


"""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


__snake_case         =					logging.get_logger(__name__)

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

__snake_case         =					{
    """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""",
    },
}

__snake_case         =					{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Dict													=						VOCAB_FILES_NAMES
   __UpperCAmelCase				: List[str]													=						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase				: Any													=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase				: Optional[Any]													=						['''input_ids''', '''attention_mask''']
   __UpperCAmelCase				: Union[str, Any]													=						GPTaTokenizer







   def __init__(			self		,			UpperCamelCase__=None		,			UpperCamelCase__=None		,			UpperCamelCase__=None		,			UpperCamelCase__="<|endoftext|>"		,			UpperCamelCase__="<|endoftext|>"		,			UpperCamelCase__="<|endoftext|>"		,			UpperCamelCase__=False		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




       super().__init__(
           UpperCamelCase__		,			UpperCamelCase__		,			tokenizer_file=UpperCamelCase__		,			unk_token=UpperCamelCase__		,			bos_token=UpperCamelCase__		,			eos_token=UpperCamelCase__		,			add_prefix_space=UpperCamelCase__		,			**UpperCamelCase__		,			)

       snake_case					:   List[str]         =					kwargs.pop("add_bos_token"		,			UpperCamelCase__		)

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

       snake_case					:   Any         =					add_prefix_space







   def 				lowerCamelCase	(			self		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	BatchEncoding:

       '''simple docstring'''




       snake_case					:   Any         =					kwargs.get("is_split_into_words"		,			UpperCamelCase__		)
       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(*UpperCamelCase__		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	BatchEncoding:

       '''simple docstring'''




       snake_case					:   int         =					kwargs.get("is_split_into_words"		,			UpperCamelCase__		)

       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(*UpperCamelCase__		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	Tuple[str]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self._tokenizer.model.save(UpperCamelCase__		,			name=UpperCamelCase__		)
       return tuple(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[int]:

       '''simple docstring'''




       snake_case					:   Tuple         =					[]
       for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(UpperCamelCase__		,			add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

       if len(UpperCamelCase__		) > self.model_max_length:
           snake_case					:   List[Any]         =					input_ids[-self.model_max_length :]
       return input_ids









"""simple docstring"""



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizer
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizerFast
   __UpperCAmelCase				: List[Any]													=						True
   __UpperCAmelCase				: Dict													=						True







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       super().setUp()

       snake_case					:   Dict         =					[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       snake_case					:   List[str]         =					os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["vocab_file"]		)
       with open(self.vocab_file		,			"w"		,			encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)







   def 				lowerCamelCase	(			self		,			**UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   str         =					"UNwant\u00E9d,running"
       snake_case					:   Optional[int]         =					"unwanted, running"
       return input_text, output_text







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.tokenizer_class(self.vocab_file		)

       snake_case					:   Optional[Any]         =					tokenizer.tokenize("UNwant\u00E9d,running"		)
       self.assertListEqual(UpperCamelCase__		,			["un", "##want", "##ed", ",", "runn", "##ing"]		)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)		,			[7, 4, 5, 10, 8, 9]		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       pass





