

"""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:
    _UpperCamelCase		     =    None


_UpperCamelCase		     =    logging.get_logger(__name__)


_UpperCamelCase		     =    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase		     =    {
    """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"""
        ),
    },
}

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

# fmt: off
_UpperCamelCase		     =    ["""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 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[Any]    =						VOCAB_FILES_NAMES
       __UpperCamelCase					:      Dict    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase					:      Dict    =						PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase					:      int    =						['input_ids', 'attention_mask']
       __UpperCamelCase					:      Union[str, Any]    =						NllbTokenizer

       __UpperCamelCase					:      List[int]    =						[]
       __UpperCamelCase					:      List[int]    =						[]






       def __init__(   self			,					snake_case=None			,					snake_case=None			,					snake_case="<s>"			,					snake_case="</s>"			,					snake_case="</s>"			,					snake_case="<s>"			,					snake_case="<unk>"			,					snake_case="<pad>"			,					snake_case="<mask>"			,					snake_case=None			,					snake_case=None			,					snake_case=None			,					snake_case=False			,					**snake_case			,					):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				AddedToken(snake_case			,					lstrip=snake_case			,					rstrip=snake_case   ) if isinstance(snake_case			,					snake_case   ) else mask_token
        lowerCAmelCase__   :				Tuple 			=				legacy_behaviour
        super().__init__(
            vocab_file=snake_case			,					tokenizer_file=snake_case			,					bos_token=snake_case			,					eos_token=snake_case			,					sep_token=snake_case			,					cls_token=snake_case			,					unk_token=snake_case			,					pad_token=snake_case			,					mask_token=snake_case			,					src_lang=snake_case			,					tgt_lang=snake_case			,					additional_special_tokens=snake_case			,					legacy_behaviour=snake_case			,					**snake_case			,					)

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

        lowerCAmelCase__   :				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}   )
        lowerCAmelCase__   :				Tuple 			=				{
            lang_code: self.convert_tokens_to_ids(snake_case   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowerCAmelCase__   :				Union[str, Any] 			=				src_lang if src_lang is not None else "eng_Latn"
        lowerCAmelCase__   :				List[str] 			=				self.convert_tokens_to_ids(self._src_lang   )
        lowerCAmelCase__   :				Any 			=				tgt_lang
        self.set_src_lang_special_tokens(self._src_lang   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self._src_lang






       @src_lang.setter
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				new_src_lang
        self.set_src_lang_special_tokens(self._src_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				[self.sep_token_id]
        lowerCAmelCase__   :				Dict 			=				[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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					**snake_case   ):





        """simple docstring"""

        if src_lang is None or tgt_lang is None:
         raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"   )
        lowerCAmelCase__   :				Any 			=				src_lang
        lowerCAmelCase__   :				Optional[Any] 			=				self(snake_case			,					add_special_tokens=snake_case			,					return_tensors=snake_case			,					**snake_case   )
        lowerCAmelCase__   :				str 			=				self.convert_tokens_to_ids(snake_case   )
        lowerCAmelCase__   :				Any 			=				tgt_lang_id
        return inputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = "eng_Latn"			,					snake_case = None			,					snake_case = "fra_Latn"			,					**snake_case			,					):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				src_lang
        lowerCAmelCase__   :				str 			=				tgt_lang
        return super().prepare_seqaseq_batch(snake_case			,					snake_case			,					**snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.set_src_lang_special_tokens(self.src_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.set_tgt_lang_special_tokens(self.tgt_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.convert_tokens_to_ids(snake_case   )

        if self.legacy_behaviour:
         lowerCAmelCase__   :				Optional[int] 			=				[]
         lowerCAmelCase__   :				Optional[int] 			=				[self.eos_token_id, self.cur_lang_code]
        else:
         lowerCAmelCase__   :				int 			=				[self.cur_lang_code]
         lowerCAmelCase__   :				List[str] 			=				[self.eos_token_id]

        lowerCAmelCase__   :				Any 			=				self.convert_ids_to_tokens(self.prefix_tokens   )
        lowerCAmelCase__   :				str 			=				self.convert_ids_to_tokens(self.suffix_tokens   )

        lowerCAmelCase__   :				Tuple 			=				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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.convert_tokens_to_ids(snake_case   )
        if self.legacy_behaviour:
         lowerCAmelCase__   :				Dict 			=				[]
         lowerCAmelCase__   :				Dict 			=				[self.eos_token_id, self.cur_lang_code]
        else:
         lowerCAmelCase__   :				str 			=				[self.cur_lang_code]
         lowerCAmelCase__   :				Tuple 			=				[self.eos_token_id]

        lowerCAmelCase__   :				Any 			=				self.convert_ids_to_tokens(self.prefix_tokens   )
        lowerCAmelCase__   :				List[Any] 			=				self.convert_ids_to_tokens(self.suffix_tokens   )

        lowerCAmelCase__   :				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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        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(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""   )
         return
        lowerCAmelCase__   :				str 			=				os.path.join(
            snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

        return (out_vocab_file,)









