





'''simple docstring'''

import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= {"""vocab_file""": """spiece.model"""}

lowercase_					= {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

lowercase_					= {
    """google/bigbird-roberta-base""": 4_096,
    """google/bigbird-roberta-large""": 4_096,
    """google/bigbird-base-trivia-itc""": 4_096,
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = VOCAB_FILES_NAMES
    UpperCamelCase     = PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase     = ['''input_ids''', '''attention_mask''']
    UpperCamelCase     = []


    def __init__(		self ,		A ,		A="<unk>" ,		A="<s>" ,		A="</s>" ,		A="<pad>" ,		A="[SEP]" ,		A="[MASK]" ,		A="[CLS]" ,		A = None ,		**A ,		)		->   None:
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else bos_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else eos_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else unk_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else pad_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else cls_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else mask_token

        _SCREAMING_SNAKE_CASE									=			{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=A ,		eos_token=A ,		unk_token=A ,		pad_token=A ,		sep_token=A ,		mask_token=A ,		cls_token=A ,		sp_model_kwargs=self.sp_model_kwargs ,		**A ,		)

        _SCREAMING_SNAKE_CASE									=			vocab_file

        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.Load(A       )


    @property
    def 			snake_case_(		self       )		->   Optional[int]:
        return self.sp_model.get_piece_size()


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			{self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
        vocab.update(self.added_tokens_encoder       )
        return vocab


    def __getstate__(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.__dict__.copy()
        _SCREAMING_SNAKE_CASE									=			None
        return state


    def __setstate__(		self ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			d

        # for backward compatibility
        if not hasattr(self ,		"""sp_model_kwargs"""       ):
            _SCREAMING_SNAKE_CASE									=			{}

        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.Load(self.vocab_file       )


    def 			snake_case_(		self ,		A       )		->   List[str]:
        return self.sp_model.encode(A ,		out_type=A       )


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        return self.sp_model.piece_to_id(A       )


    def 			snake_case_(		self ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			self.sp_model.IdToPiece(A       )
        return token


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			""""""
        _SCREAMING_SNAKE_CASE									=			False
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(A       ) + token
                _SCREAMING_SNAKE_CASE									=			True
                _SCREAMING_SNAKE_CASE									=			[]
            else:
                current_sub_tokens.append(A       )
                _SCREAMING_SNAKE_CASE									=			False
        out_string += self.sp_model.decode(A       )
        return out_string.strip()


    def 			snake_case_(		self ,		A ,		A = False ,		A = None ,		A = True ,		**A ,		)		->   str:
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""use_source_tokenizer""" ,		A       )

        _SCREAMING_SNAKE_CASE									=			self.convert_ids_to_tokens(A ,		skip_special_tokens=A       )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			[]
        for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                continue
            if token in self.added_tokens_encoder:
                if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(A       )       )
                    _SCREAMING_SNAKE_CASE									=			[]
                sub_texts.append(A       )
            else:
                current_sub_text.append(A       )
        if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(A       )       )

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
            _SCREAMING_SNAKE_CASE									=			re.sub(R""" (\[(MASK|SEP)\])""" ,		R"""\1""" ,		""" """.join(A       )       )
        else:
            _SCREAMING_SNAKE_CASE									=			"""""".join(A       )

        _SCREAMING_SNAKE_CASE									=			(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
            _SCREAMING_SNAKE_CASE									=			self.clean_up_tokenization(A       )
            return clean_text
        else:
            return text


    def 			snake_case_(		self ,		A ,		A = None       )		->   Tuple[str]:
        if not os.path.isdir(A       ):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
            return
        _SCREAMING_SNAKE_CASE									=			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       ) and os.path.isfile(self.vocab_file       ):
            copyfile(self.vocab_file ,		A       )
        elif not os.path.isfile(self.vocab_file       ):
            with open(A ,		"""wb"""       ) as fi:
                _SCREAMING_SNAKE_CASE									=			self.sp_model.serialized_model_proto()
                fi.write(A       )

        return (out_vocab_file,)


    def 			snake_case_(		self ,		A ,		A = None       )		->   List[int]:
        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _SCREAMING_SNAKE_CASE									=			[self.cls_token_id]
        _SCREAMING_SNAKE_CASE									=			[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep


    def 			snake_case_(		self ,		A ,		A = None ,		A = False       )		->   List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=A ,		token_ids_a=A ,		already_has_special_tokens=A       )

        if token_ids_a is None:
            return [1] + ([0] * len(A       )) + [1]
        return [1] + ([0] * len(A       )) + [1] + ([0] * len(A       )) + [1]





    def 			snake_case_(		self ,		A ,		A = None       )		->   List[int]:
        _SCREAMING_SNAKE_CASE									=			[self.sep_token_id]
        _SCREAMING_SNAKE_CASE									=			[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       ) * [0] + len(token_ids_a + sep       ) * [1]







