





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

UpperCAmelCase_							=				(
    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
)

UpperCAmelCase_							=				(
    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
)

UpperCAmelCase_							=				[
    """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  __magic_name__  (				lowercase				,       lowercase				)				-> Optional[Any]:

							"""simple docstring"""



							for tf_name, hf_name in patterns:
														lowercase_	:   str  				=     k.replace(lowercase				,       lowercase				)
							return k





def  __magic_name__  (				lowercase				,       lowercase				)				-> BigBirdPegasusForConditionalGeneration:

							"""simple docstring"""



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

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

							lowercase_	:   str  				=     mapping["""model.embed_positions.weight"""]
							lowercase_	:   str  				=     mapping.pop("""model.embed_positions.weight"""				)
							lowercase_				,		lowercase_	:   Union[str, Any]  				=     torch_model.load_state_dict(lowercase				,       strict=lowercase				)
							lowercase_	:   int  				=     [
							    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  __magic_name__  (				lowercase				)				-> Dict:

							"""simple docstring"""



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





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Union[str, Any]:

							"""simple docstring"""



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


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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_							=				{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """M2M100ForConditionalGeneration""",
							    """M2M100Model""",
							    """M2M100PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
							from .tokenization_mam_aaa import MaMaaaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mam_aaa import (
														    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaMaaaForConditionalGeneration,
														    MaMaaaModel,
														    MaMaaaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)