'''simple docstring'''




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwiftFormerForImageClassification, SwiftFormerModel
    from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor






class 		__A       :






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_=13				,     UpperCamelCase_=3				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=2_24				,     UpperCamelCase_=10_00				,     UpperCamelCase_=[3, 3, 6, 4]				,     UpperCamelCase_=[48, 56, 1_12, 2_20]				,     ):
              __UpperCAmelCase  :				Tuple       				=		parent
              __UpperCAmelCase  :				str       				=		batch_size
              __UpperCAmelCase  :				int       				=		num_channels
              __UpperCAmelCase  :				Union[str, Any]       				=		is_training
              __UpperCAmelCase  :				Tuple       				=		use_labels
              __UpperCAmelCase  :				List[str]       				=		hidden_dropout_prob
              __UpperCAmelCase  :				Any       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				Dict       				=		num_labels
              __UpperCAmelCase  :				List[Any]       				=		image_size
              __UpperCAmelCase  :				List[Any]       				=		layer_depths
              __UpperCAmelCase  :				str       				=		embed_dims






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

              __UpperCAmelCase  :				Tuple       				=		self.get_config()

              return config, pixel_values, labels






       def 			_snake_case						(      self      ):
              return SwiftFormerConfig(
                  depths=self.layer_depths				,     embed_dims=self.embed_dims				,     mlp_ratio=4				,     downsamples=[True, True, True, True]				,     hidden_act="gelu"				,     num_labels=self.num_labels				,     down_patch_size=3				,     down_stride=2				,     down_pad=1				,     drop_rate=0.0				,     drop_path_rate=0.0				,     use_layer_scale=A__				,     layer_scale_init_value=1E-5				,     )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[Any]       				=		SwiftFormerModel(config=A__      )
              model.to(A__      )
              model.eval()
              __UpperCAmelCase  :				List[Any]       				=		model(A__      )
              self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.embed_dims[-1], 7, 7)      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		self.num_labels
              __UpperCAmelCase  :				Tuple       				=		SwiftFormerForImageClassification(A__      )
              model.to(A__      )
              model.eval()
              __UpperCAmelCase  :				int       				=		model(A__				,     labels=A__      )
              self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)      )

              __UpperCAmelCase  :				List[str]       				=		SwiftFormerForImageClassification(A__      )
              model.to(A__      )
              model.eval()

              __UpperCAmelCase  :				Any       				=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
              __UpperCAmelCase  :				Optional[int]       				=		model(A__      )
              self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)      )







       def 			_snake_case						(      self      ):
              (__UpperCAmelCase)  :				Tuple       				=		self.prepare_config_and_inputs()
              __UpperCAmelCase  :				Optional[int]       				=		{"""pixel_values""": pixel_values}
              return config, inputs_dict






@require_torch
class 		__A       (__magic_name__ ,					__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Dict				    =					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
       snake_case       :List[Any]				    =					(
           {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       snake_case       :Dict				    =					False
       snake_case       :Union[str, Any]				    =					False
       snake_case       :Any				    =					False
       snake_case       :int				    =					False
       snake_case       :Any				    =					False






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		SwiftFormerModelTester(self      )
              __UpperCAmelCase  :				int       				=		ConfigTester(
                  self				,     config_class=A__				,     has_text_modality=A__				,     hidden_size=37				,     num_attention_heads=12				,     num_hidden_layers=12				,     )






       def 			_snake_case						(      self      ):
              self.config_tester.run_common_tests()






       @unittest.skip(reason="SwiftFormer does not use inputs_embeds"      )
       def 			_snake_case						(      self      ):
              pass






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     __UpperCAmelCase  :				List[str]       				=		model_class(A__      )
                     __UpperCAmelCase  :				Dict       				=		model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(A__				,     nn.Linear      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		self.model_tester.prepare_config_and_inputs_for_common()

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

                     __UpperCAmelCase  :				List[str]       				=		["""pixel_values"""]
                     self.assertListEqual(arg_names[:1]				,     A__      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*A__      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*A__      )






       @slow
       def 			_snake_case						(      self      ):
              for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     __UpperCAmelCase  :				Tuple       				=		SwiftFormerModel.from_pretrained(A__      )
                     self.assertIsNotNone(A__      )






       @unittest.skip(reason="SwiftFormer does not output attentions"      )
       def 			_snake_case						(      self      ):
              pass






       def 			_snake_case						(      self      ):
              def check_hidden_states_output(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				Any       				=		model_class(A__      )
                     model.to(A__      )
                     model.eval()

                     with torch.no_grad():
                            __UpperCAmelCase  :				Any       				=		model(**self._prepare_for_class(A__				,     A__      )      )

                     __UpperCAmelCase  :				str       				=		outputs.hidden_states

                     __UpperCAmelCase  :				Tuple       				=		8
                     self.assertEqual(len(A__      )				,     A__      )  # TODO

                     # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
                     # with the width and height being successively divided by 2, after every 2 blocks
                     for i in range(len(A__      )      ):
                            self.assertEqual(
                                hidden_states[i].shape				,     torch.Size(
                                    [
                                        self.model_tester.batch_size,
                                        self.model_tester.embed_dims[i // 2],
                                        (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                        (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                    ]      )				,     )

              __UpperCAmelCase  :				Dict       				=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     __UpperCAmelCase  :				Any       				=		True
                     check_hidden_states_output(A__				,     A__				,     A__      )

