




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__         =      get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
					from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCamelCase__         =      250004
UpperCamelCase__         =      250020
@require_sentencepiece
@require_tokenizers
class       a__   ( snake_case__			,    unittest.TestCase				):






						"""simple docstring"""




						_a							:   Optional[Any] =   MBartTokenizer
						_a							:   int =   MBartTokenizerFast
						_a							:   Any =   True
						_a							:   Tuple =   True






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCAmelCase           =	MBartTokenizer(_A		,      keep_accents=_A      )
											tokenizer.save_pretrained(self.tmpdirname      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MBartTokenizer(_A		,      keep_accents=_A      )

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

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(_A      )		,      [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]		,      )

											__lowerCAmelCase           =	tokenizer.tokenize("I was born in 92000, and this is falsé."      )
											self.assertListEqual(
											    _A		,      [
											        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",
											        ".",
											    ]		,      )
											__lowerCAmelCase           =	tokenizer.convert_tokens_to_ids(_A      )
											self.assertListEqual(
											    _A		,      [
											        value + tokenizer.fairseq_offset
											        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
											        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
											    ]		,      )

											__lowerCAmelCase           =	tokenizer.convert_ids_to_tokens(_A      )
											self.assertListEqual(
											    _A		,      [
											        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>",
											        ".",
											    ]		,      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											if not self.test_slow_tokenizer:
																# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																return

											__lowerCAmelCase           =	(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																					__lowerCAmelCase           =	self.rust_tokenizer_class.from_pretrained(_A		,      **_A      )
																					__lowerCAmelCase           =	self.tokenizer_class.from_pretrained(_A		,      **_A      )

																					__lowerCAmelCase           =	tempfile.mkdtemp()

																					__lowerCAmelCase           =	tokenizer_r.save_pretrained(_A      )
																					__lowerCAmelCase           =	tokenizer_p.save_pretrained(_A      )

																					# Checks it save with the same files + the tokenizer.json file for the fast one
																					self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files      )      )
																					__lowerCAmelCase           =	tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f      )
																					self.assertSequenceEqual(_A		,      _A      )

																					# Checks everything loads correctly in the same way
																					__lowerCAmelCase           =	tokenizer_r.from_pretrained(_A      )
																					__lowerCAmelCase           =	tokenizer_p.from_pretrained(_A      )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(_A		,      _A      )      )
																										# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																										# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																					shutil.rmtree(_A      )

																					# Save tokenizer rust, legacy_format=True
																					__lowerCAmelCase           =	tempfile.mkdtemp()

																					__lowerCAmelCase           =	tokenizer_r.save_pretrained(_A		,      legacy_format=_A      )
																					__lowerCAmelCase           =	tokenizer_p.save_pretrained(_A      )

																					# Checks it save with the same files
																					self.assertSequenceEqual(_A		,      _A      )

																					# Checks everything loads correctly in the same way
																					__lowerCAmelCase           =	tokenizer_r.from_pretrained(_A      )
																					__lowerCAmelCase           =	tokenizer_p.from_pretrained(_A      )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(_A		,      _A      )      )

																					shutil.rmtree(_A      )

																					# Save tokenizer rust, legacy_format=False
																					__lowerCAmelCase           =	tempfile.mkdtemp()

																					__lowerCAmelCase           =	tokenizer_r.save_pretrained(_A		,      legacy_format=_A      )
																					__lowerCAmelCase           =	tokenizer_p.save_pretrained(_A      )

																					# Checks it saved the tokenizer.json file
																					self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files      )      )

																					# Checks everything loads correctly in the same way
																					__lowerCAmelCase           =	tokenizer_r.from_pretrained(_A      )
																					__lowerCAmelCase           =	tokenizer_p.from_pretrained(_A      )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(_A		,      _A      )      )

																					shutil.rmtree(_A      )






@require_torch
@require_sentencepiece
@require_tokenizers
class       a__   ( unittest.TestCase				):






						"""simple docstring"""




						_a							:   str =   """facebook/mbart-large-en-ro"""
						_a							:   Optional[Any] =   [
						    """ UN Chief Says There Is No Military Solution in Syria""",
						    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
						]
						_a							:   Dict =   [
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
						    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
						    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
						]
						_a							:   Optional[int] =   [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]






