


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():
		lowerCamelCase						=		'''pt'''
elif is_tf_available():
		lowerCamelCase						=		'''tf'''
else:
		lowerCamelCase						=		'''jax'''





class  _a							(    _lowercase					,						unittest.TestCase):
	_a  :  Optional[Any]				   =							PerceiverTokenizer
	_a  :  List[Any]				   =							False




	def 							UpperCAmelCase__( self						:		int		)->	Any:
			super().setUp()
			lowerCAmelCase__       :      Optional[int]   					= PerceiverTokenizer()
			tokenizer.save_pretrained(self.tmpdirname		)




	@cached_property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Any:
			return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''		)




	def 							UpperCAmelCase__( self						:		List[Any]		, **_SCREAMING_SNAKE_CASE						:		List[Any]		)->	PerceiverTokenizer:
			return self.tokenizer_class.from_pretrained(self.tmpdirname		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[int]=False		, _SCREAMING_SNAKE_CASE						:		List[str]=20		, _SCREAMING_SNAKE_CASE						:		str=5		)->	Tuple[str, list]:
			# XXX The default common tokenizer tests assume that every ID is decodable on its own.
			# This assumption is invalid for Perceiver because single bytes might not be
			# valid utf-8 (byte 128 for instance).
			# Here we're overriding the smallest possible method to provide
			# a clean sequence without making the same assumption.

			lowerCAmelCase__       :      Any   					= []
			for i in range(len(_SCREAMING_SNAKE_CASE		)		):
					try:
							lowerCAmelCase__       :      Optional[int]   					= tokenizer.decode([i]		, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
					except UnicodeDecodeError:
							pass
					toks.append((i, tok)		)

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

			# Ensure consistency
			lowerCAmelCase__       :      List[Any]   					= tokenizer.decode(_SCREAMING_SNAKE_CASE		, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
			if " " not in output_txt and len(_SCREAMING_SNAKE_CASE		) > 1:
					lowerCAmelCase__       :      Tuple   					= (
					    tokenizer.decode([toks_ids[0]]		, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
					    + ''' '''
					    + tokenizer.decode(toks_ids[1:]		, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
					)
			if with_prefix_space:
					lowerCAmelCase__       :      Optional[Any]   					= ''' ''' + output_txt
			lowerCAmelCase__       :      List[str]   					= tokenizer.encode(_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		)
			return output_txt, output_ids




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Any:
			lowerCAmelCase__       :      Tuple   					= self.perceiver_tokenizer
			lowerCAmelCase__       :      Union[str, Any]   					= '''Unicode €.'''
			lowerCAmelCase__       :      Union[str, Any]   					= tokenizer(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[str]   					= [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
			self.assertEqual(encoded['''input_ids''']		, _SCREAMING_SNAKE_CASE		)

			# decoding
			lowerCAmelCase__       :      Optional[Any]   					= tokenizer.decode(_SCREAMING_SNAKE_CASE		)
			self.assertEqual(_SCREAMING_SNAKE_CASE		, '''[CLS]Unicode €.[SEP]'''		)

			lowerCAmelCase__       :      List[str]   					= tokenizer('''e è é ê ë'''		)
			lowerCAmelCase__       :      Any   					= [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
			self.assertEqual(encoded['''input_ids''']		, _SCREAMING_SNAKE_CASE		)
			# decoding
			lowerCAmelCase__       :      str   					= tokenizer.decode(_SCREAMING_SNAKE_CASE		)
			self.assertEqual(_SCREAMING_SNAKE_CASE		, '''[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 							UpperCAmelCase__( self						:		str		)->	Tuple:
			lowerCAmelCase__       :      List[Any]   					= self.perceiver_tokenizer
			lowerCAmelCase__       :      Dict   					= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
			# fmt: off
			lowerCAmelCase__       :      List[str]   					= [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
			lowerCAmelCase__       :      Optional[int]   					= tokenizer(_SCREAMING_SNAKE_CASE		, padding=_SCREAMING_SNAKE_CASE		, return_tensors=_SCREAMING_SNAKE_CASE		)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

			if FRAMEWORK != "jax":
					lowerCAmelCase__       :      Tuple   					= list(batch.input_ids.numpy()[0]		)
			else:
					lowerCAmelCase__       :      Optional[Any]   					= list(batch.input_ids.tolist()[0]		)

			self.assertListEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

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




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	str:
			lowerCAmelCase__       :      List[str]   					= self.perceiver_tokenizer
			lowerCAmelCase__       :      Optional[int]   					= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
			lowerCAmelCase__       :      Tuple   					= tokenizer(_SCREAMING_SNAKE_CASE		, padding=_SCREAMING_SNAKE_CASE		, return_tensors=_SCREAMING_SNAKE_CASE		)
			# check if input_ids are returned and no decoder_input_ids
			self.assertIn('''input_ids'''		, _SCREAMING_SNAKE_CASE		)
			self.assertIn('''attention_mask'''		, _SCREAMING_SNAKE_CASE		)
			self.assertNotIn('''decoder_input_ids'''		, _SCREAMING_SNAKE_CASE		)
			self.assertNotIn('''decoder_attention_mask'''		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[str]		)->	Dict:
			lowerCAmelCase__       :      Tuple   					= self.perceiver_tokenizer
			lowerCAmelCase__       :      int   					= [
			    '''Summary of the text.''',
			    '''Another summary.''',
			]
			lowerCAmelCase__       :      List[Any]   					= tokenizer(
			    text_target=_SCREAMING_SNAKE_CASE		, max_length=32		, padding='''max_length'''		, truncation=_SCREAMING_SNAKE_CASE		, return_tensors=_SCREAMING_SNAKE_CASE		)
			self.assertEqual(32		, targets['''input_ids'''].shape[1]		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Optional[Any]:
			# safety check on max_len default value so we are sure the test works
			lowerCAmelCase__       :      List[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
			lowerCAmelCase__       :      List[str]   					= 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
							lowerCAmelCase__       :      Optional[Any]   					= tempfile.mkdtemp()

