




'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TFDebertaVaForMaskedLM,
							    TFDebertaVaForQuestionAnswering,
							    TFDebertaVaForSequenceClassification,
							    TFDebertaVaForTokenClassification,
							    TFDebertaVaModel,
							)






class 						a				:


				def __init__(		self    :					Any       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[Any]=13       ,				lowercase_    :					Dict=7       ,				lowercase_    :					Any=True       ,				lowercase_    :					str=True       ,				lowercase_    :					int=True       ,				lowercase_    :					Any=True       ,				lowercase_    :					Any=99       ,				lowercase_    :					List[Any]=32       ,				lowercase_    :					List[Any]=2       ,				lowercase_    :					Optional[int]=4       ,				lowercase_    :					Any=37       ,				lowercase_    :					List[str]="gelu"       ,				lowercase_    :					Dict=0.1       ,				lowercase_    :					List[Any]=0.1       ,				lowercase_    :					List[str]=512       ,				lowercase_    :					int=16       ,				lowercase_    :					Dict=2       ,				lowercase_    :					Tuple=0.02       ,				lowercase_    :					Any=False       ,				lowercase_    :					int=True       ,				lowercase_    :					Union[str, Any]="None"       ,				lowercase_    :					str=3       ,				lowercase_    :					Dict=4       ,				lowercase_    :					Any=None       ,				):
									snake_case_									=				parent
									snake_case_									=				batch_size
									snake_case_									=				seq_length
									snake_case_									=				is_training
									snake_case_									=				use_input_mask
									snake_case_									=				use_token_type_ids
									snake_case_									=				use_labels
									snake_case_									=				vocab_size
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				max_position_embeddings
									snake_case_									=				type_vocab_size
									snake_case_									=				type_sequence_label_size
									snake_case_									=				initializer_range
									snake_case_									=				num_labels
									snake_case_									=				num_choices
									snake_case_									=				relative_attention
									snake_case_									=				position_biased_input
									snake_case_									=				pos_att_type
									snake_case_									=				scope


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size					)

									snake_case_									=				None
									if self.use_input_mask:
														snake_case_									=				random_attention_mask([self.batch_size, self.seq_length]					)

									snake_case_									=				None
									if self.use_token_type_ids:
														snake_case_									=				ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size					)

									snake_case_									=				None
									snake_case_									=				None
									snake_case_									=				None
									if self.use_labels:
														snake_case_									=				ids_tensor([self.batch_size]       ,				self.type_sequence_label_size					)
														snake_case_									=				ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels					)

									snake_case_									=				DebertaVaConfig(
									    vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				max_position_embeddings=self.max_position_embeddings       ,				type_vocab_size=self.type_vocab_size       ,				relative_attention=self.relative_attention       ,				position_biased_input=self.position_biased_input       ,				initializer_range=self.initializer_range       ,				return_dict=lowercase_       ,				)

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


				def   A_ (		self    :					Dict       ,				lowercase_    :					Any       ,				lowercase_    :					List[Any]       ,				lowercase_    :					List[str]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Any       ,				lowercase_    :					Any       ,				lowercase_    :					Tuple					):
									snake_case_									=				TFDebertaVaModel(config=lowercase_					)
									snake_case_									=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

									snake_case_									=				[input_ids, input_mask]
									snake_case_									=				model(lowercase_					)

									snake_case_									=				model(lowercase_					)

									self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)					)


				def   A_ (		self    :					Dict       ,				lowercase_    :					Dict       ,				lowercase_    :					List[str]       ,				lowercase_    :					int       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Any					):
									snake_case_									=				TFDebertaVaForMaskedLM(config=lowercase_					)
									snake_case_									=				{
									    '''input_ids''': input_ids,
									    '''attention_mask''': input_mask,
									    '''token_type_ids''': token_type_ids,
									}
									snake_case_									=				model(lowercase_					)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)					)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					List[str]       ,				lowercase_    :					Tuple       ,				lowercase_    :					Dict       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					int					):
									snake_case_									=				self.num_labels
									snake_case_									=				TFDebertaVaForSequenceClassification(config=lowercase_					)
									snake_case_									=				{
									    '''input_ids''': input_ids,
									    '''attention_mask''': input_mask,
									    '''token_type_ids''': token_type_ids,
									}

									snake_case_									=				model(lowercase_					)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)					)


				def   A_ (		self    :					List[str]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					List[str]       ,				lowercase_    :					Tuple       ,				lowercase_    :					Dict       ,				lowercase_    :					str       ,				lowercase_    :					List[Any]       ,				lowercase_    :					str					):
									snake_case_									=				self.num_labels
									snake_case_									=				TFDebertaVaForTokenClassification(config=lowercase_					)
									snake_case_									=				{
									    '''input_ids''': input_ids,
									    '''attention_mask''': input_mask,
									    '''token_type_ids''': token_type_ids,
									}
									snake_case_									=				model(lowercase_					)
									self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels)					)


