






"""simple docstring"""
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCamelCase_							      = (
    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
)

lowerCamelCase_							      = (
    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
)

lowerCamelCase_							      = [
    '''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 							snake_case (  A__      ,A__		):
			for tf_name, hf_name in patterns:
						UpperCAmelCase_	:					Tuple     	=   k.replace(A__      ,A__		)
			return k

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   BigBirdPegasusConfig(**A__		)
			UpperCAmelCase_	:					List[Any]     	=   BigBirdPegasusForConditionalGeneration(A__		)
			UpperCAmelCase_	:					List[str]     	=   torch_model.state_dict()
			UpperCAmelCase_	:					Any     	=   {}

			# separating decoder weights
			UpperCAmelCase_	:					Union[str, Any]     	=   {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"		)}
			UpperCAmelCase_	:					Dict     	=   {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_	:					List[Any]     	=   [k.endswith(A__		) for ending in KEYS_TO_IGNORE]
						if any(A__		):
									continue
						UpperCAmelCase_	:					Any     	=   DECODER_PATTERNS
						UpperCAmelCase_	:					int     	=   rename_state_dict_key(A__      ,A__		)
						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_	:					Optional[Any]     	=   v.T
						UpperCAmelCase_	:					str     	=   torch.from_numpy(A__		)
						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_	:					str     	=   [k.endswith(A__		) for ending in KEYS_TO_IGNORE]
						if any(A__		):
									continue
						UpperCAmelCase_	:					Optional[int]     	=   REMAINING_PATTERNS
						UpperCAmelCase_	:					Tuple     	=   rename_state_dict_key(A__      ,A__		)
						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_	:					int     	=   v.T
						UpperCAmelCase_	:					int     	=   torch.from_numpy(A__		)
						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_	:					Any     	=   mapping["model.embed_positions.weight"]
			UpperCAmelCase_	:					List[Any]     	=   mapping.pop("model.embed_positions.weight"		)
			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   torch_model.load_state_dict(A__      ,strict=A__		)
			UpperCAmelCase_	:					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 							snake_case (  A__		):
			UpperCAmelCase_	:					List[str]     	=   tf.train.list_variables(A__		)
			UpperCAmelCase_	:					List[Any]     	=   {}
			UpperCAmelCase_	:					Optional[int]     	=   ["global_step"]
			for name, shape in tqdm(A__      ,desc="converting tf checkpoint to dict"		):
						UpperCAmelCase_	:					Union[str, Any]     	=   any(pat in name for pat in ignore_name		)
						if skip_key:
									continue
						UpperCAmelCase_	:					Dict     	=   tf.train.load_variable(A__      ,A__		)
						UpperCAmelCase_	:					int     	=   array
			return tf_weights

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   get_tf_weights_as_numpy(A__		)
			UpperCAmelCase_	:					Union[str, Any]     	=   convert_bigbird_pegasus(A__      ,A__		)
			torch_model.save_pretrained(A__		)


if __name__ == "__main__":
			lowerCamelCase_							      = 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.''')
			lowerCamelCase_							      = parser.parse_args()
			lowerCamelCase_							      = {}
			convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)











"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowerCamelCase_							      = {
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__      ,A__		):
			if got_ver is None or want_ver is None:
						raise ValueError(
						    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
						    F""" reinstalling {pkg}."""		)
			if not ops[op](version.parse(A__		)      ,version.parse(A__		)		):
						raise ImportError(
						    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""		)

def 							snake_case (  A__      ,A__ = None		):
			UpperCAmelCase_	:					int     	=   F"""\n{hint}""" if hint is not None else ""

			# non-versioned check
			if re.match(r"^[\w_\-\d]+$"      ,A__		):
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   requirement, None, None
			else:
						UpperCAmelCase_	:					Optional[int]     	=   re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"      ,A__		)
						if not match:
									raise ValueError(
									    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
									    F""" got {requirement}"""		)
						UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   match[0]
						UpperCAmelCase_	:					Optional[Any]     	=   want_full.split(","		)  # there could be multiple requirements
						UpperCAmelCase_	:					int     	=   {}
						for w in want_range:
									UpperCAmelCase_	:					str     	=   re.findall(r"^([\s!=<>]{1,2})(.+)"      ,A__		)
									if not match:
												raise ValueError(
												    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
												    F""" but got {requirement}"""		)
									UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   match[0]
									UpperCAmelCase_	:					Union[str, Any]     	=   want_ver
									if op not in ops:
												raise ValueError(F"""{requirement}: need one of {list(ops.keys()		)}, but got {op}"""		)

    # special case
			if pkg == "python":
						UpperCAmelCase_	:					List[Any]     	=   ".".join([str(A__		) for x in sys.version_info[:3]]		)
						for op, want_ver in wanted.items():
									_compare_versions(A__      ,A__      ,A__      ,A__      ,A__      ,A__		)
						return

			# check if any version is installed
			try:
						UpperCAmelCase_	:					str     	=   importlib.metadata.version(A__		)
			except importlib.metadata.PackageNotFoundError:
						raise importlib.metadata.PackageNotFoundError(
						    F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""		)

			# check that the right version is installed if version number or a range was provided
			if want_ver is not None:
						for op, want_ver in wanted.items():
									_compare_versions(A__      ,A__      ,A__      ,A__      ,A__      ,A__		)




def 							snake_case (  A__		):
			UpperCAmelCase_	:					Any     	=   "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
			return require_version(A__      ,A__		)



