


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	List[str]     =			RobertaTokenizer
       lowercase_ :	Any     =			RobertaTokenizerFast
       lowercase_ :	Dict     =			True
       lowercase_ :	List[Any]     =			{"""cls_token""": """<s>"""}
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           super().setUp()

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

           _lowercase       :							Union[str, Any]      		=		os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES['vocab_file'])
           _lowercase       :							Union[str, Any]      		=		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(lowerCamelCase) + '\n')
           with open(self.merges_file,      'w',      encoding='utf-8') as fp:
               fp.write('\n'.join(lowerCamelCase))
       def 			UpperCamelCase     (					self,      **lowerCamelCase)       ->   List[Any]:




           """simple docstring"""

           kwargs.update(self.special_tokens_map)
           return self.tokenizer_class.from_pretrained(self.tmpdirname,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      **lowerCamelCase)       ->   Any:




           """simple docstring"""

           kwargs.update(self.special_tokens_map)
           return RobertaTokenizerFast.from_pretrained(self.tmpdirname,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		'lower newer'
           _lowercase       :							List[str]      		=		'lower newer'
           return input_text, output_text
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.tokenizer_class(self.vocab_file,      self.merges_file,      **self.special_tokens_map)
           _lowercase       :							Any      		=		'lower newer'
           _lowercase       :							Tuple      		=		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
           _lowercase       :							Dict      		=		tokenizer.tokenize(lowerCamelCase)  # , add_prefix_space=True)
           self.assertListEqual(lowerCamelCase,      lowerCamelCase)

           _lowercase       :							List[str]      		=		tokens + [tokenizer.unk_token]
           _lowercase       :							Any      		=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase),      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Any      		=		self.get_tokenizer()

           self.assertListEqual(tokenizer.encode('Hello world!',      add_special_tokens=lowerCamelCase),      [0, 3_14_14, 2_32, 3_28, 2])
           self.assertListEqual(
               tokenizer.encode('Hello world! cécé herlolip 418',      add_special_tokens=lowerCamelCase),      [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2],      )
       @slow
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Any      		=		self.tokenizer_class.from_pretrained('roberta-base')

           _lowercase       :							Optional[int]      		=		tokenizer.encode('sequence builders',      add_special_tokens=lowerCamelCase)
           _lowercase       :							int      		=		tokenizer.encode('multi-sequence build',      add_special_tokens=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		tokenizer.encode(
               'sequence builders',      add_special_tokens=lowerCamelCase,      add_prefix_space=lowerCamelCase)
           _lowercase       :							int      		=		tokenizer.encode(
               'sequence builders',      'multi-sequence build',      add_special_tokens=lowerCamelCase,      add_prefix_space=lowerCamelCase)

           _lowercase       :							Union[str, Any]      		=		tokenizer.build_inputs_with_special_tokens(lowerCamelCase)
           _lowercase       :							str      		=		tokenizer.build_inputs_with_special_tokens(lowerCamelCase,      lowerCamelCase)

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Any      		=		self.get_tokenizer()

           _lowercase       :							Any      		=		'Encode this sequence.'
           _lowercase       :							int      		=		tokenizer.byte_encoder[' '.encode('utf-8')[0]]

           # Testing encoder arguments
           _lowercase       :							Optional[int]      		=		tokenizer.encode(lowerCamelCase,      add_special_tokens=lowerCamelCase,      add_prefix_space=lowerCamelCase)
           _lowercase       :							Tuple      		=		tokenizer.convert_ids_to_tokens(encoded[0])[0]
           self.assertNotEqual(lowerCamelCase,      lowerCamelCase)

           _lowercase       :							Union[str, Any]      		=		tokenizer.encode(lowerCamelCase,      add_special_tokens=lowerCamelCase,      add_prefix_space=lowerCamelCase)
           _lowercase       :							Dict      		=		tokenizer.convert_ids_to_tokens(encoded[0])[0]
           self.assertEqual(lowerCamelCase,      lowerCamelCase)

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

           # Testing spaces after special tokens
           _lowercase       :							int      		=		'<mask>'
           tokenizer.add_special_tokens(
               {'mask_token': AddedToken(lowerCamelCase,      lstrip=lowerCamelCase,      rstrip=lowerCamelCase)})  # mask token has a left space
           _lowercase       :							Dict      		=		tokenizer.convert_tokens_to_ids(lowerCamelCase)

