
import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__		:Dict			  =   logging.get_logger(__name__)







class     lowercase		( SCREAMING_SNAKE_CASE__		):
       lowercase_	:	Any	 =['''input_ids''', '''attention_mask''']




       def __init__(    self       ,A__="</s>"       ,A__="<unk>"       ,A__="<pad>"       ,A__=1_2_5       ,A__=None       ,**A__       ,):

            if extra_ids > 0 and additional_special_tokens is None:
                 lowercase						     =						[f'<extra_id_{i}>' for i in range(lowerCamelCase_)]
            elif extra_ids > 0 and additional_special_tokens is not None:
                 # Check that we have the right number of extra_id special tokens
                 lowercase						     =						len(set(filter(lambda A__: bool('''extra_id''' in str(lowerCamelCase_))       ,lowerCamelCase_)))
                 if extra_tokens != extra_ids:
                      raise ValueError(
                          f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                          ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
                          ''' extra_ids tokens''')

            lowercase						     =						AddedToken(lowerCamelCase_       ,lstrip=lowerCamelCase_       ,rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_       ,lowerCamelCase_) else pad_token
            lowercase						     =						AddedToken(lowerCamelCase_       ,lstrip=lowerCamelCase_       ,rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_       ,lowerCamelCase_) else eos_token
            lowercase						     =						AddedToken(lowerCamelCase_       ,lstrip=lowerCamelCase_       ,rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_       ,lowerCamelCase_) else unk_token

            super().__init__(
                eos_token=lowerCamelCase_       ,unk_token=lowerCamelCase_       ,pad_token=lowerCamelCase_       ,extra_ids=lowerCamelCase_       ,additional_special_tokens=lowerCamelCase_       ,**lowerCamelCase_       ,)

            lowercase						     =						extra_ids

            lowercase						     =						2**8  # utf is 8 bits

            # define special tokens dict
            lowercase						     =						{
                self.pad_token: 0,
                self.eos_token: 1,
                self.unk_token: 2,
            }
            lowercase						     =						len(self.special_tokens_encoder)
            lowercase						     =						len(lowerCamelCase_)
            for i, token in enumerate(lowerCamelCase_):
                 lowercase						     =						self.vocab_size + i - n
            lowercase						     =						{v: k for k, v in self.special_tokens_encoder.items()}




       @property
       def 			A__				(    self):
            return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




       def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):

            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowerCamelCase_       ,token_ids_a=lowerCamelCase_       ,already_has_special_tokens=lowerCamelCase_)

            # normal case: some special tokens
            if token_ids_a is None:
                 return ([0] * len(lowerCamelCase_)) + [1]
            return ([0] * len(lowerCamelCase_)) + [1] + ([0] * len(lowerCamelCase_)) + [1]




       def 			A__				(    self       ,A__):

            if len(lowerCamelCase_) > 0 and token_ids[-1] == self.eos_token_id:
                 warnings.warn(
                     f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                     ''' eos tokens being added.''')
                 return token_ids
            else:
                 return token_ids + [self.eos_token_id]




       def 			A__				(    self       ,A__       ,A__ = None):
            lowercase						     =						[self.eos_token_id]

            if token_ids_a is None:
                 return len(token_ids_a + eos) * [0]
            return len(token_ids_a + eos + token_ids_a + eos) * [0]




       def 			A__				(    self       ,A__       ,A__ = None):
            lowercase						     =						self._add_eos_if_not_present(lowerCamelCase_)
            if token_ids_a is None:
                 return token_ids_a
            else:
                 lowercase						     =						self._add_eos_if_not_present(lowerCamelCase_)
                 return token_ids_a + token_ids_a




       def 			A__				(    self       ,A__):
            lowercase						     =						[chr(lowerCamelCase_) for i in text.encode('''utf-8''')]
            return tokens




       def 			A__				(    self       ,A__):

            if token in self.special_tokens_encoder:
                 lowercase						     =						self.special_tokens_encoder[token]
            elif token in self.added_tokens_encoder:
                 lowercase						     =						self.added_tokens_encoder[token]
            elif len(lowerCamelCase_) != 1:
                 lowercase						     =						self.unk_token_id
            else:
                 lowercase						     =						ord(lowerCamelCase_) + self._num_special_tokens
            return token_id




       def 			A__				(    self       ,A__):

            if index in self.special_tokens_decoder:
                 lowercase						     =						self.special_tokens_decoder[index]
            else:
                 lowercase						     =						chr(index - self._num_special_tokens)
            return token




       def 			A__				(    self       ,A__):
            lowercase						     =						B''''''
            for token in tokens:
                 if token in self.special_tokens_decoder:
                      lowercase						     =						self.special_tokens_decoder[token].encode('''utf-8''')
                 elif token in self.added_tokens_decoder:
                      lowercase						     =						self.special_tokens_decoder[token].encode('''utf-8''')
                 elif token in self.special_tokens_encoder:
                      lowercase						     =						token.encode('''utf-8''')
                 elif token in self.added_tokens_encoder:
                      lowercase						     =						token.encode('''utf-8''')
                 else:
                      lowercase						     =						bytes([ord(lowerCamelCase_)])
                 bstring += tok_string
            lowercase						     =						bstring.decode('''utf-8'''       ,errors='''ignore''')
            return string





       def 			A__				(    self       ,A__       ,A__ = None):
            return ()






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     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')

												lowercase						     =						{
												    '''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]       ,dtype=tf.intaa),  # "My dog is cute"
												    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]       ,dtype=tf.intaa),
												}

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												# compare the actual values for a slice.
												lowercase						     =						tf.convert_to_tensor(
												    [
												        [
												            [0.0681762, 0.10894451, 0.06772504],
												            [-0.06423668, 0.02366615, 0.04329344],
												            [-0.06057295, 0.09974135, -0.00070584],
												        ]
												    ]       ,dtype=tf.floataa       ,)

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




