



"""simple docstring"""




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowerCAmelCase    : Optional[int]							=							logging.getLogger(__name__)

def 						__snake_case						(				)  ->       Any:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   argparse.ArgumentParser(
         description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."    )
     parser.add_argument(
         "--dataset_name"						,  type=SCREAMING_SNAKE_CASE__						,  default="wikitext"						,  help="Name of the training. Explore datasets at: hf.co/datasets."						,  )
     parser.add_argument(
         "--dataset_config"						,  type=SCREAMING_SNAKE_CASE__						,  default="wikitext-103-raw-v1"						,  help="Configuration name of the dataset."    )
     parser.add_argument(
         "--tokenizer_name_or_path"						,  type=SCREAMING_SNAKE_CASE__						,  default="sayakpaul/unigram-tokenizer-wikitext"						,  help="Tokenizer identifier. Can be a local filepath or a Hub identifier."						,  )
     parser.add_argument(
         "--shard_size"						,  type=SCREAMING_SNAKE_CASE__						,  default=1_000						,  help="Number of entries to go in a single shard."						,  )
     parser.add_argument("--split"						,  type=SCREAMING_SNAKE_CASE__						,  default="train"						,  choices=["train", "test", "validation"]    )
     parser.add_argument(
         "--limit"						,  default=SCREAMING_SNAKE_CASE__						,  type=SCREAMING_SNAKE_CASE__						,  help="Limit the number of shards (used for debugging)."						,  )
     parser.add_argument(
         "--max_length"						,  type=SCREAMING_SNAKE_CASE__						,  default=512						,  help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
         " sequence length that is a multiple of 8."						,  )
     parser.add_argument(
         "--output_dir"						,  default="tf-tpu"						,  type=SCREAMING_SNAKE_CASE__						,  help="Output directory where the TFRecord shards will be saved. If the"
         " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
         " shards will be directly saved to a Google Cloud Storage bucket."						,  )

     _UpperCAmelCase      :     Optional[int]        =   parser.parse_args()
     return args

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[Any]    )  ->       List[Any]:





     '''simple docstring'''

     def fn(SCREAMING_SNAKE_CASE__     :						Union[str, Any]    ):
          return tokenizer(examples["text"]    )

     return fn

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str    )  ->       Any:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   []
     for i in range(len(tokenized_data["input_ids"]    )    ):
          _UpperCAmelCase      :     List[str]        =   {
              "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]    )    ),
              "attention_mask": tf.train.Feature(
                  intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]    )    ),
          }
          _UpperCAmelCase      :     Dict        =   tf.train.Features(feature=SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     List[Any]        =   tf.train.Example(features=SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     str        =   example.SerializeToString()
          records.append(SCREAMING_SNAKE_CASE__    )
     return records

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]    )  ->       Any:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   datasets.load_dataset(args.dataset_name						,  args.dataset_config						,  split=args.split    )

     if args.limit is not None:
          _UpperCAmelCase      :     Any        =   min(len(SCREAMING_SNAKE_CASE__    )						,  args.limit    )
          _UpperCAmelCase      :     Any        =   dataset.select(range(SCREAMING_SNAKE_CASE__    )    )
          print(f'Limiting the dataset to {args.limit} entries.'    )

     _UpperCAmelCase      :     int        =   AutoTokenizer.from_pretrained(args.tokenizer_name_or_path    )

     # Handle output directory creation.
     # For serializing into a Google Cloud Storage Bucket, one needs to first
     # create a bucket.
     if "gs" not in args.output_dir:
          if not os.path.exists(args.output_dir    ):
               os.makedirs(args.output_dir    )
          _UpperCAmelCase      :     Dict        =   os.path.join(args.output_dir						,  args.split    )
          if not os.path.exists(SCREAMING_SNAKE_CASE__    ):
               os.makedirs(SCREAMING_SNAKE_CASE__    )
     else:
          _UpperCAmelCase      :     Union[str, Any]        =   os.path.join(args.output_dir						,  args.split    )

     # Tokenize the whole dataset at once.
     _UpperCAmelCase      :     Optional[Any]        =   tokenize_function(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     str        =   dataset.map(SCREAMING_SNAKE_CASE__						,  batched=SCREAMING_SNAKE_CASE__						,  num_proc=4						,  remove_columns=["text"]    )

     # We need to concatenate all our texts together, and then split the result
     # into chunks of a fixed size, which we will call block_size. To do this, we
     # will use the map method again, with the option batched=True. When we use batched=True,
     # the function we pass to map() will be passed multiple inputs at once, allowing us
     # to group them into more or fewer examples than we had in the input.
     # This allows us to create our new fixed-length samples. The advantage of this
     # method is that we don't lose a whole lot of content from the dataset compared to the
     # case where we simply tokenize with a pre-defined max_length.

