
from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
     from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor




class     _SCREAMING_SNAKE_CASE     (					a__       ):
      def A_	(	self       ):
          snake_case__  = self.config_class(**self.inputs_dict       )
          self.parent.assertTrue(hasattr(lowerCamelCase_						,		"hidden_sizes"       )       )
          self.parent.assertTrue(hasattr(lowerCamelCase_						,		"neck_hidden_sizes"       )       )
          self.parent.assertTrue(hasattr(lowerCamelCase_						,		"num_attention_heads"       )       )





class     _SCREAMING_SNAKE_CASE     :
      def __init__(	self						,		lowerCamelCase						,		lowerCamelCase=13						,		lowerCamelCase=32						,		lowerCamelCase=2						,		lowerCamelCase=3						,		lowerCamelCase=6_40						,		lowerCamelCase=4						,		lowerCamelCase="silu"						,		lowerCamelCase=3						,		lowerCamelCase=32						,		lowerCamelCase=0.1						,		lowerCamelCase=0.1						,		lowerCamelCase=0.1						,		lowerCamelCase=0.0_2						,		lowerCamelCase=True						,		lowerCamelCase=True						,		lowerCamelCase=10						,		lowerCamelCase=None						,		):
          snake_case__  = parent
          snake_case__  = batch_size
          snake_case__  = image_size
          snake_case__  = patch_size
          snake_case__  = num_channels
          snake_case__  = last_hidden_size
          snake_case__  = num_attention_heads
          snake_case__  = hidden_act
          snake_case__  = conv_kernel_size
          snake_case__  = output_stride
          snake_case__  = hidden_dropout_prob
          snake_case__  = attention_probs_dropout_prob
          snake_case__  = classifier_dropout_prob
          snake_case__  = use_labels
          snake_case__  = is_training
          snake_case__  = num_labels
          snake_case__  = initializer_range
          snake_case__  = scope
      def A_	(	self       ):
          snake_case__  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

          snake_case__  = None
          snake_case__  = None
          if self.use_labels:
              snake_case__  = ids_tensor([self.batch_size]						,		self.num_labels       )
              snake_case__  = ids_tensor([self.batch_size, self.image_size, self.image_size]						,		self.num_labels       )

          snake_case__  = self.get_config()

          return config, pixel_values, labels, pixel_labels
      def A_	(	self       ):
          return MobileViTConfig(
              image_size=self.image_size						,		patch_size=self.patch_size						,		num_channels=self.num_channels						,		num_attention_heads=self.num_attention_heads						,		hidden_act=self.hidden_act						,		conv_kernel_size=self.conv_kernel_size						,		output_stride=self.output_stride						,		hidden_dropout_prob=self.hidden_dropout_prob						,		attention_probs_dropout_prob=self.attention_probs_dropout_prob						,		classifier_dropout_prob=self.classifier_dropout_prob						,		initializer_range=self.initializer_range						,		)
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = TFMobileViTModel(config=lowerCamelCase_       )
          snake_case__  = model(lowerCamelCase_						,		training=lowerCamelCase_       )
          snake_case__  = snake_case__  = self.image_size // self.output_stride
          self.parent.assertEqual(
              result.last_hidden_state.shape						,		(self.batch_size, self.last_hidden_size, expected_height, expected_width)       )
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = self.num_labels
          snake_case__  = TFMobileViTForImageClassification(lowerCamelCase_       )
          snake_case__  = model(lowerCamelCase_						,		labels=lowerCamelCase_						,		training=lowerCamelCase_       )
          self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)       )
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = self.num_labels
          snake_case__  = TFMobileViTForSemanticSegmentation(lowerCamelCase_       )
          snake_case__  = snake_case__  = self.image_size // self.output_stride

          snake_case__  = model(lowerCamelCase_						,		training=lowerCamelCase_       )
          self.parent.assertEqual(
              result.logits.shape						,		(self.batch_size, self.num_labels, expected_height, expected_width)       )

          snake_case__  = model(lowerCamelCase_						,		labels=lowerCamelCase_						,		training=lowerCamelCase_       )
          self.parent.assertEqual(
              result.logits.shape						,		(self.batch_size, self.num_labels, expected_height, expected_width)       )



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





@require_tf
class     _SCREAMING_SNAKE_CASE     (					a__				,    a__				,    unittest.TestCase       ):
      _A  :						Tuple					=   (
          (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
          if is_tf_available()
          else ()
      )
      _A  :						List[Any]					=   (
          {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
          if is_tf_available()
          else {}
      )

