from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    UpperCamelCase_    ):
  lowercase_								=  [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']




  @register_to_config
  def __init__(   self  :						str  ,			a_  :						int  ,			a_  :						int  ,			a_  :						Optional[int] = None  ,			a_  :						int = 5_0257  ,			a_  :						int = 1024  ,			a_  :						int = 768  ,			a_  :						int = 12  ,			a_  :						int = 12  ,			a_  :						Optional[int] = None  ,			a_  :						str = "gelu_new"  ,			a_  :						float = 0.1  ,			a_  :						float = 0.1  ,			a_  :						float = 0.1  ,			a_  :						float = 1e-5  ,			a_  :						float = 0.02  ,			a_  :						bool = True  ,			a_  :						bool = True  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			)->							Optional[int]:




       """simple docstring"""
       super().__init__()

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		prefix_length

       if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
            raise ValueError(
                F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
                F''' `n_embd`: {n_embd} are not equal.'''					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		prefix_inner_dim
       SCREAMING_SNAKE_CASE__					:					str      =		prefix_hidden_dim

       SCREAMING_SNAKE_CASE__					:					str      =		(
           nn.Linear(self.prefix_inner_dim  ,			self.prefix_hidden_dim					)
           if self.prefix_hidden_dim is not None
           else nn.Identity()
       )
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		(
           nn.Linear(self.prefix_hidden_dim  ,			a_					) if self.prefix_hidden_dim is not None else nn.Identity()
       )

       SCREAMING_SNAKE_CASE__					:					List[str]      =		GPTaConfig(
           vocab_size=a_  ,			n_positions=a_  ,			n_embd=a_  ,			n_layer=a_  ,			n_head=a_  ,			n_inner=a_  ,			activation_function=a_  ,			resid_pdrop=a_  ,			embd_pdrop=a_  ,			attn_pdrop=a_  ,			layer_norm_epsilon=a_  ,			initializer_range=a_  ,			scale_attn_weights=a_  ,			use_cache=a_  ,			scale_attn_by_inverse_layer_idx=a_  ,			reorder_and_upcast_attn=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		GPTaLMHeadModel(a_					)




  def 						__lowercase(   self  :						Any  ,			a_  :						torch.Tensor  ,			a_  :						torch.Tensor  ,			a_  :						Optional[torch.Tensor] = None  ,			a_  :						Optional[torch.Tensor] = None  ,			)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.transformer.transformer.wte(a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.encode_prefix(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.decode_prefix(a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.cat((prefix_embeds, embedding_text)  ,			dim=1					)

       if labels is not None:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_dummy_token(input_ids.shape[0]  ,			input_ids.device					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.cat((dummy_token, input_ids)  ,			dim=1					)
       SCREAMING_SNAKE_CASE__					:					Any      =		self.transformer(inputs_embeds=a_  ,			labels=a_  ,			attention_mask=a_					)
       if self.prefix_hidden_dim is not None:
            return out, hidden
       else:
            return out




  def 						__lowercase(   self  :						str  ,			a_  :						int  ,			a_  :						torch.device					)->							torch.Tensor:




       """simple docstring"""
       return torch.zeros(a_  ,			self.prefix_length  ,			dtype=torch.intaa  ,			device=a_					)




  def 						__lowercase(   self  :						str  ,			a_  :						int					)->							Any:




       """simple docstring"""
       return self.encode_prefix(a_					)




  @torch.no_grad()
  def 						__lowercase(   self  :						List[Any]  ,			a_  :						Tuple  ,			a_  :						int  ,			a_  :						str					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.split(a_  ,			1  ,			dim=0					)
       SCREAMING_SNAKE_CASE__					:					int      =		[]
       SCREAMING_SNAKE_CASE__					:					Dict      =		[]
       for feature in features:
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.decode_prefix(feature.to(a_					)					)  # back to the clip feature
            # Only support beam search for now
            SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.generate_beam(
                input_embeds=a_  ,			device=a_  ,			eos_token_id=a_					)
            generated_tokens.append(output_tokens[0]					)
            generated_seq_lengths.append(seq_lengths[0]					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.stack(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.stack(a_					)
       return generated_tokens, generated_seq_lengths




  @torch.no_grad()
  def 						__lowercase(   self  :						Tuple  ,			a_  :						Optional[int]=None  ,			a_  :						Optional[Any]=None  ,			a_  :						str=None  ,			a_  :						int = 5  ,			a_  :						int = 67  ,			a_  :						float = 1.0  ,			a_  :						Optional[int] = None  ,			)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		eos_token_id
       SCREAMING_SNAKE_CASE__					:					Tuple      =		None
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		None
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.ones(a_  ,			device=a_  ,			dtype=torch.int					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.zeros(a_  ,			device=a_  ,			dtype=torch.bool					)

       if input_embeds is not None:
            SCREAMING_SNAKE_CASE__					:					Dict      =		input_embeds
       else:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.transformer.transformer.wte(a_					)

