'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_nllb import NllbTokenizer
else:
 _A								=   None


_A								=   logging.get_logger(__name__)


_A								=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_A								=   {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

_A								=   {
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
_A								=   ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    VOCAB_FILES_NAMES
   A							:       Union[str, Any]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       Dict     =    PRETRAINED_VOCAB_FILES_MAP
   A							:       str     =    ['''input_ids''', '''attention_mask''']
   A							:       str     =    NllbTokenizer

   A							:       List[int]     =    []
   A							:       List[int]     =    []
   def __init__(self       ,				_a=None       ,				_a=None       ,				_a="<s>"       ,				_a="</s>"       ,				_a="</s>"       ,				_a="<s>"       ,				_a="<unk>"       ,				_a="<pad>"       ,				_a="<mask>"       ,				_a=None       ,				_a=None       ,				_a=None       ,				_a=False       ,				**_a       ,				)    ->     Optional[int]:
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase_					:       Union[str, Any]     				=					AddedToken(_a       ,				lstrip=_a       ,				rstrip=_a				) if isinstance(_a       ,				_a				) else mask_token
        lowercase_					:       Any     				=					legacy_behaviour
        super().__init__(
            vocab_file=_a       ,				tokenizer_file=_a       ,				bos_token=_a       ,				eos_token=_a       ,				sep_token=_a       ,				cls_token=_a       ,				unk_token=_a       ,				pad_token=_a       ,				mask_token=_a       ,				src_lang=_a       ,				tgt_lang=_a       ,				additional_special_tokens=_a       ,				legacy_behaviour=_a       ,				**_a       ,				)

        lowercase_					:       Optional[int]     				=					vocab_file
        lowercase_					:       str     				=					False if not self.vocab_file else True

        lowercase_					:       Union[str, Any]     				=					FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}				)
        lowercase_					:       Tuple     				=					{
            lang_code: self.convert_tokens_to_ids(_a				) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase_					:       Optional[Any]     				=					src_lang if src_lang is not None else 'eng_Latn'
        lowercase_					:       Tuple     				=					self.convert_tokens_to_ids(self._src_lang				)
        lowercase_					:       List[Any]     				=					tgt_lang
        self.set_src_lang_special_tokens(self._src_lang				)
   @property
   def 		_lowerCamelCase (self				)    ->     str:
        return self._src_lang
   @src_lang.setter
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        lowercase_					:       Optional[int]     				=					new_src_lang
        self.set_src_lang_special_tokens(self._src_lang				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     List[int]:
        if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     List[int]:
        lowercase_					:       Tuple     				=					[self.sep_token_id]
        lowercase_					:       int     				=					[self.cls_token_id]

        if token_ids_a is None:
             return len(cls + token_ids_a + sep				) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				**_a				)    ->     Optional[int]:
        if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
        lowercase_					:       Dict     				=					src_lang
        lowercase_					:       Dict     				=					self(_a       ,				add_special_tokens=_a       ,				return_tensors=_a       ,				**_a				)
        lowercase_					:       Tuple     				=					self.convert_tokens_to_ids(_a				)
        lowercase_					:       Optional[Any]     				=					tgt_lang_id
        return inputs
   def 		_lowerCamelCase (self       ,				_a       ,				_a = "eng_Latn"       ,				_a = None       ,				_a = "fra_Latn"       ,				**_a       ,				)    ->     BatchEncoding:
        lowercase_					:       Dict     				=					src_lang
        lowercase_					:       Optional[int]     				=					tgt_lang
        return super().prepare_seqaseq_batch(_a       ,				_a       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return self.set_src_lang_special_tokens(self.src_lang				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        return self.set_tgt_lang_special_tokens(self.tgt_lang				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        lowercase_					:       Dict     				=					self.convert_tokens_to_ids(_a				)

        if self.legacy_behaviour:
             lowercase_					:       Tuple     				=					[]
             lowercase_					:       str     				=					[self.eos_token_id, self.cur_lang_code]
        else:
             lowercase_					:       Union[str, Any]     				=					[self.cur_lang_code]
             lowercase_					:       Union[str, Any]     				=					[self.eos_token_id]

