

import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class       snake_case_  (						__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  int		     =							TransfoXLTokenizer
			SCREAMING_SNAKE_CASE	:  str		     =							False
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							False







			def 							snake_case__(	self					:							List[str]				) ->List[Any]:
									super().setUp()

									snake_case_  =      [
									    '''<unk>''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''want''',
									    '''unwanted''',
									    '''wa''',
									    '''un''',
									    '''running''',
									    ''',''',
									    '''low''',
									    '''l''',
									]
									snake_case_  =      os.path.join(self.tmpdirname				,  VOCAB_FILES_NAMES['''vocab_file''']				)
									with open(self.vocab_file				,  '''w'''				,  encoding='''utf-8'''				) as vocab_writer:
															vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)







			def 							snake_case__(	self					:							List[Any]				,  **_UpperCamelCase					:							Optional[Any]				) ->Dict:
									snake_case_  =      True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname				,  **_UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Dict				) ->Tuple:
									snake_case_  =      '''<unk> UNwanted , running'''
									snake_case_  =      '''<unk> unwanted, running'''
									return input_text, output_text







			def 							snake_case__(	self					:							List[Any]				) ->str:
									snake_case_  =      TransfoXLTokenizer(vocab_file=self.vocab_file				,  lower_case=_UpperCamelCase				)

									snake_case_  =      tokenizer.tokenize('''<unk> UNwanted , running'''				)
									self.assertListEqual(_UpperCamelCase				,  ['''<unk>''', '''unwanted''', ''',''', '''running''']				)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase				)				,  [0, 4, 8, 7]				)







			def 							snake_case__(	self					:							Dict				) ->int:
									snake_case_  =      TransfoXLTokenizer(lower_case=_UpperCamelCase				)

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''				)				,  ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']				)







			def 							snake_case__(	self					:							Dict				) ->int:
									snake_case_  =      TransfoXLTokenizer(lower_case=_UpperCamelCase				)

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''				)				,  ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']				)







			def 							snake_case__(	self					:							List[Any]				) ->Tuple:
									snake_case_  =      TransfoXLTokenizer(lower_case=_UpperCamelCase				)
									snake_case_  =      '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
									snake_case_  =      [
									    '''Hello''',
									    '''(''',
									    '''bracket''',
									    ''')''',
									    '''and''',
									    '''side''',
									    '''@-@''',
									    '''scrolled''',
									    '''[''',
									    '''and''',
									    ''']''',
									    '''Henry''',
									    '''\'s''',
									    '''$''',
									    '''5''',
									    '''@,@''',
									    '''000''',
									    '''with''',
									    '''3''',
									    '''@.@''',
									    '''34''',
									    '''m''',
									    '''.''',
									    '''What''',
									    '''\'s''',
									    '''up''',
									    '''!''',
									    '''?''',
									]

									self.assertListEqual(tokenizer.tokenize(_UpperCamelCase				)				,  _UpperCamelCase				)

									self.assertEqual(tokenizer.convert_tokens_to_string(_UpperCamelCase				)				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							Tuple				) ->Union[str, Any]:
									snake_case_  =      self.get_tokenizer()
									snake_case_  =      len(_UpperCamelCase				)

									tokenizer.add_tokens(['''new1''', '''new2''']				)
									tokenizer.move_added_token('''new1'''				,  1				)

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(_UpperCamelCase				)				,  original_len + 2				)
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode('''new1'''				)				,  [1]				)
									self.assertEqual(tokenizer.decode([1]				)				,  '''new1'''				)






'''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_		)