      _A  :						Any					=   False
      _A  :						Dict					=   False
      _A  :						Optional[int]					=   False
      _A  :						Any					=   False
      _A  :						Tuple					=   False
      def A_	(	self       ):
          snake_case__  = TFMobileViTModelTester(self       )
          snake_case__  = TFMobileViTConfigTester(self						,		config_class=lowerCamelCase_						,		has_text_modality=lowerCamelCase_       )
      def A_	(	self       ):
          self.config_tester.run_common_tests()
      @unittest.skip(reason="MobileViT does not use inputs_embeds"       )
      def A_	(	self       ):
          pass
      @unittest.skip(reason="MobileViT does not support input and output embeddings"       )
      def A_	(	self       ):
          pass
      @unittest.skip(reason="MobileViT does not output attentions"       )
      def A_	(	self       ):
          pass
      def A_	(	self       ):
          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case__  = ["pixel_values"]
              self.assertListEqual(arg_names[:1]						,		lowerCamelCase_       )
      def A_	(	self       ):
          snake_case__  = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase_       )
      def A_	(	self       ):
          def check_hidden_states_output(lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
              snake_case__  = model_class(lowerCamelCase_       )

              snake_case__  = model(**self._prepare_for_class(lowerCamelCase_						,		lowerCamelCase_       )       )

              snake_case__  = outputs.hidden_states

              snake_case__  = 5
              self.assertEqual(len(lowerCamelCase_       )						,		lowerCamelCase_       )

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

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

          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__  = True
              check_hidden_states_output(lowerCamelCase_						,		lowerCamelCase_						,		lowerCamelCase_       )

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

              check_hidden_states_output(lowerCamelCase_						,		lowerCamelCase_						,		lowerCamelCase_       )
      def A_	(	self       ):
          snake_case__  = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_       )
      def A_	(	self       ):
          snake_case__  = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_       )
      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("GPU"       )       ) == 0						,		reason="TF does not support backprop for grouped convolutions on CPU."						,		)
      def A_	(	self       ):
          super().test_dataset_conversion()
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase=2e-1						,		lowerCamelCase=2e-1       ):
          self.assertTrue(np.allclose(lowerCamelCase_						,		lowerCamelCase_						,		atol=lowerCamelCase_						,		rtol=lowerCamelCase_       )       )
      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices("GPU"       )       ) == 0						,		reason="TF does not support backprop for grouped convolutions on CPU."						,		)
      @slow
      def A_	(	self       ):
          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
              if model_class.__name__ != "TFMobileViTModel":
                  snake_case__  = model_class(lowerCamelCase_       )
                  if getattr(lowerCamelCase_						,		"hf_compute_loss"						,		lowerCamelCase_       ):
                      super().test_keras_fit()
      def A_	(	self       ):
          snake_case__  ,    snake_case__  = self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              # set an ignore index to correctly test the masked loss used in
              # `TFMobileViTForSemanticSegmentation`.
              if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                  snake_case__  = 5

              snake_case__  = model_class(lowerCamelCase_       )
              if getattr(lowerCamelCase_						,		"hf_compute_loss"						,		lowerCamelCase_       ):
                  # The number of elements in the loss should be the same as the number of elements in the label
                  snake_case__  = self._prepare_for_class(inputs_dict.copy()						,		lowerCamelCase_						,		return_labels=lowerCamelCase_       )
                  snake_case__  = prepared_for_class[
                      sorted(prepared_for_class.keys() - inputs_dict.keys()						,		reverse=lowerCamelCase_       )[0]
                  ]
                  snake_case__  = added_label.shape.as_list()[:1]

                  # Test that model correctly compute the loss with kwargs
                  snake_case__  = self._prepare_for_class(inputs_dict.copy()						,		lowerCamelCase_						,		return_labels=lowerCamelCase_       )
                  snake_case__  = {"input_ids", "pixel_values", "input_features"}
                  snake_case__  = possible_input_names.intersection(set(lowerCamelCase_       )       ).pop()
                  snake_case__  = prepared_for_class.pop(lowerCamelCase_       )

                  snake_case__  = model(lowerCamelCase_						,		**lowerCamelCase_       )[0]
                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

                  # Test that model correctly compute the loss when we mask some positions
                  snake_case__  = self._prepare_for_class(inputs_dict.copy()						,		lowerCamelCase_						,		return_labels=lowerCamelCase_       )
                  snake_case__  = {"input_ids", "pixel_values", "input_features"}
                  snake_case__  = possible_input_names.intersection(set(lowerCamelCase_       )       ).pop()
                  snake_case__  = prepared_for_class.pop(lowerCamelCase_       )
                  if "labels" in prepared_for_class:
                      snake_case__  = prepared_for_class["labels"].numpy()
                      if len(labels.shape       ) > 1 and labels.shape[1] != 1:
                          # labels[0] = -100
                          snake_case__  = tf.convert_to_tensor(lowerCamelCase_       )
                          snake_case__  = model(lowerCamelCase_						,		**lowerCamelCase_       )[0]
                          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
                          self.assertTrue(not np.any(np.isnan(loss.numpy()       )       )       )

