import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
					__UpperCamelCase	: Any    		=  'pt'
elif is_tf_available():
					__UpperCamelCase	: str    		=  'tf'
else:
					__UpperCamelCase	: Optional[int]    		=  'jax'





class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	PerceiverTokenizer
						UpperCamelCase_  =	False


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												super().setUp()
												SCREAMING_SNAKE_CASE							:					Any           =							PerceiverTokenizer()
												tokenizer.save_pretrained(self.tmpdirname		)


						@cached_property
						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''		)


						def  __A ( self			:       Optional[Any]					,	**UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												return self.tokenizer_class.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Union[str, Any]=False					,	UpperCamelCase__			:       Tuple=20					,	UpperCamelCase__			:       Dict=5		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							[]
												for i in range(len(UpperCamelCase__		)		):
																		try:
																								SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer.decode([i]					,	clean_up_tokenization_spaces=UpperCamelCase__		)
																		except UnicodeDecodeError:
																								pass
																		toks.append((i, tok)		)

												SCREAMING_SNAKE_CASE							:					Dict           =							list(filter(lambda UpperCamelCase__		: re.match(r'''^[ a-zA-Z]+$'''					,	t[1]		)					,	UpperCamelCase__		)		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							list(filter(lambda UpperCamelCase__		: [t[0]] == tokenizer.encode(t[1]					,	add_special_tokens=UpperCamelCase__		)					,	UpperCamelCase__		)		)
												if max_length is not None and len(UpperCamelCase__		) > max_length:
																		SCREAMING_SNAKE_CASE							:					Any           =							toks[:max_length]
												if min_length is not None and len(UpperCamelCase__		) < min_length and len(UpperCamelCase__		) > 0:
																		while len(UpperCamelCase__		) < min_length:
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							toks + toks
        # toks_str = [t[1] for t in toks]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[t[0] for t in toks]

												# Ensure consistency
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer.decode(UpperCamelCase__					,	clean_up_tokenization_spaces=UpperCamelCase__		)
												if " " not in output_txt and len(UpperCamelCase__		) > 1:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							(
																		    tokenizer.decode([toks_ids[0]]					,	clean_up_tokenization_spaces=UpperCamelCase__		)
																		    + ''' '''
																		    + tokenizer.decode(toks_ids[1:]					,	clean_up_tokenization_spaces=UpperCamelCase__		)
																		)
												if with_prefix_space:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							''' ''' + output_txt
												SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
												return output_txt, output_ids


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.perceiver_tokenizer
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''Unicode €.'''
												SCREAMING_SNAKE_CASE							:					int           =							tokenizer(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
												self.assertEqual(encoded['''input_ids''']					,	UpperCamelCase__		)

												# decoding
												SCREAMING_SNAKE_CASE							:					List[str]           =							tokenizer.decode(UpperCamelCase__		)
												self.assertEqual(UpperCamelCase__					,	'''[CLS]Unicode €.[SEP]'''		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer('''e è é ê ë'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
												self.assertEqual(encoded['''input_ids''']					,	UpperCamelCase__		)
												# decoding
												SCREAMING_SNAKE_CASE							:					Any           =							tokenizer.decode(UpperCamelCase__		)
												self.assertEqual(UpperCamelCase__					,	'''[CLS]e è é ê ë[SEP]'''		)

												# encode/decode, but with `encode` instead of `__call__`
												self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''		)		)					,	'''[CLS]e è é ê ë[SEP]'''		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.perceiver_tokenizer
												SCREAMING_SNAKE_CASE							:					List[str]           =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												# fmt: off
												SCREAMING_SNAKE_CASE							:					Any           =							[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
												# fmt: on
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer(UpperCamelCase__					,	padding=UpperCamelCase__					,	return_tensors=UpperCamelCase__		)
												self.assertIsInstance(UpperCamelCase__					,	UpperCamelCase__		)

												if FRAMEWORK != "jax":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							list(batch.input_ids.numpy()[0]		)
												else:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							list(batch.input_ids.tolist()[0]		)

												self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

												self.assertEqual((2, 38)					,	batch.input_ids.shape		)
												self.assertEqual((2, 38)					,	batch.attention_mask.shape		)


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.perceiver_tokenizer
												SCREAMING_SNAKE_CASE							:					List[str]           =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer(UpperCamelCase__					,	padding=UpperCamelCase__					,	return_tensors=UpperCamelCase__		)
												# check if input_ids are returned and no decoder_input_ids
												self.assertIn('''input_ids'''					,	UpperCamelCase__		)
												self.assertIn('''attention_mask'''					,	UpperCamelCase__		)
												self.assertNotIn('''decoder_input_ids'''					,	UpperCamelCase__		)
												self.assertNotIn('''decoder_attention_mask'''					,	UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.perceiver_tokenizer
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[
												    '''Summary of the text.''',
												    '''Another summary.''',
												]
												SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer(
												    text_target=UpperCamelCase__					,	max_length=32					,	padding='''max_length'''					,	truncation=UpperCamelCase__					,	return_tensors=UpperCamelCase__		)
												self.assertEqual(32					,	targets['''input_ids'''].shape[1]		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.get_tokenizers()
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								self.assertNotEqual(tokenizer.model_max_length					,	42		)

        # Now let's start the test
												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers()
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								# Isolate this from the other tests because we save additional tokens/etc
																								SCREAMING_SNAKE_CASE							:					int           =							tempfile.mkdtemp()

