


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 	a__						(    lowerCAmelCase_    ,							lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  Tuple				     =  StableDiffusionXLPipeline
       lowerCamelCase__:  Dict				     =  TEXT_TO_IMAGE_PARAMS
       lowerCamelCase__:  str				     =  TEXT_TO_IMAGE_BATCH_PARAMS
       lowerCamelCase__:  Tuple				     =  TEXT_TO_IMAGE_IMAGE_PARAMS
       lowerCamelCase__:  Optional[Any]				     =  TEXT_TO_IMAGE_IMAGE_PARAMS

       def     UpperCAmelCase( self   :  Any     ):
            torch.manual_seed(0     )
            a_     :  Tuple				      =     UNetaDConditionModel(
                block_out_channels=(3_2, 6_4)   ,				layers_per_block=2   ,				sample_size=3_2   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,				attention_head_dim=(2, 4)   ,				use_linear_projection=lowerCamelCase_   ,				addition_embed_type="""text_time"""   ,				addition_time_embed_dim=8   ,				transformer_layers_per_block=(1, 2)   ,				projection_class_embeddings_input_dim=8_0   ,				cross_attention_dim=6_4   ,				)
            a_     :  Optional[Any]				      =     EulerDiscreteScheduler(
                beta_start=0.0_0_0_8_5   ,				beta_end=0.0_1_2   ,				steps_offset=1   ,				beta_schedule="""scaled_linear"""   ,				timestep_spacing="""leading"""   ,				)
            torch.manual_seed(0     )
            a_     :  int				      =     AutoencoderKL(
                block_out_channels=[3_2, 6_4]   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,				latent_channels=4   ,				sample_size=1_2_8   ,				)
            torch.manual_seed(0     )
            a_     :  Optional[int]				      =     CLIPTextConfig(
                bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=3_2   ,				intermediate_size=3_7   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1_0_0_0   ,				hidden_act="""gelu"""   ,				projection_dim=3_2   ,				)
            a_     :  Dict				      =     CLIPTextModel(lowerCamelCase_     )
            a_     :  Optional[int]				      =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,				local_files_only=lowerCamelCase_     )

            a_     :  Dict				      =     CLIPTextModelWithProjection(lowerCamelCase_     )
            a_     :  Dict				      =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   ,				local_files_only=lowerCamelCase_     )

            a_     :  Dict				      =     {
                """unet""": unet,
                """scheduler""": scheduler,
                """vae""": vae,
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
                """text_encoder_2""": text_encoder_a,
                """tokenizer_2""": tokenizer_a,
                # "safety_checker": None,
                # "feature_extractor": None,
            }
            return components

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[Any]=0     ):
            if str(lowerCamelCase_     ).startswith("""mps"""     ):
                 a_     :  Dict				      =     torch.manual_seed(lowerCamelCase_     )
            else:
                 a_     :  int				      =     torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
            a_     :  Optional[Any]				      =     {
                """prompt""": """A painting of a squirrel eating a burger""",
                """generator""": generator,
                """num_inference_steps""": 2,
                """guidance_scale""": 5.0,
                """output_type""": """numpy""",
            }
            return inputs

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  Optional[Any]				      =     """cpu"""  # ensure determinism for the device-dependent torch.Generator
            a_     :  Union[str, Any]				      =     self.get_dummy_components()
            a_     :  Tuple				      =     StableDiffusionXLPipeline(**lowerCamelCase_     )
            a_     :  List[Any]				      =     sd_pipe.to(lowerCamelCase_     )
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_     )

            a_     :  Tuple				      =     self.get_dummy_inputs(lowerCamelCase_     )
            a_     :  Dict				      =     sd_pipe(**lowerCamelCase_     ).images
            a_     :  Dict				      =     image[0, -3:, -3:, -1]

            assert image.shape == (1, 6_4, 6_4, 3)
            a_     :  List[Any]				      =     np.array([0.5_8_7_3, 0.6_1_2_8, 0.4_7_9_7, 0.5_1_2_2, 0.5_6_7_4, 0.4_6_3_9, 0.5_2_2_7, 0.5_1_4_9, 0.4_7_4_7]     )

            assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            a_     :  Optional[int]				      =     self.get_dummy_components()
            a_     :  Dict				      =     StableDiffusionXLPipeline(**lowerCamelCase_     )
            a_     :  int				      =     sd_pipe.to(lowerCamelCase_     )
            a_     :  Dict				      =     sd_pipe.to(lowerCamelCase_     )
            sd_pipe.set_progress_bar_config(disable=lowerCamelCase_     )

            # forward without prompt embeds
            a_     :  List[str]				      =     self.get_dummy_inputs(lowerCamelCase_     )
            a_     :  Any				      =     3 * ["""this is a negative prompt"""]
            a_     :  Any				      =     negative_prompt
            a_     :  Union[str, Any]				      =     3 * [inputs["""prompt"""]]

            a_     :  List[str]				      =     sd_pipe(**lowerCamelCase_     )
            a_     :  Tuple				      =     output.images[0, -3:, -3:, -1]

