"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		)






class  _UpperCAmelCase       :


					def __init__( self      :Union[str, Any]					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :Any=13					,	__UpperCamelCase      :int=7					,	__UpperCamelCase      :Optional[Any]=True					,	__UpperCamelCase      :List[Any]=True					,	__UpperCamelCase      :Dict=True					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=99					,	__UpperCamelCase      :Optional[int]=32					,	__UpperCamelCase      :Union[str, Any]=2					,	__UpperCamelCase      :Optional[int]=4					,	__UpperCamelCase      :List[str]=37					,	__UpperCamelCase      :Dict="gelu"					,	__UpperCamelCase      :str=0.1					,	__UpperCamelCase      :Dict=0.1					,	__UpperCamelCase      :Optional[int]=5_12					,	__UpperCamelCase      :Optional[int]=16					,	__UpperCamelCase      :Any=2					,	__UpperCamelCase      :Any=0.02					,	__UpperCamelCase      :Tuple=3					,	__UpperCamelCase      :Tuple=4					,	__UpperCamelCase      :str=None					,	):
												A		=  parent
												A		=  13
												A		=  7
												A		=  True
												A		=  True
												A		=  True
												A		=  True
												A		=  99
												A		=  32
												A		=  2
												A		=  4
												A		=  37
												A		=  "gelu"
												A		=  0.1
												A		=  0.1
												A		=  5_12
												A		=  16
												A		=  False
												A		=  -1
												A		=  True
												A		=  2
												A		=  0.02
												A		=  3
												A		=  4
												A		=  None


					def      lowerCamelCase	( self      :str		):
												A		=  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

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

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

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

												A		=  DebertaConfig(
												    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					,	max_relative_positions=self.max_relative_positions					,	position_biased_input=self.position_biased_input					,	initializer_range=self.initializer_range					,	return_dict=__UpperCamelCase					,	)

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


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :Any		):
												A		=  TFDebertaModel(config=__UpperCamelCase		)
												A		=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

												A		=  [input_ids, input_mask]
												A		=  model(__UpperCamelCase		)

												A		=  model(__UpperCamelCase		)

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


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :Optional[Any]		):
												A		=  TFDebertaForMaskedLM(config=__UpperCamelCase		)
												A		=  {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.vocab_size)		)


					def      lowerCamelCase	( self      :List[str]					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :Union[str, Any]					,	__UpperCamelCase      :List[str]		):
												A		=  self.num_labels
												A		=  TFDebertaForSequenceClassification(config=__UpperCamelCase		)
												A		=  {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}

												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :Dict		):
												A		=  self.num_labels
												A		=  TFDebertaForTokenClassification(config=__UpperCamelCase		)
												A		=  {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.num_labels)		)


					def      lowerCamelCase	( self      :int					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Optional[Any]		):
												A		=  TFDebertaForQuestionAnswering(config=__UpperCamelCase		)
												A		=  {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}

												A		=  model(__UpperCamelCase		)
												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      lowerCamelCase	( self      :Dict		):
												A		=  self.prepare_config_and_inputs()
												(
												    (
												    A     
												),     (
												    A     
												),     (
												    A     
												),     (
												    A     
												),     (
												    A     
												),     (
												    A     
												),     (
												    A     
												),          
												)		=  config_and_inputs
												A		=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
												return config, inputs_dict







@require_tf
class  _UpperCAmelCase       (     lowercase_					,							lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							(
					    (
					        TFDebertaModel,
					        TFDebertaForMaskedLM,
					        TFDebertaForQuestionAnswering,
					        TFDebertaForSequenceClassification,
					        TFDebertaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase								=							(
					    {
					        '''feature-extraction''': TFDebertaModel,
					        '''fill-mask''': TFDebertaForMaskedLM,
					        '''question-answering''': TFDebertaForQuestionAnswering,
					        '''text-classification''': TFDebertaForSequenceClassification,
					        '''token-classification''': TFDebertaForTokenClassification,
					        '''zero-shot''': TFDebertaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					UpperCamelCase								=							False
					UpperCamelCase								=							False


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  TFDebertaModelTester(self		)
												A		=  ConfigTester(self					,	config_class=__UpperCamelCase					,	hidden_size=37		)


					def      lowerCamelCase	( self      :Optional[int]		):
												self.config_tester.run_common_tests()


					def      lowerCamelCase	( self      :Tuple		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					def      lowerCamelCase	( self      :List[str]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase		)


					def      lowerCamelCase	( self      :str		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase		)


					def      lowerCamelCase	( self      :Any		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase		)


					@slow
					def      lowerCamelCase	( self      :List[str]		):
												A		=  TFDebertaModel.from_pretrained("kamalkraj/deberta-base"		)
												self.assertIsNotNone(__UpperCamelCase		)







@require_tf
class  _UpperCAmelCase       (     unittest.TestCase			):


					@unittest.skip(reason="Model not available yet"		)
					def      lowerCamelCase	( self      :List[str]		):
												pass


					@slow
					def      lowerCamelCase	( self      :List[str]		):
												A		=  TFDebertaModel.from_pretrained("kamalkraj/deberta-base"		)
												A		=  tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]		)
												A		=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
												A		=  model(__UpperCamelCase					,	attention_mask=__UpperCamelCase		)[0]

												A		=  tf.constant(
												    [
												        [
												            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
												            [1.4_484_025, -0.93_483_794, -0.80_593_085],
												            [0.3_122_741, 0.00_316_059, -1.4_131_377],
												        ]
												    ]		)
												tf.debugging.assert_near(output[:, 1:4, 1:4]					,	__UpperCamelCase					,	atol=1e-4		)









"""simple docstring"""
import os
from shutil import copyfile
from typing import Optional, Tuple

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


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_snake_case					:		Dict							=			None


_snake_case					:		int							=			logging.get_logger(__name__)


_snake_case					:		List[Any]							=			'▁'

_snake_case					:		Tuple							=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case					:		Tuple							=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case					:		List[Any]							=			{
    'google/reformer-crime-and-punishment': 52_4288,
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							VOCAB_FILES_NAMES
					UpperCamelCase								=							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase								=							['''input_ids''', '''attention_mask''']
					UpperCamelCase								=							ReformerTokenizer


					def __init__( self      :Dict					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Dict="</s>"					,	__UpperCamelCase      :Optional[Any]="<unk>"					,	__UpperCamelCase      :Union[str, Any]=[]					,	**__UpperCamelCase      :str					,	):
												super().__init__(
												    __UpperCamelCase					,	tokenizer_file=__UpperCamelCase					,	eos_token=__UpperCamelCase					,	unk_token=__UpperCamelCase					,	additional_special_tokens=__UpperCamelCase					,	**__UpperCamelCase					,	)

												A		=  vocab_file
												A		=  False if not self.vocab_file else True


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :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(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCamelCase		):
																			copyfile(self.vocab_file					,	__UpperCamelCase		)

												return (out_vocab_file,)


