

'''simple docstring'''






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      List[str]    ,    SCREAMING_SNAKE_CASE_	:      List[str]    ,    SCREAMING_SNAKE_CASE_	:      Optional[Any]    ,    SCREAMING_SNAKE_CASE_	:      Optional[Any]="attention"       )				->  Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_ :     Dict	=			params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
      SCREAMING_SNAKE_CASE_ :     Dict	=			params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
      SCREAMING_SNAKE_CASE_ :     Optional[int]	=			params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
      SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
      return k, o, q, v


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      int    ,    SCREAMING_SNAKE_CASE_	:      Union[str, Any]    ,    SCREAMING_SNAKE_CASE_	:      str    ,    SCREAMING_SNAKE_CASE_	:      Tuple=False       )				->  str:
      """simple docstring"""

      if split_mlp_wi:
            SCREAMING_SNAKE_CASE_ :     int	=			params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
            SCREAMING_SNAKE_CASE_ :     List[Any]	=			params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
            SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			(wi_a, wi_a)
      else:
            SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

      SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
      return wi, wo


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      Optional[int]    ,    SCREAMING_SNAKE_CASE_	:      Optional[Any]    ,    SCREAMING_SNAKE_CASE_	:      Optional[int]    ,    SCREAMING_SNAKE_CASE_	:      Optional[int]       )				->  Optional[Any]:
      """simple docstring"""

      return params[F"{prefix}/layers_{i}/{layer_name}/scale"]


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      dict    ,    *, SCREAMING_SNAKE_CASE_	:      int    ,    SCREAMING_SNAKE_CASE_	:      bool       )				->  str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_ :     Any	=			traverse_util.flatten_dict(variables["target"]       )
      SCREAMING_SNAKE_CASE_ :     Optional[int]	=			{"""/""".join(A__       ): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      SCREAMING_SNAKE_CASE_ :     Tuple	=			"""encoder/layers_0/mlp/wi_0/kernel""" in old
      print("Split MLP:"    ,    A__       )

      SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			collections.OrderedDict()

      # Shared embeddings.
      SCREAMING_SNAKE_CASE_ :     Tuple	=			old["""token_embedder/embedding"""]

      # Encoder.
      for i in range(A__       ):
            # Block i, layer 0 (Self Attention).
            SCREAMING_SNAKE_CASE_ :     Dict	=			tax_layer_norm_lookup(A__    ,    A__    ,    "encoder"    ,    "pre_attention_layer_norm"       )
            SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			tax_attention_lookup(A__    ,    A__    ,    "encoder"    ,    "attention"       )
            SCREAMING_SNAKE_CASE_ :     int	=			layer_norm
            SCREAMING_SNAKE_CASE_ :     Any	=			k.T
            SCREAMING_SNAKE_CASE_ :     Dict	=			o.T
            SCREAMING_SNAKE_CASE_ :     int	=			q.T
            SCREAMING_SNAKE_CASE_ :     Optional[int]	=			v.T

            # Block i, layer 1 (MLP).
            SCREAMING_SNAKE_CASE_ :     Any	=			tax_layer_norm_lookup(A__    ,    A__    ,    "encoder"    ,    "pre_mlp_layer_norm"       )
            SCREAMING_SNAKE_CASE_ :     Any	=			tax_mlp_lookup(A__    ,    A__    ,    "encoder"    ,    A__       )
            SCREAMING_SNAKE_CASE_ :     Tuple	=			layer_norm
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE_ :     Optional[int]	=			wi[0].T
                  SCREAMING_SNAKE_CASE_ :     List[Any]	=			wi[1].T
            else:
                  SCREAMING_SNAKE_CASE_ :     int	=			wi.T
            SCREAMING_SNAKE_CASE_ :     List[Any]	=			wo.T

      SCREAMING_SNAKE_CASE_ :     str	=			old[
          """encoder/relpos_bias/rel_embedding"""
      ].T
      SCREAMING_SNAKE_CASE_ :     str	=			old["""encoder/encoder_norm/scale"""]

      if not is_encoder_only:
            # Decoder.
            for i in range(A__       ):
                  # Block i, layer 0 (Self Attention).
                  SCREAMING_SNAKE_CASE_ :     Tuple	=			tax_layer_norm_lookup(A__    ,    A__    ,    "decoder"    ,    "pre_self_attention_layer_norm"       )
                  SCREAMING_SNAKE_CASE_ :     int	=			tax_attention_lookup(A__    ,    A__    ,    "decoder"    ,    "self_attention"       )
                  SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			layer_norm
                  SCREAMING_SNAKE_CASE_ :     List[Any]	=			k.T
                  SCREAMING_SNAKE_CASE_ :     int	=			o.T
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			q.T
                  SCREAMING_SNAKE_CASE_ :     int	=			v.T

