





'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
      )
      from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor


class 					a_     :

    '''simple docstring'''


    def __init__(		self ,		A ,		A=13 ,		A=30 ,		A=2 ,		A=3 ,		A=True ,		A=True ,		A=32 ,		A=2 ,		A=4 ,		A=37 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=10 ,		A=0.02 ,		A=3 ,		A=None ,		A=2 ,		)		->   Any:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			patch_size
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			is_training
        _SCREAMING_SNAKE_CASE									=			use_labels
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			type_sequence_label_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			scope
        _SCREAMING_SNAKE_CASE									=			encoder_stride

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        _SCREAMING_SNAKE_CASE									=			(image_size // patch_size) ** 2
        _SCREAMING_SNAKE_CASE									=			num_patches + 2


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

        _SCREAMING_SNAKE_CASE									=			None
        if self.use_labels:
            _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size] ,		self.type_sequence_label_size       )

        _SCREAMING_SNAKE_CASE									=			self.get_config()

        return config, pixel_values, labels


    def 			snake_case_(		self       )		->   int:
        return DeiTConfig(
            image_size=self.image_size ,		patch_size=self.patch_size ,		num_channels=self.num_channels ,		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 ,		is_decoder=A ,		initializer_range=self.initializer_range ,		encoder_stride=self.encoder_stride ,		)


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			TFDeiTModel(config=A       )
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			TFDeiTForMaskedImageModeling(config=A       )
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(
            result.reconstruction.shape ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

        # test greyscale images
        _SCREAMING_SNAKE_CASE									=			1
        _SCREAMING_SNAKE_CASE									=			TFDeiTForMaskedImageModeling(A       )

        _SCREAMING_SNAKE_CASE									=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
        _SCREAMING_SNAKE_CASE									=			model(A       )
        self.parent.assertEqual(result.reconstruction.shape ,		(self.batch_size, 1, self.image_size, self.image_size)       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.type_sequence_label_size
        _SCREAMING_SNAKE_CASE									=			TFDeiTForImageClassification(A       )
        _SCREAMING_SNAKE_CASE									=			model(A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)       )

        # test greyscale images
        _SCREAMING_SNAKE_CASE									=			1
        _SCREAMING_SNAKE_CASE									=			TFDeiTForImageClassification(A       )

        _SCREAMING_SNAKE_CASE									=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
        _SCREAMING_SNAKE_CASE									=			model(A ,		labels=A       )
        self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)       )





    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.prepare_config_and_inputs()
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			config_and_inputs
        _SCREAMING_SNAKE_CASE									=			{"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_tf
class 					a_     (				snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    UpperCamelCase     = (
        {
            '''feature-extraction''': TFDeiTModel,
            '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False
    UpperCamelCase     = False


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			TFDeiTModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A ,		has_text_modality=A ,		hidden_size=37       )


    def 			snake_case_(		self       )		->   Optional[int]:
        self.config_tester.run_common_tests()


    @unittest.skip(reason="""DeiT does not use inputs_embeds"""       )
    def 			snake_case_(		self       )		->   Optional[int]:
        pass


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )
            self.assertIsInstance(model.get_input_embeddings() ,		(tf.keras.layers.Layer)       )
            _SCREAMING_SNAKE_CASE									=			model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(A ,		tf.keras.layers.Dense       )       )


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

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

            _SCREAMING_SNAKE_CASE									=			["""pixel_values"""]
            self.assertListEqual(arg_names[:1] ,		A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A       )


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*A       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A       )


    def 			snake_case_(		self ,		A ,		A ,		A=False       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			super()._prepare_for_class(A ,		A ,		return_labels=A       )

        if return_labels:
            if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call       ).parameters:
                del inputs_dict["labels"]

        return inputs_dict





    @slow
    def 			snake_case_(		self       )		->   Union[str, Any]:
        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE									=			TFDeiTModel.from_pretrained(A       )
            self.assertIsNotNone(A       )





def lowerCamelCase						(     )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
    return image






@require_tf
@require_vision
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    @cached_property
    def 			snake_case_(		self       )		->   Dict:
        return (
            DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )
            if is_vision_available()
            else None
        )





    @slow
    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )

        _SCREAMING_SNAKE_CASE									=			self.default_image_processor
        _SCREAMING_SNAKE_CASE									=			prepare_img()
        _SCREAMING_SNAKE_CASE									=			image_processor(images=A ,		return_tensors="""tf"""       )

        # forward pass
        _SCREAMING_SNAKE_CASE									=			model(**A       )

        # verify the logits
        _SCREAMING_SNAKE_CASE									=			tf.TensorShape((1, 1000)       )
        self.assertEqual(outputs.logits.shape ,		A       )

        _SCREAMING_SNAKE_CASE									=			tf.constant([-1.0266, 0.1912, -1.2861]       )

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









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 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  GPTaTokenizer
							UpperCamelCase__					     =  GPTaTokenizerFast
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  {'''add_prefix_space''': True}
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								super().setUp()

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

								a										=							os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]				)
								a										=							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(__magic_name__				) + """\n"""				)
								with open(self.merges_file		,      """w"""		,      encoding="""utf-8"""				) as fp:
									fp.write("""\n""".join(__magic_name__				)				)
							def       lowerCamelCase__							(						self						:Dict		,      **__magic_name__						:List[str]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizer.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:List[str]		,      **__magic_name__						:Optional[Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:List[str]				):







