



import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
		from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class 			__magic_name__ :






		def __init__(    self   :  str					,		UpperCamelCase__   :  str					,		UpperCamelCase__   :  Tuple=13					,		UpperCamelCase__   :  str=32					,		UpperCamelCase__   :  Optional[int]=3					,		UpperCamelCase__   :  str=4					,		UpperCamelCase__   :  List[str]=[10, 20, 30, 40]					,		UpperCamelCase__   :  Optional[int]=[2, 2, 3, 2]					,		UpperCamelCase__   :  int=True					,		UpperCamelCase__   :  List[Any]=True					,		UpperCamelCase__   :  List[Any]=37					,		UpperCamelCase__   :  Optional[Any]="gelu"					,		UpperCamelCase__   :  Union[str, Any]=10					,		UpperCamelCase__   :  Tuple=0.02					,		UpperCamelCase__   :  List[str]=["stage2", "stage3", "stage4"]					,		UpperCamelCase__   :  List[str]=[2, 3, 4]					,		UpperCamelCase__   :  List[str]=None					,		)					->       Optional[int]:
								'''simple docstring'''







								UpperCAmelCase								=  parent
								UpperCAmelCase								=  batch_size
								UpperCAmelCase								=  image_size
								UpperCAmelCase								=  num_channels
								UpperCAmelCase								=  num_stages
								UpperCAmelCase								=  hidden_sizes
								UpperCAmelCase								=  depths
								UpperCAmelCase								=  is_training
								UpperCAmelCase								=  use_labels
								UpperCAmelCase								=  intermediate_size
								UpperCAmelCase								=  hidden_act
								UpperCAmelCase								=  num_labels
								UpperCAmelCase								=  initializer_range
								UpperCAmelCase								=  out_features
								UpperCAmelCase								=  out_indices
								UpperCAmelCase								=  scope






		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]   )					->       List[str]:
								'''simple docstring'''







								UpperCAmelCase								=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

								UpperCAmelCase								=  None
								if self.use_labels:
														UpperCAmelCase								=  ids_tensor([self.batch_size]					,		self.num_labels   )

								UpperCAmelCase								=  self.get_config()
								return config, pixel_values, labels






		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Dict:
								'''simple docstring'''







								return ConvNextConfig(
								    num_channels=self.num_channels					,		hidden_sizes=self.hidden_sizes					,		depths=self.depths					,		num_stages=self.num_stages					,		hidden_act=self.hidden_act					,		is_decoder=UpperCamelCase__					,		initializer_range=self.initializer_range					,		out_features=self.out_features					,		out_indices=self.out_indices					,		num_labels=self.num_labels					,		)






		def SCREAMING_SNAKE_CASE_      (    self   :  str					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  Union[str, Any]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  ConvNextModel(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								UpperCAmelCase								=  model(UpperCamelCase__   )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape					,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,		)






		def SCREAMING_SNAKE_CASE_      (    self   :  int					,		UpperCamelCase__   :  Tuple					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  Tuple   )					->       int:
								'''simple docstring'''







								UpperCAmelCase								=  ConvNextForImageClassification(UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								UpperCAmelCase								=  model(UpperCamelCase__					,		labels=UpperCamelCase__   )
								self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.num_labels)   )






		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]					,		UpperCamelCase__   :  List[str]					,		UpperCamelCase__   :  Optional[Any]					,		UpperCamelCase__   :  List[str]   )					->       List[str]:
								'''simple docstring'''







								UpperCAmelCase								=  ConvNextBackbone(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								UpperCAmelCase								=  model(UpperCamelCase__   )

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps   )					,		len(config.out_features   )   )
								self.parent.assertListEqual(list(result.feature_maps[0].shape   )					,		[self.batch_size, self.hidden_sizes[1], 4, 4]   )

								# verify channels
								self.parent.assertEqual(len(model.channels   )					,		len(config.out_features   )   )
								self.parent.assertListEqual(model.channels					,		config.hidden_sizes[1:]   )

								# verify backbone works with out_features=None
								UpperCAmelCase								=  None
								UpperCAmelCase								=  ConvNextBackbone(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								UpperCAmelCase								=  model(UpperCamelCase__   )

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps   )					,		1   )
								self.parent.assertListEqual(list(result.feature_maps[0].shape   )					,		[self.batch_size, self.hidden_sizes[-1], 1, 1]   )