        lowercase_					:       Optional[Any]     				=					self.convert_ids_to_tokens(self.prefix_tokens				)
        lowercase_					:       Tuple     				=					self.convert_ids_to_tokens(self.suffix_tokens				)

        lowercase_					:       Optional[int]     				=					processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,				pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,				self.prefix_tokens + self.suffix_tokens				)				)       ,				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        lowercase_					:       str     				=					self.convert_tokens_to_ids(_a				)
        if self.legacy_behaviour:
             lowercase_					:       List[Any]     				=					[]
             lowercase_					:       Optional[Any]     				=					[self.eos_token_id, self.cur_lang_code]
        else:
             lowercase_					:       Optional[int]     				=					[self.cur_lang_code]
             lowercase_					:       Dict     				=					[self.eos_token_id]

        lowercase_					:       Dict     				=					self.convert_ids_to_tokens(self.prefix_tokens				)
        lowercase_					:       Optional[Any]     				=					self.convert_ids_to_tokens(self.suffix_tokens				)

        lowercase_					:       List[Any]     				=					processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,				pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,				self.prefix_tokens + self.suffix_tokens				)				)       ,				)





   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     Tuple[str]:
        if not self.can_save_slow_tokenizer:
             raise ValueError(
                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                 'tokenizer.'				)

        if not os.path.isdir(_a				):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''				)
             return
        lowercase_					:       Any     				=					os.path.join(
            _a       ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

        if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
             copyfile(self.vocab_file       ,				_a				)

        return (out_vocab_file,)





'''simple docstring'''




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
 from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=13       ,				_a=32       ,				_a=3       ,				_a=4       ,				_a=[10, 20, 30, 40]       ,				_a=[2, 2, 3, 2]       ,				_a=True       ,				_a=True       ,				_a=37       ,				_a="gelu"       ,				_a=10       ,				_a=0.02       ,				_a=["stage2", "stage3", "stage4"]       ,				_a=[2, 3, 4]       ,				_a=None       ,				)    ->     List[str]:
        lowercase_					:       Optional[int]     				=					parent
        lowercase_					:       int     				=					batch_size
        lowercase_					:       Union[str, Any]     				=					image_size
        lowercase_					:       Optional[Any]     				=					num_channels
        lowercase_					:       Any     				=					num_stages
        lowercase_					:       Optional[Any]     				=					hidden_sizes
        lowercase_					:       Any     				=					depths
        lowercase_					:       Tuple     				=					is_training
        lowercase_					:       int     				=					use_labels
        lowercase_					:       Tuple     				=					intermediate_size
        lowercase_					:       Any     				=					hidden_act
        lowercase_					:       Union[str, Any]     				=					num_labels
        lowercase_					:       Union[str, Any]     				=					initializer_range
        lowercase_					:       Optional[Any]     				=					out_features
        lowercase_					:       str     				=					out_indices
        lowercase_					:       Dict     				=					scope
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[Any]     				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        lowercase_					:       int     				=					None
        if self.use_labels:
             lowercase_					:       Any     				=					ids_tensor([self.batch_size]       ,				self.num_labels				)

        lowercase_					:       str     				=					self.get_config()

        return config, pixel_values, labels
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        return ConvNextVaConfig(
            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=_a       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Tuple:
        lowercase_					:       Optional[int]     				=					ConvNextVaModel(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       List[Any]     				=					model(_a				)
        # 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 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Any:
        lowercase_					:       int     				=					ConvNextVaForImageClassification(_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Dict     				=					model(_a       ,				labels=_a				)
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Optional[Any]:
        lowercase_					:       Any     				=					ConvNextVaBackbone(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)

        # 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_					:       int     				=					None
        lowercase_					:       List[str]     				=					ConvNextVaBackbone(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps				)       ,				1				)
        self.parent.assertListEqual(list(result.feature_maps[0].shape				)       ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]				)