            # forward with prompt embeds
            a_     :  List[str]				      =     self.get_dummy_inputs(lowerCamelCase_     )
            a_     :  Any				      =     3 * ["""this is a negative prompt"""]
            a_     :  List[Any]				      =     3 * [inputs.pop("""prompt"""     )]

            (
                (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      
            )     :  int				      =     sd_pipe.encode_prompt(lowerCamelCase_   ,				negative_prompt=lowerCamelCase_     )

            a_     :  Any				      =     sd_pipe(
                **lowerCamelCase_   ,				prompt_embeds=lowerCamelCase_   ,				negative_prompt_embeds=lowerCamelCase_   ,				pooled_prompt_embeds=lowerCamelCase_   ,				negative_pooled_prompt_embeds=lowerCamelCase_   ,				)
            a_     :  Dict				      =     output.images[0, -3:, -3:, -1]

            # make sure that it's equal
            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()     ).max() < 1E-4

       def     UpperCAmelCase( self   :  str     ):
            super().test_attention_slicing_forward_pass(expected_max_diff=3E-3     )

       def     UpperCAmelCase( self   :  Tuple     ):
            super().test_inference_batch_single_identical(expected_max_diff=3E-3     )


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

       def     UpperCAmelCase( self   :  int     ):
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  List[str]="cpu"   ,				lowerCamelCase_   :  List[str]=torch.floataa   ,				lowerCamelCase_   :  int=0     ):
            a_     :  str				      =     torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
            a_     :  Dict				      =     np.random.RandomState(lowerCamelCase_     ).standard_normal((1, 4, 6_4, 6_4)     )
            a_     :  Dict				      =     torch.from_numpy(lowerCamelCase_     ).to(device=lowerCamelCase_   ,				dtype=lowerCamelCase_     )
            a_     :  int				      =     {
                """prompt""": """a photograph of an astronaut riding a horse""",
                """latents""": latents,
                """generator""": generator,
                """num_inference_steps""": 3,
                """guidance_scale""": 7.5,
                """output_type""": """numpy""",
            }
            return inputs

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  List[Any]				      =     DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""     )
            pipe.to(lowerCamelCase_     )
            pipe.set_progress_bar_config(disable=lowerCamelCase_     )

            a_     :  Dict				      =     self.get_inputs(lowerCamelCase_     )
            a_     :  Any				      =     pipe(**lowerCamelCase_     ).images
            a_     :  Any				      =     image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_     :  str				      =     np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]     )
            assert np.abs(image_slice - expected_slice     ).max() < 7E-3









'''simple docstring'''





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
  from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import SwinaSRImageProcessor




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=13		,     __lowerCAmelCase=32		,     __lowerCAmelCase=1		,     __lowerCAmelCase=3		,     __lowerCAmelCase=16		,     __lowerCAmelCase=[1, 2, 1]		,     __lowerCAmelCase=[2, 2, 4]		,     __lowerCAmelCase=2		,     __lowerCAmelCase=2.0		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=False		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.02		,     __lowerCAmelCase=1e-5		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=2		,     ):







      '''simple docstring'''


      _a				:							str							       =      parent
      _a				:							str							       =      batch_size
      _a				:							int							       =      image_size
      _a				:							int							       =      patch_size
      _a				:							Any							       =      num_channels
      _a				:							Optional[Any]							       =      embed_dim
      _a				:							List[Any]							       =      depths
      _a				:							Union[str, Any]							       =      num_heads
      _a				:							List[Any]							       =      window_size
      _a				:							Optional[Any]							       =      mlp_ratio
      _a				:							Dict							       =      qkv_bias
      _a				:							Optional[int]							       =      hidden_dropout_prob
      _a				:							List[Any]							       =      attention_probs_dropout_prob
      _a				:							List[str]							       =      drop_path_rate
      _a				:							str							       =      hidden_act
      _a				:							Optional[int]							       =      use_absolute_embeddings
      _a				:							List[str]							       =      patch_norm
      _a				:							List[str]							       =      layer_norm_eps
      _a				:							Any							       =      initializer_range
      _a				:							Any							       =      is_training
      _a				:							Any							       =      scope
      _a				:							int							       =      use_labels
      _a				:							Union[str, Any]							       =      upscale

      # here we set some attributes to make tests pass
      _a				:							List[str]							       =      len(__lowerCAmelCase )
      _a				:							str							       =      embed_dim
      _a				:							Optional[int]							       =      (image_size // patch_size) ** 2



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

      _a				:							Optional[int]							       =      None
      if self.use_labels:
          _a				:							str							       =      ids_tensor([self.batch_size]		,     self.type_sequence_label_size )

      _a				:							str							       =      self.get_config()

      return config, pixel_values, labels



  def     a_							(			self ):







