"""simple docstring"""



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_UpperCamelCase								=		(
    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
)

_UpperCamelCase								=		(
    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
)

_UpperCamelCase								=		[
    "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(		lowerCAmelCase			,  lowerCAmelCase							):
							for tf_name, hf_name in patterns:
														A__       :     str								=			k.replace(UpperCamelCase_			,  UpperCamelCase_							)
							return k

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Tuple								=			BigBirdPegasusConfig(**UpperCamelCase_							)
							A__       :     List[str]								=			BigBirdPegasusForConditionalGeneration(UpperCamelCase_							)
							A__       :     Dict								=			torch_model.state_dict()
							A__       :     Union[str, Any]								=			{}

							# separating decoder weights
							A__       :     Any								=			{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""							)}
							A__       :     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"""							):
														A__       :     Union[str, Any]								=			[k.endswith(UpperCamelCase_							) for ending in KEYS_TO_IGNORE]
														if any(UpperCamelCase_							):
																					continue
														A__       :     Tuple								=			DECODER_PATTERNS
														A__       :     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"""]							):
																					A__       :     Any								=			v.T
														A__       :     Optional[Any]								=			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"""							):
														A__       :     Optional[Any]								=			[k.endswith(UpperCamelCase_							) for ending in KEYS_TO_IGNORE]
														if any(UpperCamelCase_							):
																					continue
														A__       :     List[str]								=			REMAINING_PATTERNS
														A__       :     List[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"""]							):
																					A__       :     Dict								=			v.T
														A__       :     Any								=			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}'''

							A__       :     List[Any]								=			mapping['''model.embed_positions.weight''']
							A__       :     int								=			mapping.pop("""model.embed_positions.weight"""							)
							A__       :     Union[str, Any]								=			torch_model.load_state_dict(UpperCamelCase_			,  strict=UpperCamelCase_							)
							A__       :     Any								=			[
							    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(		lowerCAmelCase							):
							A__       :     Union[str, Any]								=			tf.train.list_variables(UpperCamelCase_							)
							A__       :     Optional[Any]								=			{}
							A__       :     Optional[int]								=			['''global_step''']
							for name, shape in tqdm(UpperCamelCase_			,  desc="""converting tf checkpoint to dict"""							):
														A__       :     Any								=			any(pat in name for pat in ignore_name							)
														if skip_key:
																					continue
														A__       :     Any								=			tf.train.load_variable(UpperCamelCase_			,  UpperCamelCase_							)
														A__       :     Tuple								=			array
							return tf_weights

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			get_tf_weights_as_numpy(UpperCamelCase_							)
							A__       :     int								=			convert_bigbird_pegasus(UpperCamelCase_			,  UpperCamelCase_							)
							torch_model.save_pretrained(UpperCamelCase_							)


if __name__ == "__main__":
				_UpperCamelCase								=		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.")
				_UpperCamelCase								=		parser.parse_args()
				_UpperCamelCase								=		{}
				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=_snake_case						):
 UpperCAmelCase				=					["speech"]


 def __init__(      self							:	List[Any]			,       *__lowerCamelCase							:	List[Any]			,       **__lowerCamelCase							:	List[str] ):
    requires_backends(self			,       ['''speech'''] )




class 				UpperCAmelCase			(			metaclass=_snake_case						):
 UpperCAmelCase				=					["speech"]


 def __init__(      self							:	int			,       *__lowerCamelCase							:	List[Any]			,       **__lowerCamelCase							:	List[str] ):
    requires_backends(self			,       ['''speech'''] )



