'''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		:				int	       =		[
    (".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		:				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
)

_lowercase		:				int	       =		[
    "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			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Optional[int]				)    ->   List[Any]:
							for tf_name, hf_name in patterns:
														lowercase_      :    Optional[Any]       		= k.replace(UpperCAmelCase__   ,   UpperCAmelCase__				)
							return k




def 							lowerCamelCase			(   UpperCAmelCase__ :      dict   ,   UpperCAmelCase__ :      dict				)    ->   BigBirdPegasusForConditionalGeneration:
							lowercase_      :    Union[str, Any]       		= BigBirdPegasusConfig(**UpperCAmelCase__				)
							lowercase_      :    List[str]       		= BigBirdPegasusForConditionalGeneration(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= torch_model.state_dict()
							lowercase_      :    Dict       		= {}

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

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




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      dict				)    ->   Optional[int]:
							lowercase_      :    List[Any]       		= get_tf_weights_as_numpy(UpperCAmelCase__				)
							lowercase_      :    Any       		= convert_bigbird_pegasus(UpperCAmelCase__   ,   UpperCAmelCase__				)
							torch_model.save_pretrained(UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				int	       =		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		:				Optional[Any]	       =		parser.parse_args()
						_lowercase		:				Any	       =		{}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







'''simple docstring'''




_lowercase		:				int	       =		"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"




def 							lowerCamelCase			(   UpperCAmelCase__ :      bytes				)    ->   bytes:
							# Make sure the supplied data is a bytes-like object
							if not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    Union[str, Any]       		= F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
														raise TypeError(UpperCAmelCase__				)

							lowercase_      :    Dict       		= """""".join(bin(UpperCAmelCase__				)[2:].zfill(8				) for byte in data				)

							lowercase_      :    Union[str, Any]       		= len(UpperCAmelCase__				) % 6 != 0

							if padding_needed:
														# The padding that will be added later
														lowercase_      :    List[Any]       		= b"""=""" * ((6 - len(UpperCAmelCase__				) % 6) // 2)

														# Append binary_stream with arbitrary binary digits (0's by default) to make its
														# length a multiple of 6.
														binary_stream += "0" * (6 - len(UpperCAmelCase__				) % 6)
							else:
														lowercase_      :    Union[str, Any]       		= b""""""

							# Encode every 6 binary digits to their corresponding Base64 character
							return (
							    "".join(
							        B64_CHARSET[int(binary_stream[index : index + 6]   ,   2				)]
							        for index in range(0   ,   len(UpperCAmelCase__				)   ,   6				)				).encode()
							    + padding
							)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str				)    ->   bytes:
							# Make sure encoded_data is either a string or a bytes-like object
							if not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				) and not isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														lowercase_      :    List[str]       		= (
														    """argument should be a bytes-like object or ASCII string, """
														    F'''not \'{encoded_data.__class__.__name__}\''''
														)
														raise TypeError(UpperCAmelCase__				)

							# In case encoded_data is a bytes-like object, make sure it contains only
							# ASCII characters so we convert it to a string object
							if isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				):
														try:
																					lowercase_      :    Optional[int]       		= encoded_data.decode("""utf-8"""				)
														except UnicodeDecodeError:
																					raise ValueError("""base64 encoded data should only contain ASCII characters"""				)

							lowercase_      :    Any       		= encoded_data.count("""="""				)

							# Check if the encoded string contains non base64 characters
							if padding:
														assert all(
														    char in B64_CHARSET for char in encoded_data[:-padding]				), "Invalid base64 character(s) found."
							else:
														assert all(
														    char in B64_CHARSET for char in encoded_data				), "Invalid base64 character(s) found."

							# Check the padding
							assert len(UpperCAmelCase__				) % 4 == 0 and padding < 3, "Incorrect padding"

							if padding:
														# Remove padding if there is one
														lowercase_      :    Optional[int]       		= encoded_data[:-padding]

														lowercase_      :    Any       		= """""".join(
														    bin(B64_CHARSET.index(UpperCAmelCase__				)				)[2:].zfill(6				) for char in encoded_data				)[: -padding * 2]
							else:
														lowercase_      :    int       		= """""".join(
														    bin(B64_CHARSET.index(UpperCAmelCase__				)				)[2:].zfill(6				) for char in encoded_data				)

							lowercase_      :    Optional[int]       		= [
							    int(binary_stream[index : index + 8]   ,   2				)
							    for index in range(0   ,   len(UpperCAmelCase__				)   ,   8				)
							]

							return bytes(UpperCAmelCase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