     def group_texts(SCREAMING_SNAKE_CASE__     :						str    ):
          # Concatenate all texts.
          _UpperCAmelCase      :     Optional[int]        =   {k: sum(examples[k]						,  []    ) for k in examples.keys()}
          _UpperCAmelCase      :     Optional[int]        =   len(concatenated_examples[list(examples.keys()    )[0]]    )
          # We drop the small remainder, though you could add padding instead if the model supports it
          # In this, as in all things, we advise you to follow your heart 🫀
          _UpperCAmelCase      :     Optional[Any]        =   (total_length // args.max_length) * args.max_length
          # Split by chunks of max_len.
          _UpperCAmelCase      :     Tuple        =   {
              k: [t[i : i + args.max_length] for i in range(0						,  SCREAMING_SNAKE_CASE__						,  args.max_length    )]
              for k, t in concatenated_examples.items()
          }
          return result

     _UpperCAmelCase      :     int        =   dataset_tokenized.map(SCREAMING_SNAKE_CASE__						,  batched=SCREAMING_SNAKE_CASE__						,  batch_size=1_000						,  num_proc=4    )

     _UpperCAmelCase      :     List[Any]        =   0
     _UpperCAmelCase      :     Union[str, Any]        =   0
     for shard in range(0						,  len(SCREAMING_SNAKE_CASE__    )						,  args.shard_size    ):
          _UpperCAmelCase      :     List[Any]        =   grouped_dataset[shard : shard + args.shard_size]
          _UpperCAmelCase      :     Dict        =   len(dataset_snapshot["input_ids"]    )
          _UpperCAmelCase      :     Union[str, Any]        =   os.path.join(SCREAMING_SNAKE_CASE__						,  f'dataset-{shard_count}-{records_containing}.tfrecord'    )
          _UpperCAmelCase      :     List[Any]        =   get_serialized_examples(SCREAMING_SNAKE_CASE__    )

          with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__    ) as out_file:
               for i in range(len(SCREAMING_SNAKE_CASE__    )    ):
                    _UpperCAmelCase      :     Optional[int]        =   serialized_examples[i]
                    out_file.write(SCREAMING_SNAKE_CASE__    )
               print("Wrote file {} containing {} records".format(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )    )

          shard_count += 1
          total_records += records_containing

     with open(f'split-{args.split}-records-count.txt'						,  "w"    ) as f:
          print(f'Total {args.split} records: {total_records}'						,  file=SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 _lowerCAmelCase    : List[Any]							=							parse_args()
 main(args)





"""simple docstring"""




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_lowerCAmelCase    : List[str]							=							[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict    )  ->       List[str]:





     '''simple docstring'''

     for pegasus_name, hf_name in PATTERNS:
          _UpperCAmelCase      :     str        =   k.replace(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     return k

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						dict						,  SCREAMING_SNAKE_CASE__     :						dict    )  ->       PegasusForConditionalGeneration:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   DEFAULTS.copy()
     cfg_kwargs.update(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusConfig(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   torch_model.model.state_dict()
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     for k, v in tf_weights.items():
          _UpperCAmelCase      :     Union[str, Any]        =   rename_state_dict_key(SCREAMING_SNAKE_CASE__    )
          if new_k not in sd:
               raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'    )

          if "dense" in k or "proj" in new_k:
               _UpperCAmelCase      :     Any        =   v.T
          _UpperCAmelCase      :     str        =   torch.tensor(SCREAMING_SNAKE_CASE__						,  dtype=sd[new_k].dtype    )
          assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
     # make sure embedding.padding_idx is respected
     _UpperCAmelCase      :     Tuple        =   torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]    )
     _UpperCAmelCase      :     Any        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   {k: torch.zeros_like(SCREAMING_SNAKE_CASE__    ) for k, v in sd.items() if k.endswith("bias"    ) and k not in mapping}
     mapping.update(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__						,  strict=SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   [
         k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict="./ckpt/aeslc/model.ckpt-32000"    )  ->       Dict:





     '''simple docstring'''

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   Path(SCREAMING_SNAKE_CASE__    ).parent.name
     _UpperCAmelCase      :     Tuple        =   task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
     _UpperCAmelCase      :     Dict        =   PegasusTokenizer.from_pretrained("sshleifer/pegasus"						,  model_max_length=SCREAMING_SNAKE_CASE__    )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(SCREAMING_SNAKE_CASE__    )

     # convert model
     _UpperCAmelCase      :     Union[str, Any]        =   get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   task_specific_params[f'summarization_{dataset}']
     if dataset == "large":
          _UpperCAmelCase      :     Optional[int]        =   task_specific_params
     _UpperCAmelCase      :     str        =   convert_pegasus(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     torch_model.save_pretrained(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   torch_model.state_dict()
     sd.pop("model.decoder.embed_positions.weight"    )
     sd.pop("model.encoder.embed_positions.weight"    )
     torch.save(SCREAMING_SNAKE_CASE__						,  Path(SCREAMING_SNAKE_CASE__    ) / "pytorch_model.bin"    )


if __name__ == "__main__":
 _lowerCAmelCase    : Union[str, Any]							=							argparse.ArgumentParser()
 # Required parameters
 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    : Union[str, Any]							=							parser.parse_args()
 if args.save_dir is None:
  _lowerCAmelCase    : Tuple							=							Path(args.tf_ckpt_path).parent.name
  _lowerCAmelCase    : Dict							=							os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
