

"""simple docstring"""




import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_							:			List[Any]											= "▁"

A_							:			Tuple											= get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class    lowerCamelCase						(A__			,unittest.TestCase			):
			lowerCamelCase__   :		List[str]							=							BigBirdTokenizer
			lowerCamelCase__   :		Union[str, Any]							=							BigBirdTokenizerFast
			lowerCamelCase__   :		Tuple							=							True
			lowerCamelCase__   :		str							=							True

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[int]:
						super().setUp()

						SCREAMING_SNAKE_CASE__								=      self.tokenizer_class(__UpperCAmelCase    ,				keep_accents=__UpperCAmelCase       )
						tokenizer.save_pretrained(self.tmpdirname       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """<s>"""
						SCREAMING_SNAKE_CASE__								=      1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase       )    ,				__UpperCAmelCase       )
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase       )    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      list(self.get_tokenizer().get_vocab().keys()       )

						self.assertEqual(vocab_keys[0]    ,				"""<unk>"""       )
						self.assertEqual(vocab_keys[1]    ,				"""<s>"""       )
						self.assertEqual(vocab_keys[-1]    ,				"""[MASK]"""       )
						self.assertEqual(len(__UpperCAmelCase       )    ,				1_0_0_4       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						self.assertEqual(self.get_tokenizer().vocab_size    ,				1_0_0_0       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Dict:
						if not self.test_rust_tokenizer:
									return

						SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
						SCREAMING_SNAKE_CASE__								=      self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__								=      """I was born in 92000, and this is falsé."""

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      rust_tokenizer.tokenize(__UpperCAmelCase       )
						self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      rust_tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase       )
						self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__								=      tokenizer.encode(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      rust_tokenizer.encode(__UpperCAmelCase       )
						self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      BigBirdTokenizer(__UpperCAmelCase    ,				keep_accents=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize("""This is a test"""       )
						self.assertListEqual(__UpperCAmelCase    ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__UpperCAmelCase       )    ,				[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]    ,				)

						SCREAMING_SNAKE_CASE__								=      tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
						self.assertListEqual(
						    __UpperCAmelCase    ,				[
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """9""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """é""",
						        """.""",
						    ]    ,				)
						SCREAMING_SNAKE_CASE__								=      tokenizer.convert_tokens_to_ids(__UpperCAmelCase       )
						self.assertListEqual(
						    __UpperCAmelCase    ,				[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]    ,				)

						SCREAMING_SNAKE_CASE__								=      tokenizer.convert_ids_to_tokens(__UpperCAmelCase       )
						self.assertListEqual(
						    __UpperCAmelCase    ,				[
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """<unk>""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """<unk>""",
						        """.""",
						    ]    ,				)

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      """Hello World!"""
						SCREAMING_SNAKE_CASE__								=      [6_5, 1_8_5_3_6, 2_2_6_0, 1_0_1, 6_6]

						self.assertListEqual(__UpperCAmelCase    ,				self.big_tokenizer.encode(__UpperCAmelCase       )       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      (
						    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
						    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
						)
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      [6_5, 8_7_1, 4_1_9, 3_5_8, 9_4_6, 9_9_1, 2_5_2_1, 4_5_2, 3_5_8, 1_3_5_7, 3_8_7, 7_7_5_1, 3_5_3_6, 1_1_2, 9_8_5, 4_5_6, 1_2_6, 8_6_5, 9_3_8, 5_4_0_0, 5_7_3_4, 4_5_8, 1_3_6_8, 4_6_7, 7_8_6, 2_4_6_2, 5_2_4_6, 1_1_5_9, 6_3_3, 8_6_5, 4_5_1_9, 4_5_7, 5_8_2, 8_5_2, 2_5_5_7, 4_2_7, 9_1_6, 5_0_8, 4_0_5, 3_4_3_2_4, 4_9_7, 3_9_1, 4_0_8, 1_1_3_4_2, 1_2_4_4, 3_8_5, 1_0_0, 9_3_8, 9_8_5, 4_5_6, 5_7_4, 3_6_2, 1_2_5_9_7, 3_2_0_0, 3_1_2_9, 1_1_7_2, 6_6]  # noqa: E231
						# fmt: on
						self.assertListEqual(__UpperCAmelCase    ,				self.big_tokenizer.encode(__UpperCAmelCase       )       )

			@require_torch
			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    List[str]:
						import torch

						from transformers import BigBirdConfig, BigBirdModel

						# Build sequence
						SCREAMING_SNAKE_CASE__								=      list(self.big_tokenizer.get_vocab().keys()       )[:1_0]
						SCREAMING_SNAKE_CASE__								=      """ """.join(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.big_tokenizer.encode_plus(__UpperCAmelCase    ,				return_tensors="""pt"""    ,				return_token_type_ids=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.big_tokenizer.batch_encode_plus(
						    [sequence + """ """ + sequence]    ,				return_tensors="""pt"""    ,				return_token_type_ids=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      BigBirdConfig(attention_type="""original_full"""       )
						SCREAMING_SNAKE_CASE__								=      BigBirdModel(__UpperCAmelCase       )

						assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

						with torch.no_grad():
									model(**__UpperCAmelCase       )
									model(**__UpperCAmelCase       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""       )
						SCREAMING_SNAKE_CASE__								=      tokenizer.decode(tokenizer("""Paris is the [MASK]."""       ).input_ids       )

						self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""       )