"""simple docstring"""





import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class 						snake_case_:






     def __init__(		self     :    Dict    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Dict=1_3    , UpperCamelCase_     :    Union[str, Any]=3_2    , UpperCamelCase_     :    str=2    , UpperCamelCase_     :    int=3    , UpperCamelCase_     :    Any=1_6    , UpperCamelCase_     :    int=[1, 2, 1]    , UpperCamelCase_     :    Optional[int]=[2, 2, 4]    , UpperCamelCase_     :    Any=2    , UpperCamelCase_     :    Any=2.0    , UpperCamelCase_     :    Union[str, Any]=True    , UpperCamelCase_     :    int=0.0    , UpperCamelCase_     :    Optional[Any]=0.0    , UpperCamelCase_     :    Any=0.1    , UpperCamelCase_     :    Tuple="gelu"    , UpperCamelCase_     :    Union[str, Any]=False    , UpperCamelCase_     :    Any=True    , UpperCamelCase_     :    List[Any]=0.02    , UpperCamelCase_     :    Tuple=1E-5    , UpperCamelCase_     :    Optional[int]=True    , UpperCamelCase_     :    List[Any]=None    , UpperCamelCase_     :    str=True    , UpperCamelCase_     :    List[Any]=1_0    , UpperCamelCase_     :    Dict=8    , ):
           lowerCAmelCase    :		Union[str, Any]				=      parent
           lowerCAmelCase    :		int				=      batch_size
           lowerCAmelCase    :		List[str]				=      image_size
           lowerCAmelCase    :		Union[str, Any]				=      patch_size
           lowerCAmelCase    :		int				=      num_channels
           lowerCAmelCase    :		Any				=      embed_dim
           lowerCAmelCase    :		Any				=      depths
           lowerCAmelCase    :		Any				=      num_heads
           lowerCAmelCase    :		int				=      window_size
           lowerCAmelCase    :		List[Any]				=      mlp_ratio
           lowerCAmelCase    :		int				=      qkv_bias
           lowerCAmelCase    :		Optional[Any]				=      hidden_dropout_prob
           lowerCAmelCase    :		str				=      attention_probs_dropout_prob
           lowerCAmelCase    :		str				=      drop_path_rate
           lowerCAmelCase    :		Union[str, Any]				=      hidden_act
           lowerCAmelCase    :		int				=      use_absolute_embeddings
           lowerCAmelCase    :		Union[str, Any]				=      patch_norm
           lowerCAmelCase    :		int				=      layer_norm_eps
           lowerCAmelCase    :		str				=      initializer_range
           lowerCAmelCase    :		Optional[int]				=      is_training
           lowerCAmelCase    :		int				=      scope
           lowerCAmelCase    :		List[str]				=      use_labels
           lowerCAmelCase    :		str				=      type_sequence_label_size
           lowerCAmelCase    :		Union[str, Any]				=      encoder_stride






     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		str				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

           lowerCAmelCase    :		Union[str, Any]				=      None
           if self.use_labels:
                 lowerCAmelCase    :		Union[str, Any]				=      ids_tensor([self.batch_size]    , self.type_sequence_label_size )

           lowerCAmelCase    :		Tuple				=      self.get_config()

           return config, pixel_values, labels






     def    lowerCamelCase__   (		self     :    List[Any] ):
           return SwinvaConfig(
               image_size=self.image_size    , patch_size=self.patch_size    , num_channels=self.num_channels    , embed_dim=self.embed_dim    , depths=self.depths    , num_heads=self.num_heads    , window_size=self.window_size    , mlp_ratio=self.mlp_ratio    , qkv_bias=self.qkv_bias    , hidden_dropout_prob=self.hidden_dropout_prob    , attention_probs_dropout_prob=self.attention_probs_dropout_prob    , drop_path_rate=self.drop_path_rate    , hidden_act=self.hidden_act    , use_absolute_embeddings=self.use_absolute_embeddings    , path_norm=self.patch_norm    , layer_norm_eps=self.layer_norm_eps    , initializer_range=self.initializer_range    , encoder_stride=self.encoder_stride    , )






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Dict ):
           lowerCAmelCase    :		List[str]				=      SwinvaModel(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           lowerCAmelCase    :		List[str]				=      model(UpperCamelCase_ )

           lowerCAmelCase    :		Tuple				=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
           lowerCAmelCase    :		List[Any]				=      int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

           self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, expected_seq_len, expected_dim) )






