
"""simple docstring"""




import inspect
import unittest

from transformers import BitConfig
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 torch import nn

   from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
   from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

class 						a__		:
   def __init__(     self      ,       _a      ,       _a=3      ,       _a=32      ,       _a=3      ,       _a=10      ,       _a=[8, 16, 32, 64]      ,       _a=[1, 1, 2, 1]      ,       _a=True      ,       _a=True      ,       _a="relu"      ,       _a=3      ,       _a=None      ,       _a=["stage2", "stage3", "stage4"]      ,       _a=[2, 3, 4]      ,       _a=1      ,       ):
          lowercase       :							int									=				parent
          lowercase       :							int									=				batch_size
          lowercase       :							Tuple									=				image_size
          lowercase       :							Dict									=				num_channels
          lowercase       :							Dict									=				embeddings_size
          lowercase       :							List[str]									=				hidden_sizes
          lowercase       :							List[str]									=				depths
          lowercase       :							Optional[int]									=				is_training
          lowercase       :							Optional[Any]									=				use_labels
          lowercase       :							Optional[Any]									=				hidden_act
          lowercase       :							Union[str, Any]									=				num_labels
          lowercase       :							Dict									=				scope
          lowercase       :							Optional[int]									=				len(_a							)
          lowercase       :							Optional[int]									=				out_features
          lowercase       :							Optional[int]									=				out_indices
          lowercase       :							Optional[int]									=				num_groups
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          lowercase       :							Tuple									=				None
          if self.use_labels:
                 lowercase       :							List[str]									=				ids_tensor([self.batch_size]      ,       self.num_labels							)

          lowercase       :							str									=				self.get_config()

          return config, pixel_values, labels
   def      __magic_name__     (     self							):
          return BitConfig(
              num_channels=self.num_channels      ,       embeddings_size=self.embeddings_size      ,       hidden_sizes=self.hidden_sizes      ,       depths=self.depths      ,       hidden_act=self.hidden_act      ,       num_labels=self.num_labels      ,       out_features=self.out_features      ,       out_indices=self.out_indices      ,       num_groups=self.num_groups      ,       )
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							Union[str, Any]									=				BitModel(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							List[str]									=				model(_a							)
          self.parent.assertEqual(
              result.last_hidden_state.shape      ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,       )
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							List[Any]									=				self.num_labels
          lowercase       :							Optional[int]									=				BitForImageClassification(_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							List[str]									=				model(_a      ,       labels=_a							)
          self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							int									=				BitBackbone(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							Tuple									=				model(_a							)

          # verify feature maps
          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
          lowercase       :							Union[str, Any]									=				None
          lowercase       :							List[Any]									=				BitBackbone(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							List[Any]									=				model(_a							)

          # 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      __magic_name__     (     self							):
          lowercase       :							Any									=				self.prepare_config_and_inputs()
          lowercase						,       lowercase						,       lowercase       :							Optional[Any]									=				config_and_inputs
          lowercase       :							Optional[int]									=				{"pixel_values": pixel_values}
          return config, inputs_dict


@require_torch
class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
   __lowerCAmelCase				=							(
       {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          lowercase       :							int									=				BitModelTester(self							)
          lowercase       :							Dict									=				ConfigTester(self      ,       config_class=_a      ,       has_text_modality=_a							)
   def      __magic_name__     (     self							):
          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      __magic_name__     (     self							):
          return
   @unittest.skip(reason="Bit does not output attentions"							)
   def      __magic_name__     (     self							):
          pass
   @unittest.skip(reason="Bit does not use inputs_embeds"							)
   def      __magic_name__     (     self							):
          pass
   @unittest.skip(reason="Bit does not support input and output embeddings"							)
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Union[str, Any]									=				self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowercase       :							Optional[int]									=				["pixel_values"]
                 self.assertListEqual(arg_names[:1]      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*_a							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Any									=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase       :							Optional[Any]									=				model_class(config=_a							)
                 for name, module in model.named_modules():
                        if isinstance(_a      ,       (nn.BatchNormad, nn.GroupNorm)							):
                               self.assertTrue(
                                   torch.all(module.weight == 1							)      ,       msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,       )
                               self.assertTrue(
                                   torch.all(module.bias == 0							)      ,       msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,       )
   def      __magic_name__     (     self							):
          def check_hidden_states_output(_a      ,       _a      ,       _a							):
                 lowercase       :							List[str]									=				model_class(_a							)
                 model.to(_a							)
                 model.eval()

                 with torch.no_grad():
                        lowercase       :							Union[str, Any]									=				model(**self._prepare_for_class(_a      ,       _a							)							)

                 lowercase       :							Tuple									=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 lowercase       :							Tuple									=				self.model_tester.num_stages
                 self.assertEqual(len(_a							)      ,       expected_num_stages + 1							)

                 # Bit'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]      ,       )

          lowercase						,       lowercase       :							List[str]									=				self.model_tester.prepare_config_and_inputs_for_common()
          lowercase       :							Optional[Any]									=				["preactivation", "bottleneck"]
          for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                        lowercase       :							Any									=				layer_type
                        lowercase       :							List[Any]									=				True
                        check_hidden_states_output(_a      ,       _a      ,       _a							)

                        # check that output_hidden_states also work using config
                        del inputs_dict["output_hidden_states"]
                        lowercase       :							List[Any]									=				True

                        check_hidden_states_output(_a      ,       _a      ,       _a							)
   @unittest.skip(reason="Bit does not use feedforward chunking"							)
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_a							)

   @slow
   def      __magic_name__     (     self							):
          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase       :							Dict									=				BitModel.from_pretrained(_a							)
                 self.assertIsNotNone(_a							)


def     __magic_name__      ( )					->							List[str]:
       lowercase       :							Any									=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
       return image

@require_torch
@require_vision
class 						a__		( unittest.TestCase		):
   @cached_property
   def      __magic_name__     (     self							):
          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
          )

   @slow
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(_a							)

          lowercase       :							Optional[int]									=				self.default_image_processor
          lowercase       :							Optional[int]									=				prepare_img()
          lowercase       :							Dict									=				image_processor(images=_a      ,       return_tensors="pt"							).to(_a							)

          # forward pass
          with torch.no_grad():
                 lowercase       :							List[str]									=				model(**_a							)

          # verify the logits
          lowercase       :							Dict									=				torch.Size((1, 1_000)							)
          self.assertEqual(outputs.logits.shape      ,       _a							)

          lowercase       :							List[Any]									=				torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]							).to(_a							)

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


@require_torch
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							(BitBackbone,) if is_torch_available() else ()
   __lowerCAmelCase				=							BitConfig

   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				BitModelTester(self							)




"""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]							)


