




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_		:					Any          =  get_tests_dir("fixtures/test_sentencepiece.model")


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

UpperCAmelCase_		:					int          =  250004
UpperCAmelCase_		:					List[str]          =  250020




@require_sentencepiece
@require_tokenizers
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       MBartTokenizer
						UpperCamelCase      =       MBartTokenizerFast
						UpperCamelCase      =       True
						UpperCamelCase      =       True

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__						:						List[str]										=MBartTokenizer(__snake_case     ,			keep_accents=__snake_case			)
										tokenizer.save_pretrained(self.tmpdirname			)

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Any										=MBartTokenizer(__snake_case     ,			keep_accents=__snake_case			)

										__magic_name__						:						int										=tokenizer.tokenize("""This is a test"""			)
										self.assertListEqual(__snake_case     ,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__snake_case			)     ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,			)

										__magic_name__						:						Optional[int]										=tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
										self.assertListEqual(
										    __snake_case     ,			[
										        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""",
										        """.""",
										    ]     ,			)
										__magic_name__						:						Optional[Any]										=tokenizer.convert_tokens_to_ids(__snake_case			)
										self.assertListEqual(
										    __snake_case     ,			[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]     ,			)

										__magic_name__						:						Any										=tokenizer.convert_ids_to_tokens(__snake_case			)
										self.assertListEqual(
										    __snake_case     ,			[
										        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      A__    (		self :Tuple			):
										'''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

										__magic_name__						:						Tuple										=(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})"			):
																		__magic_name__						:						List[Any]										=self.rust_tokenizer_class.from_pretrained(__snake_case     ,			**__snake_case			)
																		__magic_name__						:						List[str]										=self.tokenizer_class.from_pretrained(__snake_case     ,			**__snake_case			)

																		__magic_name__						:						Optional[Any]										=tempfile.mkdtemp()

																		__magic_name__						:						Dict										=tokenizer_r.save_pretrained(__snake_case			)
																		__magic_name__						:						Dict										=tokenizer_p.save_pretrained(__snake_case			)

																		# 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			)			)
																		__magic_name__						:						int										=tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
																		self.assertSequenceEqual(__snake_case     ,			__snake_case			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						Any										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						List[Any]										=tokenizer_p.from_pretrained(__snake_case			)

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

																		shutil.rmtree(__snake_case			)

																		# Save tokenizer rust, legacy_format=True
																		__magic_name__						:						List[str]										=tempfile.mkdtemp()

																		__magic_name__						:						Optional[int]										=tokenizer_r.save_pretrained(__snake_case     ,			legacy_format=__snake_case			)
																		__magic_name__						:						Dict										=tokenizer_p.save_pretrained(__snake_case			)

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

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						Any										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						int										=tokenizer_p.from_pretrained(__snake_case			)

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

																		shutil.rmtree(__snake_case			)

																		# Save tokenizer rust, legacy_format=False
																		__magic_name__						:						List[Any]										=tempfile.mkdtemp()

																		__magic_name__						:						Dict										=tokenizer_r.save_pretrained(__snake_case     ,			legacy_format=__snake_case			)
																		__magic_name__						:						List[str]										=tokenizer_p.save_pretrained(__snake_case			)

																		# 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
																		__magic_name__						:						str										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						Optional[int]										=tokenizer_p.from_pretrained(__snake_case			)

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

																		shutil.rmtree(__snake_case			)




@require_torch
@require_sentencepiece
@require_tokenizers
class 		__A   (      unittest.TestCase      ):
						UpperCamelCase      =       """facebook/mbart-large-en-ro"""
						UpperCamelCase      =       [
						    """ 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.""",
						]
						UpperCamelCase      =       [
						    """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.""",
						]
						UpperCamelCase      =       [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]

						@classmethod
						def      A__    (		cls :str			):
										'''simple docstring'''

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

						def      A__    (		self :Any			):
										'''simple docstring'''

										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]     ,			25_00_01			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]     ,			25_00_04			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]     ,			25_00_20			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
										self.assertListEqual(self.expected_src_tokens     ,			__snake_case			)