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    int    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[int] ):
           lowerCAmelCase    :		Tuple				=      SwinvaForMaskedImageModeling(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           lowerCAmelCase    :		Dict				=      model(UpperCamelCase_ )
           self.parent.assertEqual(
               result.logits.shape    , (self.batch_size, self.num_channels, self.image_size, self.image_size) )

           # test greyscale images
           lowerCAmelCase    :		List[Any]				=      1
           lowerCAmelCase    :		List[str]				=      SwinvaForMaskedImageModeling(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()

           lowerCAmelCase    :		int				=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
           lowerCAmelCase    :		int				=      model(UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, 1, self.image_size, self.image_size) )






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    int ):
           lowerCAmelCase    :		List[str]				=      self.type_sequence_label_size
           lowerCAmelCase    :		Optional[Any]				=      SwinvaForImageClassification(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           lowerCAmelCase    :		Optional[int]				=      model(UpperCamelCase_    , labels=UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.type_sequence_label_size) )






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Optional[int]				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		str				=      config_and_inputs
           lowerCAmelCase    :		Dict				=      {'''pixel_values''': pixel_values}
           return config, inputs_dict



@require_torch
class 						snake_case_(				a__							,    a__							,    unittest.TestCase	):
     __UpperCamelCase     =    (
         (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
     )
     __UpperCamelCase     =    (
         {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
         if is_torch_available()
         else {}
     )

     __UpperCamelCase     =    False
     __UpperCamelCase     =    False
     __UpperCamelCase     =    False
     __UpperCamelCase     =    False






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Dict				=      SwinvaModelTester(self )
           lowerCAmelCase    :		List[str]				=      ConfigTester(self    , config_class=UpperCamelCase_    , embed_dim=3_7 )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           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[str] ):
           lowerCAmelCase    :		Tuple				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_ )






     @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''' )
     def    lowerCamelCase__   (		self     :    Dict ):
           pass






     @unittest.skip(reason='''Swinv2 does not use inputs_embeds''' )
     def    lowerCamelCase__   (		self     :    int ):
           pass






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase,					lowerCAmelCase    :		Dict				=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCAmelCase    :		Dict				=      model_class(UpperCamelCase_ )
                 self.assertIsInstance(model.get_input_embeddings()    , (nn.Module) )
                 lowerCAmelCase    :		str				=      model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(UpperCamelCase_    , nn.Linear ) )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCAmelCase    :		int				=      ['''pixel_values''']
                 self.assertListEqual(arg_names[:1]    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase,					lowerCAmelCase    :		Dict				=      self.model_tester.prepare_config_and_inputs_for_common()
           lowerCAmelCase    :		Optional[Any]				=      True

           for model_class in self.all_model_classes:
                 lowerCAmelCase    :		Any				=      True
                 lowerCAmelCase    :		List[str]				=      False
                 lowerCAmelCase    :		int				=      True
                 lowerCAmelCase    :		int				=      model_class(UpperCamelCase_ )
                 model.to(UpperCamelCase_ )
                 model.eval()
                 with torch.no_grad():
                       lowerCAmelCase    :		Optional[Any]				=      model(**self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_ ) )
                 lowerCAmelCase    :		str				=      outputs.attentions
                 lowerCAmelCase    :		int				=      len(self.model_tester.depths )
                 self.assertEqual(len(UpperCamelCase_ )    , UpperCamelCase_ )

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 lowerCAmelCase    :		Any				=      True
                 lowerCAmelCase    :		Union[str, Any]				=      config.window_size**2
                 lowerCAmelCase    :		int				=      model_class(UpperCamelCase_ )
                 model.to(UpperCamelCase_ )
                 model.eval()
                 with torch.no_grad():
                       lowerCAmelCase    :		Optional[int]				=      model(**self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_ ) )
                 lowerCAmelCase    :		Dict				=      outputs.attentions
                 self.assertEqual(len(UpperCamelCase_ )    , UpperCamelCase_ )

                 self.assertListEqual(
                     list(attentions[0].shape[-3:] )    , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    , )
                 lowerCAmelCase    :		str				=      len(UpperCamelCase_ )

                 # Check attention is always last and order is fine
                 lowerCAmelCase    :		Optional[int]				=      True
                 lowerCAmelCase    :		int				=      True
                 lowerCAmelCase    :		Optional[Any]				=      model_class(UpperCamelCase_ )
                 model.to(UpperCamelCase_ )
                 model.eval()
                 with torch.no_grad():
                       lowerCAmelCase    :		Tuple				=      model(**self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_ ) )

                 if hasattr(self.model_tester    , '''num_hidden_states_types''' ):
                       lowerCAmelCase    :		List[Any]				=      self.model_tester.num_hidden_states_types
                 else:
                       # also another +1 for reshaped_hidden_states
                       lowerCAmelCase    :		Union[str, Any]				=      2
                 self.assertEqual(out_len + added_hidden_states    , len(UpperCamelCase_ ) )

                 lowerCAmelCase    :		List[str]				=      outputs.attentions

                 self.assertEqual(len(UpperCamelCase_ )    , UpperCamelCase_ )