								# verify channels
								self.parent.assertEqual(len(model.channels   )					,		1   )
								self.parent.assertListEqual(model.channels					,		[config.hidden_sizes[-1]]   )







		def SCREAMING_SNAKE_CASE_      (    self   :  str   )					->       List[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  self.prepare_config_and_inputs()
								UpperCAmelCase								=  config_and_inputs
								UpperCAmelCase								=  {'pixel_values': pixel_values}
								return config, inputs_dict





@require_torch
class 			__magic_name__ (				A__,	A__,	unittest.TestCase    ):
		lowercase   :							Any        =(
		    (
		        ConvNextModel,
		        ConvNextForImageClassification,
		        ConvNextBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase   :							Tuple        =(
		    {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowercase   :							Union[str, Any]        =True
		lowercase   :							int        =False
		lowercase   :							Union[str, Any]        =False
		lowercase   :							Dict        =False
		lowercase   :							List[Any]        =False






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[int]   )					->       str:
								'''simple docstring'''







								UpperCAmelCase								=  ConvNextModelTester(self   )
								UpperCAmelCase								=  ConfigTester(self					,		config_class=UpperCamelCase__					,		has_text_modality=UpperCamelCase__					,		hidden_size=37   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[int]   )					->       Tuple:
								'''simple docstring'''







								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()






		def SCREAMING_SNAKE_CASE_      (    self   :  Any   )					->       Dict:
								'''simple docstring'''







								return






		@unittest.skip(reason="ConvNext does not use inputs_embeds"   )
		def SCREAMING_SNAKE_CASE_      (    self   :  Any   )					->       Tuple:
								'''simple docstring'''







								pass






		@unittest.skip(reason="ConvNext does not support input and output embeddings"   )
		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]   )					->       int:
								'''simple docstring'''







								pass






		@unittest.skip(reason="ConvNext does not use feedforward chunking"   )
		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]   )					->       Optional[int]:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_      (    self   :  str   )					->       Dict:
								'''simple docstring'''







								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCAmelCase								=  model_class(UpperCamelCase__   )
														UpperCAmelCase								=  inspect.signature(model.forward   )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														UpperCAmelCase								=  [*signature.parameters.keys()]

														UpperCAmelCase								=  ['pixel_values']
														self.assertListEqual(arg_names[:1]					,		UpperCamelCase__   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Any   )					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__   )






		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]   )					->       int:
								'''simple docstring'''







								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*UpperCamelCase__   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[int]   )					->       Dict:
								'''simple docstring'''







								def check_hidden_states_output(UpperCamelCase__   :  Union[str, Any]					,		UpperCamelCase__   :  Dict					,		UpperCamelCase__   :  int   ):
														UpperCAmelCase								=  model_class(UpperCamelCase__   )
														model.to(UpperCamelCase__   )
														model.eval()

														with torch.no_grad():
																				UpperCAmelCase								=  model(**self._prepare_for_class(UpperCamelCase__					,		UpperCamelCase__   )   )

														UpperCAmelCase								=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														UpperCAmelCase								=  self.model_tester.num_stages
														self.assertEqual(len(UpperCamelCase__   )					,		expected_num_stages + 1   )

														# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]   )					,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,		)

								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCAmelCase								=  True
														check_hidden_states_output(UpperCamelCase__					,		UpperCamelCase__					,		UpperCamelCase__   )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														UpperCAmelCase								=  True

														check_hidden_states_output(UpperCamelCase__					,		UpperCamelCase__					,		UpperCamelCase__   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Dict   )					->       str:
								'''simple docstring'''







								UpperCAmelCase								=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__   )







		@slow
		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]   )					->       Optional[Any]:
								'''simple docstring'''







								for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCAmelCase								=  ConvNextModel.from_pretrained(UpperCamelCase__   )
														self.assertIsNotNone(UpperCamelCase__   )

def  lowerCamelCase_()			->							Dict:
						UpperCAmelCase								=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
						return image





@require_torch
@require_vision
class 			__magic_name__ (				unittest.TestCase    ):






		@cached_property
		def SCREAMING_SNAKE_CASE_      (    self   :  int   )					->       Tuple:
								'''simple docstring'''







								return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224"   ) if is_vision_available() else None







		@slow
		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase								=  ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"   ).to(UpperCamelCase__   )

								UpperCAmelCase								=  self.default_image_processor
								UpperCAmelCase								=  prepare_img()
								UpperCAmelCase								=  image_processor(images=UpperCamelCase__					,		return_tensors="pt"   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														UpperCAmelCase								=  model(**UpperCamelCase__   )