      '''simple docstring'''


      return SwinaSRConfig(
          image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     embed_dim=self.embed_dim		,     depths=self.depths		,     num_heads=self.num_heads		,     window_size=self.window_size		,     mlp_ratio=self.mlp_ratio		,     qkv_bias=self.qkv_bias		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     drop_path_rate=self.drop_path_rate		,     hidden_act=self.hidden_act		,     use_absolute_embeddings=self.use_absolute_embeddings		,     path_norm=self.patch_norm		,     layer_norm_eps=self.layer_norm_eps		,     initializer_range=self.initializer_range		,     upscale=self.upscale		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      SwinaSRModel(config=__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							List[str]							       =      model(__lowerCAmelCase )

      self.parent.assertEqual(
          result.last_hidden_state.shape		,     (self.batch_size, self.embed_dim, self.image_size, self.image_size) )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Dict							       =      SwinaSRForImageSuperResolution(__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							Tuple							       =      model(__lowerCAmelCase )

      _a				:							Dict							       =      self.image_size * self.upscale
      self.parent.assertEqual(
          result.reconstruction.shape		,     (self.batch_size, self.num_channels, expected_image_size, expected_image_size) )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.prepare_config_and_inputs()
      _a       , _a       , _a				:							int							       =      config_and_inputs
      _a				:							Optional[int]							       =      {"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_torch
class      UpperCamelCase       (	snake_case__      ,		snake_case__      ,		unittest.TestCase       ):
  a     :Dict          =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
  a     :List[str]          =					{'feature-extraction': SwinaSRModel} if is_torch_available() else {}

  a     :List[Any]          =					False
  a     :Tuple          =					False
  a     :Any          =					False
  a     :Union[str, Any]          =					False
  a     :Union[str, Any]          =					False



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      SwinaSRModelTester(self )
      _a				:							Optional[int]							       =      ConfigTester(self		,     config_class=__lowerCAmelCase		,     embed_dim=37 )



  def     a_							(			self ):







      '''simple docstring'''


      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_super_resolution(*__lowerCAmelCase )



  @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not use inputs_embeds""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							int							       =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module) )
          _a				:							Optional[int]							       =      model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__lowerCAmelCase		,     nn.Linear ) )



  def     a_							(			self ):







      '''simple docstring'''


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

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

          _a				:							List[Any]							       =      ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]		,     __lowerCAmelCase )



  @slow
  def     a_							(			self ):







      '''simple docstring'''


      for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a				:							List[str]							       =      SwinaSRModel.from_pretrained(__lowerCAmelCase )
          self.assertIsNotNone(__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							str							       =      self.model_tester.prepare_config_and_inputs_for_common()

      _a				:							Union[str, Any]							       =      _config_zero_init(__lowerCAmelCase )
      for model_class in self.all_model_classes:
          _a				:							Any							       =      model_class(config=__lowerCAmelCase )
          for name, param in model.named_parameters():
              if "logit_scale" in name:
                  continue
              if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Dict							       =      self.model_tester.prepare_config_and_inputs_for_common()
      _a				:							List[str]							       =      True

      for model_class in self.all_model_classes:
          _a				:							Dict							       =      True
          _a				:							Optional[int]							       =      False
          _a				:							Any							       =      True
          _a				:							Dict							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          _a				:							Tuple							       =      len(self.model_tester.depths )
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _a				:							List[str]							       =      True
          _a				:							str							       =      config.window_size**2
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Any							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          self.assertListEqual(
              list(attentions[0].shape[-3:] )		,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,     )
          _a				:							Dict							       =      len(__lowerCAmelCase )

          # Check attention is always last and order is fine
          _a				:							Tuple							       =      True
          _a				:							Any							       =      True
          _a				:							str							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )

          self.assertEqual(out_len + 1		,     len(__lowerCAmelCase ) )

          _a				:							List[str]							       =      outputs.attentions

          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          self.assertListEqual(
              list(self_attentions[0].shape[-3:] )		,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,     )






@require_vision
@require_torch
@slow
class      UpperCamelCase       (	unittest.TestCase       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      SwinaSRImageProcessor()
      _a				:							Optional[Any]							       =      SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64""" ).to(__lowerCAmelCase )

      _a				:							Tuple							       =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
      _a				:							Optional[int]							       =      processor(images=__lowerCAmelCase		,     return_tensors="""pt""" ).to(__lowerCAmelCase )

      # forward pass
      with torch.no_grad():
          _a				:							str							       =      model(**__lowerCAmelCase )

      # verify the logits
      _a				:							List[Any]							       =      torch.Size([1, 3, 976, 1296] )
      self.assertEqual(outputs.reconstruction.shape		,     __lowerCAmelCase )
      _a				:							List[str]							       =      torch.tensor(
          [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]] ).to(__lowerCAmelCase )
      self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]		,     __lowerCAmelCase		,     atol=1e-4 ) )