				def   A_ (		self    :					Dict       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					Any       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					Any       ,				lowercase_    :					Dict       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Any					):
									snake_case_									=				TFDebertaVaForQuestionAnswering(config=lowercase_					)
									snake_case_									=				{
									    '''input_ids''': input_ids,
									    '''attention_mask''': input_mask,
									    '''token_type_ids''': token_type_ids,
									}

									snake_case_									=				model(lowercase_					)
									self.parent.assertEqual(result.start_logits.shape       ,				(self.batch_size, self.seq_length)					)
									self.parent.assertEqual(result.end_logits.shape       ,				(self.batch_size, self.seq_length)					)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)			,(
									    snake_case_
									)			,(
									    snake_case_
									)			,(
									    snake_case_
									)			,(
									    snake_case_
									)			,(
									    snake_case_
									)			,(
									    snake_case_
									)			,
									)									=				config_and_inputs
									snake_case_									=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
									return config, inputs_dict







@require_tf
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				snake_case_      =		(
				    {
				        "feature-extraction": TFDebertaVaModel,
				        "fill-mask": TFDebertaVaForMaskedLM,
				        "question-answering": TFDebertaVaForQuestionAnswering,
				        "text-classification": TFDebertaVaForSequenceClassification,
				        "token-classification": TFDebertaVaForTokenClassification,
				        "zero-shot": TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					Dict					):
									snake_case_									=				TFDebertaVaModelTester(self					)
									snake_case_									=				ConfigTester(self       ,				config_class=lowercase_       ,				hidden_size=37					)


				def   A_ (		self    :					Dict					):
									self.config_tester.run_common_tests()


				def   A_ (		self    :					int					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_					)


				def   A_ (		self    :					int					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*lowercase_					)


				def   A_ (		self    :					List[Any]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*lowercase_					)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*lowercase_					)


				@slow
				def   A_ (		self    :					List[str]					):
									snake_case_									=				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
									self.assertIsNotNone(lowercase_					)







@require_tf
class 						a				(   unittest.TestCase     ):


				@unittest.skip(reason='''Model not available yet'''					)
				def   A_ (		self    :					Optional[Any]					):
									pass


				@slow
				def   A_ (		self    :					Any					):
									snake_case_									=				TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
									snake_case_									=				tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
									snake_case_									=				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
									snake_case_									=				model(lowercase_       ,				attention_mask=lowercase_					)[0]

									snake_case_									=				tf.constant(
									    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]					)
									tf.debugging.assert_near(output[:, 1:4, 1:4]       ,				lowercase_       ,				atol=1e-4					)







'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_ta import TaTokenizer
else:
							a  :				Dict									=  None


a  :				List[Any]									=  logging.get_logger(__name__)

a  :				List[Any]									=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a  :				str									=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
a  :				List[Any]									=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		VOCAB_FILES_NAMES
				snake_case_      =		PRETRAINED_VOCAB_FILES_MAP
				snake_case_      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_      =		["input_ids", "attention_mask"]
				snake_case_      =		TaTokenizer

				snake_case_      =		[]


				def __init__(		self    :					List[Any]       ,				lowercase_    :					int=None       ,				lowercase_    :					Dict=None       ,				lowercase_    :					Dict="</s>"       ,				lowercase_    :					List[Any]="<unk>"       ,				lowercase_    :					int="<pad>"       ,				lowercase_    :					int=100       ,				lowercase_    :					List[Any]=None       ,				**lowercase_    :					List[str]       ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
														snake_case_									=				[F"<extra_id_{i}>" for i in range(lowercase_					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														snake_case_									=				len(set(filter(lambda lowercase_					: bool('''extra_id_''' in str(lowercase_					)					)       ,				lowercase_					)					)					)
														if extra_tokens != extra_ids:
																			raise ValueError(
																			    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																			    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																			    ''' tokens'''					)

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

									snake_case_									=				vocab_file
									snake_case_									=				False if not self.vocab_file else True
									snake_case_									=				extra_ids


				@staticmethod
				def   A_ (		lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														snake_case_									=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																			return init_max_model_length
														elif init_max_model_length is None:
																			warnings.warn(
																			    '''This tokenizer was incorrectly instantiated with a model max length of'''
																			    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																			    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																			    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																			    F" {pretrained_model_name_or_path} automatically truncating your input to"
																			    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																			    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																			    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																			    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,				lowercase_       ,				)

									return max_model_length


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[str] = None					):
									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''					)

									if not os.path.isdir(lowercase_					):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
														return
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					):
														copyfile(self.vocab_file       ,				lowercase_					)
														logger.info(F"Copy vocab file to {out_vocab_file}"					)

									return (out_vocab_file,)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
									else:
														snake_case_									=				token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a


				def   A_ (		self    :					int       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				[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    :					Dict					):
									return list(
									    set(filter(lambda lowercase_					: bool(re.search(R'''<extra_id_\d+>'''       ,				lowercase_					)					) is not None       ,				self.additional_special_tokens					)					)					)


				def   A_ (		self    :					Any					):
									return [self.convert_tokens_to_ids(lowercase_					) for token in self.get_sentinel_tokens()]