"""simple docstring"""

import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
    from transformers.models.mobilevitva.modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.config_class(**self.inputs_dict   )
        self.parent.assertTrue(hasattr(snake_case			,					"width_multiplier"   )   )






class 			__a					:





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=13			,					snake_case=64			,					snake_case=2			,					snake_case=3			,					snake_case="swish"			,					snake_case=3			,					snake_case=32			,					snake_case=0.1			,					snake_case=0.02			,					snake_case=True			,					snake_case=True			,					snake_case=10			,					snake_case=None			,					snake_case=0.25			,					snake_case=0.0			,					snake_case=0.0			,					):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				parent
        lowerCAmelCase__   :				Optional[Any] 			=				batch_size
        lowerCAmelCase__   :				Optional[Any] 			=				image_size
        lowerCAmelCase__   :				Tuple 			=				patch_size
        lowerCAmelCase__   :				Any 			=				num_channels
        lowerCAmelCase__   :				Tuple 			=				make_divisible(512 * width_multiplier			,					divisor=8   )
        lowerCAmelCase__   :				str 			=				hidden_act
        lowerCAmelCase__   :				Union[str, Any] 			=				conv_kernel_size
        lowerCAmelCase__   :				List[str] 			=				output_stride
        lowerCAmelCase__   :				List[Any] 			=				classifier_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				use_labels
        lowerCAmelCase__   :				Tuple 			=				is_training
        lowerCAmelCase__   :				Optional[int] 			=				num_labels
        lowerCAmelCase__   :				int 			=				initializer_range
        lowerCAmelCase__   :				str 			=				scope
        lowerCAmelCase__   :				Optional[Any] 			=				width_multiplier
        lowerCAmelCase__   :				Union[str, Any] 			=				ffn_dropout
        lowerCAmelCase__   :				Tuple 			=				attn_dropout






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        lowerCAmelCase__   :				Optional[Any] 			=				None
        lowerCAmelCase__   :				List[str] 			=				None
        if self.use_labels:
         lowerCAmelCase__   :				Dict 			=				ids_tensor([self.batch_size]			,					self.num_labels   )
         lowerCAmelCase__   :				Dict 			=				ids_tensor([self.batch_size, self.image_size, self.image_size]			,					self.num_labels   )

        lowerCAmelCase__   :				int 			=				self.get_config()

        return config, pixel_values, labels, pixel_labels






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return MobileViTVaConfig(
            image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					hidden_act=self.hidden_act			,					conv_kernel_size=self.conv_kernel_size			,					output_stride=self.output_stride			,					classifier_dropout_prob=self.classifier_dropout_prob			,					initializer_range=self.initializer_range			,					width_multiplier=self.width_multiplier			,					ffn_dropout=self.ffn_dropout_prob			,					attn_dropout=self.attn_dropout_prob			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				MobileViTVaModel(config=snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				int 			=				model(snake_case   )
        self.parent.assertEqual(
            result.last_hidden_state.shape			,					(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.num_labels
        lowerCAmelCase__   :				Any 			=				MobileViTVaForImageClassification(snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				int 			=				model(snake_case			,					labels=snake_case   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.num_labels
        lowerCAmelCase__   :				Optional[int] 			=				MobileViTVaForSemanticSegmentation(snake_case   )
        model.to(snake_case   )
        model.eval()
        lowerCAmelCase__   :				Any 			=				model(snake_case   )
        self.parent.assertEqual(
            result.logits.shape			,					(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )			,					)
        lowerCAmelCase__   :				Tuple 			=				model(snake_case			,					labels=snake_case   )
        self.parent.assertEqual(
            result.logits.shape			,					(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






@require_torch
class 			__a					(						__magic_name__       ,	__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      List[Any]    =						(
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       __UpperCamelCase					:      List[Any]    =						(
           {
               'feature-extraction': MobileViTVaModel,
               'image-classification': MobileViTVaForImageClassification,
               'image-segmentation': MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __UpperCamelCase					:      str    =						False
       __UpperCamelCase					:      int    =						False
       __UpperCamelCase					:      Tuple    =						False
       __UpperCamelCase					:      List[str]    =						False






