



"""simple docstring"""


import math
import os
import sys







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    ''''''
				try:
								with open(__snake_case					,     '''rb'''				) as binary_file:
												_UpperCAmelCase					=    binary_file.read()
								for dat in data:
												_UpperCAmelCase					=    f'{dat:08b}'
												result += curr_byte
								return result
				except OSError:
								print('''File not accessible'''				)
								sys.exit()







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				lexicon.pop(__snake_case				)
				_UpperCAmelCase					=    last_match_id

				if math.loga(__snake_case				).is_integer():
								for curr_key in lexicon:
												_UpperCAmelCase					=    '''0''' + lexicon[curr_key]

				_UpperCAmelCase					=    bin(__snake_case				)[2:]







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    {'''0''': '''0''', '''1''': '''1'''}
				_UpperCAmelCase		,						_UpperCAmelCase					=    '''''', ''''''
				_UpperCAmelCase					=    len(__snake_case				)

				for i in range(len(__snake_case				)				):
								curr_string += data_bits[i]
								if curr_string not in lexicon:
												continue

								_UpperCAmelCase					=    lexicon[curr_string]
								result += last_match_id
								add_key_to_lexicon(__snake_case					,     __snake_case					,     __snake_case					,     __snake_case				)
								index += 1
								_UpperCAmelCase					=    ''''''

				while curr_string != "" and curr_string not in lexicon:
								curr_string += "0"

				if curr_string != "":
								_UpperCAmelCase					=    lexicon[curr_string]
								result += last_match_id

				return result







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    os.path.getsize(__snake_case				)
				_UpperCAmelCase					=    bin(__snake_case				)[2:]
				_UpperCAmelCase					=    len(__snake_case				)

				return "0" * (length_length - 1) + file_length_binary + compressed







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    8
				try:
								with open(__snake_case					,     '''wb'''				) as opened_file:
												_UpperCAmelCase					=    [
												    to_write[i : i + byte_length]
												    for i in range(0					,     len(__snake_case				)					,     __snake_case				)
												]

												if len(result_byte_array[-1]				) % byte_length == 0:
																result_byte_array.append('''10000000'''				)
												else:
																result_byte_array[-1] += "1" + "0" * (
																    byte_length - len(result_byte_array[-1]				) - 1
																)

												for elem in result_byte_array:
																opened_file.write(int(__snake_case					,     2				).to_bytes(1					,     byteorder='''big'''				)				)
				except OSError:
								print('''File not accessible'''				)
								sys.exit()







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    read_file_binary(__snake_case				)
				_UpperCAmelCase					=    compress_data(__snake_case				)
				_UpperCAmelCase					=    add_file_length(__snake_case					,     __snake_case				)
				write_file_binary(__snake_case					,     __snake_case				)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])





"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A   :							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
)

__A   :							Optional[int]										=   (
    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
)

__A   :							Union[str, 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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				for tf_name, hf_name in patterns:
								_UpperCAmelCase					=    k.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				return k







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						dict					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
				_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(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    DECODER_PATTERNS
								_UpperCAmelCase					=    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''']				):
												_UpperCAmelCase					=    v.T
								_UpperCAmelCase					=    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'''				):
								_UpperCAmelCase					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    REMAINING_PATTERNS
								_UpperCAmelCase					=    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''']				):
												_UpperCAmelCase					=    v.T
								_UpperCAmelCase					=    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}'

				_UpperCAmelCase					=    mapping['''model.embed_positions.weight''']
				_UpperCAmelCase					=    mapping.pop('''model.embed_positions.weight'''				)
				_UpperCAmelCase		,						_UpperCAmelCase					=    torch_model.load_state_dict(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)
				_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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				_UpperCAmelCase					=    tf.train.list_variables(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    {}
				_UpperCAmelCase					=    ['''global_step''']
				for name, shape in tqdm(_SCREAMING_SNAKE_CASE					,     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(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    array
				return tf_weights







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				torch_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Tuple										=   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.")
					__A   :							Dict										=   parser.parse_args()
					__A   :							Tuple										=   {}
					convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)