						@classmethod
						def     __SCREAMING_SNAKE_CASE(       cls      ):




											"""simple docstring"""



											__lowerCAmelCase           =	MBartTokenizer.from_pretrained(
											    cls.checkpoint_name		,      src_lang="en_XX"		,      tgt_lang="ro_RO"      )
											__lowerCAmelCase           =	1
											return cls






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]		,      2_5_0_0_0_1      )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]		,      2_5_0_0_0_4      )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]		,      2_5_0_0_2_0      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
											self.assertListEqual(self.expected_src_tokens		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.assertIn(_A		,      self.tokenizer.all_special_ids      )
											__lowerCAmelCase           =	[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
											__lowerCAmelCase           =	self.tokenizer.decode(_A		,      skip_special_tokens=_A      )
											__lowerCAmelCase           =	self.tokenizer.decode(generated_ids[1:]		,      skip_special_tokens=_A      )
											self.assertEqual(_A		,      _A      )
											self.assertNotIn(self.tokenizer.eos_token		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	["this is gunna be a long sentence " * 2_0]
											assert isinstance(src_text[0]		,      _A      )
											__lowerCAmelCase           =	1_0
											__lowerCAmelCase           =	self.tokenizer(_A		,      max_length=_A		,      truncation=_A      ).input_ids[0]
											self.assertEqual(ids[-2]		,      2      )
											self.assertEqual(ids[-1]		,      _A      )
											self.assertEqual(len(_A      )		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]      )		,      [2_5_0_0_2_6, 2_5_0_0_0_1]      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	tempfile.mkdtemp()
											__lowerCAmelCase           =	self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(_A      )
											__lowerCAmelCase           =	MBartTokenizer.from_pretrained(_A      )
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,      _A      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.tokenizer(self.src_text		,      text_target=self.tgt_text		,      padding=_A		,      return_tensors="pt"      )
											__lowerCAmelCase           =	shift_tokens_right(batch["labels"]		,      self.tokenizer.pad_token_id      )

											# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
											assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
											assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
											assert batch.decoder_input_ids[1][-1] == 2
											assert batch.labels[1][-2:].tolist() == [2, RO_CODE]






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.tokenizer(
											    self.src_text		,      text_target=self.tgt_text		,      padding=_A		,      truncation=_A		,      max_length=len(self.expected_src_tokens      )		,      return_tensors="pt"		,      )

											__lowerCAmelCase           =	shift_tokens_right(batch["labels"]		,      self.tokenizer.pad_token_id      )

											self.assertIsInstance(_A		,      _A      )

											self.assertEqual((2, 1_4)		,      batch.input_ids.shape      )
											self.assertEqual((2, 1_4)		,      batch.attention_mask.shape      )
											__lowerCAmelCase           =	batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens		,      _A      )
											self.assertEqual(2		,      batch.decoder_input_ids[0, -1]      )  # EOS
											# Test that special tokens are reset
											self.assertEqual(self.tokenizer.prefix_tokens		,      []      )
											self.assertEqual(self.tokenizer.suffix_tokens		,      [self.tokenizer.eos_token_id, EN_CODE]      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.tokenizer(self.src_text		,      padding=_A		,      truncation=_A		,      max_length=3		,      return_tensors="pt"      )
											__lowerCAmelCase           =	self.tokenizer(
											    text_target=self.tgt_text		,      padding=_A		,      truncation=_A		,      max_length=1_0		,      return_tensors="pt"      )
											__lowerCAmelCase           =	targets["input_ids"]
											__lowerCAmelCase           =	shift_tokens_right(_A		,      self.tokenizer.pad_token_id      )

											self.assertEqual(batch.input_ids.shape[1]		,      3      )
											self.assertEqual(batch.decoder_input_ids.shape[1]		,      1_0      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.tokenizer._build_translation_inputs(
											    "A test"		,      return_tensors="pt"		,      src_lang="en_XX"		,      tgt_lang="ar_AR"      )

											self.assertEqual(
											    nested_simplify(_A      )		,      {
											        # A, test, EOS, en_XX
											        "input_ids": [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
											        "attention_mask": [[1, 1, 1, 1]],
											        # ar_AR
											        "forced_bos_token_id": 2_5_0_0_0_1,
											    }		,      )










