


'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCamelCase__          = logging.get_logger(__name__)


UpperCamelCase__          = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__          = {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase__          = {
    '''allenai/led-base-16384''': 1_6_3_8_4,
}




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	VOCAB_FILES_NAMES
      lowerCAmelCase__							=	PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__							=	LEDTokenizer
      lowerCAmelCase__							=	['input_ids', 'attention_mask']






      def __init__(   self							:       Dict		,		_A							:       Tuple=None		,		_A							:       Union[str, Any]=None		,		_A							:       str=None		,		_A							:       List[Any]="replace"		,		_A							:       Dict="<s>"		,		_A							:       Dict="</s>"		,		_A							:       Tuple="</s>"		,		_A							:       int="<s>"		,		_A							:       Optional[Any]="<unk>"		,		_A							:       str="<pad>"		,		_A							:       int="<mask>"		,		_A							:       int=False		,		_A							:       str=True		,		**_A							:       Optional[int]		,		):


          '''simple docstring'''


          super().__init__(
              _A		,		_A		,		tokenizer_file=_A		,		errors=_A		,		bos_token=_A		,		eos_token=_A		,		sep_token=_A		,		cls_token=_A		,		unk_token=_A		,		pad_token=_A		,		mask_token=_A		,		add_prefix_space=_A		,		trim_offsets=_A		,		**_A		,		)

          UpperCAmelCase__     :						Union[str, Any]						=							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__     :						int						=							add_prefix_space
              UpperCAmelCase__     :						Dict						=							pre_tok_class(**_A					)

          UpperCAmelCase__     :						Optional[Any]						=							add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          UpperCAmelCase__     :						Optional[Any]						=							'''post_processor'''
          UpperCAmelCase__     :						Tuple						=							getattr(self.backend_tokenizer		,		_A		,		_A					)
          if tokenizer_component_instance:
              UpperCAmelCase__     :						List[Any]						=							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:
                  UpperCAmelCase__     :						Optional[int]						=							tuple(state['''sep''']					)
              if "cls" in state:
                  UpperCAmelCase__     :						str						=							tuple(state['''cls''']					)

              UpperCAmelCase__     :						Dict						=							False

              if state.get('''add_prefix_space'''		,		_A					) != add_prefix_space:
                  UpperCAmelCase__     :						List[str]						=							add_prefix_space
                  UpperCAmelCase__     :						List[str]						=							True

              if state.get('''trim_offsets'''		,		_A					) != trim_offsets:
                  UpperCAmelCase__     :						List[str]						=							trim_offsets
                  UpperCAmelCase__     :						Optional[Any]						=							True

              if changes_to_apply:
                  UpperCAmelCase__     :						Optional[Any]						=							getattr(_A		,		state.pop('''type'''					)					)
                  UpperCAmelCase__     :						Union[str, Any]						=							component_class(**_A					)
                  setattr(self.backend_tokenizer		,		_A		,		_A					)






      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          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 lowercase_				(   self							:       List[str]		,		_A							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							AddedToken(_A		,		lstrip=_A		,		rstrip=_A					) if isinstance(_A		,		_A					) else value
          UpperCAmelCase__     :						Optional[Any]						=							value






      def lowercase_				(   self							:       Union[str, Any]		,		*_A							:       List[str]		,		**_A							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							kwargs.get('''is_split_into_words'''		,		_A					)

          if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  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 lowercase_				(   self							:       str		,		*_A							:       List[str]		,		**_A							:       str					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							kwargs.get('''is_split_into_words'''		,		_A					)

          if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  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 lowercase_				(   self							:       Any		,		_A							:       str		,		_A							:       Optional[str] = None					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							self._tokenizer.model.save(_A		,		name=_A					)
          return tuple(_A					)






      def lowercase_				(   self							:       Union[str, Any]		,		_A							:       int		,		_A							:       str=None					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
              return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






      def lowercase_				(   self							:       Union[str, Any]		,		_A							:       List[int]		,		_A							:       Optional[List[int]] = None					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							[self.sep_token_id]
          UpperCAmelCase__     :						Union[str, Any]						=							[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 lowercase_				(   self							:       Dict		,		_A							:       Union[Dict[str, EncodedInput], BatchEncoding]		,		_A							:       Optional[int] = None		,		_A							:       PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,		_A							:       Optional[int] = None		,		_A							:       Optional[bool] = None		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							super()._pad(
              encoded_inputs=_A		,		max_length=_A		,		padding_strategy=_A		,		pad_to_multiple_of=_A		,		return_attention_mask=_A		,		)

