





'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_a   :					List[str]           =       (
    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   :					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
)

_a   :					List[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   _lowerCAmelCase		(		lowercase   ,      lowercase      )   ->   Optional[Any]:
			for tf_name, hf_name in patterns:
						__lowerCAmelCase   		=							k.replace(UpperCamelCase_   ,      UpperCamelCase_      )
			return k





def   _lowerCAmelCase		(		lowercase   ,      lowercase      )   ->   List[Any]:
			__lowerCAmelCase   		=							BigBirdPegasusConfig(**UpperCamelCase_      )
			__lowerCAmelCase   		=							BigBirdPegasusForConditionalGeneration(UpperCamelCase_      )
			__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(UpperCamelCase_      ) for ending in KEYS_TO_IGNORE]
						if any(UpperCamelCase_      ):
									continue
						__lowerCAmelCase   		=							DECODER_PATTERNS
						__lowerCAmelCase   		=							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"""]      ):
									__lowerCAmelCase   		=							v.T
						__lowerCAmelCase   		=							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"""      ):
						__lowerCAmelCase   		=							[k.endswith(UpperCamelCase_      ) for ending in KEYS_TO_IGNORE]
						if any(UpperCamelCase_      ):
									continue
						__lowerCAmelCase   		=							REMAINING_PATTERNS
						__lowerCAmelCase   		=							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"""]      ):
									__lowerCAmelCase   		=							v.T
						__lowerCAmelCase   		=							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}'

			__lowerCAmelCase   		=							mapping["""model.embed_positions.weight"""]
			__lowerCAmelCase   		=							mapping.pop("""model.embed_positions.weight"""      )
			__lowerCAmelCase		,			__lowerCAmelCase   		=							torch_model.load_state_dict(UpperCamelCase_   ,      strict=UpperCamelCase_      )
			__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   _lowerCAmelCase		(		lowercase      )   ->   Optional[Any]:
			__lowerCAmelCase   		=							tf.train.list_variables(UpperCamelCase_      )
			__lowerCAmelCase   		=							{}
			__lowerCAmelCase   		=							["""global_step"""]
			for name, shape in tqdm(UpperCamelCase_   ,      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(UpperCamelCase_   ,      UpperCamelCase_      )
						__lowerCAmelCase   		=							array
			return tf_weights





def   _lowerCAmelCase		(		lowercase   ,      lowercase   ,      lowercase      )   ->   List[Any]:
			__lowerCAmelCase   		=							get_tf_weights_as_numpy(UpperCamelCase_      )
			__lowerCAmelCase   		=							convert_bigbird_pegasus(UpperCamelCase_   ,      UpperCamelCase_      )
			torch_model.save_pretrained(UpperCamelCase_      )


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





'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





