


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__UpperCamelCase    :						Any     =							[
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
__UpperCamelCase    :						List[str]     =							[
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

__UpperCamelCase    :						Tuple     =							(
    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    :						Union[str, Any]     =							(
    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    :						Tuple     =							[
    '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   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			Dict      ):
      """simple docstring"""




      for tf_name, hf_name in patterns:
            __lowerCamelCase     :      Dict       						=						k.replace(UpperCAmelCase						,	UpperCAmelCase      )
      return k





def   _UpperCAmelCase       (     UpperCAmelCase      :			dict						,	UpperCAmelCase      :			dict      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						BigBirdPegasusConfig(**UpperCAmelCase      )
      __lowerCamelCase     :      Optional[Any]       						=						BigBirdPegasusForConditionalGeneration(UpperCAmelCase      )
      __lowerCamelCase     :      Optional[Any]       						=						torch_model.state_dict()
      __lowerCamelCase     :      Optional[int]       						=						{}

      # separating decoder weights
      __lowerCamelCase     :      Optional[int]       						=						{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""      )}
      __lowerCamelCase     :      Tuple       						=						{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     :      List[str]       						=						[k.endswith(UpperCAmelCase      ) for ending in KEYS_TO_IGNORE]
            if any(UpperCAmelCase      ):
                  continue
            __lowerCamelCase     :      List[Any]       						=						DECODER_PATTERNS
            __lowerCamelCase     :      Any       						=						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     :      str       						=						v.T
            __lowerCamelCase     :      List[str]       						=						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     :      Dict       						=						[k.endswith(UpperCAmelCase      ) for ending in KEYS_TO_IGNORE]
            if any(UpperCAmelCase      ):
                  continue
            __lowerCamelCase     :      List[Any]       						=						REMAINING_PATTERNS
            __lowerCamelCase     :      List[Any]       						=						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     :      str       						=						v.T
            __lowerCamelCase     :      List[Any]       						=						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     :      Tuple       						=						mapping["""model.embed_positions.weight"""]
      __lowerCamelCase     :      Optional[int]       						=						mapping.pop("""model.embed_positions.weight"""      )
      __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						torch_model.load_state_dict(UpperCAmelCase						,	strict=UpperCAmelCase      )
      __lowerCamelCase     :      Dict       						=						[
          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   _UpperCAmelCase       (     UpperCAmelCase      :			Dict      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			dict      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						get_tf_weights_as_numpy(UpperCAmelCase      )
      __lowerCamelCase     :      Dict       						=						convert_bigbird_pegasus(UpperCAmelCase						,	UpperCAmelCase      )
      torch_model.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						Optional[Any]     =							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    :						Union[str, Any]     =							parser.parse_args()
    __UpperCamelCase    :						Union[str, Any]     =							{}
    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







def   _UpperCAmelCase       (     UpperCAmelCase      :			list      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[int]       						=						len(UpperCAmelCase      )
      for _ in range(UpperCAmelCase      ):
            for i in range(_ % 2						,	arr_size - 1						,	2      ):
                  if arr[i + 1] < arr[i]:
                        __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						arr[i + 1], arr[i]

      return arr


if __name__ == "__main__":
    __UpperCamelCase    :						str     =							list(range(10, 0, -1))
    print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')