        # verify channels
        self.parent.assertEqual(len(model.channels				)       ,				1				)
        self.parent.assertListEqual(model.channels       ,				[config.hidden_sizes[-1]]				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Union[str, Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       str     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'pixel_values': pixel_values}
        return config, inputs_dict





   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Optional[Any]     				=					config_and_inputs
        lowercase_					:       Dict     				=					{'pixel_values': pixel_values, 'labels': labels}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       List[str]     =    (
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   A							:       Optional[int]     =    (
       {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A							:       Optional[int]     =    False
   A							:       str     =    False
   A							:       int     =    False
   A							:       Dict     =    False
   A							:       Optional[int]     =    False
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					ConvNextVaModelTester(self				)
        lowercase_					:       List[str]     				=					ConfigTester(self       ,				config_class=_a       ,				has_text_modality=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     int:
        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 		_lowerCamelCase (self				)    ->     List[Any]:
        return
   @unittest.skip(reason='ConvNextV2 does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        pass
   @unittest.skip(reason='ConvNextV2 does not support input and output embeddings'				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        pass
   @unittest.skip(reason='ConvNextV2 does not use feedforward chunking'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        if not self.model_tester.is_training:
             return

        for model_class in self.all_model_classes:
             lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_with_labels()
             lowercase_					:       Dict     				=					True

             if model_class.__name__ in [
                 *get_values(_a				),
                 *get_values(_a				),
             ]:
                  continue

             lowercase_					:       str     				=					model_class(_a				)
             model.to(_a				)
             model.train()
             lowercase_					:       Tuple     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       List[Any]     				=					model(**_a				).loss
             loss.backward()
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        if not self.model_tester.is_training:
             return

        for model_class in self.all_model_classes:
             lowercase_				,lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs_with_labels()
             lowercase_					:       Optional[int]     				=					False
             lowercase_					:       Union[str, Any]     				=					True

             if (
                 model_class.__name__
                 in [*get_values(_a				), *get_values(_a				)]
                 or not model_class.supports_gradient_checkpointing
             ):
                  continue

             lowercase_					:       Union[str, Any]     				=					model_class(_a				)
             model.to(_a				)
             model.gradient_checkpointing_enable()
             model.train()
             lowercase_					:       List[Any]     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       Optional[int]     				=					model(**_a				).loss
             loss.backward()
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_				,lowercase_					:       int     				=					self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_					:       List[str]     				=					['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        def check_hidden_states_output(_a       ,				_a       ,				_a				):
             lowercase_					:       Tuple     				=					model_class(_a				)
             model.to(_a				)
             model.eval()

             with torch.no_grad():
                  lowercase_					:       str     				=					model(**self._prepare_for_class(_a       ,				_a				)				)

             lowercase_					:       str     				=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             lowercase_					:       Optional[int]     				=					self.model_tester.num_stages
             self.assertEqual(len(_a				)       ,				expected_num_stages + 1				)

             # ConvNextV2'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_					:       Optional[int]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       Optional[Any]     				=					True
             check_hidden_states_output(_a       ,				_a       ,				_a				)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_					:       int     				=					True

             check_hidden_states_output(_a       ,				_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_a				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Any:
        for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       Optional[int]     				=					ConvNextVaModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)



def _UpperCamelCase   (       ):
     lowercase_					:       Union[str, Any]     				=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
     return image






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






   """simple docstring"""
   @cached_property
   def 		_lowerCamelCase (self				)    ->     str:
        return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'				) if is_vision_available() else None





   @slow
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       List[Any]     				=					ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'				).to(_a				)

        lowercase_					:       Any     				=					self.default_image_processor
        lowercase_					:       Union[str, Any]     				=					prepare_img()
        lowercase_					:       List[str]     				=					preprocessor(images=_a       ,				return_tensors='pt'				).to(_a				)

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

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

        lowercase_					:       Tuple     				=					torch.tensor([0.99_96, 0.19_66, -0.43_86]				).to(_a				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				_a       ,				atol=1e-4				)				)




