
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_lowerCamelCase			:							List[str]         =     (
    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
)

_lowerCamelCase			:							List[str]         =     (
    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
)

_lowerCamelCase			:							Dict         =     [
    '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_						(			__lowercase  :      List[Any]			,   __lowercase  :      List[str]					)					->    Union[str, Any]:
			for tf_name, hf_name in patterns:
						_snake_case            =     k.replace(lowerCamelCase_			,   lowerCamelCase_					)
			return k






def 			a_						(			__lowercase  :      List[Any]			,   __lowercase  :      Optional[int]					)					->    Any:
			_snake_case            =     BigBirdPegasusConfig(**lowerCamelCase_					)
			_snake_case            =     BigBirdPegasusForConditionalGeneration(lowerCamelCase_					)
			_snake_case            =     torch_model.state_dict()
			_snake_case            =     {}

			# separating decoder weights
			_snake_case            =     {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'					)}
			_snake_case            =     {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'					):
						_snake_case            =     [k.endswith(lowerCamelCase_					) for ending in KEYS_TO_IGNORE]
						if any(lowerCamelCase_					):
									continue
						_snake_case            =     DECODER_PATTERNS
						_snake_case            =     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']					):
									_snake_case            =     v.T
						_snake_case            =     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'					):
						_snake_case            =     [k.endswith(lowerCamelCase_					) for ending in KEYS_TO_IGNORE]
						if any(lowerCamelCase_					):
									continue
						_snake_case            =     REMAINING_PATTERNS
						_snake_case            =     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']					):
									_snake_case            =     v.T
						_snake_case            =     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}'''

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






def 			a_						(			__lowercase  :      Any			,   __lowercase  :      Optional[Any]			,   __lowercase  :      Tuple					)					->    Dict:
			_snake_case            =     get_tf_weights_as_numpy(lowerCamelCase_					)
			_snake_case            =     convert_bigbird_pegasus(lowerCamelCase_			,   lowerCamelCase_					)
			torch_model.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
			_lowerCamelCase			:							Optional[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.''')
			_lowerCamelCase			:							Tuple         =     parser.parse_args()
			_lowerCamelCase			:							int         =     {}
			convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







def     a     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




