





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TFMobileBertForMaskedLM,
					    TFMobileBertForMultipleChoice,
					    TFMobileBertForNextSentencePrediction,
					    TFMobileBertForPreTraining,
					    TFMobileBertForQuestionAnswering,
					    TFMobileBertForSequenceClassification,
					    TFMobileBertForTokenClassification,
					    TFMobileBertModel,
					)
@require_tf
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =(
				    (
				        TFMobileBertModel,
				        TFMobileBertForMaskedLM,
				        TFMobileBertForNextSentencePrediction,
				        TFMobileBertForPreTraining,
				        TFMobileBertForQuestionAnswering,
				        TFMobileBertForSequenceClassification,
				        TFMobileBertForTokenClassification,
				        TFMobileBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__a            =(
				    {
				        'feature-extraction': TFMobileBertModel,
				        'fill-mask': TFMobileBertForMaskedLM,
				        'question-answering': TFMobileBertForQuestionAnswering,
				        'text-classification': TFMobileBertForSequenceClassification,
				        'token-classification': TFMobileBertForTokenClassification,
				        'zero-shot': TFMobileBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__a            =False
				__a            =False




				def   UpperCamelCase__   (  self     :					str					,				__a     :					Tuple					,				__a     :					Optional[Any]					,				__a     :					List[str]=False					):
								_a								= super()._prepare_for_class(__a					,				__a					,				return_labels=__a					)

								if return_labels:
												if model_class in get_values(__a					):
																_a								= tf.zeros(self.model_tester.batch_size					,				dtype=tf.intaa					)

								return inputs_dict
				class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






								"""simple docstring"""







								def __init__(  self     :					List[str]					,				__a     :					int					,				__a     :					Union[str, Any]=13					,				__a     :					int=7					,				__a     :					List[str]=True					,				__a     :					str=True					,				__a     :					Union[str, Any]=True					,				__a     :					Union[str, Any]=True					,				__a     :					Tuple=99					,				__a     :					Any=32					,				__a     :					Tuple=32					,				__a     :					Any=2					,				__a     :					int=4					,				__a     :					Dict=37					,				__a     :					Union[str, Any]="gelu"					,				__a     :					Optional[Any]=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Dict=5_12					,				__a     :					int=16					,				__a     :					str=2					,				__a     :					Tuple=0.02					,				__a     :					Any=3					,				__a     :					Tuple=4					,				__a     :					Any=None					,				):
												_a								= parent
												_a								= batch_size
												_a								= seq_length
												_a								= is_training
												_a								= use_input_mask
												_a								= use_token_type_ids
												_a								= use_labels
												_a								= vocab_size
												_a								= hidden_size
												_a								= num_hidden_layers
												_a								= num_attention_heads
												_a								= intermediate_size
												_a								= hidden_act
												_a								= hidden_dropout_prob
												_a								= attention_probs_dropout_prob
												_a								= max_position_embeddings
												_a								= type_vocab_size
												_a								= type_sequence_label_size
												_a								= initializer_range
												_a								= num_labels
												_a								= num_choices
												_a								= scope
												_a								= embedding_size




								def   UpperCamelCase__   (  self     :					Any					):
												_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size					)

												_a								= None
												if self.use_input_mask:
																_a								= random_attention_mask([self.batch_size, self.seq_length]					)

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

												_a								= None
												_a								= None
												_a								= None
												if self.use_labels:
																_a								= ids_tensor([self.batch_size]					,				self.type_sequence_label_size					)
																_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.num_labels					)
																_a								= ids_tensor([self.batch_size]					,				self.num_choices					)

												_a								= MobileBertConfig(
												    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					,				initializer_range=self.initializer_range					,				embedding_size=self.embedding_size					,				)

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




								def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					Optional[Any]					,				__a     :					int					,				__a     :					Tuple					,				__a     :					str					,				__a     :					List[Any]					,				__a     :					List[Any]					,				__a     :					str					):
												_a								= TFMobileBertModel(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)

												_a								= [input_ids, input_mask]
												_a								= model(__a					)