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						# fmt: off
						SCREAMING_SNAKE_CASE__								=      {"""input_ids""": [[6_5, 3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4, 6_6], [6_5, 4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6_5, 4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__UpperCAmelCase    ,				model_name="""google/bigbird-roberta-base"""    ,				revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""    ,				)





"""simple docstring"""




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    DeiTForImageClassification,
						    DeiTForImageClassificationWithTeacher,
						    DeiTForMaskedImageModeling,
						    DeiTModel,
						)
						from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor





class    lowerCamelCase						:

			def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]=1_3    ,				__UpperCAmelCase				:						Optional[Any]=3_0    ,				__UpperCAmelCase				:						Tuple=2    ,				__UpperCAmelCase				:						Optional[int]=3    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						Optional[int]=3_2    ,				__UpperCAmelCase				:						Optional[Any]=5    ,				__UpperCAmelCase				:						List[str]=4    ,				__UpperCAmelCase				:						int=3_7    ,				__UpperCAmelCase				:						str="gelu"    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=1_0    ,				__UpperCAmelCase				:						Dict=0.02    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						List[Any]=None    ,				__UpperCAmelCase				:						str=2    ,				)   ->    Tuple:
						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 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[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 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						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=__UpperCAmelCase    ,				initializer_range=self.initializer_range    ,				encoder_stride=self.encoder_stride    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      DeiTModel(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      DeiTForMaskedImageModeling(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						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__								=      DeiTForMaskedImageModeling(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						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_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Tuple							=							(
			    (
			        DeiTModel,
			        DeiTForImageClassification,
			        DeiTForImageClassificationWithTeacher,
			        DeiTForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__   :		Any							=							(
			    {
			        'feature-extraction': DeiTModel,
			        'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__   :		Any							=							False
			lowerCamelCase__   :		Optional[int]							=							False
			lowerCamelCase__   :		str							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      DeiTModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Tuple:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""DeiT does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Any:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						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(__UpperCAmelCase       )
									self.assertIsInstance(model.get_input_embeddings()    ,				(nn.Module)       )
									SCREAMING_SNAKE_CASE__								=      model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(__UpperCAmelCase    ,				nn.Linear       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    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(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      inspect.signature(model.forward       )
									# 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]    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any=False       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

						if return_labels:
									if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
												del inputs_dict["labels"]

						return inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						if not self.model_tester.is_training:
									return

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__								=      True

						for model_class in self.all_model_classes:
									# DeiTForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(__UpperCAmelCase       )
									    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
									):
												continue
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									model.to(__UpperCAmelCase       )
									model.train()
									SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).loss
									loss.backward()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
									return

						SCREAMING_SNAKE_CASE__								=      False
						SCREAMING_SNAKE_CASE__								=      True

						for model_class in self.all_model_classes:
									if model_class in get_values(__UpperCAmelCase       ) or not model_class.supports_gradient_checkpointing:
												continue
									# DeiTForImageClassificationWithTeacher supports inference-only
									if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
												continue
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									model.gradient_checkpointing_enable()
									model.to(__UpperCAmelCase       )
									model.train()
									SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).loss
									loss.backward()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_SNAKE_CASE__								=      [
						    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
						    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
						    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
						]

						for model_class in self.all_model_classes:
									if (
									    model_class
									    not in [
									        *get_values(__UpperCAmelCase       ),
									        *get_values(__UpperCAmelCase       ),
									    ]
									    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
									):
												continue

									for problem_type in problem_types:
												with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""       ):
															SCREAMING_SNAKE_CASE__								=      problem_type["""title"""]
															SCREAMING_SNAKE_CASE__								=      problem_type["""num_labels"""]

															SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
															model.to(__UpperCAmelCase       )
															model.train()

															SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

															if problem_type["num_labels"] > 1:
																		SCREAMING_SNAKE_CASE__								=      inputs["""labels"""].unsqueeze(1       ).repeat(1    ,				problem_type["""num_labels"""]       )

															SCREAMING_SNAKE_CASE__								=      inputs["""labels"""].to(problem_type["""dtype"""]       )

															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
															# they have the same size." which is a symptom something in wrong for the regression problem.
															# See https://github.com/huggingface/transformers/issues/11780
															with warnings.catch_warnings(record=__UpperCAmelCase       ) as warning_list:
																		SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).loss
															for w in warning_list:
																		if "Using a target size that is different to the input size" in str(w.message       ):
																					raise ValueError(
																					    F"""Something is going wrong in the regression problem: intercepted {w.message}"""       )

															loss.backward()





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      DeiTModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image





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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    List[Any]:
						return (
						    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )
						    if is_vision_available()
						    else None
						)

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       ).to(
						    __UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor([-1.0_266, 0.1_912, -1.2_861]       ).to(__UpperCAmelCase       )

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





			@slow
			@require_accelerate
			@require_torch_gpu
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      DeiTModel.from_pretrained(
						    """facebook/deit-base-distilled-patch16-224"""    ,				torch_dtype=torch.floataa    ,				device_map="""auto"""       )
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor

						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       )
						SCREAMING_SNAKE_CASE__								=      inputs.pixel_values.to(__UpperCAmelCase       )

						# forward pass to make sure inference works in fp16
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )


