






"""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_      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.config_class(**self.inputs_dict			)
      self.parent.assertTrue(hasattr(_lowerCamelCase				,						'hidden_sizes'			)			)
      self.parent.assertTrue(hasattr(_lowerCamelCase				,						'num_encoder_blocks'			)			)


class 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=1_3				,						_lowerCamelCase=6_4				,						_lowerCamelCase=3				,						_lowerCamelCase=4				,						_lowerCamelCase=[2, 2, 2, 2]				,						_lowerCamelCase=[8, 4, 2, 1]				,						_lowerCamelCase=[1_6, 3_2, 6_4, 1_2_8]				,						_lowerCamelCase=[1, 4, 8, 1_6]				,						_lowerCamelCase=False				,						_lowerCamelCase=True				,						_lowerCamelCase="gelu"				,						_lowerCamelCase=0.1				,						_lowerCamelCase=0.0_2				,						_lowerCamelCase=3				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			image_size
      lowercase				       =			num_channels
      lowercase				       =			num_encoder_blocks
      lowercase				       =			sr_ratios
      lowercase				       =			depths
      lowercase				       =			hidden_sizes
      lowercase				       =			downsampling_rates
      lowercase				       =			is_training
      lowercase				       =			use_labels
      lowercase				       =			hidden_act
      lowercase				       =			hidden_dropout_prob
      lowercase				       =			initializer_range
      lowercase				       =			num_labels
      lowercase				       =			scope







    def      UpperCamelCase_ (     self			):
      lowercase				       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

      lowercase				       =			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      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			PoolFormerModel(config=_lowerCamelCase			)
      model.to(_lowerCamelCase			)
      model.eval()
      lowercase				       =			model(_lowerCamelCase			)
      lowercase				       =			lowercase				       =			self.image_size // 3_2.0
      self.parent.assertEqual(
          result.last_hidden_state.shape				,						(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      lowercase       ,	lowercase       ,	lowercase				       =			config_and_inputs
      lowercase				       =			{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	List[str]      	=(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
    UpperCAmelCase_					:	List[str]      	=(
        {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase_					:	List[Any]      	=False
    UpperCAmelCase_					:	Union[str, Any]      	=False
    UpperCAmelCase_					:	int      	=False
    UpperCAmelCase_					:	List[str]      	=False
    UpperCAmelCase_					:	str      	=False







    def      UpperCamelCase_ (     self			):
      lowercase				       =			PoolFormerModelTester(self			)
      lowercase				       =			PoolFormerConfigTester(self				,						config_class=_lowerCamelCase			)







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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase			)







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







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







    def      UpperCamelCase_ (     self			):
      def check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			model_class(_lowerCamelCase			)
        model.to(_lowerCamelCase			)
        model.eval()

        with torch.no_grad():
          lowercase				       =			model(**self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)			)

        lowercase				       =			outputs.hidden_states

        lowercase				       =			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,
            ]				,						)

      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			True
        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)

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

        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      if not self.model_tester.is_training:
        return

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

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







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

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

        lowercase				       =			['pixel_values']
        self.assertListEqual(arg_names[:1]				,						_lowerCamelCase			)







    @slow
    def      UpperCamelCase_ (     self			):
      for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase				       =			PoolFormerModel.from_pretrained(_lowerCamelCase			)
        self.assertIsNotNone(_lowerCamelCase			)


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image



@require_torch
class 			a	(  unittest.TestCase      ):







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			PoolFormerImageProcessor()
      lowercase				       =			PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'			).to(_lowerCamelCase			)

      lowercase				       =			image_processor(images=prepare_img()				,						return_tensors='pt'			).to(_lowerCamelCase			)

      # forward pass
      with torch.no_grad():
        lowercase				       =			model(**_lowerCamelCase			)

      # verify the logits
      lowercase				       =			torch.Size((1, 1_0_0_0)			)
      self.assertEqual(outputs.logits.shape				,						_lowerCamelCase			)

      lowercase				       =			torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]			).to(_lowerCamelCase			)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)




import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class 							lowercase						(   unittest.TestCase						):




							def 					A__ (							self):
												lowercase		      =		'''runwayml/stable-diffusion-v1-5'''
												lowercase		      =		TextToVideoZeroPipeline.from_pretrained(A__  ,torch_dtype=torch.floataa).to('''cuda''')
												lowercase		      =		DDIMScheduler.from_config(pipe.scheduler.config)
												lowercase		      =		torch.Generator(device='''cuda''').manual_seed(0)

												lowercase		      =		'''A bear is playing a guitar on Times Square'''
												lowercase		      =		pipe(prompt=A__  ,generator=A__).images

												lowercase		      =		load_pt(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt''')

												assert_mean_pixel_difference(A__  ,A__)




