





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

UpperCAmelCase__  :     Optional[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
)

UpperCAmelCase__  :     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
)

UpperCAmelCase__  :     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   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Tuple:
				for tf_name, hf_name in patterns:
								lowerCamelCase			=k.replace(__lowerCamelCase     , __lowerCamelCase			)
				return k





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[Any]:
				lowerCamelCase			=BigBirdPegasusConfig(**__lowerCamelCase			)
				lowerCamelCase			=BigBirdPegasusForConditionalGeneration(__lowerCamelCase			)
				lowerCamelCase			=torch_model.state_dict()
				lowerCamelCase			={}

				# separating decoder weights
				lowerCamelCase			={k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""			)}
				lowerCamelCase			={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"""			):
								lowerCamelCase			=[k.endswith(__lowerCamelCase			) for ending in KEYS_TO_IGNORE]
								if any(__lowerCamelCase			):
												continue
								lowerCamelCase			=DECODER_PATTERNS
								lowerCamelCase			=rename_state_dict_key(__lowerCamelCase     , __lowerCamelCase			)
								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"""]			):
												lowerCamelCase			=v.T
								lowerCamelCase			=torch.from_numpy(__lowerCamelCase			)
								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"""			):
								lowerCamelCase			=[k.endswith(__lowerCamelCase			) for ending in KEYS_TO_IGNORE]
								if any(__lowerCamelCase			):
												continue
								lowerCamelCase			=REMAINING_PATTERNS
								lowerCamelCase			=rename_state_dict_key(__lowerCamelCase     , __lowerCamelCase			)
								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"""]			):
												lowerCamelCase			=v.T
								lowerCamelCase			=torch.from_numpy(__lowerCamelCase			)
								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}"""

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





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					str:
				lowerCamelCase			=get_tf_weights_as_numpy(__lowerCamelCase			)
				lowerCamelCase			=convert_bigbird_pegasus(__lowerCamelCase     , __lowerCamelCase			)
				torch_model.save_pretrained(__lowerCamelCase			)


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












import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def        _lowercase   (  _UpperCAmelCase			)  ->					str:
				lowerCamelCase			=[]
				for line in lines:
								lowerCamelCase			=re.sub(r"""#.*"""     , """"""     , _UpperCAmelCase			)  # remove comments
								if line:
												filtered_lines.append(_UpperCAmelCase			)
				lowerCamelCase			="""\n""".join(_UpperCAmelCase			)

				# Make a hash from all this code
				lowerCamelCase			=full_str.encode("""utf-8"""			)
				return shaaaa(_UpperCAmelCase			).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase__  :     str   			={
    '''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    '''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    '''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    '''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    '''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    '''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    '''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    '''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
UpperCAmelCase__  :     Tuple   			={
    '''.csv''': ('''csv''', {}),
    '''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
    '''.json''': ('''json''', {}),
    '''.jsonl''': ('''json''', {}),
    '''.parquet''': ('''parquet''', {}),
    '''.arrow''': ('''arrow''', {}),
    '''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase__  :     Optional[Any]   			={'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
UpperCAmelCase__  :     Dict[str, List[str]]   			={}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
							_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')




_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')





