



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowercase_						=       (
    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_						=       (
    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_						=       [
    """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	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			for tf_name, hf_name in patterns:
						lowercase__   					=			k.replace(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			return k



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			BigBirdPegasusConfig(**SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			BigBirdPegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			torch_model.state_dict()
			lowercase__   					=			{}

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

			lowercase__   					=			mapping["model.embed_positions.weight"]
			lowercase__   					=			mapping.pop("model.embed_positions.weight"					)
			lowercase__					,    lowercase__   					=			torch_model.load_state_dict(SCREAMING_SNAKE_CASE_  ,					strict=SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			[
			    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	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			tf.train.list_variables(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{}
			lowercase__   					=			["global_step"]
			for name, shape in tqdm(SCREAMING_SNAKE_CASE_  ,					desc="converting tf checkpoint to dict"					):
						lowercase__   					=			any(pat in name for pat in ignore_name					)
						if skip_key:
									continue
						lowercase__   					=			tf.train.load_variable(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			array
			return tf_weights



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			convert_bigbird_pegasus(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			torch_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


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









from __future__ import annotations



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return len(set(SCREAMING_SNAKE_CASE_					)					) == len(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