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										self.assertIn(__snake_case     ,			self.tokenizer.all_special_ids			)
										__magic_name__						:						Union[str, Any]										=[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
										__magic_name__						:						Optional[int]										=self.tokenizer.decode(__snake_case     ,			skip_special_tokens=__snake_case			)
										__magic_name__						:						List[str]										=self.tokenizer.decode(generated_ids[1:]     ,			skip_special_tokens=__snake_case			)
										self.assertEqual(__snake_case     ,			__snake_case			)
										self.assertNotIn(self.tokenizer.eos_token     ,			__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						str										=["""this is gunna be a long sentence """ * 20]
										assert isinstance(src_text[0]     ,			__snake_case			)
										__magic_name__						:						Dict										=10
										__magic_name__						:						Optional[Any]										=self.tokenizer(__snake_case     ,			max_length=__snake_case     ,			truncation=__snake_case			).input_ids[0]
										self.assertEqual(ids[-2]     ,			2			)
										self.assertEqual(ids[-1]     ,			__snake_case			)
										self.assertEqual(len(__snake_case			)     ,			__snake_case			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]			)     ,			[25_00_26, 25_00_01]			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=tempfile.mkdtemp()
										__magic_name__						:						Dict										=self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__snake_case			)
										__magic_name__						:						Dict										=MBartTokenizer.from_pretrained(__snake_case			)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,			__snake_case			)

						@require_torch
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer(self.src_text     ,			text_target=self.tgt_text     ,			padding=__snake_case     ,			return_tensors="""pt"""			)
										__magic_name__						:						str										=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      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.tokenizer(
										    self.src_text     ,			text_target=self.tgt_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=len(self.expected_src_tokens			)     ,			return_tensors="""pt"""     ,			)

										__magic_name__						:						Any										=shift_tokens_right(batch["""labels"""]     ,			self.tokenizer.pad_token_id			)

										self.assertIsInstance(__snake_case     ,			__snake_case			)

										self.assertEqual((2, 14)     ,			batch.input_ids.shape			)
										self.assertEqual((2, 14)     ,			batch.attention_mask.shape			)
										__magic_name__						:						int										=batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens     ,			__snake_case			)
										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      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.tokenizer(self.src_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=3     ,			return_tensors="""pt"""			)
										__magic_name__						:						Tuple										=self.tokenizer(
										    text_target=self.tgt_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=10     ,			return_tensors="""pt"""			)
										__magic_name__						:						List[Any]										=targets["""input_ids"""]
										__magic_name__						:						List[str]										=shift_tokens_right(__snake_case     ,			self.tokenizer.pad_token_id			)

										self.assertEqual(batch.input_ids.shape[1]     ,			3			)
										self.assertEqual(batch.decoder_input_ids.shape[1]     ,			10			)


						@require_torch
						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.tokenizer._build_translation_inputs(
										    """A test"""     ,			return_tensors="""pt"""     ,			src_lang="""en_XX"""     ,			tgt_lang="""ar_AR"""			)

										self.assertEqual(
										    nested_simplify(__snake_case			)     ,			{
										        # A, test, EOS, en_XX
										        """input_ids""": [[62, 30_34, 2, 25_00_04]],
										        """attention_mask""": [[1, 1, 1, 1]],
										        # ar_AR
										        """forced_bos_token_id""": 25_00_01,
										    }     ,			)





"""simple docstring"""






import inspect
import unittest

from transformers import ConvNextConfig
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 transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
       from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=13						,			__lowerCAmelCase=32						,			__lowerCAmelCase=3						,			__lowerCAmelCase=4						,			__lowerCAmelCase=[10, 20, 30, 40]						,			__lowerCAmelCase=[2, 2, 3, 2]						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=37						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=10						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=["stage2", "stage3", "stage4"]						,			__lowerCAmelCase=[2, 3, 4]						,			__lowerCAmelCase=None						,			):







            """simple docstring"""



            lowercase								=      parent
            lowercase								=      batch_size
            lowercase								=      image_size
            lowercase								=      num_channels
            lowercase								=      num_stages
            lowercase								=      hidden_sizes
            lowercase								=      depths
            lowercase								=      is_training
            lowercase								=      use_labels
            lowercase								=      intermediate_size
            lowercase								=      hidden_act
            lowercase								=      num_labels
            lowercase								=      initializer_range
            lowercase								=      out_features
            lowercase								=      out_indices
            lowercase								=      scope

      def 							A__			(		self		):