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				MobileViTVaModelTester(self   )
        lowerCAmelCase__   :				List[str] 			=				MobileViTVaConfigTester(self			,					config_class=snake_case			,					has_text_modality=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        self.config_tester.run_common_tests()






       @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @unittest.skip(reason="MobileViTV2 does not output attentions"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @require_torch_multi_gpu
       @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__			,						lowerCAmelCase__   :				Tuple 			=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__   :				Union[str, Any] 			=				model_class(snake_case   )
         lowerCAmelCase__   :				Tuple 			=				inspect.signature(model.forward   )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCAmelCase__   :				int 			=				[*signature.parameters.keys()]

         lowerCAmelCase__   :				Dict 			=				["pixel_values"]
         self.assertListEqual(arg_names[:1]			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        def check_hidden_states_output(snake_case			,					snake_case			,					snake_case   ):
         lowerCAmelCase__   :				List[str] 			=				model_class(snake_case   )
         model.to(snake_case   )
         model.eval()

         with torch.no_grad():
          lowerCAmelCase__   :				Optional[int] 			=				model(**self._prepare_for_class(snake_case			,					snake_case   )   )

         lowerCAmelCase__   :				str 			=				outputs.hidden_states

         lowerCAmelCase__   :				List[Any] 			=				5
         self.assertEqual(len(snake_case   )			,					snake_case   )

         # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
         # with the width and height being successively divided by 2.
         lowerCAmelCase__   :				str 			=				2
         for i in range(len(snake_case   )   ):
          self.assertListEqual(
              list(hidden_states[i].shape[-2:]   )			,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,					)
          divisor *= 2

         self.assertEqual(self.model_tester.output_stride			,					divisor // 2   )

        lowerCAmelCase__			,						lowerCAmelCase__   :				Tuple 			=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__   :				int 			=				True
         check_hidden_states_output(snake_case			,					snake_case			,					snake_case   )

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

         check_hidden_states_output(snake_case			,					snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*snake_case   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__   :				Dict 			=				MobileViTVaModel.from_pretrained(snake_case   )
         self.assertIsNotNone(snake_case   )




def       SCREAMING_SNAKE_CASE							( )			->						Optional[int]:
 lowerCAmelCase__   :				Optional[Any] 			=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
 return image






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





       """simple docstring"""


       @cached_property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return (
            MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"   )
            if is_vision_available()
            else None
        )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"   ).to(
            snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				self.default_image_processor
        lowerCAmelCase__   :				Union[str, Any] 			=				prepare_img()
        lowerCAmelCase__   :				List[str] 			=				image_processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__   :				str 			=				model(**snake_case   )

        # verify the logits
        lowerCAmelCase__   :				int 			=				torch.Size((1, 1_000)   )
        self.assertEqual(outputs.logits.shape			,					snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]   ).to(snake_case   )

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






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )
        lowerCAmelCase__   :				Tuple 			=				model.to(snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )

        lowerCAmelCase__   :				Union[str, Any] 			=				prepare_img()
        lowerCAmelCase__   :				str 			=				image_processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__   :				Any 			=				model(**snake_case   )
        lowerCAmelCase__   :				List[str] 			=				outputs.logits

        # verify the logits
        lowerCAmelCase__   :				Tuple 			=				torch.Size((1, 21, 32, 32)   )
        self.assertEqual(logits.shape			,					snake_case   )

        lowerCAmelCase__   :				Dict 			=				torch.tensor(
            [
                [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
            ]			,					device=snake_case			,					)

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






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )
        lowerCAmelCase__   :				List[Any] 			=				model.to(snake_case   )

        lowerCAmelCase__   :				str 			=				MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"   )

        lowerCAmelCase__   :				Optional[int] 			=				prepare_img()
        lowerCAmelCase__   :				List[Any] 			=				image_processor(images=snake_case			,					return_tensors="pt"   ).to(snake_case   )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__   :				Union[str, Any] 			=				model(**snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				outputs.logits.detach().cpu()

        lowerCAmelCase__   :				Optional[int] 			=				image_processor.post_process_semantic_segmentation(outputs=snake_case			,					target_sizes=[(50, 60)]   )
        lowerCAmelCase__   :				Union[str, Any] 			=				torch.Size((50, 60)   )
        self.assertEqual(segmentation[0].shape			,					snake_case   )

        lowerCAmelCase__   :				Any 			=				image_processor.post_process_semantic_segmentation(outputs=snake_case   )
        lowerCAmelCase__   :				Optional[int] 			=				torch.Size((32, 32)   )
        self.assertEqual(segmentation[0].shape			,					snake_case   )






