


import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
				from .tokenization_fnet import FNetTokenizer
else:
				lowerCAmelCase__			:      List[str]								=None

lowerCAmelCase__			:      str								=logging.get_logger(__name__)
lowerCAmelCase__			:      Any								={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__			:      List[Any]								={
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__			:      Any								={
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

lowerCAmelCase__			:      Optional[Any]								='▁'







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						VOCAB_FILES_NAMES
							_UpperCAmelCase      =						PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase      =						["""input_ids""", """token_type_ids"""]
							_UpperCAmelCase      =						FNetTokenizer






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=False			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__="<unk>"			,						lowerCAmelCase__="[SEP]"			,						lowerCAmelCase__="<pad>"			,						lowerCAmelCase__="[CLS]"			,						lowerCAmelCase__="[MASK]"			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					(
										    AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__			,						normalized=lowerCAmelCase__       )
										    if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       )
										    else mask_token
										)

										super().__init__(
										    lowerCAmelCase__			,						tokenizer_file=lowerCAmelCase__			,						do_lower_case=lowerCAmelCase__			,						remove_space=lowerCAmelCase__			,						keep_accents=lowerCAmelCase__			,						unk_token=lowerCAmelCase__			,						sep_token=lowerCAmelCase__			,						pad_token=lowerCAmelCase__			,						cls_token=lowerCAmelCase__			,						mask_token=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					do_lower_case
										SCREAMING_SNAKE_CASE_		:     int					=					remove_space
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					keep_accents
										SCREAMING_SNAKE_CASE_		:     Any					=					vocab_file
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					False if not self.vocab_file else True






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[self.sep_token_id]
										SCREAMING_SNAKE_CASE_		:     Any					=					[self.cls_token_id]
										if token_ids_a is None:
													return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[self.sep_token_id]
										SCREAMING_SNAKE_CASE_		:     int					=					[self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep       ) * [0]
										return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										if not os.path.isdir(lowerCAmelCase__       ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										SCREAMING_SNAKE_CASE_		:     Tuple					=					os.path.join(
										    lowerCAmelCase__			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

										return (out_vocab_file,)









import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				from transformers.models.roberta.modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				)







class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=7			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=9_9			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=5			,						lowerCAmelCase__=4			,						lowerCAmelCase__=3_7			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=4			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					parent
										SCREAMING_SNAKE_CASE_		:     List[str]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					seq_length
										SCREAMING_SNAKE_CASE_		:     Any					=					is_training
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					use_attention_mask
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_token_type_ids
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					use_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     List[str]					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     int					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Tuple					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     Any					=					type_vocab_size
										SCREAMING_SNAKE_CASE_		:     str					=					type_sequence_label_size
										SCREAMING_SNAKE_CASE_		:     str					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					num_choices






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

										SCREAMING_SNAKE_CASE_		:     str					=					None
										if self.use_attention_mask:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					random_attention_mask([self.batch_size, self.seq_length]       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					None
										if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

										SCREAMING_SNAKE_CASE_		:     Any					=					RobertaConfig(
										    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			,						is_decoder=lowerCAmelCase__			,						initializer_range=self.initializer_range			,						)

										return config, input_ids, token_type_ids, attention_mask






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					config_and_inputs
										SCREAMING_SNAKE_CASE_		:     List[str]					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					config_and_inputs

										SCREAMING_SNAKE_CASE_		:     str					=					True
										SCREAMING_SNAKE_CASE_		:     Dict					=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
										SCREAMING_SNAKE_CASE_		:     int					=					ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2       )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)







@require_flax
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						True

							_UpperCAmelCase      =						(
							    (
							        FlaxRobertaModel,
							        FlaxRobertaForCausalLM,
							        FlaxRobertaForMaskedLM,
							        FlaxRobertaForSequenceClassification,
							        FlaxRobertaForTokenClassification,
							        FlaxRobertaForMultipleChoice,
							        FlaxRobertaForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					FlaxRobertaModelTester(self       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					model_class_name.from_pretrained('roberta-base'			,						from_pt=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     str					=					model(np.ones((1, 1)       )       )
													self.assertIsNotNone(lowerCAmelCase__       )