            """simple docstring"""



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

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

            lowercase								=      self.get_config()
            return config, pixel_values, labels

      def 							A__			(		self		):







            """simple docstring"""



            return ConvNextConfig(
                num_channels=self.num_channels						,			hidden_sizes=self.hidden_sizes						,			depths=self.depths						,			num_stages=self.num_stages						,			hidden_act=self.hidden_act						,			is_decoder=__lowerCAmelCase						,			initializer_range=self.initializer_range						,			out_features=self.out_features						,			out_indices=self.out_indices						,			num_labels=self.num_labels						,			)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      ConvNextModel(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase		)
            # expected last hidden states: B, C, H // 32, W // 32
            self.parent.assertEqual(
                result.last_hidden_state.shape						,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,			)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      ConvNextForImageClassification(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase						,			labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      ConvNextBackbone(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase		)

            # verify hidden states
            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								=      None
            lowercase								=      ConvNextBackbone(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase		)

            # 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 							A__			(		self		):







            """simple docstring"""



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






@require_torch
class 					_A		(			lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  List[str]				    =      (
          (
              ConvNextModel,
              ConvNextForImageClassification,
              ConvNextBackbone,
          )
          if is_torch_available()
          else ()
      )
      snake_case__     :  Optional[Any]				    =      (
          {'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
          if is_torch_available()
          else {}
      )

      snake_case__     :  List[str]				    =      True
      snake_case__     :  Dict				    =      False
      snake_case__     :  Optional[int]				    =      False
      snake_case__     :  Tuple				    =      False
      snake_case__     :  List[str]				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ConvNextModelTester(self		)
            lowercase								=      ConfigTester(self						,			config_class=__lowerCAmelCase						,			has_text_modality=__lowerCAmelCase						,			hidden_size=37		)

      def 							A__			(		self		):







            """simple docstring"""



            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 							A__			(		self		):







            """simple docstring"""



            return

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







            """simple docstring"""



            pass

      @unittest.skip(reason="""ConvNext does not support input and output embeddings"""		)
      def 							A__			(		self		):







            """simple docstring"""



            pass

      @unittest.skip(reason="""ConvNext does not use feedforward chunking"""		)
      def 							A__			(		self		):







            """simple docstring"""



            pass

      def 							A__			(		self		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

      def 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            def check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):
                  lowercase								=      model_class(__lowerCAmelCase		)
                  model.to(__lowerCAmelCase		)
                  model.eval()

                  with torch.no_grad():
                        lowercase								=      model(**self._prepare_for_class(__lowerCAmelCase						,			__lowerCAmelCase		)		)

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

                  lowercase								=      self.model_tester.num_stages
                  self.assertEqual(len(__lowerCAmelCase		)						,			expected_num_stages + 1		)

                  # ConvNext'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								=      self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowercase								=      True
                  check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)

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

                  check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



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







      @slow
      def 							A__			(		self		):







            """simple docstring"""



            for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase								=      ConvNextModel.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)






def      UpperCAmelCase__			(							)					->       List[str]:
      '''simple docstring'''


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





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

      @cached_property
      def 							A__			(		self		):







            """simple docstring"""



            return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""		) if is_vision_available() else None







      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""		).to(__lowerCAmelCase		)

            lowercase								=      self.default_image_processor
            lowercase								=      prepare_img()
            lowercase								=      image_processor(images=__lowerCAmelCase						,			return_tensors="""pt"""		).to(__lowerCAmelCase		)

            # forward pass
            with torch.no_grad():
                  lowercase								=      model(**__lowerCAmelCase		)

            # verify the logits
            lowercase								=      torch.Size((1, 1000)		)
            self.assertEqual(outputs.logits.shape						,			__lowerCAmelCase		)

            lowercase								=      torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]		).to(__lowerCAmelCase		)

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






@require_torch
class 					_A		(			unittest.TestCase    ,       lowerCAmelCase      ):
      snake_case__     :  int				    =      (ConvNextBackbone,) if is_torch_available() else ()
      snake_case__     :  Tuple				    =      ConvNextConfig

      snake_case__     :  Dict				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ConvNextModelTester(self		)



