





"""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 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    self.config_class(**self.inputs_dict    )
        self.parent.assertTrue(hasattr(snake_case_   , 'hidden_sizes'    )    )
        self.parent.assertTrue(hasattr(snake_case_   , 'num_encoder_blocks'    )    )


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=1_3   , snake_case_=6_4   , snake_case_=3   , snake_case_=4   , snake_case_=[2, 2, 2, 2]   , snake_case_=[8, 4, 2, 1]   , snake_case_=[1_6, 3_2, 6_4, 1_2_8]   , snake_case_=[1, 4, 8, 1_6]   , snake_case_=False   , snake_case_=True   , snake_case_="gelu"   , snake_case_=0.1   , snake_case_=0.02   , snake_case_=3   , snake_case_=None   , ):

        """simple docstring"""

        A_    :			Any									=    parent
        A_    :			Optional[Any]									=    batch_size
        A_    :			List[str]									=    image_size
        A_    :			Optional[int]									=    num_channels
        A_    :			Tuple									=    num_encoder_blocks
        A_    :			Tuple									=    sr_ratios
        A_    :			Optional[int]									=    depths
        A_    :			Dict									=    hidden_sizes
        A_    :			str									=    downsampling_rates
        A_    :			Dict									=    is_training
        A_    :			Union[str, Any]									=    use_labels
        A_    :			List[str]									=    hidden_act
        A_    :			Any									=    hidden_dropout_prob
        A_    :			int									=    initializer_range
        A_    :			List[str]									=    num_labels
        A_    :			str									=    scope

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        A_    :			Optional[int]									=    None
        if self.use_labels:
          A_    :			Tuple									=    ids_tensor([self.batch_size, self.image_size, self.image_size]   , self.num_labels    )

        A_    :			Union[str, Any]									=    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    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    PoolFormerModel(config=snake_case_    )
        model.to(snake_case_    )
        model.eval()
        A_    :			Dict									=    model(snake_case_    )
        A_    :			Union[str, Any]									=    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    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.prepare_config_and_inputs()
        A_		,			A_		,			A_    :			Optional[int]									=    config_and_inputs
        A_    :			Any									=    {'pixel_values': pixel_values}
        return config, inputs_dict


@require_torch
class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					int             =  (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
      lowercase_ :					Any             =  (
          {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      lowercase_ :					Union[str, Any]             =  False
      lowercase_ :					Optional[Any]             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Any             =  False
      lowercase_ :					List[Any]             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    PoolFormerModelTester(self    )
        A_    :			Optional[Any]									=    PoolFormerConfigTester(self   , config_class=snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.config_tester.run_common_tests()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_    )

      @unittest.skip('PoolFormer does not use inputs_embeds'    )
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'    )
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        def check_hidden_states_output(snake_case_   , snake_case_   , snake_case_    ):
          A_    :			Dict									=    model_class(snake_case_    )
          model.to(snake_case_    )
          model.eval()

          with torch.no_grad():
            A_    :			str									=    model(**self._prepare_for_class(snake_case_   , snake_case_    )    )

          A_    :			List[Any]									=    outputs.hidden_states

          A_    :			str									=    self.model_tester.num_encoder_blocks
          self.assertEqual(len(snake_case_    )   , snake_case_    )

          # 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,
              ]   , )

        A_		,			A_    :			Tuple									=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_    :			List[str]									=    True
          check_hidden_states_output(snake_case_   , snake_case_   , snake_case_    )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          A_    :			Optional[int]									=    True

          check_hidden_states_output(snake_case_   , snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if not self.model_tester.is_training:
          return

        A_		,			A_    :			str									=    self.model_tester.prepare_config_and_inputs_for_common()
        A_    :			str									=    True

        for model_class in self.all_model_classes:
          if model_class in get_values(snake_case_    ):
            continue
          A_    :			List[str]									=    model_class(snake_case_    )
          model.to(snake_case_    )
          model.train()
          A_    :			Any									=    self._prepare_for_class(snake_case_   , snake_case_   , return_labels=snake_case_    )
          A_    :			List[str]									=    model(**snake_case_    ).loss
          loss.backward()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			List[str]									=    self.model_tester.prepare_config_and_inputs_for_common()

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

          A_    :			List[Any]									=    ['pixel_values']
          self.assertListEqual(arg_names[:1]   , snake_case_    )

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_    :			Any									=    PoolFormerModel.from_pretrained(snake_case_    )
          self.assertIsNotNone(snake_case_    )





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			List[str]									=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
  return image





@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    PoolFormerImageProcessor()
        A_    :			List[Any]									=    PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'    ).to(snake_case_    )

        A_    :			List[str]									=    image_processor(images=prepare_img()   , return_tensors='pt'    ).to(snake_case_    )

        # forward pass
        with torch.no_grad():
          A_    :			Any									=    model(**snake_case_    )

        # verify the logits
        A_    :			Union[str, Any]									=    torch.Size((1, 1_0_0_0)    )
        self.assertEqual(outputs.logits.shape   , snake_case_    )

        A_    :			List[Any]									=    torch.tensor([-0.61_13, 0.16_85, -0.04_92]    ).to(snake_case_    )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]   , snake_case_   , atol=1E-4    )    )

"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()      ,	reason='XFormers attention is only available with CUDA and `xformers` installed'      ,	)
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'      ,	reason='float16 requires CUDA' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2      ,	)