								# verify the logits
								UpperCAmelCase								=  torch.Size((1, 10_00)   )
								self.assertEqual(outputs.logits.shape					,		UpperCamelCase__   )

								UpperCAmelCase								=  torch.tensor([-0.02_60, -0.47_39, 0.19_11]   ).to(UpperCamelCase__   )

								self.assertTrue(torch.allclose(outputs.logits[0, :3]					,		UpperCamelCase__					,		atol=1e-4   )   )





@require_torch
class 			__magic_name__ (				unittest.TestCase,	A__    ):
		lowercase   :							str        =(ConvNextBackbone,) if is_torch_available() else ()
		lowercase   :							List[str]        =ConvNextConfig

		lowercase   :							Union[str, Any]        =False






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]   )					->       List[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  ConvNextModelTester(self   )







'''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    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =CodeGenTokenizer
       UpperCAmelCase		 =CodeGenTokenizerFast
       UpperCAmelCase		 =True
       UpperCAmelCase		 ={"add_prefix_space": True}
       UpperCAmelCase		 =False






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          super().setUp()

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

          _UpperCAmelCase  :  List[Any]				       =os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file'])
          _UpperCAmelCase  :  List[str]				       =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(snake_case) + '\n')
          with open(self.merges_file    ,				'w'    ,				encoding='utf-8') as fp:
             fp.write('\n'.join(snake_case))






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    str:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizerFast.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'
          _UpperCAmelCase  :  int				       ='lower newer'
          return input_text, output_text






       def      lowerCAmelCase						(     self)		->    int:




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

          _UpperCAmelCase  :  Any				       =tokens + [tokenizer.unk_token]
          _UpperCAmelCase  :  List[str]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          if not self.test_rust_tokenizer:
             return

          _UpperCAmelCase  :  Tuple				       =self.get_tokenizer()
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)

          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'

          # Testing tokenization
          _UpperCAmelCase  :  List[str]				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.tokenize(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids without special tokens
          _UpperCAmelCase  :  str				       =tokenizer.encode(snake_case    ,				add_special_tokens=snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.encode(snake_case    ,				add_special_tokens=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids with special tokens
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer.encode(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  int				       =rust_tokenizer.encode(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing the unknown token
          _UpperCAmelCase  :  List[str]				       =tokens + [rust_tokenizer.unk_token]
          _UpperCAmelCase  :  Union[str, Any]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Any:




          '''simple docstring'''
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass






       def      lowerCAmelCase						(     self    ,				snake_case=1_5)		->    Optional[int]:




          '''simple docstring'''
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                _UpperCAmelCase  :  Optional[Any]				       =self.rust_tokenizer_class.from_pretrained(snake_case    ,				**snake_case)

                # Simple input
                _UpperCAmelCase  :  List[str]				       ='This is a simple input'
                _UpperCAmelCase  :  Optional[Any]				       =['This is a simple input 1', 'This is a simple input 2']
                _UpperCAmelCase  :  int				       =('This is a simple input', 'This is a pair')
                _UpperCAmelCase  :  Union[str, 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(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='<pad>')

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

          _UpperCAmelCase  :  List[Any]				       =tokenizer.pad_token_id

          _UpperCAmelCase  :  Union[str, Any]				       =tokenizer(snake_case    ,				padding='max_length'    ,				max_length=3_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')
          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(*snake_case    ,				padding='max_length'    ,				max_length=6_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')

          # s
          # test single string max_length padding
          self.assertEqual(out_s['input_ids'].shape[-1]    ,				3_0)
          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]    ,				3_3)
          # 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]    ,				6_0)
          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]    ,				5_2)
          # 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      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ='$$$'
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				bos_token=snake_case    ,				add_bos_token=snake_case)

          _UpperCAmelCase  :  Optional[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input 1', 'This is a simple input 2']

          _UpperCAmelCase  :  int				       =tokenizer.bos_token_id

          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer(snake_case)

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

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

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






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

          _UpperCAmelCase  :  Optional[Any]				       ='\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
          _UpperCAmelCase  :  Tuple				       ='\nif len_a > len_b:      result = a\nelse:      result = b'

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.encode(snake_case)
          _UpperCAmelCase  :  List[str]				       =['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^"""', '\n\n\n']
          _UpperCAmelCase  :  Dict				       =tokenizer.decode(snake_case    ,				truncate_before_pattern=snake_case)
          self.assertEqual(snake_case    ,				snake_case)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          pass

