


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class        a					(			unittest.TestCase   ):





					@slow
					def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
								__lowerCamelCase:				Dict     =	TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""							)

								__lowerCamelCase:				Tuple     =	{
								    "input_ids": tf.convert_to_tensor([[0, 2646, 10269, 83, 99942, 2]]    ,	dtype=tf.intaa							),  # "My dog is cute"
								    "attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,	dtype=tf.intaa							),
								}

								__lowerCamelCase:				Any     =	model(lowercase__							)["last_hidden_state"]
								__lowerCamelCase:				Any     =	tf.TensorShape((1, 6, 768)							)
								self.assertEqual(output.shape    ,	lowercase__							)
								# compare the actual values for a slice.
								__lowerCamelCase:				Dict     =	tf.convert_to_tensor(
								    [
								        [
								            [0.068_1762, 0.1089_4451, 0.0677_2504],
								            [-0.0642_3668, 0.0236_6615, 0.0432_9344],
								            [-0.0605_7295, 0.0997_4135, -0.0007_0584],
								        ]
								    ]    ,	dtype=tf.floataa    ,	)

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,	expected_slice.numpy()    ,	atol=1E-4							)							)






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_A		:				List[str]									=       logging.get_logger(__name__)


_A		:				Any									=       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
_A		:				int									=       {
    '''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''',
    },
}

_A		:				Dict									=       {
    '''allenai/led-base-16384''': 16_384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	(
       list(range(ord("""!""" )						,      ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" )						,      ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" )						,      ord("""ÿ""" ) + 1 ) )
   )
   __lowerCamelCase:				List[str]     =	bs[:]
   __lowerCamelCase:				Tuple     =	0
   for b in range(2**8 ):
      if b not in bs:
         bs.append(snake_case )
         cs.append(2**8 + n )
         n += 1
   __lowerCamelCase:				List[Any]     =	[chr(snake_case ) for n in cs]
   return dict(zip(snake_case						,      snake_case ) )



def     __lowerCAmelCase						(							snake_case	:    List[str] )       ->							str:
   __lowerCamelCase:				int     =	set()
   __lowerCamelCase:				str     =	word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      __lowerCamelCase:				Any     =	char
   return pairs







class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	VOCAB_FILES_NAMES
     UpperCAmelCase__ :  Optional[Any]						      =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__ :  List[str]						      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__ :  List[str]						      =	["input_ids", "attention_mask"]





     def __init__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict="replace"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]="</s>"    ,	SCREAMING_SNAKE_CASE_					:				int="</s>"    ,	SCREAMING_SNAKE_CASE_					:				Any="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="<unk>"    ,	SCREAMING_SNAKE_CASE_					:				Tuple="<pad>"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<mask>"    ,	SCREAMING_SNAKE_CASE_					:				int=False    ,	**SCREAMING_SNAKE_CASE_					:				List[str]    ,	):
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else bos_token
        __lowerCamelCase:				Union[str, Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else eos_token
        __lowerCamelCase:				List[Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else sep_token
        __lowerCamelCase:				Dict     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else cls_token
        __lowerCamelCase:				Any     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else unk_token
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __lowerCamelCase:				List[str]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else mask_token

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

        with open(SCREAMING_SNAKE_CASE_    ,	encoding="""utf-8"""							) as vocab_handle:
           __lowerCamelCase:				str     =	json.load(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	{v: k for k, v in self.encoder.items()}
        __lowerCamelCase:				Any     =	errors  # how to handle errors in decoding
        __lowerCamelCase:				List[Any]     =	bytes_to_unicode()
        __lowerCamelCase:				Optional[int]     =	{v: k for k, v in self.byte_encoder.items()}
        with open(SCREAMING_SNAKE_CASE_    ,	encoding="""utf-8"""							) as merges_handle:
           __lowerCamelCase:				List[Any]     =	merges_handle.read().split("""\n"""							)[1:-1]
        __lowerCamelCase:				Tuple     =	[tuple(merge.split()							) for merge in bpe_merges]
        __lowerCamelCase:				str     =	dict(zip(SCREAMING_SNAKE_CASE_    ,	range(len(SCREAMING_SNAKE_CASE_							)							)							)							)
        __lowerCamelCase:				List[str]     =	{}
        __lowerCamelCase:				Optional[int]     =	add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __lowerCamelCase:				str     =	re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)





