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  :str    						=	k.replace(_lowercase						,    _lowercase			)
    return k



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







    __UpperCamelCase  :Union[str, Any]    						=	BigBirdPegasusConfig(**_lowercase			)
    __UpperCamelCase  :Tuple    						=	BigBirdPegasusForConditionalGeneration(_lowercase			)
    __UpperCamelCase  :Optional[int]    						=	torch_model.state_dict()
    __UpperCamelCase  :Any    						=	{}

    # separating decoder weights
    __UpperCamelCase  :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  :int    						=	[k.endswith(_lowercase			) for ending in KEYS_TO_IGNORE]
        if any(_lowercase			):
            continue
        __UpperCamelCase  :Tuple    						=	DECODER_PATTERNS
        __UpperCamelCase  :Tuple    						=	rename_state_dict_key(_lowercase						,    _lowercase			)
        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  :Union[str, Any]    						=	v.T
        __UpperCamelCase  :int    						=	torch.from_numpy(_lowercase			)
        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(_lowercase			) for ending in KEYS_TO_IGNORE]
        if any(_lowercase			):
            continue
        __UpperCamelCase  :Optional[int]    						=	REMAINING_PATTERNS
        __UpperCamelCase  :Dict    						=	rename_state_dict_key(_lowercase						,    _lowercase			)
        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  :Optional[Any]    						=	v.T
        __UpperCamelCase  :List[str]    						=	torch.from_numpy(_lowercase			)
        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  :Dict    						=	mapping['''model.embed_positions.weight''']
    __UpperCamelCase  :Dict    						=	mapping.pop('''model.embed_positions.weight'''			)
    __UpperCamelCase  :Any    						=	torch_model.load_state_dict(_lowercase						,    strict=_lowercase			)
    __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  :List[str]    						=	tf.train.list_variables(_lowercase			)
    __UpperCamelCase  :Any    						=	{}
    __UpperCamelCase  :Optional[Any]    						=	['''global_step''']
    for name, shape in tqdm(_lowercase						,    desc='''converting tf checkpoint to dict'''			):
        __UpperCamelCase  :List[Any]    						=	any(pat in name for pat in ignore_name			)
        if skip_key:
            continue
        __UpperCamelCase  :List[Any]    						=	tf.train.load_variable(_lowercase						,    _lowercase			)
        __UpperCamelCase  :List[str]    						=	array
    return tf_weights



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







    __UpperCamelCase  :Optional[Any]    						=	get_tf_weights_as_numpy(_lowercase			)
    __UpperCamelCase  :List[str]    						=	convert_bigbird_pegasus(_lowercase						,    _lowercase			)
    torch_model.save_pretrained(_lowercase			)


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}.')





