



'''simple docstring'''

import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







def 					lowercase__				(   __UpperCamelCase					)->				Optional[int]:
      UpperCamelCase       						=      np.inf

      def set_batch_size(__UpperCamelCase					) -> None:
            nonlocal batch_size
            if isinstance(__UpperCamelCase	,		__UpperCamelCase					):
                  UpperCamelCase       						=      min(__UpperCamelCase	,		config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
            elif isinstance(__UpperCamelCase	,		__UpperCamelCase					):
                  UpperCamelCase       						=      min(__UpperCamelCase	,		config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
            elif isinstance(__UpperCamelCase	,		__UpperCamelCase					) and feature.dtype == "binary":
                  UpperCamelCase       						=      min(__UpperCamelCase	,		config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS					)

      _visit(__UpperCamelCase	,		__UpperCamelCase					)

      return None if batch_size is np.inf else batch_size






class        a_		(    lowerCamelCase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Any:



        """simple docstring"""






        super().__init__(
            _SCREAMING_SNAKE_CASE    ,	split=_SCREAMING_SNAKE_CASE    ,	features=_SCREAMING_SNAKE_CASE    ,	cache_dir=_SCREAMING_SNAKE_CASE    ,	keep_in_memory=_SCREAMING_SNAKE_CASE    ,	streaming=_SCREAMING_SNAKE_CASE    ,	num_proc=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)
        UpperCamelCase       						=      path_or_paths if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) else {self.split: path_or_paths}
        UpperCamelCase       						=      _PACKAGED_DATASETS_MODULES["""parquet"""][1]
        UpperCamelCase       						=      Parquet(
            cache_dir=_SCREAMING_SNAKE_CASE    ,	data_files=_SCREAMING_SNAKE_CASE    ,	features=_SCREAMING_SNAKE_CASE    ,	hash=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)


  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        if self.streaming:
              UpperCamelCase       						=      self.builder.as_streaming_dataset(split=self.split   )
        # Build regular (map-style) dataset
        else:
              UpperCamelCase       						=      None
              UpperCamelCase       						=      None
              UpperCamelCase       						=      None
              UpperCamelCase       						=      None

              self.builder.download_and_prepare(
                  download_config=_SCREAMING_SNAKE_CASE    ,	download_mode=_SCREAMING_SNAKE_CASE    ,	verification_mode=_SCREAMING_SNAKE_CASE    ,	base_path=_SCREAMING_SNAKE_CASE    ,	num_proc=self.num_proc    ,	)
              UpperCamelCase       						=      self.builder.as_dataset(
                  split=self.split    ,	verification_mode=_SCREAMING_SNAKE_CASE    ,	in_memory=self.keep_in_memory   )
        return dataset






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      dataset
        UpperCamelCase       						=      path_or_buf
        UpperCamelCase       						=      batch_size or get_writer_batch_size(dataset.features   )
        UpperCamelCase       						=      parquet_writer_kwargs

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf    ,	(str, bytes, os.PathLike)   ):
              with open(self.path_or_buf    ,	"""wb+"""   ) as buffer:
                    UpperCamelCase       						=      self._write(file_obj=_SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	**self.parquet_writer_kwargs   )
        else:
              UpperCamelCase       						=      self._write(file_obj=self.path_or_buf    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	**self.parquet_writer_kwargs   )
        return written


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      0
        UpperCamelCase       						=      parquet_writer_kwargs.pop("""path_or_buf"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.dataset.features.arrow_schema

        UpperCamelCase       						=      pq.ParquetWriter(_SCREAMING_SNAKE_CASE    ,	schema=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

        for offset in logging.tqdm(
            range(0    ,	len(self.dataset   )    ,	_SCREAMING_SNAKE_CASE   )    ,	unit="""ba"""    ,	disable=not logging.is_progress_bar_enabled()    ,	desc="""Creating parquet from Arrow format"""    ,	):
              UpperCamelCase       						=      query_table(
                  table=self.dataset._data    ,	key=slice(_SCREAMING_SNAKE_CASE    ,	offset + batch_size   )    ,	indices=self.dataset._indices if self.dataset._indices is not None else None    ,	)
              writer.write_table(_SCREAMING_SNAKE_CASE   )
              written += batch.nbytes
        writer.close()
        return written








'''simple docstring'''

import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import MaskFormerSwinBackbone
     from transformers.models.maskformer import MaskFormerSwinModel