							lowerCAmelCase__       :      List[str]   					= ''' He is very happy, UNwant\u00E9d,running'''
							lowerCAmelCase__       :      Union[str, Any]   					= tokenizer.encode(_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		)
							tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)

							lowerCAmelCase__       :      Any   					= tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE		)
							lowerCAmelCase__       :      Dict   					= after_tokenizer.encode(_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		)
							self.assertListEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

							shutil.rmtree(_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Dict   					= 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
							lowerCAmelCase__       :      Union[str, Any]   					= tempfile.mkdtemp()

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

							lowerCAmelCase__       :      int   					= tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE		)
							lowerCAmelCase__       :      Dict   					= after_tokenizer.encode(_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		)
							self.assertListEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
							self.assertIn('''new_additional_special_token'''		, after_tokenizer.additional_special_tokens		)
							self.assertEqual(after_tokenizer.model_max_length		, 42		)

							lowerCAmelCase__       :      int   					= tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE		, model_max_length=43		)
							self.assertEqual(tokenizer.model_max_length		, 43		)

							shutil.rmtree(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	List[Any]:
			lowerCAmelCase__       :      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(_SCREAMING_SNAKE_CASE		)

							with open(os.path.join(_SCREAMING_SNAKE_CASE		, '''special_tokens_map.json'''		)		, encoding='''utf-8'''		) as json_file:
									lowerCAmelCase__       :      List[str]   					= json.load(_SCREAMING_SNAKE_CASE		)

							with open(os.path.join(_SCREAMING_SNAKE_CASE		, '''tokenizer_config.json'''		)		, encoding='''utf-8'''		) as json_file:
									lowerCAmelCase__       :      Any   					= json.load(_SCREAMING_SNAKE_CASE		)

							lowerCAmelCase__       :      Optional[int]   					= [F'<extra_id_{i}>' for i in range(125		)]

							lowerCAmelCase__       :      str   					= added_tokens_extra_ids + [
							    '''an_additional_special_token'''
							]
							lowerCAmelCase__       :      Tuple   					= added_tokens_extra_ids + [
							    '''an_additional_special_token'''
							]

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

							# 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
							lowerCAmelCase__       :      Tuple   					= tokenizer_class.from_pretrained(
							    _SCREAMING_SNAKE_CASE		, )
							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
							lowerCAmelCase__       :      Optional[int]   					= added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''		, lstrip=_SCREAMING_SNAKE_CASE		)]
							lowerCAmelCase__       :      str   					= tokenizer_class.from_pretrained(
							    _SCREAMING_SNAKE_CASE		, additional_special_tokens=_SCREAMING_SNAKE_CASE		, )

							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 							UpperCAmelCase__( self						:		List[str]		)->	Any:
			lowerCAmelCase__       :      str   					= self.perceiver_tokenizer
			self.assertEqual(tokenizer.decode([178]		)		, '''�'''		)




	def 							UpperCAmelCase__( self						:		str		)->	Union[str, Any]:
			pass




	def 							UpperCAmelCase__( self						:		int		)->	List[str]:
			pass




	def 							UpperCAmelCase__( self						:		int		)->	str:
			pass




	def 							UpperCAmelCase__( self						:		str		)->	List[str]:
			pass




	def 							UpperCAmelCase__( self						:		int		)->	Tuple:
			# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
			# strings and special added tokens as tokens
			lowerCAmelCase__       :      Dict   					= self.get_tokenizers(fast=_SCREAMING_SNAKE_CASE		, do_lower_case=_SCREAMING_SNAKE_CASE		)
			for tokenizer in tokenizers:
					with self.subTest(F'{tokenizer.__class__.__name__}'		):
							lowerCAmelCase__       :      Tuple   					= ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
							lowerCAmelCase__       :      Any   					= tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)

							self.assertIsInstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)








from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class  _a							(    _lowercase):
	_a  :  Dict				   =							'''convbert'''




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str=3_0522		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=12		, _SCREAMING_SNAKE_CASE						:		Any=3072		, _SCREAMING_SNAKE_CASE						:		List[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		int=512		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2		, _SCREAMING_SNAKE_CASE						:		Tuple=9		, _SCREAMING_SNAKE_CASE						:		int=1		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(
			    pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      Tuple   					= num_hidden_layers
			lowerCAmelCase__       :      Union[str, Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Dict   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      Tuple   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= layer_norm_eps
			lowerCAmelCase__       :      int   					= embedding_size
			lowerCAmelCase__       :      Union[str, Any]   					= head_ratio
			lowerCAmelCase__       :      Optional[int]   					= conv_kernel_size
			lowerCAmelCase__       :      List[str]   					= num_groups
			lowerCAmelCase__       :      Dict   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      Optional[Any]   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[str]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			        ('''token_type_ids''', dynamic_axis),
			    ]		)




