






'''simple docstring'''
import inspect
import unittest

from transformers import MobileNetVaConfig
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_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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
	from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileNetVaImageProcessor







class 	UpperCamelCase__					(     a_			):

		"""simple docstring"""




		def       snake_case     ( self						:						str    ):







							"""simple docstring"""
							_lowercase			= self.config_class(**self.inputs_dict    )
							self.parent.assertTrue(hasattr(_a							,    "tf_padding"    )    )
							self.parent.assertTrue(hasattr(_a							,    "depth_multiplier"    )    )







class 	UpperCamelCase__					:

		"""simple docstring"""




		def __init__( self						:						Dict							,    __A						:						Optional[Any]							,    __A						:						int=1_3							,    __A						:						Optional[int]=3							,    __A						:						int=3_2							,    __A						:						Dict=0.2_5							,    __A						:						Optional[int]=8							,    __A						:						List[Any]=8							,    __A						:						Tuple=6							,    __A						:						int=3_2							,    __A						:						Optional[Any]=True							,    __A						:						Union[str, Any]=True							,    __A						:						Tuple=True							,    __A						:						Union[str, Any]="relu6"							,    __A						:						str=1_2_8_0							,    __A						:						Any=0.1							,    __A						:						str=0.0_2							,    __A						:						Optional[int]=True							,    __A						:						Optional[Any]=True							,    __A						:						int=1_0							,    __A						:						Optional[Any]=None							,    ):







							"""simple docstring"""
							_lowercase			= parent
							_lowercase			= batch_size
							_lowercase			= num_channels
							_lowercase			= image_size
							_lowercase			= depth_multiplier
							_lowercase			= depth_divisible_by
							_lowercase			= min_depth
							_lowercase			= expand_ratio
							_lowercase			= tf_padding
							_lowercase			= output_stride
							_lowercase			= first_layer_is_expansion
							_lowercase			= finegrained_output
							_lowercase			= hidden_act
							_lowercase			= last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier    )
							_lowercase			= classifier_dropout_prob
							_lowercase			= use_labels
							_lowercase			= is_training
							_lowercase			= num_labels
							_lowercase			= initializer_range
							_lowercase			= scope




		def       snake_case     ( self						:						Any    ):







							"""simple docstring"""
							_lowercase			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

							_lowercase			= self.get_config()

							return config, pixel_values, labels, pixel_labels




		def       snake_case     ( self						:						str    ):







							"""simple docstring"""
							return MobileNetVaConfig(
							    num_channels=self.num_channels							,    image_size=self.image_size							,    depth_multiplier=self.depth_multiplier							,    depth_divisible_by=self.depth_divisible_by							,    min_depth=self.min_depth							,    expand_ratio=self.expand_ratio							,    output_stride=self.output_stride							,    first_layer_is_expansion=self.first_layer_is_expansion							,    finegrained_output=self.finegrained_output							,    hidden_act=self.hidden_act							,    tf_padding=self.tf_padding							,    classifier_dropout_prob=self.classifier_dropout_prob							,    initializer_range=self.initializer_range							,    )




		def       snake_case     ( self						:						Dict							,    __A						:						int							,    __A						:						Dict							,    __A						:						Dict							,    __A						:						int    ):







							"""simple docstring"""
							_lowercase			= MobileNetVaModel(config=_a    )
							model.to(_a    )
							model.eval()
							_lowercase			= model(_a    )
							self.parent.assertEqual(
							    result.last_hidden_state.shape							,    (
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )							,    )
							self.parent.assertEqual(
							    result.pooler_output.shape							,    (self.batch_size, self.last_hidden_size)							,    )




		def       snake_case     ( self						:						int							,    __A						:						Optional[int]							,    __A						:						List[str]							,    __A						:						Tuple							,    __A						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= self.num_labels
							_lowercase			= MobileNetVaForImageClassification(_a    )
							model.to(_a    )
							model.eval()
							_lowercase			= model(_a							,    labels=_a    )
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)    )




		def       snake_case     ( self						:						List[Any]							,    __A						:						int							,    __A						:						Optional[Any]							,    __A						:						Union[str, Any]							,    __A						:						str    ):







							"""simple docstring"""
							_lowercase			= self.num_labels
							_lowercase			= MobileNetVaForSemanticSegmentation(_a    )
							model.to(_a    )
							model.eval()
							_lowercase			= model(_a    )
							self.parent.assertEqual(
							    result.logits.shape							,    (
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )							,    )
							_lowercase			= model(_a							,    labels=_a    )
							self.parent.assertEqual(
							    result.logits.shape							,    (
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )							,    )




		def       snake_case     ( self						:						str    ):







							"""simple docstring"""
							_lowercase			= self.prepare_config_and_inputs()
							_lowercase			= config_and_inputs
							_lowercase			= {"pixel_values": pixel_values}
							return config, inputs_dict







@require_torch
class 	UpperCamelCase__					(     a_      ,     a_      ,     unittest.TestCase			):