												_a								= model(__a					)

												self.parent.assertEqual(
												    result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)					)
												self.parent.assertEqual(result.pooler_output.shape					,				(self.batch_size, self.hidden_size)					)




								def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[str]					,				__a     :					Dict					,				__a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Dict					,				__a     :					List[Any]					,				__a     :					List[str]					):
												_a								= TFMobileBertForMaskedLM(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)					)




								def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Dict					,				__a     :					Dict					,				__a     :					Any					,				__a     :					Any					,				__a     :					str					,				__a     :					Tuple					,				__a     :					str					):
												_a								= TFMobileBertForNextSentencePrediction(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, 2)					)




								def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					Tuple					,				__a     :					Optional[Any]					,				__a     :					List[Any]					,				__a     :					List[Any]					,				__a     :					List[str]					,				__a     :					Optional[Any]					,				__a     :					List[Any]					):
												_a								= TFMobileBertForPreTraining(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(
												    result.prediction_logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)					)
												self.parent.assertEqual(result.seq_relationship_logits.shape					,				(self.batch_size, 2)					)




								def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[Any]					,				__a     :					str					,				__a     :					str					,				__a     :					Union[str, Any]					,				__a     :					List[Any]					,				__a     :					List[Any]					,				__a     :					List[Any]					):
												_a								= self.num_labels
												_a								= TFMobileBertForSequenceClassification(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)					)




								def   UpperCamelCase__   (  self     :					Any					,				__a     :					str					,				__a     :					Any					,				__a     :					Optional[int]					,				__a     :					int					,				__a     :					Optional[int]					,				__a     :					int					,				__a     :					List[Any]					):
												_a								= self.num_choices
												_a								= TFMobileBertForMultipleChoice(config=__a					)
												_a								= tf.tile(tf.expand_dims(__a					,				1					)					,				(1, self.num_choices, 1)					)
												_a								= tf.tile(tf.expand_dims(__a					,				1					)					,				(1, self.num_choices, 1)					)
												_a								= tf.tile(tf.expand_dims(__a					,				1					)					,				(1, self.num_choices, 1)					)
												_a								= {
												    "input_ids": multiple_choice_inputs_ids,
												    "attention_mask": multiple_choice_input_mask,
												    "token_type_ids": multiple_choice_token_type_ids,
												}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_choices)					)




								def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[str]					,				__a     :					List[Any]					,				__a     :					Dict					,				__a     :					Dict					,				__a     :					int					,				__a     :					str					,				__a     :					List[str]					):
												_a								= self.num_labels
												_a								= TFMobileBertForTokenClassification(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.num_labels)					)




								def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					Tuple					,				__a     :					List[Any]					,				__a     :					str					,				__a     :					int					,				__a     :					Any					,				__a     :					int					,				__a     :					List[str]					):
												_a								= TFMobileBertForQuestionAnswering(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.start_logits.shape					,				(self.batch_size, self.seq_length)					)
												self.parent.assertEqual(result.end_logits.shape					,				(self.batch_size, self.seq_length)					)




								def   UpperCamelCase__   (  self     :					Any					):
												_a								= self.prepare_config_and_inputs()
												(
												    (
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					
												)								= config_and_inputs
												_a								= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
												return config, inputs_dict




				def   UpperCamelCase__   (  self     :					str					):
								_a								= TFMobileBertModelTest.TFMobileBertModelTester(self					)
								_a								= ConfigTester(self					,				config_class=__a					,				hidden_size=37					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								self.config_tester.run_common_tests()




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*__a					)




				@slow
				def   UpperCamelCase__   (  self     :					List[str]					):
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
												_a								= TFMobileBertModel.from_pretrained(__a					)
												self.assertIsNotNone(__a					)


@require_tf
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				@slow
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased"					)
								_a								= tf.constant([[0, 1, 2, 3, 4, 5]]					)
								_a								= model(__a					)[0]

								_a								= [1, 6, 3_05_22]
								self.assertEqual(output.shape					,				__a					)

								_a								= tf.constant(
								    [
								        [
								            [-4.5919547, -9.248295, -9.645256],
								            [-6.7306175, -6.440284, -6.6052837],
								            [-7.2743506, -6.7847915, -6.024673],
								        ]
								    ]					)
								tf.debugging.assert_near(output[:, :3, :3]					,				__a					,				atol=1e-4					)






"""simple docstring"""


import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					CodeGenTokenizer
 lowercase__ 					=					CodeGenTokenizerFast
 lowercase__ 					=					True
 lowercase__ 					=					{"add_prefix_space": True}
 lowercase__ 					=					False


