


"""simple docstring"""





import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	List[Any]       ,      _snake_case :	List[Any]       ,      _snake_case :	List[Any]       ,      _snake_case :	Optional[int]=True       ,      _snake_case :	str="pt"	):
      lowerCAmelCase    :		int				=      {'''add_prefix_space''': True} if isinstance(_snake_case       ,      _snake_case	) and not line.startswith(''' '''	) else {}
      lowerCAmelCase    :		str				=      padding_side
      return tokenizer(
          [line]       ,      max_length=_snake_case       ,      padding='''max_length''' if pad_to_max_length else None       ,      truncation=_snake_case       ,      return_tensors=_snake_case       ,      add_special_tokens=_snake_case       ,      **_snake_case       ,      )


def    _snake_case  (  _snake_case :	Any       ,      _snake_case :	Optional[Any]       ,      _snake_case :	str=None       ,      ):
      lowerCAmelCase    :		Tuple				=      input_ids.ne(_snake_case	).any(dim=0	)
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class 						snake_case_(				a__	):






     def __init__(		self     :    Any    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    Optional[int]="train"    , UpperCamelCase_     :    List[str]=None    , UpperCamelCase_     :    Dict=None    , UpperCamelCase_     :    Optional[Any]=None    , UpperCamelCase_     :    Optional[Any]=""    , ):
           super().__init__()
           lowerCAmelCase    :		Any				=      Path(UpperCamelCase_ ).joinpath(type_path + '''.source''' )
           lowerCAmelCase    :		int				=      Path(UpperCamelCase_ ).joinpath(type_path + '''.target''' )
           lowerCAmelCase    :		Any				=      self.get_char_lens(self.src_file )
           lowerCAmelCase    :		Optional[Any]				=      max_source_length
           lowerCAmelCase    :		Any				=      max_target_length
           assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
           lowerCAmelCase    :		List[Any]				=      tokenizer
           lowerCAmelCase    :		Optional[Any]				=      prefix
           if n_obs is not None:
                 lowerCAmelCase    :		Union[str, Any]				=      self.src_lens[:n_obs]
           lowerCAmelCase    :		Optional[Any]				=      src_lang
           lowerCAmelCase    :		Optional[int]				=      tgt_lang






     def __len__(		self     :    List[str] ):
           return len(self.src_lens )






     def __getitem__(		self     :    Any    , UpperCamelCase_     :    List[str] ):
           lowerCAmelCase    :		int				=      index + 1  # linecache starts at 1
           lowerCAmelCase    :		List[Any]				=      self.prefix + linecache.getline(str(self.src_file )    , UpperCamelCase_ ).rstrip('''\n''' )
           lowerCAmelCase    :		Tuple				=      linecache.getline(str(self.tgt_file )    , UpperCamelCase_ ).rstrip('''\n''' )
           assert source_line, F'''empty source line for index {index}'''
           assert tgt_line, F'''empty tgt line for index {index}'''

           # Need to add eos token manually for T5
           if isinstance(self.tokenizer    , UpperCamelCase_ ):
                 source_line += self.tokenizer.eos_token
                 tgt_line += self.tokenizer.eos_token

           # Pad source and target to the right
           lowerCAmelCase    :		List[Any]				=      (
               self.tokenizer.question_encoder if isinstance(self.tokenizer    , UpperCamelCase_ ) else self.tokenizer
           )
           lowerCAmelCase    :		Tuple				=      self.tokenizer.generator if isinstance(self.tokenizer    , UpperCamelCase_ ) else self.tokenizer

           lowerCAmelCase    :		Optional[Any]				=      encode_line(UpperCamelCase_    , UpperCamelCase_    , self.max_source_length    , '''right''' )
           lowerCAmelCase    :		Optional[Any]				=      encode_line(UpperCamelCase_    , UpperCamelCase_    , self.max_target_length    , '''right''' )

           lowerCAmelCase    :		List[str]				=      source_inputs['''input_ids'''].squeeze()
           lowerCAmelCase    :		List[Any]				=      target_inputs['''input_ids'''].squeeze()
           lowerCAmelCase    :		int				=      source_inputs['''attention_mask'''].squeeze()
           return {
               "input_ids": source_ids,
               "attention_mask": src_mask,
               "decoder_input_ids": target_ids,
           }






     @staticmethod
     def    lowerCamelCase__   (		UpperCamelCase_     :    int ):
           return [len(UpperCamelCase_ ) for x in Path(UpperCamelCase_ ).open().readlines()]






     def    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    str ):
           lowerCAmelCase    :		str				=      torch.stack([x['''input_ids'''] for x in batch] )
           lowerCAmelCase    :		Dict				=      torch.stack([x['''attention_mask'''] for x in batch] )
           lowerCAmelCase    :		int				=      torch.stack([x['''decoder_input_ids'''] for x in batch] )
           lowerCAmelCase    :		Union[str, Any]				=      (
               self.tokenizer.generator.pad_token_id
               if isinstance(self.tokenizer    , UpperCamelCase_ )
               else self.tokenizer.pad_token_id
           )
           lowerCAmelCase    :		Tuple				=      (
               self.tokenizer.question_encoder.pad_token_id
               if isinstance(self.tokenizer    , UpperCamelCase_ )
               else self.tokenizer.pad_token_id
           )
           lowerCAmelCase    :		Optional[Any]				=      trim_batch(UpperCamelCase_    , UpperCamelCase_ )
           lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      trim_batch(UpperCamelCase_    , UpperCamelCase_    , attention_mask=UpperCamelCase_ )
           lowerCAmelCase    :		List[Any]				=      {
               '''input_ids''': source_ids,
               '''attention_mask''': source_mask,
               '''decoder_input_ids''': y,
           }
           return batch



