


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			LongformerTokenizer
 SCREAMING_SNAKE_CASE__ =			True
 SCREAMING_SNAKE_CASE__ =			LongformerTokenizerFast
 SCREAMING_SNAKE_CASE__ =			True




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       a		:List[Any]									=			[
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       a		:Dict									=			dict(zip(UpperCamelCase_   ,    range(len(UpperCamelCase_       )       )       )       )
       a		:Optional[Any]									=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       a		:Optional[Any]									=			{'''unk_token''': '''<unk>'''}

       a		:int									=			os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES['''vocab_file''']       )
       a		:int									=			os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES['''merges_file''']       )
       with open(self.vocab_file   ,    '''w'''   ,    encoding='''utf-8'''       ) as fp:
             fp.write(json.dumps(UpperCamelCase_       ) + '''\n'''       )
       with open(self.merges_file   ,    '''w'''   ,    encoding='''utf-8'''       ) as fp:
             fp.write('''\n'''.join(UpperCamelCase_       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       kwargs.update(self.special_tokens_map       )
       return self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **UpperCamelCase_       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       kwargs.update(self.special_tokens_map       )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,    **UpperCamelCase_       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Union[str, Any]									=			'''lower newer'''
       a		:Union[str, Any]									=			'''lower newer'''
       return input_text, output_text




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			self.tokenizer_class(self.vocab_file   ,    self.merges_file   ,    **self.special_tokens_map       )
       a		:int									=			'''lower newer'''
       a		:Dict									=			['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
       a		:Dict									=			tokenizer.tokenize(UpperCamelCase_       )  # , add_prefix_space=True)
       self.assertListEqual(UpperCamelCase_   ,    UpperCamelCase_       )

       a		:List[Any]									=			tokens + [tokenizer.unk_token]
       a		:Any									=			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_       )   ,    UpperCamelCase_       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			self.get_tokenizer()

       self.assertListEqual(tokenizer.encode('''Hello world!'''   ,    add_special_tokens=UpperCamelCase_       )   ,    [0, 3_1414, 232, 328, 2]       )
       self.assertListEqual(
           tokenizer.encode('''Hello world! cécé herlolip 418'''   ,    add_special_tokens=UpperCamelCase_       )   ,    [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]   ,    )




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''       )

       a		:Optional[int]									=			tokenizer.encode('''sequence builders'''   ,    add_special_tokens=UpperCamelCase_       )
       a		:Union[str, Any]									=			tokenizer.encode('''multi-sequence build'''   ,    add_special_tokens=UpperCamelCase_       )

       a		:Tuple									=			tokenizer.encode(
           '''sequence builders'''   ,    add_special_tokens=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_       )
       a		:Dict									=			tokenizer.encode(
           '''sequence builders'''   ,    '''multi-sequence build'''   ,    add_special_tokens=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_       )

       a		:Union[str, Any]									=			tokenizer.build_inputs_with_special_tokens(UpperCamelCase_       )
       a		:List[Any]									=			tokenizer.build_inputs_with_special_tokens(UpperCamelCase_   ,    UpperCamelCase_       )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			self.get_tokenizer()

       a		:List[str]									=			'''Encode this sequence.'''
       a		:str									=			tokenizer.byte_encoder[''' '''.encode('''utf-8'''       )[0]]

       # Testing encoder arguments
       a		:List[str]									=			tokenizer.encode(UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_       )
       a		:Union[str, Any]									=			tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
       self.assertNotEqual(UpperCamelCase_   ,    UpperCamelCase_       )

       a		:Optional[int]									=			tokenizer.encode(UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_       )
       a		:Optional[int]									=			tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
       self.assertEqual(UpperCamelCase_   ,    UpperCamelCase_       )

       tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}       )
       a		:Union[str, Any]									=			tokenizer.encode(UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_       )
       a		:Union[str, Any]									=			tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
       self.assertNotEqual(UpperCamelCase_   ,    UpperCamelCase_       )