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _snake_case							:		Tuple								=  [
       """l""",
       """o""",
       """w""",
       """e""",
       """r""",
       """s""",
       """t""",
       """i""",
       """d""",
       """n""",
       """\u0120""",
       """\u0120l""",
       """\u0120n""",
       """\u0120lo""",
       """\u0120low""",
       """er""",
       """\u0120lowest""",
       """\u0120newer""",
       """\u0120wider""",
       """<unk>""",
       """<|endoftext|>""",
   ]
   _snake_case							:		Tuple								=  dict(zip(a_,      range(len(a_		)		)		)		)
   _snake_case							:		str								=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
   _snake_case							:		List[Any]								=  {"""unk_token""": """<unk>"""}

   _snake_case							:		Optional[int]								=  os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["""vocab_file"""]		)
   _snake_case							:		Optional[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(a_		) + """\n"""		)
   with open(self.merges_file,      """w""",      encoding="""utf-8"""		) as fp:
     fp.write("""\n""".join(a_		)		)


 def  UpperCamelCase_   (				self:		Any,      **a_:		int		):




   '''simple docstring'''



   kwargs.update(self.special_tokens_map		)
   return CodeGenTokenizer.from_pretrained(self.tmpdirname,      **a_		)


 def  UpperCamelCase_   (				self:		Any,      **a_:		str		):




   '''simple docstring'''



   kwargs.update(self.special_tokens_map		)
   return CodeGenTokenizerFast.from_pretrained(self.tmpdirname,      **a_		)


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		Dict		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  """lower newer"""
   _snake_case							:		Tuple								=  """lower newer"""
   return input_text, output_text


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  CodeGenTokenizer(self.vocab_file,      self.merges_file,      **self.special_tokens_map		)
   _snake_case							:		Optional[Any]								=  """lower newer"""
   _snake_case							:		Optional[int]								=  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
   _snake_case							:		int								=  tokenizer.tokenize(a_,      add_prefix_space=a_		)
   self.assertListEqual(a_,      a_		)

   _snake_case							:		str								=  tokens + [tokenizer.unk_token]
   _snake_case							:		Optional[int]								=  [14, 15, 10, 9, 3, 2, 15, 19]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		),      a_		)


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		int								=  self.get_tokenizer()
   _snake_case							:		int								=  self.get_rust_tokenizer(add_prefix_space=a_		)

   _snake_case							:		Dict								=  """lower newer"""

   # Testing tokenization
   _snake_case							:		Dict								=  tokenizer.tokenize(a_,      add_prefix_space=a_		)
   _snake_case							:		List[str]								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,      a_		)

   # Testing conversion to ids without special tokens
   _snake_case							:		Optional[Any]								=  tokenizer.encode(a_,      add_special_tokens=a_,      add_prefix_space=a_		)
   _snake_case							:		Tuple								=  rust_tokenizer.encode(a_,      add_special_tokens=a_		)
   self.assertListEqual(a_,      a_		)

   # Testing conversion to ids with special tokens
   _snake_case							:		Tuple								=  self.get_rust_tokenizer(add_prefix_space=a_		)
   _snake_case							:		int								=  tokenizer.encode(a_,      add_prefix_space=a_		)
   _snake_case							:		Optional[Any]								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,      a_		)

   # Testing the unknown token
   _snake_case							:		Tuple								=  tokens + [rust_tokenizer.unk_token]
   _snake_case							:		List[Any]								=  [14, 15, 10, 9, 3, 2, 15, 19]
   self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a_		),      a_		)


 def  UpperCamelCase_   (				self:		Dict,      *a_:		Dict,      **a_:		int		):




   '''simple docstring'''



   pass


 def  UpperCamelCase_   (				self:		int,      a_:		List[Any]=15		):




   '''simple docstring'''



   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
       _snake_case							:		List[Any]								=  self.rust_tokenizer_class.from_pretrained(a_,      **a_		)

       # Simple input
       _snake_case							:		Any								=  """This is a simple input"""
       _snake_case							:		Optional[int]								=  ["""This is a simple input 1""", """This is a simple input 2"""]
       _snake_case							:		Optional[int]								=  ("""This is a simple input""", """This is a pair""")
       _snake_case							:		Optional[Any]								=  [
           ("""This is a simple input 1""", """This is a simple input 2"""),
           ("""This is a simple pair 1""", """This is a simple pair 2"""),
       ]

       # Simple input tests
       self.assertRaises(a_,      tokenizer_r.encode,      a_,      max_length=a_,      padding="""max_length"""		)

       # Simple input
       self.assertRaises(a_,      tokenizer_r.encode_plus,      a_,      max_length=a_,      padding="""max_length"""		)

       # Simple input
       self.assertRaises(
           a_,      tokenizer_r.batch_encode_plus,      a_,      max_length=a_,      padding="""max_length""",      )