       for i in range(a_					):
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.transformer(inputs_embeds=a_					)
            SCREAMING_SNAKE_CASE__					:					int      =		outputs.logits
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		logits.softmax(-1					).log()

            if scores is None:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		logits.topk(a_  ,			-1					)
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		generated.expand(a_  ,			*generated.shape[1:]					)
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		next_tokens.permute(1  ,			0					), scores.squeeze(0					)
                 if tokens is None:
                      SCREAMING_SNAKE_CASE__					:					Dict      =		next_tokens
                 else:
                      SCREAMING_SNAKE_CASE__					:					Dict      =		tokens.expand(a_  ,			*tokens.shape[1:]					)
                      SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.cat((tokens, next_tokens)  ,			dim=1					)
            else:
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		-float(np.inf					)
                 SCREAMING_SNAKE_CASE__					:					Any      =		0
                 SCREAMING_SNAKE_CASE__					:					Any      =		scores[:, None] + logits
                 seq_lengths[~is_stopped] += 1
                 SCREAMING_SNAKE_CASE__					:					Dict      =		scores_sum / seq_lengths[:, None]
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		scores_sum_average.view(-1					).topk(a_  ,			-1					)
                 SCREAMING_SNAKE_CASE__					:					str      =		next_tokens // scores_sum.shape[1]
                 SCREAMING_SNAKE_CASE__					:					str      =		seq_lengths[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		next_tokens % scores_sum.shape[1]
                 SCREAMING_SNAKE_CASE__					:					Any      =		next_tokens.unsqueeze(1					)
                 SCREAMING_SNAKE_CASE__					:					int      =		tokens[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.cat((tokens, next_tokens)  ,			dim=1					)
                 SCREAMING_SNAKE_CASE__					:					Any      =		generated[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		scores_sum_average * seq_lengths
                 SCREAMING_SNAKE_CASE__					:					int      =		is_stopped[next_tokens_source]

            SCREAMING_SNAKE_CASE__					:					int      =		self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0]  ,			1  ,			-1					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.cat((generated, next_token_embed)  ,			dim=1					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		is_stopped + next_tokens.eq(a_					).squeeze()
            if is_stopped.all():
                 break

       SCREAMING_SNAKE_CASE__					:					Dict      =		scores / seq_lengths
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		scores.argsort(descending=a_					)
       # tokens tensors are already padded to max_seq_length
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[tokens[i] for i in order]
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.stack(a_  ,			dim=0					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.tensor([seq_lengths[i] for i in order]  ,			dtype=seq_lengths.dtype					)
       return output_texts, seq_lengths










"""simple docstring"""


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():
		__magic_name__       :					Any						=			"""pt"""
elif is_tf_available():
		__magic_name__       :					Optional[Any]						=			"""tf"""
else:
		__magic_name__       :					int						=			"""jax"""






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  PerceiverTokenizer
			__lowerCAmelCase     :				int										=  False







			def   _a      (      self    ):






									'''simple docstring'''







									super().setUp()
									UpperCamelCase				:		List[Any]				   =			PerceiverTokenizer()
									tokenizer.save_pretrained(self.tmpdirname    )







			@cached_property
			def   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self						,   **_A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A						,   _A=False						,   _A=2_0						,   _A=5    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			[]
									for i in range(len(_A    )    ):
															try:
																					UpperCamelCase				:		List[str]				   =			tokenizer.decode([i]						,   clean_up_tokenization_spaces=_A    )
															except UnicodeDecodeError:
																					pass
															toks.append((i, tok)    )

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

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.perceiver_tokenizer
									UpperCamelCase				:		str				   =			"""Unicode €."""
									UpperCamelCase				:		Dict				   =			tokenizer(_A    )
									UpperCamelCase				:		Optional[Any]				   =			[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
									self.assertEqual(encoded["""input_ids"""]						,   _A    )

									# decoding
									UpperCamelCase				:		Optional[int]				   =			tokenizer.decode(_A    )
									self.assertEqual(_A						,   """[CLS]Unicode €.[SEP]"""    )

									UpperCamelCase				:		str				   =			tokenizer("""e è é ê ë"""    )
									UpperCamelCase				:		str				   =			[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
									self.assertEqual(encoded["""input_ids"""]						,   _A    )
									# decoding
									UpperCamelCase				:		Any				   =			tokenizer.decode(_A    )
									self.assertEqual(_A						,   """[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    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self.perceiver_tokenizer
									UpperCamelCase				:		Optional[Any]				   =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									# fmt: off
									UpperCamelCase				:		List[Any]				   =			[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
									# fmt: on
									UpperCamelCase				:		Dict				   =			tokenizer(_A						,   padding=_A						,   return_tensors=_A    )
									self.assertIsInstance(_A						,   _A    )