snake_case__						:   Dict			 = getLogger(__name__)


def    _snake_case  (  _snake_case :	List[List]	):
      return list(itertools.chain.from_iterable(_snake_case	)	)


def    _snake_case  (  _snake_case :	str	):
      lowerCAmelCase    :		Tuple				=      get_git_info()
      save_json(_snake_case       ,      os.path.join(_snake_case       ,      '''git_log.json'''	)	)


def    _snake_case  (  _snake_case :	Any       ,      _snake_case :	Tuple       ,      _snake_case :	List[Any]=4       ,      **_snake_case :	Optional[int]	):
      with open(_snake_case       ,      '''w'''	) as f:
            json.dump(_snake_case       ,      _snake_case       ,      indent=_snake_case       ,      **_snake_case	)


def    _snake_case  (  _snake_case :	Tuple	):
      with open(_snake_case	) as f:
            return json.load(_snake_case	)


def    _snake_case  (  ):
      lowerCAmelCase    :		Dict				=      git.Repo(search_parent_directories=_snake_case	)
      lowerCAmelCase    :		Dict				=      {
          '''repo_id''': str(_snake_case	),
          '''repo_sha''': str(repo.head.object.hexsha	),
          '''repo_branch''': str(repo.active_branch	),
          '''hostname''': str(socket.gethostname()	),
      }
      return repo_infos


def    _snake_case  (  _snake_case :	Callable       ,      _snake_case :	Iterable	):
      return list(map(_snake_case       ,      _snake_case	)	)


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	Tuple	):
      with open(_snake_case       ,      '''wb'''	) as f:
            return pickle.dump(_snake_case       ,      _snake_case	)


def    _snake_case  (  _snake_case :	Any	):

      def remove_articles(_snake_case :	Dict	):
            return re.sub(r'''\b(a|an|the)\b'''       ,      ''' '''       ,      _snake_case	)

      def white_space_fix(_snake_case :	int	):
            return " ".join(text.split()	)

      def remove_punc(_snake_case :	str	):
            lowerCAmelCase    :		Optional[Any]				=      set(string.punctuation	)
            return "".join(ch for ch in text if ch not in exclude	)

      def lower(_snake_case :	Dict	):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(_snake_case	)	)	)	)


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	List[Any]	):
      lowerCAmelCase    :		str				=      normalize_answer(_snake_case	).split()
      lowerCAmelCase    :		Union[str, Any]				=      normalize_answer(_snake_case	).split()
      lowerCAmelCase    :		str				=      Counter(_snake_case	) & Counter(_snake_case	)
      lowerCAmelCase    :		Tuple				=      sum(common.values()	)
      if num_same == 0:
            return 0
      lowerCAmelCase    :		Dict				=      1.0 * num_same / len(_snake_case	)
      lowerCAmelCase    :		str				=      1.0 * num_same / len(_snake_case	)
      lowerCAmelCase    :		List[Any]				=      (2 * precision * recall) / (precision + recall)
      return fa


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	List[str]	):
      return normalize_answer(_snake_case	) == normalize_answer(_snake_case	)


def    _snake_case  (  _snake_case :	List[str]       ,      _snake_case :	List[str]	):
      assert len(_snake_case	) == len(_snake_case	)
      lowerCAmelCase    :		Dict				=      0
      for hypo, pred in zip(_snake_case       ,      _snake_case	):
            em += exact_match_score(_snake_case       ,      _snake_case	)
      if len(_snake_case	) > 0:
            em /= len(_snake_case	)
      return {"em": em}


def    _snake_case  (  _snake_case :	str	):
      return model_prefix.startswith('''rag'''	)






def    _snake_case  (  _snake_case :	str       ,      _snake_case :	Any       ,      _snake_case :	str	):
      lowerCAmelCase    :		int				=      {p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      lowerCAmelCase    :		List[str]				=      '''dropout_rate'''
      for p in extra_params:
            if getattr(_snake_case       ,      _snake_case       ,      _snake_case	):
                  if not hasattr(_snake_case       ,      _snake_case	) and not hasattr(_snake_case       ,      equivalent_param[p]	):
                        logger.info('''config doesn\'t have a `{}` attribute'''.format(_snake_case	)	)
                        delattr(_snake_case       ,      _snake_case	)
                        continue
                  lowerCAmelCase    :		Dict				=      p if hasattr(_snake_case       ,      _snake_case	) else equivalent_param[p]
                  setattr(_snake_case       ,      _snake_case       ,      getattr(_snake_case       ,      _snake_case	)	)
                  delattr(_snake_case       ,      _snake_case	)
      return hparams, config






"""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 ) )


