




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   :						Optional[int]				=			'''pt'''
elif is_tf_available():
	__lowerCamelCase   :						Optional[Any]				=			'''tf'''
else:
	__lowerCamelCase   :						Any				=			'''jax'''


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=PerceiverTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Dict  )						->   List[str]:
								super().setUp()
								__magic_name__							:	Optional[int]    							=							PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname  )

			@cached_property
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						**lowerCamelCase_			:   Any  )						->   PerceiverTokenizer:
								return self.tokenizer_class.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Union[str, Any]=False       ,						lowerCamelCase_			:   Tuple=20       ,						lowerCamelCase_			:   Dict=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.

								__magic_name__							:	List[str]    							=							[]
								for i in range(len(lowerCamelCase_  )  ):
													try:
																		__magic_name__							:	Optional[int]    							=							tokenizer.decode([i]       ,						clean_up_tokenization_spaces=lowerCamelCase_  )
													except UnicodeDecodeError:
																		pass
													toks.append((i, tok)  )

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

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

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

								# decoding
								__magic_name__							:	List[Any]    							=							tokenizer.decode(lowerCamelCase_  )
								self.assertEqual(lowerCamelCase_       ,						'''[CLS]Unicode €.[SEP]'''  )

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

								if FRAMEWORK != "jax":
													__magic_name__							:	List[Any]    							=							list(batch.input_ids.numpy()[0]  )
								else:
													__magic_name__							:	Union[str, Any]    							=							list(batch.input_ids.tolist()[0]  )

								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

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

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

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

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

																		__magic_name__							:	Dict    							=							''' He is very happy, UNwant\u00E9d,running'''
																		__magic_name__							:	Tuple    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		tokenizer.save_pretrained(lowerCamelCase_  )

																		__magic_name__							:	Dict    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_  )
																		__magic_name__							:	List[Any]    							=							after_tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

																		shutil.rmtree(lowerCamelCase_  )

								__magic_name__							:	List[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
																		__magic_name__							:	List[Any]    							=							tempfile.mkdtemp()

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

																		__magic_name__							:	List[Any]    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_  )
																		__magic_name__							:	Optional[Any]    							=							after_tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )
																		self.assertIn('''new_additional_special_token'''       ,						after_tokenizer.additional_special_tokens  )
																		self.assertEqual(after_tokenizer.model_max_length       ,						42  )

																		__magic_name__							:	Optional[int]    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_       ,						model_max_length=43  )
																		self.assertEqual(tokenizer.model_max_length       ,						43  )

																		shutil.rmtree(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any  )						->   Tuple:
								__magic_name__							:	List[Any]    							=							[]
								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(lowerCamelCase_  )

																		with open(os.path.join(lowerCamelCase_       ,						'''special_tokens_map.json'''  )       ,						encoding='''utf-8'''  ) as json_file:
																							__magic_name__							:	Dict    							=							json.load(lowerCamelCase_  )

																		with open(os.path.join(lowerCamelCase_       ,						'''tokenizer_config.json'''  )       ,						encoding='''utf-8'''  ) as json_file:
																							__magic_name__							:	Dict    							=							json.load(lowerCamelCase_  )

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

																		__magic_name__							:	str    							=							added_tokens_extra_ids + [
																		    '''an_additional_special_token'''
																		]
																		__magic_name__							:	Dict    							=							added_tokens_extra_ids + [
																		    '''an_additional_special_token'''
																		]

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

																		# 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
																		__magic_name__							:	Optional[Any]    							=							tokenizer_class.from_pretrained(
																		    lowerCamelCase_       ,						)
																		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
																		__magic_name__							:	Optional[int]    							=							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''       ,						lstrip=lowerCamelCase_  )]
																		__magic_name__							:	Optional[Any]    							=							tokenizer_class.from_pretrained(
																		    lowerCamelCase_       ,						additional_special_tokens=lowerCamelCase_       ,						)

																		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			:   Tuple  )						->   Any:
								__magic_name__							:	int    							=							self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([178]  )       ,						'''�'''  )

			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Dict:
								pass

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

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

			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								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
								__magic_name__							:	Any    							=							self.get_tokenizers(fast=lowerCamelCase_       ,						do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							:	Any    							=							['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
																		__magic_name__							:	Tuple    							=							tokenizer.convert_tokens_to_string(lowerCamelCase_  )

																		self.assertIsInstance(lowerCamelCase_       ,						lowerCamelCase_  )









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



