


'''simple docstring'''


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__magic_name__			=      (
    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
)

__magic_name__			=      (
    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
)

__magic_name__			=      [
    "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  :      Optional[int]							,						lowerCamelCase  :      List[Any]):



      for tf_name, hf_name in patterns:
            A_						:  Optional[Any]          =				k.replace(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE)
      return k




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      dict):
      A_						:  Any          =				BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE)
      A_						:  str          =				BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE)
      A_						:  Any          =				torch_model.state_dict()
      A_						:  Union[str, Any]          =				{}

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

      A_						:  Union[str, Any]          =				mapping["""model.embed_positions.weight"""]
      A_						:  Optional[Any]          =				mapping.pop("""model.embed_positions.weight""")
      A_   ,					A_						:  int          =				torch_model.load_state_dict(_SCREAMING_SNAKE_CASE							,						strict=_SCREAMING_SNAKE_CASE)
      A_						:  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					(      lowerCamelCase  :      Any):
      A_						:  Optional[int]          =				tf.train.list_variables(_SCREAMING_SNAKE_CASE)
      A_						:  Any          =				{}
      A_						:  List[Any]          =				["""global_step"""]
      for name, shape in tqdm(_SCREAMING_SNAKE_CASE							,						desc="""converting tf checkpoint to dict"""):
            A_						:  List[Any]          =				any(pat in name for pat in ignore_name)
            if skip_key:
                  continue
            A_						:  Optional[Any]          =				tf.train.load_variable(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE)
            A_						:  int          =				array
      return tf_weights




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      str							,						lowerCamelCase  :      dict):
      A_						:  Any          =				get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE)
      A_						:  Dict          =				convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE)
      torch_model.save_pretrained(_SCREAMING_SNAKE_CASE)


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




'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""deberta-v2"""






  def __init__(							self :       Optional[Any]	,_a :       Union[str, Any]=128100	,_a :       Optional[int]=1536	,_a :       Dict=24	,_a :       int=24	,_a :       Tuple=6144	,_a :       Union[str, Any]="gelu"	,_a :       List[Any]=0.1	,_a :       Dict=0.1	,_a :       int=512	,_a :       int=0	,_a :       int=0.02	,_a :       int=1e-7	,_a :       List[str]=False	,_a :       Union[str, Any]=-1	,_a :       List[Any]=0	,_a :       Optional[Any]=True	,_a :       Tuple=None	,_a :       Any=0	,_a :       int="gelu"	,**_a :       Any	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  Union[str, Any]          =				hidden_size
        A_						:  Dict          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  List[Any]          =				intermediate_size
        A_						:  List[Any]          =				hidden_act
        A_						:  Optional[int]          =				hidden_dropout_prob
        A_						:  Dict          =				attention_probs_dropout_prob
        A_						:  int          =				max_position_embeddings
        A_						:  Any          =				type_vocab_size
        A_						:  List[Any]          =				initializer_range
        A_						:  int          =				relative_attention
        A_						:  Tuple          =				max_relative_positions
        A_						:  int          =				pad_token_id
        A_						:  Tuple          =				position_biased_input

        # Backwards compatibility
        if type(_a ) == str:
              A_						:  str          =				[x.strip() for x in pos_att_type.lower().split("""|""" )]

        A_						:  Any          =				pos_att_type
        A_						:  Optional[int]          =				vocab_size
        A_						:  Tuple          =				layer_norm_eps

        A_						:  Any          =				kwargs.get("""pooler_hidden_size"""	,_a )
        A_						:  Union[str, Any]          =				pooler_dropout
        A_						:  List[Any]          =				pooler_hidden_act







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        if self.task == "multiple-choice":
              A_						:  Any          =				{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              A_						:  Any          =				{0: """batch""", 1: """sequence"""}
        if self._config.type_vocab_size > 0:
              return OrderedDict(
                  [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
        else:
              return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return 12






  def        _a							(							self :       int	,_a :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,_a :       int = -1	,_a :       int = -1	,_a :       int = -1	,_a :       bool = False	,_a :       Optional["TensorType"] = None	,_a :       int = 3	,_a :       int = 40	,_a :       int = 40	,_a :       "PreTrainedTokenizerBase" = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(preprocessor=_a	,framework=_a )
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
        return dummy_inputs
