


'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_lowercase    :    Tuple			     =  (
    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
)

_lowercase    :    List[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
)

_lowercase    :    Tuple			     =  [
    """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__   ( A     :    str   ,     A     :    int  ):




						'''simple docstring'''






						for tf_name, hf_name in patterns:
												UpperCAmelCase         =			k.replace(A   ,     A  )
						return k


def    lowerCamelCase__   ( A     :    dict   ,     A     :    dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			BigBirdPegasusConfig(**A  )
						UpperCAmelCase         =			BigBirdPegasusForConditionalGeneration(A  )
						UpperCAmelCase         =			torch_model.state_dict()
						UpperCAmelCase         =			{}

						# separating decoder weights
						UpperCAmelCase         =			{k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''  )}
						UpperCAmelCase         =			{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'''  ):
												UpperCAmelCase         =			[k.endswith(A  ) for ending in KEYS_TO_IGNORE]
												if any(A  ):
																		continue
												UpperCAmelCase         =			DECODER_PATTERNS
												UpperCAmelCase         =			rename_state_dict_key(A   ,     A  )
												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''']  ):
																		UpperCAmelCase         =			v.T
												UpperCAmelCase         =			torch.from_numpy(A  )
												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'''  ):
												UpperCAmelCase         =			[k.endswith(A  ) for ending in KEYS_TO_IGNORE]
												if any(A  ):
																		continue
												UpperCAmelCase         =			REMAINING_PATTERNS
												UpperCAmelCase         =			rename_state_dict_key(A   ,     A  )
												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''']  ):
																		UpperCAmelCase         =			v.T
												UpperCAmelCase         =			torch.from_numpy(A  )
												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}"""

						UpperCAmelCase         =			mapping['''model.embed_positions.weight''']
						UpperCAmelCase         =			mapping.pop('''model.embed_positions.weight'''  )
						UpperCAmelCase       ,       UpperCAmelCase         =			torch_model.load_state_dict(A   ,     strict=A  )
						UpperCAmelCase         =			[
						    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__   ( A     :    int  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    str   ,     A     :    str   ,     A     :    dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			get_tf_weights_as_numpy(A  )
						UpperCAmelCase         =			convert_bigbird_pegasus(A   ,     A  )
						torch_model.save_pretrained(A  )


if __name__ == "__main__":
	_lowercase    :    List[str]			     =  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.""")
	_lowercase    :    Union[str, Any]			     =  parser.parse_args()
	_lowercase    :    Dict			     =  {}
	convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)









'''simple docstring'''



from ..utils import DummyObject, requires_backends






class  UpperCamelCase__(     metaclass=lowerCAmelCase ):
				__magic_name__       :   List[str]							= ["note_seq"]




				def __init__(	self    :				Any   ,					*lowerCAmelCase    :				List[str]   ,					**lowerCAmelCase    :				int   )->  Optional[int]:

										"""simple docstring"""




										requires_backends(self   ,					['''note_seq''']   )




				@classmethod
				def   a__(	cls    :				Dict   ,					*lowerCAmelCase    :				int   ,					**lowerCAmelCase    :				Optional[int]   )->  Dict:

										"""simple docstring"""




										requires_backends(cls   ,					['''note_seq''']   )





				@classmethod
				def   a__(	cls    :				int   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				Optional[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										requires_backends(cls   ,					['''note_seq''']   )