          # Load from model defaults
          if return_attention_mask is None:
              UpperCAmelCase__     :						Tuple						=							'''attention_mask''' in self.model_input_names

          if return_attention_mask and "global_attention_mask" in encoded_inputs:
              UpperCAmelCase__     :						int						=							encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              UpperCAmelCase__     :						int						=							len(encoded_inputs['''global_attention_mask''']					) != len(_A					)

              if needs_to_be_padded:
                  UpperCAmelCase__     :						int						=							len(_A					) - len(encoded_inputs['''global_attention_mask''']					)

                  if self.padding_side == "right":
                      # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                      UpperCAmelCase__     :						Tuple						=							(
                          encoded_inputs['''global_attention_mask'''] + [-1] * difference
                      )
                  elif self.padding_side == "left":
                      UpperCAmelCase__     :						Optional[Any]						=							[-1] * difference + encoded_inputs[
                          '''global_attention_mask'''
                      ]
                  else:
                      raise ValueError('''Invalid padding strategy:''' + str(self.padding_side					)					)

          return encoded_inputs








"""simple docstring"""





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       __A		(   SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				OpenAIGPTTokenizer
					UpperCAmelCase__								=				OpenAIGPTTokenizerFast
					UpperCAmelCase__								=				True
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[Any]:
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											__magic_name__:	str							=       [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """w</w>""",
											    """r</w>""",
											    """t</w>""",
											    """lo""",
											    """low""",
											    """er</w>""",
											    """low</w>""",
											    """lowest</w>""",
											    """newer</w>""",
											    """wider</w>""",
											    """<unk>""",
											]
											__magic_name__:	Any							=       dict(zip(__snake_case     ,		range(len(__snake_case					)					)					)					)
											__magic_name__:	List[str]							=       ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

											__magic_name__:	Optional[Any]							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
											__magic_name__:	Any							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""merges_file"""]					)
											with open(self.vocab_file     ,		"""w"""					) as fp:
																	fp.write(json.dumps(__snake_case					)					)
											with open(self.merges_file     ,		"""w"""					) as fp:
																	fp.write("""\n""".join(__snake_case					)					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Optional[Any]					)			->							Optional[int]:
											return "lower newer", "lower newer"

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Union[str, Any]:
											__magic_name__:	Optional[Any]							=       OpenAIGPTTokenizer(self.vocab_file     ,		self.merges_file					)

											__magic_name__:	Union[str, Any]							=       """lower"""
											__magic_name__:	Optional[int]							=       ["""low""", """er</w>"""]
											__magic_name__:	List[Any]							=       tokenizer.tokenize(__snake_case					)
											self.assertListEqual(__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       tokens + ["""<unk>"""]
											__magic_name__:	List[str]							=       [1_4, 1_5, 2_0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Any=1_5					)			->							Tuple:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																							__magic_name__:	str							=       self.rust_tokenizer_class.from_pretrained(__snake_case     ,		**__snake_case					)

																							# Simple input
																							__magic_name__:	Dict							=       """This is a simple input"""
																							__magic_name__:	Any							=       ["""This is a simple input 1""", """This is a simple input 2"""]
																							__magic_name__:	int							=       ("""This is a simple input""", """This is a pair""")
																							__magic_name__:	int							=       [
																							    ("""This is a simple input 1""", """This is a simple input 2"""),
																							    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																							]

																							# Simple input tests
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Simple input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Simple input
																							self.assertRaises(
																							    __snake_case     ,		tokenizer_r.batch_encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""     ,		)

																							# Pair input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Pair input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Pair input
																							self.assertRaises(
																							    __snake_case     ,		tokenizer_r.batch_encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""     ,		)






					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											pass



@require_ftfy
@require_spacy
@require_tokenizers
class       __A		(   SCREAMING_SNAKE_CASE_			):
					pass



