





'''simple docstring'''


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_SCREAMING_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
)

_SCREAMING_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
)

_SCREAMING_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 	__a(SCREAMING_SNAKE_CASE_  :					Any		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    for tf_name, hf_name in patterns:
        _lowerCAmelCase				=      k.replace(UpperCAmelCase__		,       UpperCAmelCase__    )
    return k


def 	__a(SCREAMING_SNAKE_CASE_  :					dict		,       SCREAMING_SNAKE_CASE_  :					dict    ):



    '''simple docstring'''





    _lowerCAmelCase				=      BigBirdPegasusConfig(**UpperCAmelCase__    )
    _lowerCAmelCase				=      BigBirdPegasusForConditionalGeneration(UpperCAmelCase__    )
    _lowerCAmelCase				=      torch_model.state_dict()
    _lowerCAmelCase				=      {}

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

    _lowerCAmelCase				=      mapping["""model.embed_positions.weight"""]
    _lowerCAmelCase				=      mapping.pop("model.embed_positions.weight"    )
    _lowerCAmelCase				=      torch_model.load_state_dict(UpperCAmelCase__		,       strict=UpperCAmelCase__    )
    _lowerCAmelCase				=      [
        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 	__a(SCREAMING_SNAKE_CASE_  :					Any    ):



    '''simple docstring'''





    _lowerCAmelCase				=      tf.train.list_variables(UpperCAmelCase__    )
    _lowerCAmelCase				=      {}
    _lowerCAmelCase				=      ["""global_step"""]
    for name, shape in tqdm(UpperCAmelCase__		,       desc="converting tf checkpoint to dict"    ):
        _lowerCAmelCase				=      any(pat in name for pat in ignore_name    )
        if skip_key:
            continue
        _lowerCAmelCase				=      tf.train.load_variable(UpperCAmelCase__		,       UpperCAmelCase__    )
        _lowerCAmelCase				=      array
    return tf_weights


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					dict    ):



    '''simple docstring'''





    _lowerCAmelCase				=      get_tf_weights_as_numpy(UpperCAmelCase__    )
    _lowerCAmelCase				=      convert_bigbird_pegasus(UpperCAmelCase__		,       UpperCAmelCase__    )
    torch_model.save_pretrained(UpperCAmelCase__    )


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









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							str         =					"speech_to_text_2"
 __lowerCamelCase						:							Union[str, Any]         =					["past_key_values"]
 __lowerCamelCase						:							Tuple         =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}




 def __init__(	self			,     _lowerCAmelCase=10000			,     _lowerCAmelCase=6			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=4			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=True			,     _lowerCAmelCase="relu"			,     _lowerCAmelCase=256			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=2			,     _lowerCAmelCase=True			,     _lowerCAmelCase=1			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1024			,     **_lowerCAmelCase			,     )    ->							Dict:
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      d_model
     _lowerCAmelCase				=      decoder_ffn_dim
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      decoder_attention_heads
     _lowerCAmelCase				=      dropout
     _lowerCAmelCase				=      attention_dropout
     _lowerCAmelCase				=      activation_dropout
     _lowerCAmelCase				=      activation_function
     _lowerCAmelCase				=      init_std
     _lowerCAmelCase				=      decoder_layerdrop
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      scale_embedding  # scale factor will be sqrt(d_model) if True
     _lowerCAmelCase				=      max_target_positions

     super().__init__(
         pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     decoder_start_token_id=_lowerCAmelCase			,     **_lowerCAmelCase			,     )


