import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

__lowerCAmelCase	:       str     =	logging.get_logger(__name__)


__lowerCAmelCase	:       int     =	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__lowerCAmelCase	:       Tuple     =	{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

__lowerCAmelCase	:       str     =	{'facebook/blenderbot-3B': 128}




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_ :							Dict											=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_ :							Dict											=      BlenderbotTokenizer





				def __init__(   self       :  Any      ,   __lowerCamelCase       :  List[Any]=None      ,   __lowerCamelCase       :  Any=None      ,   __lowerCamelCase       :  Optional[int]=None      ,   __lowerCamelCase       :  Optional[Any]="replace"      ,   __lowerCamelCase       :  List[str]="<s>"      ,   __lowerCamelCase       :  Optional[Any]="</s>"      ,   __lowerCamelCase       :  Dict="</s>"      ,   __lowerCamelCase       :  Union[str, Any]="<s>"      ,   __lowerCamelCase       :  int="<unk>"      ,   __lowerCamelCase       :  str="<pad>"      ,   __lowerCamelCase       :  List[str]="<mask>"      ,   __lowerCamelCase       :  Dict=False      ,   __lowerCamelCase       :  int=True      ,   **__lowerCamelCase       :  Optional[int]      ,   )      ->  List[Any]:
							super().__init__(
							    __lowerCamelCase      ,   __lowerCamelCase      ,   tokenizer_file=__lowerCamelCase      ,   errors=__lowerCamelCase      ,   bos_token=__lowerCamelCase      ,   eos_token=__lowerCamelCase      ,   sep_token=__lowerCamelCase      ,   cls_token=__lowerCamelCase      ,   unk_token=__lowerCamelCase      ,   pad_token=__lowerCamelCase      ,   mask_token=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase      ,   **__lowerCamelCase      ,   )

							a					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get("add_prefix_space"      ,   __lowerCamelCase							) != add_prefix_space:
										a					=    getattr(__lowerCamelCase      ,   pre_tok_state.pop("type"							)							)
										a					=    add_prefix_space
										a					=    pre_tok_class(**__lowerCamelCase							)

							a					=    add_prefix_space

							a					=    "post_processor"
							a					=    getattr(self.backend_tokenizer      ,   __lowerCamelCase      ,   __lowerCamelCase							)
							if tokenizer_component_instance:
										a					=    json.loads(tokenizer_component_instance.__getstate__()							)

										# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
										if "sep" in state:
													a					=    tuple(state["sep"]							)
										if "cls" in state:
													a					=    tuple(state["cls"]							)

										a					=    False

										if state.get("add_prefix_space"      ,   __lowerCamelCase							) != add_prefix_space:
													a					=    add_prefix_space
													a					=    True

										if state.get("trim_offsets"      ,   __lowerCamelCase							) != trim_offsets:
													a					=    trim_offsets
													a					=    True

										if changes_to_apply:
													a					=    getattr(__lowerCamelCase      ,   state.pop("type"							)							)
													a					=    component_class(**__lowerCamelCase							)
													setattr(self.backend_tokenizer      ,   __lowerCamelCase      ,   __lowerCamelCase							)





				@property
				# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  str:
							if self._mask_token is None:
										if self.verbose:
													logger.error("Using mask_token, but it is not set yet."							)
										return None
							return str(self._mask_token							)





				@mask_token.setter
				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Union[str, Any]:
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else value
							a					=    value





				def 							__UpperCAmelCase							(   self       :  Any      ,   *__lowerCamelCase       :  Union[str, Any]      ,   **__lowerCamelCase       :  List[str]							)      ->  BatchEncoding:
							a					=    kwargs.get("is_split_into_words"      ,   __lowerCamelCase							)
							assert self.add_prefix_space or not is_split_into_words, (
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*__lowerCamelCase      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   *__lowerCamelCase       :  List[str]      ,   **__lowerCamelCase       :  Tuple							)      ->  BatchEncoding:
							a					=    kwargs.get("is_split_into_words"      ,   __lowerCamelCase							)

							assert self.add_prefix_space or not is_split_into_words, (
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*__lowerCamelCase      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[str] = None							)      ->  Tuple[str]:
							a					=    self._tokenizer.model.save(__lowerCamelCase      ,   name=__lowerCamelCase							)
							return tuple(__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None							)      ->  List[int]:
							a					=    [self.sep_token_id]
							a					=    [self.cls_token_id]

							if token_ids_a is None:
										return len(cls + token_ids_a + sep							) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None							)      ->  str:
							return token_ids_a + [self.eos_token_id]





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  "Conversation"							)      ->  List[int]:
							a					=    []
							for is_user, text in conversation.iter_texts():
										if is_user:
													# We need to space prefix as it's being done within blenderbot
													inputs.append(" " + text							)
										else:
													# Generated responses should contain them already.
													inputs.append(__lowerCamelCase							)

							a					=    "  ".join(__lowerCamelCase							)
							a					=    self.encode(__lowerCamelCase							)
							if len(__lowerCamelCase							) > self.model_max_length:
										a					=    input_ids[-self.model_max_length :]
										logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""							)
							return input_ids












import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__lowerCAmelCase	:       List[Any]     =	logging.getLogger(__name__)




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    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.",						)

			a					=    parser.parse_args()
			return args




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			def fn(A	:      Tuple     ):
						return tokenizer(examples["text"]     )

			return fn




def     __magic_name__       (				A	:      Any     ):






			'''simple docstring'''







			a					=    []
			for i in range(len(tokenized_data["input_ids"]     )     ):
						a					=    {
						    "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]     )     ),
						}
						a					=    tf.train.Features(feature=A     )
						a					=    tf.train.Example(features=A     )
						a					=    example.SerializeToString()
						records.append(A     )
			return records




def     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    datasets.load_dataset(args.dataset_name,						args.dataset_config,						split=args.split     )

			if args.limit is not None:
						a					=    min(len(A     ),						args.limit     )
						a					=    dataset.select(range(A     )     )
						print(F"""Limiting the dataset to {args.limit} entries."""     )

			a					=    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     )
						a					=    os.path.join(args.output_dir,						args.split     )
						if not os.path.exists(A     ):
									os.makedirs(A     )
			else:
						a					=    os.path.join(args.output_dir,						args.split     )

			# Tokenize the whole dataset at once.
			a					=    tokenize_function(A     )
			a					=    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	:      List[Any]     ):
						# Concatenate all texts.
						a					=    {k: sum(examples[k],						[]     ) for k in examples.keys()}
						a					=    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 🫀
						a					=    (total_length // args.max_length) * args.max_length
						# Split by chunks of max_len.
						a					=    {
						    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

			a					=    dataset_tokenized.map(A,						batched=A,						batch_size=1000,						num_proc=4     )

			a					=    0
			a					=    0
			for shard in range(0,						len(A     ),						args.shard_size     ):
						a					=    grouped_dataset[shard : shard + args.shard_size]
						a					=    len(dataset_snapshot["input_ids"]     )
						a					=    os.path.join(A,						F"""dataset-{shard_count}-{records_containing}.tfrecord"""     )
						a					=    get_serialized_examples(A     )

						with tf.io.TFRecordWriter(A     ) as out_file:
									for i in range(len(A     )     ):
												a					=    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__":
		__lowerCAmelCase	:       Optional[int]     =	parse_args()
		main(args)






