


"""simple docstring"""



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__snake_case   			=    (
    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   			=    (
    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   			=    [
    """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						(						lowercase  :  Any						,							lowercase  :  List[str] )	->						List[str]:


    """simple docstring"""





    for tf_name, hf_name in patterns:
        snake_case    :      Tuple      					=    k.replace(lowercase						,							lowercase )
    return k







def   __lowerCAmelCase						(						lowercase  :  dict						,							lowercase  :  dict )	->						BigBirdPegasusForConditionalGeneration:


    """simple docstring"""





    snake_case    :      List[str]      					=    BigBirdPegasusConfig(**lowercase )
    snake_case    :      Dict      					=    BigBirdPegasusForConditionalGeneration(lowercase )
    snake_case    :      int      					=    torch_model.state_dict()
    snake_case    :      Optional[Any]      					=    {}

    # separating decoder weights
    snake_case    :      Union[str, Any]      					=    {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder" )}
    snake_case    :      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" ):
        snake_case    :      Optional[Any]      					=    [k.endswith(lowercase ) for ending in KEYS_TO_IGNORE]
        if any(lowercase ):
            continue
        snake_case    :      Any      					=    DECODER_PATTERNS
        snake_case    :      Dict      					=    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"] ):
            snake_case    :      Dict      					=    v.T
        snake_case    :      List[Any]      					=    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" ):
        snake_case    :      List[str]      					=    [k.endswith(lowercase ) for ending in KEYS_TO_IGNORE]
        if any(lowercase ):
            continue
        snake_case    :      Optional[int]      					=    REMAINING_PATTERNS
        snake_case    :      Tuple      					=    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"] ):
            snake_case    :      List[str]      					=    v.T
        snake_case    :      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}'

    snake_case    :      Optional[int]      					=    mapping["model.embed_positions.weight"]
    snake_case    :      List[Any]      					=    mapping.pop("model.embed_positions.weight" )
    snake_case					,snake_case    :      List[str]      					=    torch_model.load_state_dict(lowercase						,							strict=lowercase )
    snake_case    :      List[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   __lowerCAmelCase						(						lowercase  :  Tuple )	->						Dict:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  str						,							lowercase  :  dict )	->						Optional[int]:


    """simple docstring"""





    snake_case    :      Optional[Any]      					=    get_tf_weights_as_numpy(lowercase )
    snake_case    :      str      					=    convert_bigbird_pegasus(lowercase						,							lowercase )
    torch_model.save_pretrained(lowercase )


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









"""simple docstring"""



from typing import List

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  dict )	->						int:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    {key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase						,							lowercase )}
    if len(set(lists_lengths.values() ) ) > 1:
        raise RuntimeError(
            (
                "Sharding is ambiguous for this dataset: "
                + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
                + "\n".join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
                + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
                + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
            ) )
    snake_case    :      int      					=    max(lists_lengths.values()						,							default=0 )
    return max(1						,							lowercase )







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int )	->						List[range]:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    []
    for group_idx in range(lowercase ):
        snake_case    :      Union[str, Any]      					=    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        snake_case    :      int      					=    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        snake_case    :      Dict      					=    range(lowercase						,							start + num_shards_to_add )
        shards_indices_per_group.append(lowercase )
    return shards_indices_per_group







def   __lowerCAmelCase						(						lowercase  :  dict						,							lowercase  :  int )	->						List[dict]:


    """simple docstring"""





    snake_case    :      int      					=    _number_of_shards_in_gen_kwargs(lowercase )
    if num_shards == 1:
        return [dict(lowercase )]
    else:
        snake_case    :      Optional[int]      					=    _distribute_shards(num_shards=lowercase						,							max_num_jobs=lowercase )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(lowercase						,							lowercase )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(lowercase ) )
        ]







def   __lowerCAmelCase						(						lowercase  :  List[dict] )	->						dict:


    """simple docstring"""





    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key]						,							lowercase )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }







def   __lowerCAmelCase						(						lowercase  :  np.random.Generator						,							lowercase  :  dict )	->						dict:


    """simple docstring"""





    snake_case    :      Tuple      					=    {len(lowercase ) for value in gen_kwargs.values() if isinstance(lowercase						,							lowercase )}
    snake_case    :      str      					=    {}
    for size in list_sizes:
        snake_case    :      Optional[int]      					=    list(range(lowercase ) )
        rng.shuffle(indices_per_size[size] )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    snake_case    :      Dict      					=    dict(lowercase )
    for key, value in shuffled_kwargs.items():
        if isinstance(lowercase						,							lowercase ):
            snake_case    :      Dict      					=    [value[i] for i in indices_per_size[len(lowercase )]]
    return shuffled_kwargs