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__   :






						def __init__(       self		,      _A		,      _A=1_3		,      _A=3_0		,      _A=2		,      _A=3		,      _A=True		,      _A=True		,      _A=3_2		,      _A=2		,      _A=4		,      _A=3_7		,      _A="gelu"		,      _A=0.1		,      _A=0.1		,      _A=1_0		,      _A=0.02		,      _A=3		,      _A=None		,      _A=2		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	parent
											__lowerCAmelCase           =	batch_size
											__lowerCAmelCase           =	image_size
											__lowerCAmelCase           =	patch_size
											__lowerCAmelCase           =	num_channels
											__lowerCAmelCase           =	is_training
											__lowerCAmelCase           =	use_labels
											__lowerCAmelCase           =	hidden_size
											__lowerCAmelCase           =	num_hidden_layers
											__lowerCAmelCase           =	num_attention_heads
											__lowerCAmelCase           =	intermediate_size
											__lowerCAmelCase           =	hidden_act
											__lowerCAmelCase           =	hidden_dropout_prob
											__lowerCAmelCase           =	attention_probs_dropout_prob
											__lowerCAmelCase           =	type_sequence_label_size
											__lowerCAmelCase           =	initializer_range
											__lowerCAmelCase           =	scope
											__lowerCAmelCase           =	encoder_stride

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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

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

											__lowerCAmelCase           =	self.get_config()

											return config, pixel_values, labels






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTModel(config=_A      )
											__lowerCAmelCase           =	model(_A      )
											self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTForMaskedImageModeling(config=_A      )
											__lowerCAmelCase           =	model(_A      )
											self.parent.assertEqual(
											    result.reconstruction.shape		,      (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

											# test greyscale images
											__lowerCAmelCase           =	1
											__lowerCAmelCase           =	TFDeiTForMaskedImageModeling(_A      )

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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.type_sequence_label_size
											__lowerCAmelCase           =	TFDeiTForImageClassification(_A      )
											__lowerCAmelCase           =	model(_A		,      labels=_A      )
											self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)      )

											# test greyscale images
											__lowerCAmelCase           =	1
											__lowerCAmelCase           =	TFDeiTForImageClassification(_A      )

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






@require_tf
class       a__   ( snake_case__			,    snake_case__			,    unittest.TestCase				):
						_a							:   Optional[Any] =   (
						    (
						        TFDeiTModel,
						        TFDeiTForImageClassification,
						        TFDeiTForImageClassificationWithTeacher,
						        TFDeiTForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:   Optional[Any] =   (
						    {
						        """feature-extraction""": TFDeiTModel,
						        """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
						    }
						    if is_tf_available()
						    else {}
						)

						_a							:   str =   False
						_a							:   str =   False
						_a							:   List[str] =   False
						_a							:   Optional[int] =   False






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTModelTester(self      )
											__lowerCAmelCase           =	ConfigTester(self		,      config_class=_A		,      has_text_modality=_A		,      hidden_size=3_7      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						@unittest.skip(reason="DeiT does not use inputs_embeds"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs_for_common()

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

																__lowerCAmelCase           =	["pixel_values"]
																self.assertListEqual(arg_names[:1]		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A=False      ):




											"""simple docstring"""



											__lowerCAmelCase           =	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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase           =	TFDeiTModel.from_pretrained(_A      )
																self.assertIsNotNone(_A      )







def      _a				(						):
					__lowerCAmelCase           =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
					return image


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






						@cached_property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return (
											    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"      )
											    if is_vision_available()
											    else None
											)






						@slow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"      )

											__lowerCAmelCase           =	self.default_image_processor
											__lowerCAmelCase           =	prepare_img()
											__lowerCAmelCase           =	image_processor(images=_A		,      return_tensors="tf"      )

											# forward pass
											__lowerCAmelCase           =	model(**_A      )

											# verify the logits
											__lowerCAmelCase           =	tf.TensorShape((1, 1_0_0_0)      )
											self.assertEqual(outputs.logits.shape		,      _A      )

											__lowerCAmelCase           =	tf.constant([-1.02_66, 0.19_12, -1.28_61]      )

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