       # Pair input
       self.assertRaises(a_,      tokenizer_r.encode,      a_,      max_length=a_,      padding="""max_length"""		)

       # Pair input
       self.assertRaises(a_,      tokenizer_r.encode_plus,      a_,      max_length=a_,      padding="""max_length"""		)

       # Pair input
       self.assertRaises(
           a_,      tokenizer_r.batch_encode_plus,      a_,      max_length=a_,      padding="""max_length""",      )


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  CodeGenTokenizer.from_pretrained(self.tmpdirname,      pad_token="""<pad>"""		)

   # Simple input
   _snake_case							:		List[Any]								=  """This is a simple input"""
   _snake_case							:		int								=  ["""This is a simple input looooooooong""", """This is a simple input"""]
   _snake_case							:		Any								=  ("""This is a simple input""", """This is a pair""")
   _snake_case							:		str								=  [
       ("""This is a simple input loooooong""", """This is a simple input"""),
       ("""This is a simple pair loooooong""", """This is a simple pair"""),
   ]

   _snake_case							:		str								=  tokenizer.pad_token_id

   _snake_case							:		Optional[int]								=  tokenizer(a_,      padding="""max_length""",      max_length=30,      return_tensors="""np"""		)
   _snake_case							:		Dict								=  tokenizer(a_,      padding=a_,      truncate=a_,      return_tensors="""np"""		)
   _snake_case							:		Tuple								=  tokenizer(*a_,      padding="""max_length""",      max_length=60,      return_tensors="""np"""		)
   _snake_case							:		Optional[Any]								=  tokenizer(a_,      padding=a_,      truncate=a_,      return_tensors="""np"""		)

   # s
   # test single string max_length padding
   self.assertEqual(out_s["""input_ids"""].shape[-1],      30		)
   self.assertTrue(pad_token_id in out_s["""input_ids"""]		)
   self.assertTrue(0 in out_s["""attention_mask"""]		)

   # s2
   # test automatic padding
   self.assertEqual(out_sa["""input_ids"""].shape[-1],      33		)
   # long slice doesn't have padding
   self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]		)
   self.assertFalse(0 in out_sa["""attention_mask"""][0]		)
   # short slice does have padding
   self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]		)
   self.assertTrue(0 in out_sa["""attention_mask"""][1]		)

   # p
   # test single pair max_length padding
   self.assertEqual(out_p["""input_ids"""].shape[-1],      60		)
   self.assertTrue(pad_token_id in out_p["""input_ids"""]		)
   self.assertTrue(0 in out_p["""attention_mask"""]		)

   # p2
   # test automatic padding pair
   self.assertEqual(out_pa["""input_ids"""].shape[-1],      52		)
   # long slice pair doesn't have padding
   self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]		)
   self.assertFalse(0 in out_pa["""attention_mask"""][0]		)
   # short slice pair does have padding
   self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]		)
   self.assertTrue(0 in out_pa["""attention_mask"""][1]		)


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  """$$$"""
   _snake_case							:		List[Any]								=  CodeGenTokenizer.from_pretrained(self.tmpdirname,      bos_token=a_,      add_bos_token=a_		)

   _snake_case							:		str								=  """This is a simple input"""
   _snake_case							:		int								=  ["""This is a simple input 1""", """This is a simple input 2"""]

   _snake_case							:		Union[str, Any]								=  tokenizer.bos_token_id

   _snake_case							:		Tuple								=  tokenizer(a_		)
   _snake_case							:		Optional[Any]								=  tokenizer(a_		)

   self.assertEqual(out_s.input_ids[0],      a_		)
   self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids		)		)

   _snake_case							:		Optional[int]								=  tokenizer.decode(out_s.input_ids		)
   _snake_case							:		int								=  tokenizer.batch_decode(out_sa.input_ids		)

   self.assertEqual(decode_s.split()[0],      a_		)
   self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa		)		)


 @slow
 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""		)

   _snake_case							:		Dict								=  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
   _snake_case							:		Union[str, Any]								=  """\nif len_a > len_b:      result = a\nelse:      result = b"""

   _snake_case							:		Optional[Any]								=  tokenizer.encode(a_		)
   _snake_case							:		Dict								=  ["""^#""", re.escape("""<|endoftext|>"""		), """^'''""", """^\"\"\"""", """\n\n\n"""]
   _snake_case							:		Optional[Any]								=  tokenizer.decode(a_,      truncate_before_pattern=a_		)
   self.assertEqual(a_,      a_		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   pass