																								SCREAMING_SNAKE_CASE							:					int           =							''' He is very happy, UNwant\u00E9d,running'''
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								tokenizer.save_pretrained(UpperCamelCase__		)

																								SCREAMING_SNAKE_CASE							:					str           =							tokenizer.__class__.from_pretrained(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Tuple           =							after_tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

																								shutil.rmtree(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.get_tokenizers(model_max_length=42		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								# Isolate this from the other tests because we save additional tokens/etc
																								SCREAMING_SNAKE_CASE							:					Tuple           =							tempfile.mkdtemp()

																								SCREAMING_SNAKE_CASE							:					Dict           =							''' He is very happy, UNwant\u00E9d,running'''
																								tokenizer.add_tokens(['''bim''', '''bambam''']		)
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer.additional_special_tokens
																								additional_special_tokens.append('''new_additional_special_token'''		)
																								tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}		)
																								SCREAMING_SNAKE_CASE							:					List[str]           =							tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								tokenizer.save_pretrained(UpperCamelCase__		)

																								SCREAMING_SNAKE_CASE							:					List[str]           =							tokenizer.__class__.from_pretrained(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Tuple           =							after_tokenizer.encode(UpperCamelCase__					,	add_special_tokens=UpperCamelCase__		)
																								self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)
																								self.assertIn('''new_additional_special_token'''					,	after_tokenizer.additional_special_tokens		)
																								self.assertEqual(after_tokenizer.model_max_length					,	42		)

																								SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer.__class__.from_pretrained(UpperCamelCase__					,	model_max_length=43		)
																								self.assertEqual(tokenizer.model_max_length					,	43		)

																								shutil.rmtree(UpperCamelCase__		)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							[]
												if self.test_slow_tokenizer:
																		tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

												if self.test_rust_tokenizer:
																		tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

												for tokenizer_class, tokenizer_utils in tokenizer_list:
																		with tempfile.TemporaryDirectory() as tmp_dir:
																								tokenizer_utils.save_pretrained(UpperCamelCase__		)

																								with open(os.path.join(UpperCamelCase__					,	'''special_tokens_map.json'''		)					,	encoding='''utf-8'''		) as json_file:
																														SCREAMING_SNAKE_CASE							:					Tuple           =							json.load(UpperCamelCase__		)

																								with open(os.path.join(UpperCamelCase__					,	'''tokenizer_config.json'''		)					,	encoding='''utf-8'''		) as json_file:
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							json.load(UpperCamelCase__		)

																								SCREAMING_SNAKE_CASE							:					Tuple           =							[f"""<extra_id_{i}>""" for i in range(125		)]

																								SCREAMING_SNAKE_CASE							:					int           =							added_tokens_extra_ids + [
																								    '''an_additional_special_token'''
																								]
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							added_tokens_extra_ids + [
																								    '''an_additional_special_token'''
																								]

																								with open(os.path.join(UpperCamelCase__					,	'''special_tokens_map.json'''		)					,	'''w'''					,	encoding='''utf-8'''		) as outfile:
																														json.dump(UpperCamelCase__					,	UpperCamelCase__		)
																								with open(os.path.join(UpperCamelCase__					,	'''tokenizer_config.json'''		)					,	'''w'''					,	encoding='''utf-8'''		) as outfile:
																														json.dump(UpperCamelCase__					,	UpperCamelCase__		)

																								# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																								# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																								# "special_tokens_map.json" files
																								SCREAMING_SNAKE_CASE							:					Any           =							tokenizer_class.from_pretrained(
																								    UpperCamelCase__					,	)
																								self.assertIn(
																								    '''an_additional_special_token'''					,	tokenizer_without_change_in_init.additional_special_tokens		)
																								self.assertEqual(
																								    ['''an_additional_special_token''']					,	tokenizer_without_change_in_init.convert_ids_to_tokens(
																								        tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']		)		)					,	)

																								# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''					,	lstrip=UpperCamelCase__		)]
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer_class.from_pretrained(
																								    UpperCamelCase__					,	additional_special_tokens=UpperCamelCase__					,	)

																								self.assertIn('''a_new_additional_special_token'''					,	tokenizer.additional_special_tokens		)
																								self.assertEqual(
																								    ['''a_new_additional_special_token''']					,	tokenizer.convert_ids_to_tokens(
																								        tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']		)		)					,	)


						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.perceiver_tokenizer
												self.assertEqual(tokenizer.decode([178]		)					,	'''�'''		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												pass


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												pass


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												pass


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												pass



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_tokenizers(fast=UpperCamelCase__					,	do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
																								SCREAMING_SNAKE_CASE							:					int           =							['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
																								SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer.convert_tokens_to_string(UpperCamelCase__		)

																								self.assertIsInstance(UpperCamelCase__					,	UpperCamelCase__		)






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							hidden_size
												SCREAMING_SNAKE_CASE							:					int           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					List[Any]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Dict           =							type_vocab_size
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							relative_attention
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							max_relative_positions
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pad_token_id
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							position_biased_input

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{0: '''batch''', 1: '''sequence'''}
												if self._config.type_vocab_size > 0:
																		return OrderedDict(
																		    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]		)
												else:
																		return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]		)


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().generate_dummy_inputs(preprocessor=UpperCamelCase__					,	framework=UpperCamelCase__		)
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs





