




"""simple docstring"""



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

SCREAMING_SNAKE_CASE    =	(
    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
)

SCREAMING_SNAKE_CASE    =	(
    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
)

SCREAMING_SNAKE_CASE    =	[
    "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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Optional[int]:
 for tf_name, hf_name in patterns:
  A__	=    k.replace(lowercase_      ,			lowercase_						)
 return k







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		BigBirdPegasusForConditionalGeneration:
 A__	=    BigBirdPegasusConfig(**lowercase_						)
 A__	=    BigBirdPegasusForConditionalGeneration(lowercase_						)
 A__	=    torch_model.state_dict()
 A__	=    {}

 # separating decoder weights
 A__	=    {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"						)}
 A__	=    {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__	=    [k.endswith(lowercase_						) for ending in KEYS_TO_IGNORE]
  if any(lowercase_						):
   continue
  A__	=    DECODER_PATTERNS
  A__	=    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"]						):
   A__	=    v.T
  A__	=    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"						):
  A__	=    [k.endswith(lowercase_						) for ending in KEYS_TO_IGNORE]
  if any(lowercase_						):
   continue
  A__	=    REMAINING_PATTERNS
  A__	=    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"]						):
   A__	=    v.T
  A__	=    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}"""

 A__	=    mapping["model.embed_positions.weight"]
 A__	=    mapping.pop("model.embed_positions.weight"						)
 A__,				A__	=    torch_model.load_state_dict(lowercase_      ,			strict=lowercase_						)
 A__	=    [
     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   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    tf.train.list_variables(lowercase_						)
 A__	=    {}
 A__	=    ["global_step"]
 for name, shape in tqdm(lowercase_      ,			desc="converting tf checkpoint to dict"						):
  A__	=    any(pat in name for pat in ignore_name						)
  if skip_key:
   continue
  A__	=    tf.train.load_variable(lowercase_      ,			lowercase_						)
  A__	=    array
 return tf_weights







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		str:
 A__	=    get_tf_weights_as_numpy(lowercase_						)
 A__	=    convert_bigbird_pegasus(lowercase_      ,			lowercase_						)
 torch_model.save_pretrained(lowercase_						)


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











"""simple docstring"""



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE    =	{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Any:
 if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
  raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()						)}."""						)

 if tokenizer_name is None:
  A__	=    TOKENIZER_CLASSES
 else:
  A__	=    {tokenizer_name: getattr(lowercase_      ,			tokenizer_name + "Fast"						)}

 logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""						)

 for tokenizer_name in tokenizer_names:
  A__	=    TOKENIZER_CLASSES[tokenizer_name]

  A__	=    True
  if checkpoint_name is None:
   A__	=    list(tokenizer_class.max_model_input_sizes.keys()						)
  else:
   A__	=    [checkpoint_name]

  logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""						)

  for checkpoint in checkpoint_names:
   logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""						)

   # Load tokenizer
   A__	=    tokenizer_class.from_pretrained(lowercase_      ,			force_download=lowercase_						)

   # Save fast tokenizer
   logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""						)

   # For organization names we create sub-directories
   if "/" in checkpoint:
    A__,				A__	=    checkpoint.split("/"						)
    A__	=    os.path.join(lowercase_      ,			lowercase_						)
   elif add_prefix:
    A__	=    checkpoint
    A__	=    dump_path
   else:
    A__	=    None
    A__	=    dump_path

   logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()						)[0]:
    A__	=    list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
    A__	=    file_path.split(lowercase_						)[-1][0]
    if next_char == "/":
     A__	=    os.path.join(lowercase_      ,			lowercase_						)
     A__	=    None

    logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   A__	=    tokenizer.save_pretrained(
       lowercase_      ,			legacy_format=lowercase_      ,			filename_prefix=lowercase_						)
   logger.info(f"""=> File names {file_names}"""						)

   for file_name in file_names:
    if not file_name.endswith("tokenizer.json"						):
     os.remove(lowercase_						)
     logger.info(f"""=> removing {file_name}"""						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
      )
      parser.add_argument(
          "--tokenizer_name",
          default=None,
          type=str,
          help=(
              f'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
              "download and convert all the checkpoints from AWS."
          ),
      )
      parser.add_argument(
          "--checkpoint_name",
          default=None,
          type=str,
          help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
      )
      parser.add_argument(
          "--force_download",
          action="store_true",
          help="Re-download checkpoints.",
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





