'''simple docstring'''

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

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

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      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				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      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				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])

         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids







'''simple docstring'''

import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowerCAmelCase_ : Union[str, Any]						  =      datasets.utils.logging.get_logger(__name__)





@dataclass
class 							lowercase						(						datasets.BuilderConfig ):
   lowerCamelCase_    					=None
   lowerCamelCase_    					="utf-8"
   lowerCamelCase_    					=None
   lowerCamelCase_    					=None
   lowerCamelCase_    					=True  # deprecated
   lowerCamelCase_    					=None  # deprecated
   lowerCamelCase_    					=1_0 << 2_0  # 10MB
   lowerCamelCase_    					=None





class 							lowercase						(						datasets.ArrowBasedBuilder ):
   lowerCamelCase_    					=JsonConfig


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         if self.config.block_size is not None:
               logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead")
               lowercase_     =      self.config.block_size
         if self.config.use_threads is not True:
               logger.warning(
                   "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore.")
         if self.config.newlines_in_values is not None:
               raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported")
         return datasets.DatasetInfo(features=self.config.features)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	int)      ->					Any:
         if not self.config.data_files:
               raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
         lowercase_     =      dl_manager.download_and_extract(self.config.data_files)
         if isinstance(__lowerCAmelCase   ,		(str, list, tuple)):
               lowercase_     =      data_files
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      [files]
               lowercase_     =      [dl_manager.iter_files(__lowerCAmelCase) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,		gen_kwargs={"files": files})]
         lowercase_     =      []
         for split_name, files in data_files.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      [files]
               lowercase_     =      [dl_manager.iter_files(__lowerCAmelCase) for file in files]
               splits.append(datasets.SplitGenerator(name=__lowerCAmelCase   ,		gen_kwargs={"files": files}))
         return splits


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	pa.Table)      ->					pa.Table:
         if self.config.features is not None:
               # adding missing columns
               for column_name in set(self.config.features) - set(pa_table.column_names):
                     lowercase_     =      self.config.features.arrow_schema.field(__lowerCAmelCase).type
                     lowercase_     =      pa_table.append_column(__lowerCAmelCase   ,		pa.array([None] * len(__lowerCAmelCase)   ,		type=__lowerCAmelCase))
               # more expensive cast to support nested structures with keys in a different order
               # allows str <-> int/float or str to Audio for example
               lowercase_     =      table_cast(__lowerCAmelCase   ,		self.config.features.arrow_schema)
         return pa_table


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	List[Any])      ->					List[Any]:
         for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase)):
               # If the file is one json object and if we need to look at the list of items in one specific field
               if self.config.field is not None:
                     with open(__lowerCAmelCase   ,		encoding=self.config.encoding   ,		errors=self.config.encoding_errors) as f:
                           lowercase_     =      json.load(__lowerCAmelCase)

                     # We keep only the field we are interested in
                     lowercase_     =      dataset[self.config.field]

                     # We accept two format: a list of dicts or a dict of lists
                     if isinstance(__lowerCAmelCase   ,		(list, tuple)):
                           lowercase_     =      set().union(*[row.keys() for row in dataset])
                           lowercase_     =      {col: [row.get(__lowerCAmelCase) for row in dataset] for col in keys}
                     else:
                           lowercase_     =      dataset
                     lowercase_     =      pa.Table.from_pydict(__lowerCAmelCase)
                     yield file_idx, self._cast_table(__lowerCAmelCase)

               # If the file has one json object per line
               else:
                     with open(__lowerCAmelCase   ,		"rb") as f:
                           lowercase_     =      0
                           # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                           # Set a default minimum value of 16kB if the chunk size is really small
                           lowercase_     =      max(self.config.chunksize // 32   ,		16 << 10)
                           lowercase_     =      (
                               self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                           )
                           while True:
                                 lowercase_     =      f.read(self.config.chunksize)
                                 if not batch:
                                       break
                                 # Finish current line
                                 try:
                                       batch += f.readline()
                                 except (AttributeError, io.UnsupportedOperation):
                                       batch += readline(__lowerCAmelCase)
                                 # PyArrow only accepts utf-8 encoded bytes
                                 if self.config.encoding != "utf-8":
                                       lowercase_     =      batch.decode(self.config.encoding   ,		errors=__lowerCAmelCase).encode("utf-8")
                                 try:
                                       while True:
                                             try:
                                                   lowercase_     =      paj.read_json(
                                                       io.BytesIO(__lowerCAmelCase)   ,		read_options=paj.ReadOptions(block_size=__lowerCAmelCase))
                                                   break
                                             except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                   if (
                                                       isinstance(__lowerCAmelCase   ,		pa.ArrowInvalid)
                                                       and "straddling" not in str(__lowerCAmelCase)
                                                       or block_size > len(__lowerCAmelCase)
                                                   ):
                                                         raise
                                                   else:
                                                         # Increase the block size in case it was too small.
                                                         # The block size will be reset for the next file.
                                                         logger.debug(
                                                             F'Batch of {len(__lowerCAmelCase)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.')
                                                         block_size *= 2
                                 except pa.ArrowInvalid as e:
                                       try:
                                             with open(
                                                 __lowerCAmelCase   ,		encoding=self.config.encoding   ,		errors=self.config.encoding_errors) as f:
                                                   lowercase_     =      json.load(__lowerCAmelCase)
                                       except json.JSONDecodeError:
                                             logger.error(F'Failed to read file \'{file}\' with error {type(__lowerCAmelCase)}: {e}')
                                             raise e
                                       # If possible, parse the file as a list of json objects and exit the loop
                                       if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):  # list is the only sequence type supported in JSON
                                             try:
                                                   lowercase_     =      set().union(*[row.keys() for row in dataset])
                                                   lowercase_     =      {col: [row.get(__lowerCAmelCase) for row in dataset] for col in keys}
                                                   lowercase_     =      pa.Table.from_pydict(__lowerCAmelCase)
                                             except (pa.ArrowInvalid, AttributeError) as e:
                                                   logger.error(F'Failed to read file \'{file}\' with error {type(__lowerCAmelCase)}: {e}')
                                                   raise ValueError(F'Not able to read records in the JSON file at {file}.') from None
                                             yield file_idx, self._cast_table(__lowerCAmelCase)
                                             break
                                       else:
                                             logger.error(F'Failed to read file \'{file}\' with error {type(__lowerCAmelCase)}: {e}')
                                             raise ValueError(
                                                 F'Not able to read records in the JSON file at {file}. '
                                                 F'You should probably indicate the field of the JSON file containing your records. '
                                                 F'This JSON file contain the following fields: {str(list(dataset.keys()))}. '
                                                 F'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ') from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                 yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase)
                                 batch_idx += 1