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

									self.assertListEqual(_A						,   _A    )

									self.assertEqual((2, 3_8)						,   batch.input_ids.shape    )
									self.assertEqual((2, 3_8)						,   batch.attention_mask.shape    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.perceiver_tokenizer
									UpperCamelCase				:		Optional[int]				   =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									UpperCamelCase				:		Any				   =			tokenizer(_A						,   padding=_A						,   return_tensors=_A    )
									# check if input_ids are returned and no decoder_input_ids
									self.assertIn("""input_ids"""						,   _A    )
									self.assertIn("""attention_mask"""						,   _A    )
									self.assertNotIn("""decoder_input_ids"""						,   _A    )
									self.assertNotIn("""decoder_attention_mask"""						,   _A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			self.perceiver_tokenizer
									UpperCamelCase				:		int				   =			[
									    """Summary of the text.""",
									    """Another summary.""",
									]
									UpperCamelCase				:		int				   =			tokenizer(
									    text_target=_A						,   max_length=3_2						,   padding="""max_length"""						,   truncation=_A						,   return_tensors=_A    )
									self.assertEqual(3_2						,   targets["""input_ids"""].shape[1]    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.get_tokenizers()
									for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																					self.assertNotEqual(tokenizer.model_max_length						,   4_2    )

        # Now let's start the test
									UpperCamelCase				:		Any				   =			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
																					UpperCamelCase				:		int				   =			tempfile.mkdtemp()

																					UpperCamelCase				:		Tuple				   =			""" He is very happy, UNwant\u00E9d,running"""
																					UpperCamelCase				:		Dict				   =			tokenizer.encode(_A						,   add_special_tokens=_A    )
																					tokenizer.save_pretrained(_A    )

																					UpperCamelCase				:		Any				   =			tokenizer.__class__.from_pretrained(_A    )
																					UpperCamelCase				:		Tuple				   =			after_tokenizer.encode(_A						,   add_special_tokens=_A    )
																					self.assertListEqual(_A						,   _A    )

																					shutil.rmtree(_A    )

									UpperCamelCase				:		Union[str, Any]				   =			self.get_tokenizers(model_max_length=4_2    )
									for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																					# Isolate this from the other tests because we save additional tokens/etc
																					UpperCamelCase				:		List[Any]				   =			tempfile.mkdtemp()

																					UpperCamelCase				:		Union[str, Any]				   =			""" He is very happy, UNwant\u00E9d,running"""
																					tokenizer.add_tokens(["""bim""", """bambam"""]    )
																					UpperCamelCase				:		int				   =			tokenizer.additional_special_tokens
																					additional_special_tokens.append("""new_additional_special_token"""    )
																					tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}    )
																					UpperCamelCase				:		List[str]				   =			tokenizer.encode(_A						,   add_special_tokens=_A    )
																					tokenizer.save_pretrained(_A    )

																					UpperCamelCase				:		List[str]				   =			tokenizer.__class__.from_pretrained(_A    )
																					UpperCamelCase				:		Tuple				   =			after_tokenizer.encode(_A						,   add_special_tokens=_A    )
																					self.assertListEqual(_A						,   _A    )
																					self.assertIn("""new_additional_special_token"""						,   after_tokenizer.additional_special_tokens    )
																					self.assertEqual(after_tokenizer.model_max_length						,   4_2    )

																					UpperCamelCase				:		Any				   =			tokenizer.__class__.from_pretrained(_A						,   model_max_length=4_3    )
																					self.assertEqual(tokenizer.model_max_length						,   4_3    )

																					shutil.rmtree(_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			[]
									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(_A    )

																					with open(os.path.join(_A						,   """special_tokens_map.json"""    )						,   encoding="""utf-8"""    ) as json_file:
																											UpperCamelCase				:		Union[str, Any]				   =			json.load(_A    )

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

																					UpperCamelCase				:		Optional[int]				   =			[f"""<extra_id_{i}>""" for i in range(1_2_5    )]

																					UpperCamelCase				:		List[Any]				   =			added_tokens_extra_ids + [
																					    """an_additional_special_token"""
																					]
																					UpperCamelCase				:		int				   =			added_tokens_extra_ids + [
																					    """an_additional_special_token"""
																					]

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

																					# 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
																					UpperCamelCase				:		Tuple				   =			tokenizer_class.from_pretrained(
																					    _A						,   )
																					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
																					UpperCamelCase				:		List[str]				   =			added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""						,   lstrip=_A    )]
																					UpperCamelCase				:		Optional[Any]				   =			tokenizer_class.from_pretrained(
																					    _A						,   additional_special_tokens=_A						,   )

																					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    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			self.perceiver_tokenizer
									self.assertEqual(tokenizer.decode([1_7_8]    )						,   """�"""    )







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.get_tokenizers(fast=_A						,   do_lower_case=_A    )
									for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																					UpperCamelCase				:		Optional[Any]				   =			["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
																					UpperCamelCase				:		Dict				   =			tokenizer.convert_tokens_to_string(_A    )

																					self.assertIsInstance(_A						,   _A    )