       # Testing spaces after special tokens
       a		:Union[str, Any]									=			'''<mask>'''
       tokenizer.add_special_tokens(
           {'''mask_token''': AddedToken(UpperCamelCase_   ,    lstrip=UpperCamelCase_   ,    rstrip=UpperCamelCase_       )}       )  # mask token has a left space
       a		:int									=			tokenizer.convert_tokens_to_ids(UpperCamelCase_       )

       a		:Optional[Any]									=			'''Encode <mask> sequence'''
       a		:Union[str, Any]									=			'''Encode <mask>sequence'''

       a		:List[Any]									=			tokenizer.encode(UpperCamelCase_       )
       a		:List[str]									=			encoded.index(UpperCamelCase_       )
       a		:int									=			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
       self.assertEqual(UpperCamelCase_   ,    UpperCamelCase_       )

       a		:List[str]									=			tokenizer.encode(UpperCamelCase_       )
       a		:Union[str, Any]									=			encoded.index(UpperCamelCase_       )
       a		:Any									=			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
       self.assertNotEqual(UpperCamelCase_   ,    UpperCamelCase_       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   a		:Optional[Any]									=			self.rust_tokenizer_class.from_pretrained(UpperCamelCase_   ,    **UpperCamelCase_       )
                   a		:str									=			self.tokenizer_class.from_pretrained(UpperCamelCase_   ,    **UpperCamelCase_       )
                   a		:str									=			'''A, <mask> AllenNLP sentence.'''
                   a		:Any									=			tokenizer_r.encode_plus(UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_   ,    return_token_type_ids=UpperCamelCase_       )
                   a		:List[Any]									=			tokenizer_p.encode_plus(UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_   ,    return_token_type_ids=UpperCamelCase_       )

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['''token_type_ids''']       )   ,    sum(tokens_p['''token_type_ids''']       )       )

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']       ) / len(tokens_r['''attention_mask''']       )   ,    sum(tokens_p['''attention_mask''']       ) / len(tokens_p['''attention_mask''']       )   ,    )

                   a		:int									=			tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']       )
                   a		:List[str]									=			tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']       )

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['''input_ids''']   ,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )
                   self.assertSequenceEqual(tokens_r['''input_ids''']   ,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )

                   self.assertSequenceEqual(
                       UpperCamelCase_   ,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )
                   self.assertSequenceEqual(
                       UpperCamelCase_   ,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for trim_offsets, add_prefix_space in itertools.product([True, False]   ,    repeat=2       ):
             a		:List[Any]									=			self.rust_tokenizer_class.from_pretrained(
                 self.tmpdirname   ,    use_fast=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_   ,    trim_offsets=UpperCamelCase_       )

             a		:List[Any]									=			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
             a		:Optional[int]									=			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

             self.assertEqual(pre_tokenizer_state['''add_prefix_space''']   ,    UpperCamelCase_       )

             self.assertEqual(post_processor_state['''add_prefix_space''']   ,    UpperCamelCase_       )
             self.assertEqual(post_processor_state['''trim_offsets''']   ,    UpperCamelCase_       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
       # `trim_offsets`
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   a		:Optional[Any]									=			'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                   a		:Optional[int]									=			F'''{text_of_1_token} {text_of_1_token}'''

                   a		:List[Any]									=			self.rust_tokenizer_class.from_pretrained(
                       UpperCamelCase_   ,    use_fast=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_   ,    trim_offsets=UpperCamelCase_       )
                   a		:Any									=			tokenizer_r(UpperCamelCase_   ,    return_offsets_mapping=UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_       )
                   self.assertEqual(encoding.offset_mapping[0]   ,    (0, len(UpperCamelCase_       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]   ,    (len(UpperCamelCase_       ) + 1, len(UpperCamelCase_       ) + 1 + len(UpperCamelCase_       ))   ,    )

                   a		:Union[str, Any]									=			self.rust_tokenizer_class.from_pretrained(
                       UpperCamelCase_   ,    use_fast=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_   ,    trim_offsets=UpperCamelCase_       )
                   a		:int									=			tokenizer_r(UpperCamelCase_   ,    return_offsets_mapping=UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_       )
                   self.assertEqual(encoding.offset_mapping[0]   ,    (0, len(UpperCamelCase_       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]   ,    (len(UpperCamelCase_       ) + 1, len(UpperCamelCase_       ) + 1 + len(UpperCamelCase_       ))   ,    )

                   a		:Tuple									=			self.rust_tokenizer_class.from_pretrained(
                       UpperCamelCase_   ,    use_fast=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_   ,    trim_offsets=UpperCamelCase_       )
                   a		:str									=			tokenizer_r(UpperCamelCase_   ,    return_offsets_mapping=UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_       )
                   self.assertEqual(encoding.offset_mapping[0]   ,    (0, len(UpperCamelCase_       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]   ,    (len(UpperCamelCase_       ), len(UpperCamelCase_       ) + 1 + len(UpperCamelCase_       ))   ,    )

