


"""simple docstring"""

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 lowerCAmelCase_		(				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Dict ):






 """simple docstring"""







 for tf_name, hf_name in patterns:
  __lowercase									=	k.replace(_UpperCAmelCase				,   _UpperCAmelCase )
 return k


def lowerCAmelCase_		(				UpperCamelCase__	: int				,   UpperCamelCase__	: Optional[Any] ):






 """simple docstring"""







 __lowercase									=	BigBirdPegasusConfig(**_UpperCAmelCase )
 __lowercase									=	BigBirdPegasusForConditionalGeneration(_UpperCAmelCase )
 __lowercase									=	torch_model.state_dict()
 __lowercase									=	{}

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

 __lowercase									=	mapping["""model.embed_positions.weight"""]
 __lowercase									=	mapping.pop("""model.embed_positions.weight""" )
 __lowercase							,		__lowercase									=	torch_model.load_state_dict(_UpperCAmelCase				,   strict=_UpperCAmelCase )
 __lowercase									=	[
     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_		(				UpperCamelCase__	: Any ):






 """simple docstring"""







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


def lowerCAmelCase_		(				UpperCamelCase__	: Any				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: Optional[Any] ):






 """simple docstring"""







 __lowercase									=	get_tf_weights_as_numpy(_UpperCAmelCase )
 __lowercase									=	convert_bigbird_pegasus(_UpperCAmelCase				,   _UpperCAmelCase )
 torch_model.save_pretrained(_UpperCAmelCase )


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)




"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCAmelCase__  							={"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__  							=[
           "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VanForImageClassification",
           "VanModel",
           "VanPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_van import (
                  VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VanForImageClassification,
                  VanModel,
                  VanPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase__  							=_LazyModule(__name__, globals()["__file__"], _import_structure)
