'''simple docstring'''





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase_	:		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
)

lowerCAmelCase_	:		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
)

lowerCAmelCase_	:		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 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Tuple:




						'''simple docstring'''
						for tf_name, hf_name in patterns:
												_UpperCamelCase			:			Any   			=						k.replace(UpperCAmelCase     ,UpperCAmelCase					)
						return k
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			BigBirdPegasusForConditionalGeneration:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						BigBirdPegasusConfig(**UpperCAmelCase					)
						_UpperCamelCase			:			Optional[int]   			=						BigBirdPegasusForConditionalGeneration(UpperCAmelCase					)
						_UpperCamelCase			:			Optional[int]   			=						torch_model.state_dict()
						_UpperCamelCase			:			Dict   			=						{}

						# separating decoder weights
						_UpperCamelCase			:			int   			=						{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"					)}
						_UpperCamelCase			:			List[Any]   			=						{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			:			Dict   			=						[k.endswith(UpperCAmelCase					) for ending in KEYS_TO_IGNORE]
												if any(UpperCAmelCase					):
																		continue
												_UpperCamelCase			:			str   			=						DECODER_PATTERNS
												_UpperCamelCase			:			Tuple   			=						rename_state_dict_key(UpperCAmelCase     ,UpperCAmelCase					)
												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			:			Union[str, Any]   			=						v.T
												_UpperCamelCase			:			int   			=						torch.from_numpy(UpperCAmelCase					)
												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			:			int   			=						[k.endswith(UpperCAmelCase					) for ending in KEYS_TO_IGNORE]
												if any(UpperCAmelCase					):
																		continue
												_UpperCamelCase			:			Dict   			=						REMAINING_PATTERNS
												_UpperCamelCase			:			str   			=						rename_state_dict_key(UpperCAmelCase     ,UpperCAmelCase					)
												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			:			Optional[int]   			=						v.T
												_UpperCamelCase			:			str   			=						torch.from_numpy(UpperCAmelCase					)
												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			:			Tuple   			=						mapping["model.embed_positions.weight"]
						_UpperCamelCase			:			Tuple   			=						mapping.pop("model.embed_positions.weight"					)
						_UpperCamelCase			,       _UpperCamelCase			:			Optional[int]   			=						torch_model.load_state_dict(UpperCAmelCase     ,strict=UpperCAmelCase					)
						_UpperCamelCase			:			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							(     UpperCAmelCase					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[Any]   			=						tf.train.list_variables(UpperCAmelCase					)
						_UpperCamelCase			:			Dict   			=						{}
						_UpperCamelCase			:			Optional[int]   			=						["global_step"]
						for name, shape in tqdm(UpperCAmelCase     ,desc="converting tf checkpoint to dict"					):
												_UpperCamelCase			:			Optional[int]   			=						any(pat in name for pat in ignore_name					)
												if skip_key:
																		continue
												_UpperCamelCase			:			Dict   			=						tf.train.load_variable(UpperCAmelCase     ,UpperCAmelCase					)
												_UpperCamelCase			:			Union[str, Any]   			=						array
						return tf_weights
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Union[str, Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						get_tf_weights_as_numpy(UpperCAmelCase					)
						_UpperCamelCase			:			Optional[Any]   			=						convert_bigbird_pegasus(UpperCAmelCase     ,UpperCAmelCase					)
						torch_model.save_pretrained(UpperCAmelCase					)


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



'''simple docstring'''





lowerCAmelCase_	:		Union[str, Any]			=     """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCAmelCase_	:		List[Any]			=     [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCAmelCase_	:		int			=     {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