                # Test that model correctly compute the loss with a dict
                  snake_case__  = self._prepare_for_class(inputs_dict.copy()						,		lowerCamelCase_						,		return_labels=lowerCamelCase_       )
                  snake_case__  = model(lowerCamelCase_       )[0]
                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

                  # Test that model correctly compute the loss with a tuple
                  snake_case__  = self._prepare_for_class(inputs_dict.copy()						,		lowerCamelCase_						,		return_labels=lowerCamelCase_       )

                  # Get keys that were added with the _prepare_for_class function
                  snake_case__  = prepared_for_class.keys() - inputs_dict.keys()
                  snake_case__  = inspect.signature(model.call       ).parameters
                  snake_case__  = list(signature.keys()       )

                  # Create a dictionary holding the location of the tensors in the tuple
                  snake_case__  = {0: input_name}
                  for label_key in label_keys:
                      snake_case__  = signature_names.index(lowerCamelCase_       )
                      snake_case__  = label_key
                  snake_case__  = sorted(tuple_index_mapping.items()       )
                  # Initialize a list with their default values, update the values and convert to a tuple
                  snake_case__  = []

                  for name in signature_names:
                      if name != "kwargs":
                          list_input.append(signature[name].default       )

                  for index, value in sorted_tuple_index_mapping:
                      snake_case__  = prepared_for_class[value]

                  snake_case__  = tuple(lowerCamelCase_       )

                  # Send to model
                  snake_case__  = model(tuple_input[:-1]       )[0]

                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )



      @slow
      def A_	(	self       ):
          for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__  = TFMobileViTModel.from_pretrained(lowerCamelCase_       )
              self.assertIsNotNone(lowerCamelCase_       )





def 		SCREAMING_SNAKE_CASE__			(					):
    snake_case__  = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
    return image





@require_tf
class     _SCREAMING_SNAKE_CASE     (					unittest.TestCase       ):
      @slow
      def A_	(	self       ):
          snake_case__  = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"       )

          snake_case__  = MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"       )
          snake_case__  = prepare_img()
          snake_case__  = image_processor(images=lowerCamelCase_						,		return_tensors="tf"       )

          # forward pass
          snake_case__  = model(**lowerCamelCase_						,		training=lowerCamelCase_       )

          # verify the logits
          snake_case__  = tf.TensorShape((1, 10_00)       )
          self.assertEqual(outputs.logits.shape						,		lowerCamelCase_       )

          snake_case__  = tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]       )

          tf.debugging.assert_near(outputs.logits[0, :3]						,		lowerCamelCase_						,		atol=1e-4						,		rtol=1e-04       )



      @slow
      def A_	(	self       ):
          # `from_pt` will be removed
          snake_case__  = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"       )

          snake_case__  = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"       )

          snake_case__  = prepare_img()
          snake_case__  = image_processor(images=lowerCamelCase_						,		return_tensors="tf"       )

          # forward pass
          snake_case__  = model(inputs.pixel_values						,		training=lowerCamelCase_       )
          snake_case__  = outputs.logits

          # verify the logits
          snake_case__  = tf.TensorShape((1, 21, 32, 32)       )
          self.assertEqual(logits.shape						,		lowerCamelCase_       )

          snake_case__  = tf.constant(
              [
                  [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
                  [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
                  [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
              ]       )

          tf.debugging.assert_near(logits[0, :3, :3, :3]						,		lowerCamelCase_						,		rtol=1e-4						,		atol=1e-4       )







'''simple docstring'''






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
		from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import PoolFormerImageProcessor




class 		a__				(							a__  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	self.config_class(**self.inputs_dict   )
														self.parent.assertTrue(hasattr(lowerCamelCase_					,    '''hidden_sizes'''   )   )
														self.parent.assertTrue(hasattr(lowerCamelCase_					,    '''num_encoder_blocks'''   )   )




class 		a__				:





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_=13					,    lowerCamelCase_=64					,    lowerCamelCase_=3					,    lowerCamelCase_=4					,    lowerCamelCase_=[2, 2, 2, 2]					,    lowerCamelCase_=[8, 4, 2, 1]					,    lowerCamelCase_=[16, 32, 64, 1_28]					,    lowerCamelCase_=[1, 4, 8, 16]					,    lowerCamelCase_=False					,    lowerCamelCase_=True					,    lowerCamelCase_="gelu"					,    lowerCamelCase_=0.1					,    lowerCamelCase_=0.02					,    lowerCamelCase_=3					,    lowerCamelCase_=None					,    )       ->							Any:
														lowerCAmelCase__          =	parent
														lowerCAmelCase__          =	batch_size
														lowerCAmelCase__          =	image_size
														lowerCAmelCase__          =	num_channels
														lowerCAmelCase__          =	num_encoder_blocks
														lowerCAmelCase__          =	sr_ratios
														lowerCAmelCase__          =	depths
														lowerCAmelCase__          =	hidden_sizes
														lowerCAmelCase__          =	downsampling_rates
														lowerCAmelCase__          =	is_training
														lowerCAmelCase__          =	use_labels
														lowerCAmelCase__          =	hidden_act
														lowerCAmelCase__          =	hidden_dropout_prob
														lowerCAmelCase__          =	initializer_range
														lowerCAmelCase__          =	num_labels
														lowerCAmelCase__          =	scope