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=[1, 2, 1]    ,	_SCREAMING_SNAKE_CASE=[2, 2, 4]    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=2.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE="gelu"    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=1e-5    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=["stage1", "stage2", "stage3"]    ,	_SCREAMING_SNAKE_CASE=[1, 2, 3]    ,	)						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      image_size
        UpperCamelCase       						=      patch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      embed_dim
        UpperCamelCase       						=      depths
        UpperCamelCase       						=      num_heads
        UpperCamelCase       						=      window_size
        UpperCamelCase       						=      mlp_ratio
        UpperCamelCase       						=      qkv_bias
        UpperCamelCase       						=      hidden_dropout_prob
        UpperCamelCase       						=      attention_probs_dropout_prob
        UpperCamelCase       						=      drop_path_rate
        UpperCamelCase       						=      hidden_act
        UpperCamelCase       						=      use_absolute_embeddings
        UpperCamelCase       						=      patch_norm
        UpperCamelCase       						=      layer_norm_eps
        UpperCamelCase       						=      initializer_range
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      scope
        UpperCamelCase       						=      use_labels
        UpperCamelCase       						=      type_sequence_label_size
        UpperCamelCase       						=      encoder_stride
        UpperCamelCase       						=      out_features
        UpperCamelCase       						=      out_indices

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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

        UpperCamelCase       						=      None
        if self.use_labels:
              UpperCamelCase       						=      ids_tensor([self.batch_size]    ,	self.type_sequence_label_size   )

        UpperCamelCase       						=      self.get_config()

        return config, pixel_values, labels

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        return MaskFormerSwinConfig(
            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    ,	out_features=self.out_features    ,	out_indices=self.out_indices    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      MaskFormerSwinModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
        UpperCamelCase       						=      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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      MaskFormerSwinBackbone(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps   )    ,	len(config.out_features   )   )
        self.parent.assertListEqual(list(result.feature_maps[0].shape   )    ,	[13, 16, 16, 16]   )

        # verify channels
        self.parent.assertEqual(len(model.channels   )    ,	len(config.out_features   )   )
        self.parent.assertListEqual(model.channels    ,	[16, 32, 64]   )

        # verify ValueError
        with self.parent.assertRaises(_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      ["""stem"""]
              UpperCamelCase       						=      MaskFormerSwinBackbone(config=_SCREAMING_SNAKE_CASE   )


  def 						A__		(   self   )						->   str:



        """simple docstring"""






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






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (
      (
          MaskFormerSwinModel,
          MaskFormerSwinBackbone,
      )
      if is_torch_available()
      else ()
  )
  lowercase  	=   {"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MaskFormerSwinModelTester(self   )
        UpperCamelCase       						=      ConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	embed_dim=37   )

  @require_torch_multi_gpu
  @unittest.skip(
      reason=(
          """`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
          """ `nn.DataParallel`"""
      )   )
  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE   )

  @unittest.skip("""Swin does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        pass

  @unittest.skip("""Swin does not support feedforward chunking"""   )
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCamelCase       						=      ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,	_SCREAMING_SNAKE_CASE   )

  @unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions"""   )
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone"""   )
  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        pass

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()

        with torch.no_grad():
              UpperCamelCase       						=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )   )

        UpperCamelCase       						=      outputs.hidden_states

        UpperCamelCase       						=      getattr(
            self.model_tester    ,	"""expected_num_hidden_layers"""    ,	len(self.model_tester.depths   ) + 1   )
        self.assertEqual(len(_SCREAMING_SNAKE_CASE   )    ,	_SCREAMING_SNAKE_CASE   )

        # Swin has a different seq_length
        UpperCamelCase       						=      (
            config.patch_size
            if isinstance(config.patch_size    ,	collections.abc.Iterable   )
            else (config.patch_size, config.patch_size)
        )

        UpperCamelCase       						=      (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]    ,	)

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        UpperCamelCase       						=      (
            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:
              UpperCamelCase       						=      True
              self.check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

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

              self.check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase       						=      3

        UpperCamelCase       						=      (
            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)
        )
        UpperCamelCase       						=      (
            config.patch_size
            if isinstance(config.patch_size    ,	collections.abc.Iterable   )
            else (config.patch_size, config.patch_size)
        )

        UpperCamelCase       						=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        UpperCamelCase       						=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      True
              self.check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	(padded_height, padded_width)   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCamelCase       						=      True
              self.check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	(padded_height, padded_width)   )