                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     __UpperCAmelCase  :				List[Any]       				=		True

                     check_hidden_states_output(A__				,     A__				,     A__      )






       def 			_snake_case						(      self      ):
              def _config_zero_init(UpperCamelCase_      ):
                     __UpperCAmelCase  :				str       				=		copy.deepcopy(A__      )
                     for key in configs_no_init.__dict__.keys():
                            if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                                   setattr(A__				,     A__				,     1E-10      )
                            if isinstance(getattr(A__				,     A__				,     A__      )				,     A__      ):
                                   __UpperCAmelCase  :				Optional[Any]       				=		_config_zero_init(getattr(A__				,     A__      )      )
                                   setattr(A__				,     A__				,     A__      )
                     return configs_no_init

              __UpperCAmelCase  :				str       				=		self.model_tester.prepare_config_and_inputs_for_common()

              __UpperCAmelCase  :				List[Any]       				=		_config_zero_init(A__      )
              for model_class in self.all_model_classes:
                     __UpperCAmelCase  :				str       				=		model_class(config=A__      )
                     for name, param in model.named_parameters():
                            if param.requires_grad:
                                   self.assertIn(
                                       ((param.data.mean() * 1E9) / 1E9).round().item()				,     [0.0, 1.0]				,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""				,     )







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





def _lowercase	(      )					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
       return image






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






       @cached_property
       def 			_snake_case						(      self      ):
              return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"      ) if is_vision_available() else None







       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"      ).to(A__      )

              __UpperCAmelCase  :				Any       				=		self.default_image_processor
              __UpperCAmelCase  :				List[Any]       				=		prepare_img()
              __UpperCAmelCase  :				Optional[Any]       				=		image_processor(images=A__				,     return_tensors="pt"      ).to(A__      )

              # forward pass
              with torch.no_grad():
                     __UpperCAmelCase  :				Dict       				=		model(**A__      )

              # verify the logits
              __UpperCAmelCase  :				Union[str, Any]       				=		torch.Size((1, 10_00)      )
              self.assertEqual(outputs.logits.shape				,     A__      )

              __UpperCAmelCase  :				Any       				=		torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]      ).to(A__      )
              self.assertTrue(torch.allclose(outputs.logits[0, :3]				,     A__				,     atol=1E-4      )      )







'''simple docstring'''




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import torch






class 		__A       (TensorFormatter[Mapping, "torch.Tensor", Mapping]   ):






       def __init__(      self				,     UpperCamelCase_=None				,     **UpperCamelCase_      ):
              super().__init__(features=UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		torch_tensor_kwargs
              import torch  # noqa import torch at initialization






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) and column:
                     if all(
                         isinstance(UpperCamelCase_				,     torch.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                         for x in column      ):
                            return torch.stack(UpperCamelCase_      )
              return column






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              if isinstance(UpperCamelCase_				,     (str, bytes, type(UpperCamelCase_      ))      ):
                     return value
              elif isinstance(UpperCamelCase_				,     (np.character, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.character      ):
                     return value.tolist()

              __UpperCAmelCase  :				int       				=		{}

              if isinstance(UpperCamelCase_				,     (np.number, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.integer      ):
                     __UpperCAmelCase  :				Optional[int]       				=		{"dtype": torch.intaa}
              elif isinstance(UpperCamelCase_				,     (np.number, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.floating      ):
                     __UpperCAmelCase  :				str       				=		{"dtype": torch.floataa}
              elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                     import PIL.Image

                     if isinstance(UpperCamelCase_				,     PIL.Image.Image      ):
                            __UpperCAmelCase  :				str       				=		np.asarray(UpperCamelCase_      )
              return torch.tensor(UpperCamelCase_				,     **{**default_dtype, **self.torch_tensor_kwargs}      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              # support for torch, tf, jax etc.
              if hasattr(UpperCamelCase_				,     "__array__"      ) and not isinstance(UpperCamelCase_				,     torch.Tensor      ):
                     __UpperCAmelCase  :				Dict       				=		data_struct.__array__()
              # support for nested types like struct of list of struct
              if isinstance(UpperCamelCase_				,     np.ndarray      ):
                     if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                            return self._consolidate([self.recursive_tensorize(UpperCamelCase_      ) for substruct in data_struct]      )
              elif isinstance(UpperCamelCase_				,     (list, tuple)      ):
                     return self._consolidate([self.recursive_tensorize(UpperCamelCase_      ) for substruct in data_struct]      )
              return self._tensorize(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return map_nested(self._recursive_tensorize				,     UpperCamelCase_				,     map_list=UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		self.numpy_arrow_extractor().extract_row(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		self.python_features_decoder.decode_row(UpperCamelCase_      )
              return self.recursive_tensorize(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.numpy_arrow_extractor().extract_column(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[Any]       				=		self.python_features_decoder.decode_column(UpperCamelCase_				,     pa_table.column_names[0]      )
              __UpperCAmelCase  :				List[Any]       				=		self.recursive_tensorize(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		self._consolidate(UpperCamelCase_      )
              return column







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		self.numpy_arrow_extractor().extract_batch(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		self.python_features_decoder.decode_batch(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		self.recursive_tensorize(UpperCamelCase_      )
              for column_name in batch:
                     __UpperCAmelCase  :				Tuple       				=		self._consolidate(batch[column_name]      )
              return batch