                   a		:Dict									=			self.rust_tokenizer_class.from_pretrained(
                       UpperCamelCase_   ,    use_fast=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_   ,    trim_offsets=UpperCamelCase_       )
                   a		:str									=			tokenizer_r(UpperCamelCase_   ,    return_offsets_mapping=UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_       )
                   self.assertEqual(encoding.offset_mapping[0]   ,    (0, len(UpperCamelCase_       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]   ,    (len(UpperCamelCase_       ), len(UpperCamelCase_       ) + 1 + len(UpperCamelCase_       ))   ,    )

                   a		:Optional[Any]									=			F''' {text}'''

                   # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                   #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                   # )
                   # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                   # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                   # self.assertEqual(
                   #     encoding.offset_mapping[1],
                   #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                   # )

                   a		:Any									=			self.rust_tokenizer_class.from_pretrained(
                       UpperCamelCase_   ,    use_fast=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_   ,    trim_offsets=UpperCamelCase_       )
                   a		:List[str]									=			tokenizer_r(UpperCamelCase_   ,    return_offsets_mapping=UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_       )
                   self.assertEqual(encoding.offset_mapping[0]   ,    (1, 1 + len(UpperCamelCase_       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]   ,    (1 + len(UpperCamelCase_       ) + 1, 1 + len(UpperCamelCase_       ) + 1 + len(UpperCamelCase_       ))   ,    )

                   a		:Optional[Any]									=			self.rust_tokenizer_class.from_pretrained(
                       UpperCamelCase_   ,    use_fast=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_   ,    trim_offsets=UpperCamelCase_       )
                   a		:Union[str, Any]									=			tokenizer_r(UpperCamelCase_   ,    return_offsets_mapping=UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_       )
                   self.assertEqual(encoding.offset_mapping[0]   ,    (0, 1 + len(UpperCamelCase_       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]   ,    (1 + len(UpperCamelCase_       ), 1 + len(UpperCamelCase_       ) + 1 + len(UpperCamelCase_       ))   ,    )

                   a		:List[str]									=			self.rust_tokenizer_class.from_pretrained(
                       UpperCamelCase_   ,    use_fast=UpperCamelCase_   ,    add_prefix_space=UpperCamelCase_   ,    trim_offsets=UpperCamelCase_       )
                   a		:Union[str, Any]									=			tokenizer_r(UpperCamelCase_   ,    return_offsets_mapping=UpperCamelCase_   ,    add_special_tokens=UpperCamelCase_       )
                   self.assertEqual(encoding.offset_mapping[0]   ,    (0, 1 + len(UpperCamelCase_       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]   ,    (1 + len(UpperCamelCase_       ), 1 + len(UpperCamelCase_       ) + 1 + len(UpperCamelCase_       ))   ,    )












'''simple docstring'''




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 				__snake_case     (					unittest.TestCase							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=7      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=99      ,       UpperCamelCase_=32      ,       UpperCamelCase_=5      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=512      ,       UpperCamelCase_=16      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=4      ,       )      ->		List[Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   seq_length
		snake_case__	       =   is_training
		snake_case__	       =   use_attention_mask
		snake_case__	       =   use_token_type_ids
		snake_case__	       =   use_labels
		snake_case__	       =   vocab_size
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   type_vocab_size
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   num_choices





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size		)

		snake_case__	       =   None
		if self.use_attention_mask:
			snake_case__	       =   random_attention_mask([self.batch_size, self.seq_length]		)

		snake_case__	       =   None
		if self.use_token_type_ids:
			snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size		)

		snake_case__	       =   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=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )

		return config, input_ids, token_type_ids, attention_mask





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
		return config, inputs_dict





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs

		snake_case__	       =   True
		snake_case__	       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
		snake_case__	       =   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 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  True

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





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   FlaxRobertaModelTester(self		)





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_class_name in self.all_model_classes:
			snake_case__	       =   model_class_name.from_pretrained('roberta-base'      ,       from_pt=UpperCamelCase_		)
			snake_case__	       =   model(np.ones((1, 1)		)		)
			self.assertIsNotNone(UpperCamelCase_		)