		"""simple docstring"""
		UpperCAmelCase__     			=						(
		    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		UpperCAmelCase__     			=						(
		    {
		        'feature-extraction': MobileNetVaModel,
		        'image-classification': MobileNetVaForImageClassification,
		        'image-segmentation': MobileNetVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		UpperCAmelCase__     			=						False
		UpperCAmelCase__     			=						False
		UpperCAmelCase__     			=						False
		UpperCAmelCase__     			=						False




		def       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= MobileNetVaModelTester(self    )
							_lowercase			= MobileNetVaConfigTester(self							,    config_class=_a							,    has_text_modality=_a    )




		def       snake_case     ( self						:						Optional[int]    ):







							"""simple docstring"""
							self.config_tester.run_common_tests()




		@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"    )
		def       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""
							pass




		@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"    )
		def       snake_case     ( self						:						Any    ):







							"""simple docstring"""
							pass




		@unittest.skip(reason="MobileNetV2 does not output attentions"    )
		def       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""
							pass




		def       snake_case     ( self						:						Dict    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs_for_common()

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

												_lowercase			= ["pixel_values"]
												self.assertListEqual(arg_names[:1]							,    _a    )




		def       snake_case     ( self						:						Optional[Any]    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a    )




		def       snake_case     ( self						:						Dict    ):







							"""simple docstring"""
							def check_hidden_states_output(__A						:						List[Any]							,    __A						:						List[str]							,    __A						:						List[str]    ):
												_lowercase			= model_class(_a    )
												model.to(_a    )
												model.eval()

												with torch.no_grad():
																	_lowercase			= model(**self._prepare_for_class(_a							,    _a    )    )

												_lowercase			= outputs.hidden_states

												_lowercase			= 1_6
												self.assertEqual(len(_a    )							,    _a    )

							_lowercase			= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_lowercase			= True
												check_hidden_states_output(_a							,    _a							,    _a    )

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

												check_hidden_states_output(_a							,    _a							,    _a    )




		def       snake_case     ( self						:						Optional[int]    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a    )




		def       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*_a    )




		@slow
		def       snake_case     ( self						:						Dict    ):







							"""simple docstring"""
							for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowercase			= MobileNetVaModel.from_pretrained(_a    )
												self.assertIsNotNone(_a    )





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







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

		"""simple docstring"""




		@cached_property
		def       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""
							return (
							    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"    ) if is_vision_available() else None
							)




		@slow
		def       snake_case     ( self						:						Dict    ):







							"""simple docstring"""
							_lowercase			= MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"    ).to(_a    )

							_lowercase			= self.default_image_processor
							_lowercase			= prepare_img()
							_lowercase			= image_processor(images=_a							,    return_tensors="pt"    ).to(_a    )

							# forward pass
							with torch.no_grad():
												_lowercase			= model(**_a    )

							# verify the logits
							_lowercase			= torch.Size((1, 1_0_0_1)    )
							self.assertEqual(outputs.logits.shape							,    _a    )

							_lowercase			= torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]    ).to(_a    )

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




		@slow
		def       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"    )
							_lowercase			= model.to(_a    )

							_lowercase			= MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"    )

							_lowercase			= prepare_img()
							_lowercase			= image_processor(images=_a							,    return_tensors="pt"    ).to(_a    )

							# forward pass
							with torch.no_grad():
												_lowercase			= model(**_a    )
							_lowercase			= outputs.logits

							# verify the logits
							_lowercase			= torch.Size((1, 2_1, 6_5, 6_5)    )
							self.assertEqual(logits.shape							,    _a    )

							_lowercase			= torch.tensor(
							    [
							        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
							        [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
							        [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
							    ]							,    device=_a							,    )

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



"""simple docstring"""




import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							GPTaTokenizer
   __lowerCAmelCase				=							GPTaTokenizerFast
   __lowerCAmelCase				=							True
   __lowerCAmelCase				=							{"""add_prefix_space""": True}
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          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>",
              "<|endoftext|>",
          ]
          lowercase       :							Any									=				dict(zip(_a      ,       range(len(_a							)							)							)							)
          lowercase       :							Dict									=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          lowercase       :							int									=				{"unk_token": "<unk>"}

          lowercase       :							Tuple									=				os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["vocab_file"]							)
          lowercase       :							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      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizer.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Tuple									=				"lower newer"
          lowercase       :							Dict									=				"lower newer"
          return input_text, output_text
   def      __magic_name__     (     self							):
          lowercase       :							str									=				GPTaTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map							)
          lowercase       :							Union[str, Any]									=				"lower newer"
          lowercase       :							Any									=				["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
          lowercase       :							int									=				tokenizer.tokenize(_a      ,       add_prefix_space=_a							)
          self.assertListEqual(_a      ,       _a							)

          lowercase       :							int									=				tokens + [tokenizer.unk_token]
          lowercase       :							List[Any]									=				[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a							)      ,       _a							)
   def      __magic_name__     (     self							):
          if not self.test_rust_tokenizer:
                 return

          lowercase       :							Union[str, Any]									=				self.get_tokenizer()
          lowercase       :							int									=				self.get_rust_tokenizer(add_prefix_space=_a							)

          lowercase       :							Union[str, Any]									=				"lower newer"

