


"""simple docstring"""






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_lowerCAmelCase							:    List[Any]					   =	(
    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							:    Dict					   =	(
    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							:    List[str]					   =	[
    """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_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Optional[Any]:
   '''simple docstring'''





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







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     BigBirdPegasusForConditionalGeneration:
   '''simple docstring'''





   _lowerCamelCase   :				List[str]							=     BigBirdPegasusConfig(**_lowerCAmelCase       )
   _lowerCamelCase   :				Optional[int]							=     BigBirdPegasusForConditionalGeneration(_lowerCAmelCase       )
   _lowerCamelCase   :				int							=     torch_model.state_dict()
   _lowerCamelCase   :				Any							=     {}

   # separating decoder weights
   _lowerCamelCase   :				Any							=     {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   :				Any							=     [k.endswith(_lowerCAmelCase       ) for ending in KEYS_TO_IGNORE]
      if any(_lowerCAmelCase       ):
         continue
      _lowerCamelCase   :				Tuple							=     DECODER_PATTERNS
      _lowerCamelCase   :				List[str]							=     rename_state_dict_key(_lowerCAmelCase			,		_lowerCAmelCase       )
      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   :				int							=     v.T
      _lowerCamelCase   :				int							=     torch.from_numpy(_lowerCAmelCase       )
      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   :				List[Any]							=     [k.endswith(_lowerCAmelCase       ) for ending in KEYS_TO_IGNORE]
      if any(_lowerCAmelCase       ):
         continue
      _lowerCamelCase   :				Optional[Any]							=     REMAINING_PATTERNS
      _lowerCamelCase   :				int							=     rename_state_dict_key(_lowerCAmelCase			,		_lowerCAmelCase       )
      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   :				str							=     v.T
      _lowerCamelCase   :				List[str]							=     torch.from_numpy(_lowerCAmelCase       )
      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   :				List[Any]							=     mapping["model.embed_positions.weight"]
   _lowerCamelCase   :				Union[str, Any]							=     mapping.pop("model.embed_positions.weight"       )
   _lowerCamelCase   :				Optional[Any]							=     torch_model.load_state_dict(_lowerCAmelCase			,		strict=_lowerCAmelCase       )
   _lowerCamelCase   :				Tuple							=     [
       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_(		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





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







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     get_tf_weights_as_numpy(_lowerCAmelCase       )
   _lowerCamelCase   :				Optional[int]							=     convert_bigbird_pegasus(_lowerCAmelCase			,		_lowerCAmelCase       )
   torch_model.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
  _lowerCAmelCase							:    int					   =	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							:    List[Any]					   =	parser.parse_args()
  _lowerCAmelCase							:    List[str]					   =	{}
  convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase			:	Optional[Any]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):





      def __init__(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       warnings.warn(
           "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use BeitImageProcessor instead."			,				A_			,				)
       super().__init__(*A_			,				**A_  )