           _lowercase       :							str      		=		'Encode <mask> sequence'
           _lowercase       :							List[Any]      		=		'Encode <mask>sequence'

           _lowercase       :							Optional[Any]      		=		tokenizer.encode(lowerCamelCase)
           _lowercase       :							Tuple      		=		encoded.index(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
           self.assertEqual(lowerCamelCase,      lowerCamelCase)

           _lowercase       :							Tuple      		=		tokenizer.encode(lowerCamelCase)
           _lowercase       :							List[Any]      		=		encoded.index(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
           self.assertNotEqual(lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

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

                   # 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']),      )

                   _lowercase       :							List[str]      		=		tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
                   _lowercase       :							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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
                   self.assertSequenceEqual(tokens_r['input_ids'],      [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

                   self.assertSequenceEqual(
                       lowerCamelCase,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
                   self.assertSequenceEqual(
                       lowerCamelCase,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           for trim_offsets, add_prefix_space in itertools.product([True, False],      repeat=2):
               _lowercase       :							str      		=		self.rust_tokenizer_class.from_pretrained(
                   self.tmpdirname,      use_fast=lowerCamelCase,      add_prefix_space=lowerCamelCase,      trim_offsets=lowerCamelCase)

               _lowercase       :							int      		=		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
               _lowercase       :							Tuple      		=		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

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

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




       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
                   _lowercase       :							Optional[int]      		=		'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                   _lowercase       :							List[Any]      		=		F'''{text_of_1_token} {text_of_1_token}'''

                   _lowercase       :							Tuple      		=		self.rust_tokenizer_class.from_pretrained(
                       lowerCamelCase,      use_fast=lowerCamelCase,      add_prefix_space=lowerCamelCase,      trim_offsets=lowerCamelCase)
                   _lowercase       :							Optional[Any]      		=		tokenizer_r(lowerCamelCase,      return_offsets_mapping=lowerCamelCase,      add_special_tokens=lowerCamelCase)
                   self.assertEqual(encoding.offset_mapping[0],      (0, len(lowerCamelCase)))
                   self.assertEqual(
                       encoding.offset_mapping[1],      (len(lowerCamelCase) + 1, len(lowerCamelCase) + 1 + len(lowerCamelCase)),      )

                   _lowercase       :							Optional[Any]      		=		self.rust_tokenizer_class.from_pretrained(
                       lowerCamelCase,      use_fast=lowerCamelCase,      add_prefix_space=lowerCamelCase,      trim_offsets=lowerCamelCase)
                   _lowercase       :							Dict      		=		tokenizer_r(lowerCamelCase,      return_offsets_mapping=lowerCamelCase,      add_special_tokens=lowerCamelCase)
                   self.assertEqual(encoding.offset_mapping[0],      (0, len(lowerCamelCase)))
                   self.assertEqual(
                       encoding.offset_mapping[1],      (len(lowerCamelCase) + 1, len(lowerCamelCase) + 1 + len(lowerCamelCase)),      )

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

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

                   _lowercase       :							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)),
                   # )

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

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

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








"""simple docstring"""




import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)






class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=7		, _a=True		, _a=True		, _a=True		, _a=True		, _a=99		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=512		, _a=16		, _a=2		, _a=0.02		, _a=4		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					seq_length
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_attention_mask
							lowerCamelCase			      =					use_token_type_ids
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					vocab_size
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					type_vocab_size
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					num_choices





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size )

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

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

							lowerCamelCase			      =					RoFormerConfig(
							    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=_a		, initializer_range=self.initializer_range		, )

							return config, input_ids, token_type_ids, attention_mask





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict






@require_flax
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						True

				__UpperCamelCase			  =						(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerModelTester(self )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_class_name in self.all_model_classes:
										lowerCamelCase			      =					model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""		, from_pt=_a )
										lowerCamelCase			      =					model(np.ones((1, 1) ) )
										self.assertIsNotNone(_a )






@require_flax
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
							lowerCamelCase			      =					jnp.array([[0, 1, 2, 3, 4, 5]] )
							lowerCamelCase			      =					model(_a )[0]

							lowerCamelCase			      =					50_000

							lowerCamelCase			      =					(1, 6, vocab_size)
							self.assertEqual(output.shape		, _a )

							lowerCamelCase			      =					jnp.array(
							    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )

							self.assertTrue(jnp.allclose(output[:, :3, :3]		, _a		, atol=1e-4 ) )



