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 		_lowerCamelCase		:
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=13			,			lowerCAmelCase=3			,			lowerCAmelCase=True			,			lowerCAmelCase=True			,			lowerCAmelCase=0.1			,			lowerCAmelCase=0.1			,			lowerCAmelCase=224			,			lowerCAmelCase=1000			,			lowerCAmelCase=[3, 3, 6, 4]			,			lowerCAmelCase=[48, 56, 112, 220]			,			)							->       Dict:
     SCREAMING_SNAKE_CASE__: Any=   parent
     SCREAMING_SNAKE_CASE__: Dict=   batch_size
     SCREAMING_SNAKE_CASE__: Tuple=   num_channels
     SCREAMING_SNAKE_CASE__: Any=   is_training
     SCREAMING_SNAKE_CASE__: List[Any]=   use_labels
     SCREAMING_SNAKE_CASE__: str=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__: Any=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__: Dict=   num_labels
     SCREAMING_SNAKE_CASE__: Optional[int]=   image_size
     SCREAMING_SNAKE_CASE__: List[str]=   layer_depths
     SCREAMING_SNAKE_CASE__: int=   embed_dims
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

     SCREAMING_SNAKE_CASE__: int=   self.get_config()

     return config, pixel_values, labels
    def     UpperCamelCase_					(	self )							->       Tuple:
     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=lowerCAmelCase			,			layer_scale_init_value=1e-5			,			)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: int=   SwiftFormerModel(config=lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.eval()
     SCREAMING_SNAKE_CASE__: str=   model(lowerCAmelCase )
     self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.embed_dims[-1], 7, 7) )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.num_labels
     SCREAMING_SNAKE_CASE__: Dict=   SwiftFormerForImageClassification(lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.eval()
     SCREAMING_SNAKE_CASE__: Dict=   model(lowerCAmelCase			,			labels=lowerCAmelCase )
     self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels) )

     SCREAMING_SNAKE_CASE__: List[Any]=   SwiftFormerForImageClassification(lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.eval()

     SCREAMING_SNAKE_CASE__: Dict=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
     SCREAMING_SNAKE_CASE__: List[str]=   model(lowerCAmelCase )
     self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels) )



    def     UpperCamelCase_					(	self )							->       int:
     ((SCREAMING_SNAKE_CASE__),						(SCREAMING_SNAKE_CASE__),						(SCREAMING_SNAKE_CASE__)): List[Any]=   self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__: Optional[int]=   {'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    __a						=   (
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: str=   SwiftFormerModelTester(self )
     SCREAMING_SNAKE_CASE__: Dict=   ConfigTester(
         self			,			config_class=lowerCAmelCase			,			has_text_modality=lowerCAmelCase			,			hidden_size=37			,			num_attention_heads=12			,			num_hidden_layers=12			,			)
    def     UpperCamelCase_					(	self )							->       Tuple:
     self.config_tester.run_common_tests()
    @unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     pass
    def     UpperCamelCase_					(	self )							->       Any:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   model_class(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Dict=   model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(lowerCAmelCase			,			nn.Linear ) )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Any=   self.model_tester.prepare_config_and_inputs_for_common()

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

      SCREAMING_SNAKE_CASE__: List[Any]=   ['''pixel_values''']
      self.assertListEqual(arg_names[:1]			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: int=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
    @slow
    def     UpperCamelCase_					(	self )							->       Any:
     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__: List[Any]=   SwiftFormerModel.from_pretrained(lowerCAmelCase )
      self.assertIsNotNone(lowerCAmelCase )
    @unittest.skip(reason='''SwiftFormer does not output attentions''' )
    def     UpperCamelCase_					(	self )							->       List[str]:
     pass
    def     UpperCamelCase_					(	self )							->       Tuple:
     def check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   model_class(lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()

      with torch.no_grad():
       SCREAMING_SNAKE_CASE__: Any=   model(**self._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase ) )

      SCREAMING_SNAKE_CASE__: Union[str, Any]=   outputs.hidden_states

      SCREAMING_SNAKE_CASE__: str=   8
      self.assertEqual(len(lowerCAmelCase )			,			lowerCAmelCase )  # 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(lowerCAmelCase ) ):
       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),
               ] )			,			)

     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[int]=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: Optional[int]=   True
      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   True

      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[str]:
     def _config_zero_init(lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: List[str]=   copy.deepcopy(lowerCAmelCase )
      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(lowerCAmelCase			,			lowerCAmelCase			,			1e-10 )
       if isinstance(getattr(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )			,			lowerCAmelCase ):
        SCREAMING_SNAKE_CASE__: str=   _config_zero_init(getattr(lowerCAmelCase			,			lowerCAmelCase ) )
        setattr(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
      return configs_no_init

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

     SCREAMING_SNAKE_CASE__: int=   _config_zero_init(lowerCAmelCase )
     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: List[str]=   model_class(config=lowerCAmelCase )
      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     UpperCamelCase_					(	self )							->       List[Any]:
     pass







def       A__   (						):
 SCREAMING_SNAKE_CASE__: List[Any]=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
 return image