     @property
     # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return len(self.encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        return dict(self.encoder    ,	**self.added_tokens_encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int							):
        if token in self.cache:
           return self.cache[token]
        __lowerCamelCase:				List[str]     =	tuple(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	get_pairs(SCREAMING_SNAKE_CASE_							)

        if not pairs:
           return token

        while True:
           __lowerCamelCase:				List[str]     =	min(SCREAMING_SNAKE_CASE_    ,	key=lambda SCREAMING_SNAKE_CASE_							: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_    ,	float("""inf"""							)							)							)
           if bigram not in self.bpe_ranks:
              break
           __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	bigram
           __lowerCamelCase:				Optional[int]     =	[]
           __lowerCamelCase:				Optional[Any]     =	0
           while i < len(SCREAMING_SNAKE_CASE_							):
              try:
                 __lowerCamelCase:				int     =	word.index(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              except ValueError:
                 new_word.extend(word[i:]							)
                 break
              else:
                 new_word.extend(word[i:j]							)
                 __lowerCamelCase:				Tuple     =	j

              if word[i] == first and i < len(SCREAMING_SNAKE_CASE_							) - 1 and word[i + 1] == second:
                 new_word.append(first + second							)
                 i += 2
              else:
                 new_word.append(word[i]							)
                 i += 1
           __lowerCamelCase:				Optional[int]     =	tuple(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Any     =	new_word
           if len(SCREAMING_SNAKE_CASE_							) == 1:
              break
           else:
              __lowerCamelCase:				Tuple     =	get_pairs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	""" """.join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	word
        return word





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				Any     =	[]
        for token in re.findall(self.pat    ,	SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				int     =	"""""".join(
               self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_							).split(""" """							)							)
        return bpe_tokens





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        return self.encoder.get(SCREAMING_SNAKE_CASE_    ,	self.encoder.get(self.unk_token							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str							):
        return self.decoder.get(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	"""""".join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""    ,	errors=self.errors							)
        return text





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None							):
        if not os.path.isdir(SCREAMING_SNAKE_CASE_							):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
           return
        __lowerCamelCase:				Dict     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
        __lowerCamelCase:				str     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

        with open(SCREAMING_SNAKE_CASE_    ,	"""w"""    ,	encoding="""utf-8"""							) as f:
           f.write(json.dumps(self.encoder    ,	indent=2    ,	sort_keys=SCREAMING_SNAKE_CASE_    ,	ensure_ascii=SCREAMING_SNAKE_CASE_							) + """\n"""							)

        __lowerCamelCase:				List[str]     =	0
        with open(SCREAMING_SNAKE_CASE_    ,	"""w"""    ,	encoding="""utf-8"""							) as writer:
           writer.write("""#version: 0.2\n"""							)
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,	key=lambda SCREAMING_SNAKE_CASE_							: kv[1]							):
              if index != token_index:
                 logger.warning(
                     F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                     """ Please check that the tokenizer is not corrupted!"""							)
                 __lowerCamelCase:				Tuple     =	token_index
              writer.write(""" """.join(SCREAMING_SNAKE_CASE_							) + """\n"""							)
              index += 1

        return vocab_file, merge_file





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __lowerCamelCase:				List[str]     =	[self.cls_token_id]
        __lowerCamelCase:				int     =	[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=SCREAMING_SNAKE_CASE_    ,	token_ids_a=SCREAMING_SNAKE_CASE_    ,	already_has_special_tokens=SCREAMING_SNAKE_CASE_							)

        if token_ids_a is None:
           return [1] + ([0] * len(SCREAMING_SNAKE_CASE_							)) + [1]
        return [1] + ([0] * len(SCREAMING_SNAKE_CASE_							)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_							)) + [1]





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        __lowerCamelCase:				int     =	[self.sep_token_id]
        __lowerCamelCase:				Tuple     =	[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  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	kwargs.pop("""add_prefix_space"""    ,	self.add_prefix_space							)
        if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_							) > 0 and not text[0].isspace()):
           __lowerCamelCase:				Dict     =	""" """ + text
        return (text, kwargs)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[Dict[str, EncodedInput], BatchEncoding]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	):
        __lowerCamelCase:				Optional[Any]     =	super()._pad(
            encoded_inputs=SCREAMING_SNAKE_CASE_    ,	max_length=SCREAMING_SNAKE_CASE_    ,	padding_strategy=SCREAMING_SNAKE_CASE_    ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_    ,	return_attention_mask=SCREAMING_SNAKE_CASE_    ,	)

        # Load from model defaults
        if return_attention_mask is None:
           __lowerCamelCase:				Optional[Any]     =	"""attention_mask""" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
           __lowerCamelCase:				List[str]     =	encoded_inputs[self.model_input_names[0]]
           # `global_attention_mask` need to have the same length as other (sequential) inputs.
           __lowerCamelCase:				str     =	len(encoded_inputs["""global_attention_mask"""]							) != len(SCREAMING_SNAKE_CASE_							)

           if needs_to_be_padded:
              __lowerCamelCase:				Union[str, Any]     =	len(SCREAMING_SNAKE_CASE_							) - 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`
                 __lowerCamelCase:				str     =	(
                     encoded_inputs["""global_attention_mask"""] + [-1] * difference
                 )
              elif self.padding_side == "left":
                 __lowerCamelCase:				Optional[int]     =	[-1] * difference + encoded_inputs[
                     """global_attention_mask"""
                 ]
              else:
                 raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

        return encoded_inputs


