





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

snake_case_							=			(
    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
)

snake_case_							=			(
    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
)

snake_case_							=			[
    '''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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):




		for tf_name, hf_name in patterns:
				lowerCamelCase   : Optional[Any]								=k.replace(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		return k


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
		lowerCamelCase   : List[str]								=BigBirdPegasusConfig(**SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Optional[Any]								=BigBirdPegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Tuple								=torch_model.state_dict()
		lowerCamelCase   : str								={}

		# separating decoder weights
		lowerCamelCase   : List[Any]								={k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''		)}
		lowerCamelCase   : int								={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   : List[str]								=[k.endswith(SCREAMING_SNAKE_CASE_		) for ending in KEYS_TO_IGNORE]
				if any(SCREAMING_SNAKE_CASE_		):
						continue
				lowerCamelCase   : Any								=DECODER_PATTERNS
				lowerCamelCase   : List[Any]								=rename_state_dict_key(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
				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   : Union[str, Any]								=v.T
				lowerCamelCase   : List[Any]								=torch.from_numpy(SCREAMING_SNAKE_CASE_		)
				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   : Tuple								=[k.endswith(SCREAMING_SNAKE_CASE_		) for ending in KEYS_TO_IGNORE]
				if any(SCREAMING_SNAKE_CASE_		):
						continue
				lowerCamelCase   : Optional[Any]								=REMAINING_PATTERNS
				lowerCamelCase   : List[str]								=rename_state_dict_key(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
				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   : Any								=v.T
				lowerCamelCase   : Optional[int]								=torch.from_numpy(SCREAMING_SNAKE_CASE_		)
				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   : Optional[Any]								=mapping['''model.embed_positions.weight''']
		lowerCamelCase   : int								=mapping.pop('''model.embed_positions.weight'''		)
		lowerCamelCase		,   lowerCamelCase   : Any								=torch_model.load_state_dict(SCREAMING_SNAKE_CASE_							,       strict=SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Dict								=[
		    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 					A__	(       SCREAMING_SNAKE_CASE_		):
		lowerCamelCase   : Tuple								=tf.train.list_variables(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : int								={}
		lowerCamelCase   : Union[str, Any]								=['''global_step''']
		for name, shape in tqdm(SCREAMING_SNAKE_CASE_							,       desc='''converting tf checkpoint to dict'''		):
				lowerCamelCase   : Any								=any(pat in name for pat in ignore_name		)
				if skip_key:
						continue
				lowerCamelCase   : Union[str, Any]								=tf.train.load_variable(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
				lowerCamelCase   : Optional[int]								=array
		return tf_weights


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
		lowerCamelCase   : Optional[int]								=get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : str								=convert_bigbird_pegasus(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		torch_model.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				snake_case_							=			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.''')
				snake_case_							=			parser.parse_args()
				snake_case_							=			{}
				convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		if len(SCREAMING_SNAKE_CASE_		) < 2:
				raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''		)
		if any(i <= 0 for i in nums		):
				raise ValueError('''All values must be greater than 0'''		)
		lowerCamelCase   : List[Any]								=nums.copy()
		copy_nums.sort()
		return copy_nums[-1] < sum(copy_nums[:-1]		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