                  # Block i, layer 1 (Cross Attention).
                  SCREAMING_SNAKE_CASE_ :     int	=			tax_layer_norm_lookup(A__    ,    A__    ,    "decoder"    ,    "pre_cross_attention_layer_norm"       )
                  SCREAMING_SNAKE_CASE_ :     Optional[int]	=			tax_attention_lookup(A__    ,    A__    ,    "decoder"    ,    "encoder_decoder_attention"       )
                  SCREAMING_SNAKE_CASE_ :     int	=			layer_norm
                  SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			k.T
                  SCREAMING_SNAKE_CASE_ :     List[str]	=			o.T
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			q.T
                  SCREAMING_SNAKE_CASE_ :     List[Any]	=			v.T

                  # Block i, layer 2 (MLP).
                  SCREAMING_SNAKE_CASE_ :     List[str]	=			tax_layer_norm_lookup(A__    ,    A__    ,    "decoder"    ,    "pre_mlp_layer_norm"       )
                  SCREAMING_SNAKE_CASE_ :     Dict	=			tax_mlp_lookup(A__    ,    A__    ,    "decoder"    ,    A__       )
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			layer_norm
                  if split_mlp_wi:
                        SCREAMING_SNAKE_CASE_ :     List[str]	=			wi[0].T
                        SCREAMING_SNAKE_CASE_ :     Dict	=			wi[1].T
                  else:
                        SCREAMING_SNAKE_CASE_ :     Any	=			wi.T
                  SCREAMING_SNAKE_CASE_ :     Optional[int]	=			wo.T

            SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			old["""decoder/decoder_norm/scale"""]
            SCREAMING_SNAKE_CASE_ :     int	=			old[
                """decoder/relpos_bias/rel_embedding"""
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  SCREAMING_SNAKE_CASE_ :     Any	=			old["""decoder/logits_dense/kernel"""].T

      return new


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      Optional[Any]    ,    SCREAMING_SNAKE_CASE_	:      bool       )				->  Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_ :     str	=			collections.OrderedDict([(k, torch.from_numpy(v.copy()       )) for (k, v) in converted_params.items()]       )

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            SCREAMING_SNAKE_CASE_ :     Dict	=			state_dict["""shared.weight"""]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			state_dict["""shared.weight"""]

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print("Using shared word embeddings as lm_head."       )
                  SCREAMING_SNAKE_CASE_ :     Any	=			state_dict["""shared.weight"""]

      return state_dict


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      Tuple    ,    SCREAMING_SNAKE_CASE_	:      List[Any]    ,    SCREAMING_SNAKE_CASE_	:      List[Any]    ,    SCREAMING_SNAKE_CASE_	:      Any       )				->  Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_ :     Tuple	=			checkpoints.load_tax_checkpoint(A__       )
      SCREAMING_SNAKE_CASE_ :     Optional[int]	=			convert_tax_to_pytorch(A__    ,    num_layers=config.num_layers    ,    is_encoder_only=A__       )
      SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			make_state_dict(A__    ,    A__       )
      model.load_state_dict(A__    ,    strict=A__       )


def    __lowerCamelCase     ( SCREAMING_SNAKE_CASE_	:      Optional[Any]    ,    SCREAMING_SNAKE_CASE_	:      int    ,    SCREAMING_SNAKE_CASE_	:      Tuple    ,    SCREAMING_SNAKE_CASE_	:      bool = False       )				->  Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_ :     Tuple	=			TaConfig.from_json_file(A__       )
      print(F"Building PyTorch model from configuration: {config}"       )
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            SCREAMING_SNAKE_CASE_ :     Dict	=			TaEncoderModel(A__       )
      else:
            SCREAMING_SNAKE_CASE_ :     Optional[int]	=			TaForConditionalGeneration(A__       )

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(A__    ,    A__    ,    A__    ,    A__       )

