



"""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							:   Any				=				logging.get_logger(__name__)

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

_lowerCAmelCase							:   List[str]				=				{
    "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							:   List[str]				=				{
    "gpt2": 10_24,
    "gpt2-medium": 10_24,
    "gpt2-large": 10_24,
    "gpt2-xl": 10_24,
    "distilgpt2": 10_24,
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  int						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:  Dict						=					GPTaTokenizer



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

           _UpperCAmelCase     :      List[Any]														=       kwargs.pop("add_bos_token"	,      A		)

           _UpperCAmelCase     :      List[str]														=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
           if pre_tok_state.get("add_prefix_space"	,      A		) != add_prefix_space:
                _UpperCAmelCase     :      Dict														=       getattr(A	,      pre_tok_state.pop("type"		)		)
                _UpperCAmelCase     :      Optional[Any]														=       add_prefix_space
                _UpperCAmelCase     :      Optional[Any]														=       pre_tok_class(**A		)

           _UpperCAmelCase     :      List[Any]														=       add_prefix_space



      def 		snake_case_   (	self	:      List[Any]	,      *A	:      List[Any]	,      **A	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)
           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(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[Any]	,      *A	:      List[Any]	,      **A	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)

           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(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      str	,      A	:      Optional[str] = None		):
           _UpperCAmelCase     :      List[Any]														=       self._tokenizer.model.save(A	,      name=A		)
           return tuple(A		)



      def 		snake_case_   (	self	:      int	,      A	:      "Conversation"		):
           _UpperCAmelCase     :      List[str]														=       []
           for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(A	,      add_special_tokens=A		) + [self.eos_token_id]		)

           if len(A		) > self.model_max_length:
                _UpperCAmelCase     :      Optional[int]														=       input_ids[-self.model_max_length :]
           return input_ids





"""simple docstring"""




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       tmp_path / "file.csv"
     _UpperCAmelCase     :      Union[str, Any]														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       tmp_path / "malformed_file.csv"
     _UpperCAmelCase     :      Dict														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20,\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       tmp_path / "csv_with_image.csv"
     _UpperCAmelCase     :      List[str]														=       textwrap.dedent(
         f'\\n        image\n        {image_file}\n        '							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       tmp_path / "csv_with_label.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        label\n        good\n        bad\n        good\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       tmp_path / "csv_with_int_list.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

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





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       Csv()
     _UpperCAmelCase     :      Optional[int]														=       csv._generate_tables([[csv_file, malformed_csv_file]]							)
     with pytest.raises(SCREAMING_SNAKE_CASE__		,    match="Error tokenizing data"							):
          for _ in generator:
               pass
     assert any(
         record.levelname == "ERROR"
         and "Failed to read file" in record.message
         and os.path.basename(SCREAMING_SNAKE_CASE__							) in record.message
         for record in caplog.records							)

@require_pil
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Union[str, Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Union[str, Any]														=       f.read().splitlines()[1]
     _UpperCAmelCase     :      Tuple														=       Csv(encoding="utf-8"		,    features=Features({"image": Image()}							)							)
     _UpperCAmelCase     :      Tuple														=       csv._generate_tables([[csv_file_with_image]]							)
     _UpperCAmelCase     :      List[str]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("image"							).type == Image()()
     _UpperCAmelCase     :      Optional[Any]														=       pa_table.to_pydict()["image"]
     assert generated_content == [{"path": image_file, "bytes": None}]

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





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Tuple														=       f.read().splitlines()[1:]
     _UpperCAmelCase     :      Optional[Any]														=       Csv(encoding="utf-8"		,    features=Features({"label": ClassLabel(names=["good", "bad"]							)}							)							)
     _UpperCAmelCase     :      int														=       csv._generate_tables([[csv_file_with_label]]							)
     _UpperCAmelCase     :      Dict														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("label"							).type == ClassLabel(names=["good", "bad"]							)()
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["label"]
     assert generated_content == [ClassLabel(names=["good", "bad"]							).straint(SCREAMING_SNAKE_CASE__							) for label in labels]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       Csv(encoding="utf-8"		,    sep=","		,    converters={"int_list": lambda SCREAMING_SNAKE_CASE__							: [int(SCREAMING_SNAKE_CASE__							) for i in x.split()]}							)
     _UpperCAmelCase     :      Dict														=       csv._generate_tables([[csv_file_with_int_list]]							)
     _UpperCAmelCase     :      List[Any]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa.types.is_list(pa_table.schema.field("int_list"							).type							)
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["int_list"]
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