          # Testing tokenization
          lowercase       :							Union[str, Any]									=				tokenizer.tokenize(_a      ,       add_prefix_space=_a							)
          lowercase       :							List[Any]									=				rust_tokenizer.tokenize(_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing conversion to ids without special tokens
          lowercase       :							Dict									=				tokenizer.encode(_a      ,       add_special_tokens=_a      ,       add_prefix_space=_a							)
          lowercase       :							Dict									=				rust_tokenizer.encode(_a      ,       add_special_tokens=_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing conversion to ids with special tokens
          lowercase       :							Any									=				self.get_rust_tokenizer(add_prefix_space=_a							)
          lowercase       :							Any									=				tokenizer.encode(_a      ,       add_prefix_space=_a							)
          lowercase       :							Any									=				rust_tokenizer.encode(_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing the unknown token
          lowercase       :							Union[str, Any]									=				tokens + [rust_tokenizer.unk_token]
          lowercase       :							str									=				[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a							)      ,       _a							)
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass
   def      __magic_name__     (     self      ,       _a=15							):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                        lowercase       :							int									=				self.rust_tokenizer_class.from_pretrained(_a      ,       **_a							)

                        # Simple input
                        lowercase       :							int									=				"This is a simple input"
                        lowercase       :							Optional[int]									=				["This is a simple input 1", "This is a simple input 2"]
                        lowercase       :							Union[str, Any]									=				("This is a simple input", "This is a pair")
                        lowercase       :							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      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				GPTaTokenizer.from_pretrained(self.tmpdirname      ,       pad_token="<pad>"							)

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

          lowercase       :							List[Any]									=				tokenizer.pad_token_id

          lowercase       :							List[Any]									=				tokenizer(_a      ,       padding="max_length"      ,       max_length=30      ,       return_tensors="np"							)
          lowercase       :							List[str]									=				tokenizer(_a      ,       padding=_a      ,       truncate=_a      ,       return_tensors="np"							)
          lowercase       :							List[Any]									=				tokenizer(*_a      ,       padding="max_length"      ,       max_length=60      ,       return_tensors="np"							)
          lowercase       :							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      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				"$$$"
          lowercase       :							List[str]									=				GPTaTokenizer.from_pretrained(self.tmpdirname      ,       bos_token=_a      ,       add_bos_token=_a							)

          lowercase       :							List[Any]									=				"This is a simple input"
          lowercase       :							str									=				["This is a simple input 1", "This is a simple input 2"]

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

          lowercase       :							List[str]									=				tokenizer(_a							)
          lowercase       :							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							)							)

          lowercase       :							List[Any]									=				tokenizer.decode(out_s.input_ids							)
          lowercase       :							Tuple									=				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							)							)
   def      __magic_name__     (     self							):
          pass

   def      __magic_name__     (     self							):
          # TODO: change to self.get_tokenizers() when the fast version is implemented
          lowercase       :							Dict									=				[self.get_tokenizer(do_lower_case=_a      ,       add_bos_token=_a							)]
          for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
                        lowercase       :							Optional[Any]									=				"Encode this."
                        lowercase       :							Union[str, Any]									=				"This one too please."
                        lowercase       :							Dict									=				tokenizer.encode(_a      ,       add_special_tokens=_a							)
                        encoded_sequence += tokenizer.encode(_a      ,       add_special_tokens=_a							)
                        lowercase       :							Optional[Any]									=				tokenizer.encode_plus(
                            _a      ,       _a      ,       add_special_tokens=_a      ,       return_special_tokens_mask=_a      ,       )
                        lowercase       :							Tuple									=				encoded_sequence_dict["input_ids"]
                        lowercase       :							int									=				encoded_sequence_dict["special_tokens_mask"]
                        self.assertEqual(len(_a							)      ,       len(_a							)							)

                        lowercase       :							List[str]									=				[
                            (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a							)
                        ]
                        lowercase       :							List[str]									=				[x for x in filtered_sequence if x is not None]
                        self.assertEqual(_a      ,       _a							)


@require_tokenizers
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # More context:
          # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
          # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
          # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       from_slow=_a							)
          lowercase       :							Any									=				"A photo of a cat"

          lowercase       :							List[str]									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("test_opt"							)

          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("./test_opt"							)
          lowercase       :							Tuple									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       use_slow=_a							)
          lowercase       :							Union[str, Any]									=				"A photo of a cat"

          lowercase       :							Union[str, Any]									=				tokenizer.encode(
              _a      ,       )
          # Same as above
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)

   @unittest.skip("This test is failing because of a bug in the fast tokenizer"							)
   def      __magic_name__     (     self							):
          lowercase       :							int									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       from_slow=_a							)

          lowercase       :							List[Any]									=				"bos"
          lowercase       :							Optional[int]									=				tokenizer.get_vocab()["bos"]

          lowercase       :							List[str]									=				"A photo of a cat"
          lowercase       :							Dict									=				tokenizer.encode(
              _a      ,       )
          # We changed the bos token
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("./tok"							)
          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("./tok"							)
          self.assertTrue(tokenizer.is_fast							)
          lowercase       :							str									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)