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														lowerCAmelCase__          =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

														lowerCAmelCase__          =	None
														if self.use_labels:
																					lowerCAmelCase__          =	ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels   )

														lowerCAmelCase__          =	PoolFormerConfig(
														    image_size=self.image_size					,    num_channels=self.num_channels					,    num_encoder_blocks=self.num_encoder_blocks					,    depths=self.depths					,    hidden_sizes=self.hidden_sizes					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    initializer_range=self.initializer_range					,    )

														return config, pixel_values, labels







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	PoolFormerModel(config=lowerCamelCase_   )
														model.to(lowerCamelCase_   )
														model.eval()
														lowerCAmelCase__          =	model(lowerCamelCase_   )
														lowerCAmelCase__          =	lowerCAmelCase__          =	self.image_size // 32.0
														self.parent.assertEqual(
														    result.last_hidden_state.shape					,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														lowerCAmelCase__          =	self.prepare_config_and_inputs()
														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	config_and_inputs
														lowerCAmelCase__          =	{'''pixel_values''': pixel_values}
														return config, inputs_dict




@require_torch
class 		a__				(							a__				,				a__				,				unittest.TestCase  ):





							'''simple docstring'''






							lowercase__			:   Dict							=      (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
							lowercase__			:   Any							=      (
							    {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__			:   Optional[int]							=      False
							lowercase__			:   Optional[int]							=      False
							lowercase__			:   Union[str, Any]							=      False
							lowercase__			:   List[Any]							=      False
							lowercase__			:   Tuple							=      False







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	PoolFormerModelTester(self   )
														lowerCAmelCase__          =	PoolFormerConfigTester(self					,    config_class=lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														self.config_tester.run_common_tests()







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_   )







							@unittest.skip('''PoolFormer does not use inputs_embeds'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														pass







							@unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														def check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   ):
																					lowerCAmelCase__          =	model_class(lowerCamelCase_   )
																					model.to(lowerCamelCase_   )
																					model.eval()

																					with torch.no_grad():
																												lowerCAmelCase__          =	model(**self._prepare_for_class(lowerCamelCase_					,    lowerCamelCase_   )   )

																					lowerCAmelCase__          =	outputs.hidden_states

																					lowerCAmelCase__          =	self.model_tester.num_encoder_blocks
																					self.assertEqual(len(lowerCamelCase_   )					,    lowerCamelCase_   )

																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-3:]   )					,    [
																					        self.model_tester.hidden_sizes[0],
																					        self.model_tester.image_size // 4,
																					        self.model_tester.image_size // 4,
																					    ]					,    )

														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()

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

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

																					check_hidden_states_output(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														if not self.model_tester.is_training:
																					return

														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase__          =	True

														for model_class in self.all_model_classes:
																					if model_class in get_values(lowerCamelCase_   ):
																												continue
																					lowerCAmelCase__          =	model_class(lowerCamelCase_   )
																					model.to(lowerCamelCase_   )
																					model.train()
																					lowerCAmelCase__          =	self._prepare_for_class(lowerCamelCase_					,    lowerCamelCase_					,    return_labels=lowerCamelCase_   )
																					lowerCAmelCase__          =	model(**lowerCamelCase_   ).loss
																					loss.backward()







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCAmelCase__          =	['''pixel_values''']
																					self.assertListEqual(arg_names[:1]					,    lowerCamelCase_   )







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__          =	PoolFormerModel.from_pretrained(lowerCamelCase_   )
																					self.assertIsNotNone(lowerCamelCase_   )


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






@require_torch
class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							@slow
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	PoolFormerImageProcessor()
														lowerCAmelCase__          =	PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''   ).to(lowerCamelCase_   )

														lowerCAmelCase__          =	image_processor(images=prepare_img()					,    return_tensors='''pt'''   ).to(lowerCamelCase_   )

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

														# verify the logits
														lowerCAmelCase__          =	torch.Size((1, 10_00)   )
														self.assertEqual(outputs.logits.shape					,    lowerCamelCase_   )

														lowerCAmelCase__          =	torch.tensor([-0.6_113, 0.1_685, -0.0_492]   ).to(lowerCamelCase_   )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]					,    lowerCamelCase_					,    atol=1e-4   )   )