import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowercase											=      (
    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											=      (
    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											=      [
    '''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						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    for tf_name, hf_name in patterns:
        __UpperCamelCase  :Any    						=	k.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    return k



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	BigBirdPegasusConfig(**SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	BigBirdPegasusForConditionalGeneration(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	torch_model.state_dict()
    __UpperCamelCase  :Dict    						=	{}

    # separating decoder weights
    __UpperCamelCase  :List[str]    						=	{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[str]    						=	[k.endswith(SCREAMING_SNAKE_CASE			) for ending in KEYS_TO_IGNORE]
        if any(SCREAMING_SNAKE_CASE			):
            continue
        __UpperCamelCase  :int    						=	DECODER_PATTERNS
        __UpperCamelCase  :List[Any]    						=	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  :str    						=	v.T
        __UpperCamelCase  :List[str]    						=	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  :List[Any]    						=	[k.endswith(SCREAMING_SNAKE_CASE			) for ending in KEYS_TO_IGNORE]
        if any(SCREAMING_SNAKE_CASE			):
            continue
        __UpperCamelCase  :Any    						=	REMAINING_PATTERNS
        __UpperCamelCase  :Any    						=	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  :Union[str, Any]    						=	v.T
        __UpperCamelCase  :int    						=	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  :Tuple    						=	mapping['''model.embed_positions.weight''']
    __UpperCamelCase  :int    						=	mapping.pop('''model.embed_positions.weight'''			)
    __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	torch_model.load_state_dict(SCREAMING_SNAKE_CASE						,    strict=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	[
        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						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	tf.train.list_variables(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :int    						=	{}
    __UpperCamelCase  :List[Any]    						=	['''global_step''']
    for name, shape in tqdm(SCREAMING_SNAKE_CASE						,    desc='''converting tf checkpoint to dict'''			):
        __UpperCamelCase  :str    						=	any(pat in name for pat in ignore_name			)
        if skip_key:
            continue
        __UpperCamelCase  :Dict    						=	tf.train.load_variable(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Dict    						=	array
    return tf_weights



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[int]    						=	convert_bigbird_pegasus(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    torch_model.save_pretrained(SCREAMING_SNAKE_CASE			)


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






from __future__ import annotations

import bisect



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :str    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[int]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] < item:
            __UpperCamelCase  :List[Any]    						=	mid + 1
        else:
            __UpperCamelCase  :Any    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[Any]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] <= item:
            __UpperCamelCase  :Dict    						=	mid + 1
        else:
            __UpperCamelCase  :Optional[Any]    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_right(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	0
    __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			) - 1

    while left <= right:
        __UpperCamelCase  :Optional[int]    						=	left + (right - left) // 2
        __UpperCamelCase  :List[str]    						=	sorted_collection[midpoint]
        if current_item == item:
            return midpoint
        elif item < current_item:
            __UpperCamelCase  :Union[str, Any]    						=	midpoint - 1
        else:
            __UpperCamelCase  :Dict    						=	midpoint + 1
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bisect.bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    if index != len(SCREAMING_SNAKE_CASE			) and sorted_collection[index] == item:
        return index
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if right < left:
        return None

    __UpperCamelCase  :str    						=	left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint - 1			)
    else:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint + 1						,    SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      input('''Enter numbers separated by comma:\n''').strip()
    __lowercase											=      sorted(int(item) for item in user_input.split(''','''))
    __lowercase											=      int(input('''Enter a single number to be found in the list:\n'''))
    __lowercase											=      binary_search(collection, target)
    if result is None:
        print(F'{target} was not found in {collection}.')
    else:
        print(F'{target} was found at position {result} in {collection}.')





