



'''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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    for tf_name, hf_name in patterns:
        lowerCAmelCase__			:  List[str]											= k.replace(_a							,   _a      )
    return k
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Tuple											= BigBirdPegasusConfig(**_a      )
    lowerCAmelCase__			:  Any											= BigBirdPegasusForConditionalGeneration(_a      )
    lowerCAmelCase__			:  int											= torch_model.state_dict()
    lowerCAmelCase__			:  Any											= {}

    # separating decoder weights
    lowerCAmelCase__			:  List[str]											= {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""      )}
    lowerCAmelCase__			:  Any											= {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__			:  str											= [k.endswith(_a      ) for ending in KEYS_TO_IGNORE]
        if any(_a      ):
            continue
        lowerCAmelCase__			:  Any											= DECODER_PATTERNS
        lowerCAmelCase__			:  Optional[Any]											= 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"""]      ):
            lowerCAmelCase__			:  str											= v.T
        lowerCAmelCase__			:  Optional[int]											= 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"""      ):
        lowerCAmelCase__			:  Optional[int]											= [k.endswith(_a      ) for ending in KEYS_TO_IGNORE]
        if any(_a      ):
            continue
        lowerCAmelCase__			:  List[Any]											= REMAINING_PATTERNS
        lowerCAmelCase__			:  str											= 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"""]      ):
            lowerCAmelCase__			:  Dict											= v.T
        lowerCAmelCase__			:  Union[str, Any]											= 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}"""

    lowerCAmelCase__			:  Optional[int]											= mapping["model.embed_positions.weight"]
    lowerCAmelCase__			:  str											= mapping.pop("""model.embed_positions.weight"""      )
    lowerCAmelCase__			:  Optional[int]											= torch_model.load_state_dict(_a							,   strict=_a      )
    lowerCAmelCase__			:  Union[str, 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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Any											= tf.train.list_variables(_a      )
    lowerCAmelCase__			:  int											= {}
    lowerCAmelCase__			:  Dict											= ["global_step"]
    for name, shape in tqdm(_a							,   desc="""converting tf checkpoint to dict"""      ):
        lowerCAmelCase__			:  str											= any(pat in name for pat in ignore_name      )
        if skip_key:
            continue
        lowerCAmelCase__			:  str											= tf.train.load_variable(_a							,   _a      )
        lowerCAmelCase__			:  Dict											= array
    return tf_weights
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[Any]											= get_tf_weights_as_numpy(_a      )
    lowerCAmelCase__			:  List[str]											= 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'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations(UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item      ) for item in array      )

    return count_of_possible_combinations(UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase							,   UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        lowerCAmelCase__			:  Any											= sum(
            count_of_possible_combinations_with_dp_array(target - item							,   UpperCamelCase      )
            for item in array      )
        lowerCAmelCase__			:  Tuple											= answer
        return answer

    lowerCAmelCase__			:  Optional[int]											= [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,   UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= [0] * (target + 1)
    lowerCAmelCase__			:  List[Any]											= 1

    for i in range(1							,   target + 1      ):
        for j in range(UpperCamelCase      ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCAmelCase										=     3
 _lowerCAmelCase										=     5
 _lowerCAmelCase										=     [1, 2, 5]
 print(combination_sum_iv(n, array, target))