								'''simple docstring'''







								a										=							"""lower newer"""
								a										=							"""lower newer"""
								return input_text, output_text
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							GPTaTokenizer(self.vocab_file		,      self.merges_file		,      **self.special_tokens_map				)
								a										=							"""lower newer"""
								a										=							["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								a										=							tokenizer.tokenize(__magic_name__		,      add_prefix_space=__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								a										=							tokens + [tokenizer.unk_token]
								a										=							[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								if not self.test_rust_tokenizer:
									return

								a										=							self.get_tokenizer()
								a										=							self.get_rust_tokenizer(add_prefix_space=__magic_name__				)

								a										=							"""lower newer"""

								# Testing tokenization
								a										=							tokenizer.tokenize(__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.tokenize(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing conversion to ids without special tokens
								a										=							tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing conversion to ids with special tokens
								a										=							self.get_rust_tokenizer(add_prefix_space=__magic_name__				)
								a										=							tokenizer.encode(__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.encode(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing the unknown token
								a										=							tokens + [rust_tokenizer.unk_token]
								a										=							[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:Optional[int]		,      *__magic_name__						:Tuple		,      **__magic_name__						:str				):







								'''simple docstring'''







								pass
							def       lowerCamelCase__							(						self						:Optional[Any]		,      __magic_name__						:Union[str, Any]=15				):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
										a										=							self.rust_tokenizer_class.from_pretrained(__magic_name__		,      **__magic_name__				)

										# Simple input
										a										=							"""This is a simple input"""
										a										=							["""This is a simple input 1""", """This is a simple input 2"""]
										a										=							("""This is a simple input""", """This is a pair""")
										a										=							[
										    ("""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(__magic_name__		,      tokenizer_r.encode		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

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

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

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

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

										# Pair input
										self.assertRaises(
										    __magic_name__		,      tokenizer_r.batch_encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""		,      )
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							GPTaTokenizer.from_pretrained(self.tmpdirname		,      pad_token="""<pad>"""				)

								# Simple input
								a										=							"""This is a simple input"""
								a										=							["""This is a simple input looooooooong""", """This is a simple input"""]
								a										=							("""This is a simple input""", """This is a pair""")
								a										=							[
								    ("""This is a simple input loooooong""", """This is a simple input"""),
								    ("""This is a simple pair loooooong""", """This is a simple pair"""),
								]

								a										=							tokenizer.pad_token_id

								a										=							tokenizer(__magic_name__		,      padding="""max_length"""		,      max_length=30		,      return_tensors="""np"""				)
								a										=							tokenizer(__magic_name__		,      padding=__magic_name__		,      truncate=__magic_name__		,      return_tensors="""np"""				)
								a										=							tokenizer(*__magic_name__		,      padding="""max_length"""		,      max_length=60		,      return_tensors="""np"""				)
								a										=							tokenizer(__magic_name__		,      padding=__magic_name__		,      truncate=__magic_name__		,      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       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							"""$$$"""
								a										=							GPTaTokenizer.from_pretrained(self.tmpdirname		,      bos_token=__magic_name__		,      add_bos_token=__magic_name__				)

								a										=							"""This is a simple input"""
								a										=							["""This is a simple input 1""", """This is a simple input 2"""]

								a										=							tokenizer.bos_token_id

								a										=							tokenizer(__magic_name__				)
								a										=							tokenizer(__magic_name__				)

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

								a										=							tokenizer.decode(out_s.input_ids				)
								a										=							tokenizer.batch_decode(out_sa.input_ids				)

								self.assertEqual(decode_s.split()[0]		,      __magic_name__				)
								self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								pass





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							[self.get_tokenizer(do_lower_case=__magic_name__		,      add_bos_token=__magic_name__				)]
								for tokenizer in tokenizers:
									with self.subTest(F'{tokenizer.__class__.__name__}'				):
										a										=							"""Encode this."""
										a										=							"""This one too please."""
										a										=							tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
										encoded_sequence += tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
										a										=							tokenizer.encode_plus(
										    __magic_name__		,      __magic_name__		,      add_special_tokens=__magic_name__		,      return_special_tokens_mask=__magic_name__		,      )
										a										=							encoded_sequence_dict["""input_ids"""]
										a										=							encoded_sequence_dict["""special_tokens_mask"""]
										self.assertEqual(len(__magic_name__				)		,      len(__magic_name__				)				)

										a										=							[
										    (x if not special_tokens_mask[i] else None) for i, x in enumerate(__magic_name__				)
										]
										a										=							[x for x in filtered_sequence if x is not None]
										self.assertEqual(__magic_name__		,      __magic_name__				)







@require_tokenizers
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      from_slow=__magic_name__				)
								a										=							"""A photo of a cat"""

								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)
								tokenizer.save_pretrained("""test_opt"""				)

								a										=							AutoTokenizer.from_pretrained("""./test_opt"""				)
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      use_slow=__magic_name__				)
								a										=							"""A photo of a cat"""

								a										=							tokenizer.encode(
								    __magic_name__		,      )
								# Same as above
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)





							@unittest.skip("""This test is failing because of a bug in the fast tokenizer"""				)
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      from_slow=__magic_name__				)

								a										=							"""bos"""
								a										=							tokenizer.get_vocab()["""bos"""]

								a										=							"""A photo of a cat"""
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								# We changed the bos token
								self.assertEqual(__magic_name__		,      [3_1957, 250, 1345, 9, 10, 4758]				)
								tokenizer.save_pretrained("""./tok"""				)
								a										=							AutoTokenizer.from_pretrained("""./tok"""				)
								self.assertTrue(tokenizer.is_fast				)
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [3_1957, 250, 1345, 9, 10, 4758]				)