@require_torch
@require_vision
class 		_lowerCamelCase		( unittest.TestCase     ):
    @cached_property
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None



    @slow
    def     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__: Tuple=   SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.default_image_processor
     SCREAMING_SNAKE_CASE__: Dict=   prepare_img()
     SCREAMING_SNAKE_CASE__: List[Any]=   image_processor(images=lowerCAmelCase			,			return_tensors='''pt''' ).to(lowerCAmelCase )

     # forward pass
     with torch.no_grad():
      SCREAMING_SNAKE_CASE__: int=   model(**lowerCAmelCase )

     # verify the logits
     SCREAMING_SNAKE_CASE__: int=   torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape			,			lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[Any]=   torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]] ).to(lowerCAmelCase )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,			lowerCAmelCase			,			atol=1e-4 ) )



"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		(DDIMParallelScheduler,)
							_snake_case						      =		(('''eta''', 0.0), ('''num_inference_steps''', 5_0))




							def    A__						( self    ,				**snake_case_				)       ->  Optional[Any]:
													__lowerCAmelCase     =  {
													    """num_train_timesteps""": 1_000,
													    """beta_start""": 0.0_001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													    """clip_sample""": True,
													}

													config.update(**snake_case_				)
													return config




							def    A__						( self    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(**snake_case_				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter

													scheduler.set_timesteps(snake_case_				)

													for t in scheduler.timesteps:
																			__lowerCAmelCase     =  model(snake_case_    ,				snake_case_				)
																			__lowerCAmelCase     =  scheduler.step(snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_				).prev_sample

													return sample




							def    A__						( self				)       ->  List[str]:
													for timesteps in [100, 500, 1_000]:
																			self.check_over_configs(num_train_timesteps=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for steps_offset in [0, 1]:
																			self.check_over_configs(steps_offset=snake_case_				)

													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(steps_offset=1				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)
													scheduler.set_timesteps(5				)
													assert torch.equal(scheduler.timesteps    ,				torch.LongTensor([801, 601, 401, 201, 1]				)				)




							def    A__						( self				)       ->  int:
													for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,				[0.002, 0.02, 0.2, 2]				):
																			self.check_over_configs(beta_start=snake_case_    ,				beta_end=snake_case_				)




							def    A__						( self				)       ->  str:
													for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=snake_case_				)




							def    A__						( self				)       ->  Dict:
													for timestep_spacing in ["trailing", "leading"]:
																			self.check_over_configs(timestep_spacing=snake_case_				)




							def    A__						( self				)       ->  str:
													for rescale_betas_zero_snr in [True, False]:
																			self.check_over_configs(rescale_betas_zero_snr=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													self.check_over_configs(thresholding=snake_case_				)
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=snake_case_    ,				prediction_type=snake_case_    ,				sample_max_value=snake_case_    ,				)




							def    A__						( self				)       ->  Union[str, Any]:
													for t in [1, 10, 49]:
																			self.check_over_forward(time_step=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, num_inference_steps in zip([1, 10, 50]    ,				[10, 50, 500]				):
																			self.check_over_forward(time_step=snake_case_    ,				num_inference_steps=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, eta in zip([1, 10, 49]    ,				[0.0, 0.5, 1.0]				):
																			self.check_over_forward(time_step=snake_case_    ,				eta=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(420    ,				400				) - 0.14_771				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(980    ,				960				) - 0.32_460				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487    ,				486				) - 0.00_979				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999    ,				998				) - 0.02				)				) < 1e-5




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0
													scheduler.set_timesteps(snake_case_				)

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter
													__lowerCAmelCase     =  self.dummy_sample_deter + 0.1
													__lowerCAmelCase     =  self.dummy_sample_deter - 0.1

													__lowerCAmelCase     =  samplea.shape[0]
													__lowerCAmelCase     =  torch.stack([samplea, samplea, samplea]    ,				dim=0				)
													__lowerCAmelCase     =  torch.arange(snake_case_				)[0:3, None].repeat(1    ,				snake_case_				)

													__lowerCAmelCase     =  model(samples.flatten(0    ,				1				)    ,				timesteps.flatten(0    ,				1				)				)
													__lowerCAmelCase     =  scheduler.batch_step_no_noise(snake_case_    ,				timesteps.flatten(0    ,				1				)    ,				samples.flatten(0    ,				1				)    ,				snake_case_				)

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
													assert abs(result_mean.item() - 0.4_982				) < 1e-3




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  self.full_loop()

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 172.0_067				) < 1e-2
													assert abs(result_mean.item() - 0.223_967				) < 1e-3




							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  self.full_loop(prediction_type="""v_prediction"""				)

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 52.5_302				) < 1e-2
													assert abs(result_mean.item() - 0.0_684				) < 1e-3




							def    A__						( self				)       ->  Tuple:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.8_295				) < 1e-2
													assert abs(result_mean.item() - 0.1_951				) < 1e-3




							def    A__						( self				)       ->  Union[str, Any]:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.0_784				) < 1e-2
													assert abs(result_mean.item() - 0.1_941				) < 1e-3