  @unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""   )
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        pass

  @unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""   )
  def 						A__		(   self   )						->   str:



        """simple docstring"""






        pass

  @unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""   )
  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        pass


  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        def set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      0
              return t

        def check_equivalence(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE={}   ):
              with torch.no_grad():
                    UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE    ,	return_dict=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE    ,	return_dict=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).to_tuple()

                    def recursive_check(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                          if isinstance(_SCREAMING_SNAKE_CASE    ,	(List, Tuple)   ):
                                for tuple_iterable_value, dict_iterable_value in zip(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                                      recursive_check(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
                          elif isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                                for tuple_iterable_value, dict_iterable_value in zip(
                                    tuple_object.values()    ,	dict_object.values()   ):
                                      recursive_check(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
                          elif tuple_object is None:
                                return
                          else:
                                self.assertTrue(
                                    torch.allclose(
                                        set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE   )    ,	set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE   )    ,	atol=1e-5   )    ,	msg=(
                                        """Tuple and dict output are not equal. Difference:"""
                                        F" {torch.max(torch.abs(tuple_object - dict_object   )   )}. Tuple has `nan`:"
                                        F" {torch.isnan(_SCREAMING_SNAKE_CASE   ).any()} and `inf`: {torch.isinf(_SCREAMING_SNAKE_CASE   )}. Dict has"
                                        F" `nan`: {torch.isnan(_SCREAMING_SNAKE_CASE   ).any()} and `inf`: {torch.isinf(_SCREAMING_SNAKE_CASE   )}."
                                    )    ,	)

                    recursive_check(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              model.to(_SCREAMING_SNAKE_CASE   )
              model.eval()

              UpperCamelCase       						=      self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
              check_equivalence(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              UpperCamelCase       						=      self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_labels=_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_labels=_SCREAMING_SNAKE_CASE   )
              check_equivalence(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              UpperCamelCase       						=      self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
              check_equivalence(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	{"""output_hidden_states""": True}   )

              UpperCamelCase       						=      self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_labels=_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	return_labels=_SCREAMING_SNAKE_CASE   )
              check_equivalence(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	{"""output_hidden_states""": True}   )






@require_torch
class        a_		(    unittest.TestCase			, lowerCamelCase       ):
  lowercase  	=   (MaskFormerSwinBackbone,) if is_torch_available() else ()
  lowercase  	=   MaskFormerSwinConfig

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      MaskFormerSwinModelTester(self   )


  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase       						=      inputs_dict["""pixel_values"""].shape[0]

        for backbone_class in self.all_model_classes:
              UpperCamelCase       						=      backbone_class(_SCREAMING_SNAKE_CASE   )
              backbone.to(_SCREAMING_SNAKE_CASE   )
              backbone.eval()

              UpperCamelCase       						=      backbone(**_SCREAMING_SNAKE_CASE   )

              # Test default outputs and verify feature maps
              self.assertIsInstance(outputs.feature_maps    ,	_SCREAMING_SNAKE_CASE   )
              self.assertTrue(len(outputs.feature_maps   ) == len(backbone.channels   )   )
              for feature_map, n_channels in zip(outputs.feature_maps    ,	backbone.channels   ):
                    self.assertTrue(feature_map.shape[:2]    ,	(batch_size, n_channels)   )
              self.assertIsNone(outputs.hidden_states   )
              self.assertIsNone(outputs.attentions   )

              # Test output_hidden_states=True
              UpperCamelCase       						=      backbone(**_SCREAMING_SNAKE_CASE    ,	output_hidden_states=_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(outputs.hidden_states   )
              self.assertTrue(len(outputs.hidden_states   )    ,	len(backbone.stage_names   )   )
              # We skip the stem layer
              for hidden_states, n_channels in zip(outputs.hidden_states[1:]    ,	backbone.channels   ):
                    for hidden_state in hidden_states:
                          # Hidden states are in the format (batch_size, (height * width), n_channels)
                          UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      hidden_state.shape
                          self.assertTrue((h_batch_size, h_n_channels)    ,	(batch_size, n_channels)   )

            # Test output_attentions=True
              if self.has_attentions:
                    UpperCamelCase       						=      backbone(**_SCREAMING_SNAKE_CASE    ,	output_attentions=_SCREAMING_SNAKE_CASE   )
                    self.assertIsNotNone(outputs.attentions   )