                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:] )    , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    , )






     def    lowerCamelCase__   (		self     :    int    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Optional[Any] ):
           lowerCAmelCase    :		int				=      model_class(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()

           with torch.no_grad():
                 lowerCAmelCase    :		Union[str, Any]				=      model(**self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_ ) )

           lowerCAmelCase    :		str				=      outputs.hidden_states

           lowerCAmelCase    :		List[str]				=      getattr(
               self.model_tester    , '''expected_num_hidden_layers'''    , len(self.model_tester.depths ) + 1 )
           self.assertEqual(len(UpperCamelCase_ )    , UpperCamelCase_ )

           # Swinv2 has a different seq_length
           lowerCAmelCase    :		Any				=      (
               config.patch_size
               if isinstance(config.patch_size    , collections.abc.Iterable )
               else (config.patch_size, config.patch_size)
           )

           lowerCAmelCase    :		str				=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:] )    , [num_patches, self.model_tester.embed_dim]    , )

           lowerCAmelCase    :		List[str]				=      outputs.reshaped_hidden_states
           self.assertEqual(len(UpperCamelCase_ )    , UpperCamelCase_ )

           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		str				=      reshaped_hidden_states[0].shape
           lowerCAmelCase    :		Optional[Any]				=      (
               reshaped_hidden_states[0].view(UpperCamelCase_    , UpperCamelCase_    , height * width ).permute(0    , 2    , 1 )
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:] )    , [num_patches, self.model_tester.embed_dim]    , )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase,					lowerCAmelCase    :		Union[str, Any]				=      self.model_tester.prepare_config_and_inputs_for_common()

           lowerCAmelCase    :		Any				=      (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size    , collections.abc.Iterable )
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
                 lowerCAmelCase    :		Union[str, Any]				=      True
                 self.check_hidden_states_output(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )

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

                 self.check_hidden_states_output(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase,					lowerCAmelCase    :		Union[str, Any]				=      self.model_tester.prepare_config_and_inputs_for_common()
           lowerCAmelCase    :		Dict				=      3

           lowerCAmelCase    :		Dict				=      (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size    , collections.abc.Iterable )
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           lowerCAmelCase    :		Dict				=      (
               config.patch_size
               if isinstance(config.patch_size    , collections.abc.Iterable )
               else (config.patch_size, config.patch_size)
           )

           lowerCAmelCase    :		List[str]				=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           lowerCAmelCase    :		Tuple				=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
                 lowerCAmelCase    :		str				=      True
                 self.check_hidden_states_output(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , (padded_height, padded_width) )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCAmelCase    :		Optional[int]				=      True
                 self.check_hidden_states_output(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , (padded_height, padded_width) )






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		str				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Dict				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )






     @slow
     def    lowerCamelCase__   (		self     :    int ):
           for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase    :		int				=      SwinvaModel.from_pretrained(UpperCamelCase_ )
                 self.assertIsNotNone(UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase,					lowerCAmelCase    :		int				=      self.model_tester.prepare_config_and_inputs_for_common()

           lowerCAmelCase    :		Union[str, Any]				=      _config_zero_init(UpperCamelCase_ )
           for model_class in self.all_model_classes:
                 lowerCAmelCase    :		Union[str, Any]				=      model_class(config=UpperCamelCase_ )
                 for name, param in model.named_parameters():
                       if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()    , [0.0, 1.0]    , msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    , )



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






     @cached_property
     def    lowerCamelCase__   (		self     :    Dict ):
           return (
               AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''' )
               if is_vision_available()
               else None
           )






     @slow
     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		str				=      SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''' ).to(
               UpperCamelCase_ )
           lowerCAmelCase    :		List[Any]				=      self.default_image_processor

           lowerCAmelCase    :		int				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
           lowerCAmelCase    :		Union[str, Any]				=      image_processor(images=UpperCamelCase_    , return_tensors='''pt''' ).to(UpperCamelCase_ )

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase    :		Dict				=      model(**UpperCamelCase_ )

           # verify the logits
           lowerCAmelCase    :		List[Any]				=      torch.Size((1, 1_0_0_0) )
           self.assertEqual(outputs.logits.shape    , UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      torch.tensor([-0.3_947, -0.4_306, 0.0_026] ).to(UpperCamelCase_ )
           self.assertTrue(torch.allclose(outputs.logits[0, :3]    , UpperCamelCase_    , atol=1E-4 ) )