      # Save pytorch-model
      print(F"Save PyTorch model to {pytorch_dump_path}"       )
      model.save_pretrained(A__       )

      # Verify that we can load the checkpoint.
      model.from_pretrained(A__       )
      print("Done"       )


if __name__ == "__main__":
      snake_case_	    =			argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
      # Required parameters
      parser.add_argument(
          '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          required=True,
          help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
      )
      snake_case_	    =			parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


snake_case__				:	Dict					       =					logging.getLogger(__name__)


def    __lowerCamelCase   (      )     ->    Any:
   lowerCamelCase_						:   str		= argparse.ArgumentParser(
       description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."""					)
   parser.add_argument(
       """--dataset_name"""						,						type=A__						,						default="""wikitext"""						,						help="""Name of the training. Explore datasets at: hf.co/datasets."""						,						)
   parser.add_argument(
       """--dataset_config"""						,						type=A__						,						default="""wikitext-103-raw-v1"""						,						help="""Configuration name of the dataset."""					)
   parser.add_argument(
       """--tokenizer_name_or_path"""						,						type=A__						,						default="""sayakpaul/unigram-tokenizer-wikitext"""						,						help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""						,						)
   parser.add_argument(
       """--shard_size"""						,						type=A__						,						default=1000						,						help="""Number of entries to go in a single shard."""						,						)
   parser.add_argument("""--split"""						,						type=A__						,						default="""train"""						,						choices=["""train""", """test""", """validation"""]					)
   parser.add_argument(
       """--limit"""						,						default=A__						,						type=A__						,						help="""Limit the number of shards (used for debugging)."""						,						)
   parser.add_argument(
       """--max_length"""						,						type=A__						,						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=A__						,						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."""						,						)

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


def    __lowerCamelCase   (      A__						: Tuple					)     ->    str:
   def fn(A__						: Dict					):
      return tokenizer(examples["""text"""]					)

   return fn


def    __lowerCamelCase   (      A__						: Union[str, Any]					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Dict		= []
   for i in range(len(tokenized_data["""input_ids"""]					)					):
      lowerCamelCase_						:   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]					)					),
      }
      lowerCamelCase_						:   Optional[int]		= tf.train.Features(feature=A__					)
      lowerCamelCase_						:   Tuple		= tf.train.Example(features=A__					)
      lowerCamelCase_						:   Optional[Any]		= example.SerializeToString()
      records.append(A__					)
   return records


def    __lowerCamelCase   (      A__						: Tuple					)     ->    Union[str, Any]:
   lowerCamelCase_						:   int		= datasets.load_dataset(args.dataset_name						,						args.dataset_config						,						split=args.split					)

   if args.limit is not None:
      lowerCamelCase_						:   str		= min(len(A__					)						,						args.limit					)
      lowerCamelCase_						:   Dict		= dataset.select(range(A__					)					)
      print(f'''Limiting the dataset to {args.limit} entries.'''					)

   lowerCamelCase_						:   str		= 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					)
      lowerCamelCase_						:   Optional[int]		= os.path.join(args.output_dir						,						args.split					)
      if not os.path.exists(A__					):
         os.makedirs(A__					)
   else:
      lowerCamelCase_						:   Any		= os.path.join(args.output_dir						,						args.split					)

   # Tokenize the whole dataset at once.
   lowerCamelCase_						:   List[str]		= tokenize_function(A__					)
   lowerCamelCase_						:   List[Any]		= dataset.map(A__						,						batched=A__						,						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(A__						: Union[str, Any]					):
      # Concatenate all texts.
      lowerCamelCase_						:   Optional[int]		= {k: sum(examples[k]						,						[]					) for k in examples.keys()}
      lowerCamelCase_						:   Union[str, Any]		= 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 🫀
      lowerCamelCase_						:   Union[str, Any]		= (total_length // args.max_length) * args.max_length
      # Split by chunks of max_len.
      lowerCamelCase_						:   List[str]		= {
          k: [t[i : i + args.max_length] for i in range(0						,						A__						,						args.max_length					)]
          for k, t in concatenated_examples.items()
      }
      return result

   lowerCamelCase_						:   Optional[int]		= dataset_tokenized.map(A__						,						batched=A__						,						batch_size=1000						,						num_proc=4					)

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

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

      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=A__					)


if __name__ == "__main__":
      snake_case__				:	str					       =					parse_args()
      main(args)





