



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__snake_case		:Optional[Any]   	=  (
    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
)

__snake_case		:Optional[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
)

__snake_case		:Tuple   	=  [
    '''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			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    for tf_name, hf_name in patterns:
        __a		 =       k.replace(_UpperCAmelCase     ,				_UpperCAmelCase       )
    return k





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       BigBirdPegasusConfig(**_UpperCAmelCase       )
    __a		 =       BigBirdPegasusForConditionalGeneration(_UpperCAmelCase       )
    __a		 =       torch_model.state_dict()
    __a		 =       {}

    # separating decoder weights
    __a		 =       {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''       )}
    __a		 =       {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'''       ):
        __a		 =       [k.endswith(_UpperCAmelCase       ) for ending in KEYS_TO_IGNORE]
        if any(_UpperCAmelCase       ):
            continue
        __a		 =       DECODER_PATTERNS
        __a		 =       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''']       ):
            __a		 =       v.T
        __a		 =       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'''       ):
        __a		 =       [k.endswith(_UpperCAmelCase       ) for ending in KEYS_TO_IGNORE]
        if any(_UpperCAmelCase       ):
            continue
        __a		 =       REMAINING_PATTERNS
        __a		 =       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''']       ):
            __a		 =       v.T
        __a		 =       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}'

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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       get_tf_weights_as_numpy(_UpperCAmelCase       )
    __a		 =       convert_bigbird_pegasus(_UpperCAmelCase     ,				_UpperCAmelCase       )
    torch_model.save_pretrained(_UpperCAmelCase       )


if __name__ == "__main__":
 __snake_case		:Optional[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.''')
 __snake_case		:int   	=  parser.parse_args()
 __snake_case		:str   	=  {}
 convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)










from __future__ import annotations





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    print(f'Vertex\tShortest Distance from vertex {src}'       )
    for i, d in enumerate(_UpperCAmelCase       ):
        print(f'{i}\t\t{d}'       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for j in range(_UpperCAmelCase       ):
        __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
            return True
    return False





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [float('''inf'''       )] * vertex_count
    __a		 =       0.0

    for _ in range(vertex_count - 1       ):
        for j in range(_UpperCAmelCase       ):
            __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
                __a		 =       distance[u] + w

    __a		 =       check_negative_cycle(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''       )

    return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __snake_case		:Dict   	=  int(input('''Enter number of vertices: ''').strip())
 __snake_case		:Any   	=  int(input('''Enter number of edges: ''').strip())

 __snake_case		:list[dict[str, int]]   	=  [{} for _ in range(E)]

 for i in range(E):
  print('''Edge ''', i + 1)
  __snake_case	,__snake_case	,__snake_case		:int   	=  (
      int(x)
      for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
  )
  __snake_case		:Any   	=  {'''src''': src, '''dst''': dest, '''weight''': weight}

 __snake_case		:List[str]   	=  int(input('''\nEnter shortest path source:''').strip())
 __snake_case		:Optional[Any]   	=  bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)





