(_lowercase					)[1] in (".py", ".ipynb"):
				yield os.path.join(_lowercase,				_lowercase					).lstrip('./'					)







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	return f'''{i * "  "}*''' if i else "\n##"







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Optional[Any]       =		old_path.split(os.sep					)
	for i, new_part in enumerate(new_path.split(os.sep					)					):
		if (i + 1 > len(_lowercase					) or old_parts[i] != new_part) and new_part:
			print(f'''{md_prefix(_lowercase					)} {new_part.replace("_",				" "					).title()}'''					)
	return new_path







def 						__UpperCamelCase				(							_lowercase = "."					) ->			None:
	_lowercase					:       Dict       =		''
	for filepath in sorted(good_file_paths(_lowercase					)					):
		_lowercase  ,    _lowercase					:       Optional[Any]       =		os.path.split(_lowercase					)
		if filepath != old_path:
			_lowercase					:       Dict       =		print_path(_lowercase,				_lowercase					)
		_lowercase					:       Optional[int]       =		(filepath.count(os.sep					) + 1) if filepath else 0
		_lowercase					:       Dict       =		f'''{filepath}/{filename}'''.replace(' ',				'%20'					)
		_lowercase					:       Optional[int]       =		os.path.splitext(filename.replace('_',				' '					).title()					)[0]
		print(f'''{md_prefix(_lowercase					)} [{filename}]({url})'''					)


if __name__ == "__main__":
		print_directory_md('''.''')
