
'''simple docstring'''





import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
 from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class 			UpperCamelCase__	:






 def __init__(      self	:List[str]						,					_A	:str						,					_A	:Optional[Any]=13						,					_A	:Tuple=32						,					_A	:Dict=2						,					_A	:str=3						,					_A	:Union[str, Any]=16						,					_A	:Optional[Any]=[1, 2, 1]						,					_A	:Tuple=[2, 2, 4]						,					_A	:Dict=2						,					_A	:Optional[int]=2.0						,					_A	:int=True						,					_A	:List[Any]=0.0						,					_A	:Dict=0.0						,					_A	:int=0.1						,					_A	:Optional[Any]="gelu"						,					_A	:Union[str, Any]=False						,					_A	:Optional[Any]=True						,					_A	:Union[str, Any]=0.02						,					_A	:Optional[Any]=1E-5						,					_A	:Union[str, Any]=True						,					_A	:Optional[int]=None						,					_A	:int=True						,					_A	:Optional[Any]=10						,					_A	:List[Any]=8						,					_A	:Optional[Any]=["stage1", "stage2"]						,					_A	:Any=[1, 2]						,					)					->      List[Any]:







       '''simple docstring'''



       __A													=      parent
       __A													=      batch_size
       __A													=      image_size
       __A													=      patch_size
       __A													=      num_channels
       __A													=      embed_dim
       __A													=      depths
       __A													=      num_heads
       __A													=      window_size
       __A													=      mlp_ratio
       __A													=      qkv_bias
       __A													=      hidden_dropout_prob
       __A													=      attention_probs_dropout_prob
       __A													=      drop_path_rate
       __A													=      hidden_act
       __A													=      use_absolute_embeddings
       __A													=      patch_norm
       __A													=      layer_norm_eps
       __A													=      initializer_range
       __A													=      is_training
       __A													=      scope
       __A													=      use_labels
       __A													=      type_sequence_label_size
       __A													=      encoder_stride
       __A													=      out_features
       __A													=      out_indices






 def 						lowercase_    (      self	:Tuple      )					->      List[Any]:







       '''simple docstring'''



       __A													=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       __A													=      None
       if self.use_labels:
             __A													=      ids_tensor([self.batch_size]						,					self.type_sequence_label_size      )

       __A													=      self.get_config()

       return config, pixel_values, labels






 def 						lowercase_    (      self	:str      )					->      List[str]:







       '''simple docstring'''



       return SwinConfig(
           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						,					encoder_stride=self.encoder_stride						,					out_features=self.out_features						,					out_indices=self.out_indices						,					)






 def 						lowercase_    (      self	:Tuple						,					_A	:Optional[int]						,					_A	:Dict						,					_A	:List[Any]      )					->      str:







       '''simple docstring'''



       __A													=      SwinModel(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )

       __A													=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
       __A													=      int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

       self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, expected_seq_len, expected_dim)      )






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Optional[Any]						,					_A	:List[str]						,					_A	:List[Any]      )					->      Dict:







       '''simple docstring'''



       __A													=      SwinBackbone(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps      )						,					len(config.out_features      )      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,					[self.batch_size, model.channels[0], 16, 16]      )

       # verify channels
       self.parent.assertEqual(len(model.channels      )						,					len(config.out_features      )      )

       # verify backbone works with out_features=None
       __A													=      None
       __A													=      SwinBackbone(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps      )						,					1      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,					[self.batch_size, model.channels[-1], 4, 4]      )

       # verify channels
       self.parent.assertEqual(len(model.channels      )						,					1      )






 def 						lowercase_    (      self	:int						,					_A	:Optional[int]						,					_A	:int						,					_A	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      SwinForMaskedImageModeling(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A      )
       self.parent.assertEqual(
           result.logits.shape						,					(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       __A													=      1
       __A													=      SwinForMaskedImageModeling(_A      )
       model.to(_A      )
       model.eval()

       __A													=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       __A													=      model(_A      )
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, 1, self.image_size, self.image_size)      )






 def 						lowercase_    (      self	:List[Any]						,					_A	:Union[str, Any]						,					_A	:List[Any]						,					_A	:Tuple      )					->      Any:







       '''simple docstring'''



       __A													=      self.type_sequence_label_size
       __A													=      SwinForImageClassification(_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(_A						,					labels=_A      )
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       __A													=      1
       __A													=      SwinForImageClassification(_A      )
       model.to(_A      )
       model.eval()

       __A													=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       __A													=      model(_A      )
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.type_sequence_label_size)      )






 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



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


@require_torch
class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     str    					=       (
     (
         SwinModel,
         SwinBackbone,
         SwinForImageClassification,
         SwinForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__     :     Optional[Any]    					=       (
     {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__     :     Dict    					=       True

 UpperCAmelCase__     :     List[Any]    					=       False
 UpperCAmelCase__     :     Tuple    					=       False
 UpperCAmelCase__     :     str    					=       False






 def 						lowercase_    (      self	:Optional[Any]      )					->      List[Any]:







       '''simple docstring'''



       __A													=      SwinModelTester(self      )
       __A													=      ConfigTester(self						,					config_class=_A						,					embed_dim=37      )






 def 						lowercase_    (      self	:List[Any]      )					->      str:







       '''simple docstring'''



       self.create_and_test_config_common_properties()
       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 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[Any]:







       '''simple docstring'''



       return






 def 						lowercase_    (      self	:Union[str, Any]      )					->      List[Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A      )






 @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'      )
 def 						lowercase_    (      self	:int      )					->      Any:







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



       super().test_training_gradient_checkpointing()






 def 						lowercase_    (      self	:List[str]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*_A      )






 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_A      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      Dict:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_A      )






 @unittest.skip(reason='Swin does not use inputs_embeds'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      Dict:







       '''simple docstring'''



       pass






 @unittest.skip(reason='Swin Transformer does not use feedforward chunking'      )
 def 						lowercase_    (      self	:str      )					->      Any:







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:List[str]      )					->      Dict:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A													=      model_class(_A      )
             self.assertIsInstance(model.get_input_embeddings()						,					(nn.Module)      )
             __A													=      model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_A						,					nn.Linear      )      )






 def 						lowercase_    (      self	:List[str]      )					->      List[str]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

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

             __A													=      ['pixel_values']
             self.assertListEqual(arg_names[:1]						,					_A      )






 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



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

       for model_class in self.all_model_classes:
             __A													=      True
             __A													=      False
             __A													=      True
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()
             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )
             __A													=      outputs.attentions
             __A													=      len(self.model_tester.depths      )
             self.assertEqual(len(_A      )						,					_A      )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             __A													=      True
             __A													=      config.window_size**2
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()
             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )
             __A													=      outputs.attentions
             self.assertEqual(len(_A      )						,					_A      )

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

             # Check attention is always last and order is fine
             __A													=      True
             __A													=      True
             __A													=      model_class(_A      )
             model.to(_A      )
             model.eval()
             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(_A						,					_A      )      )

             # also another +1 for reshaped_hidden_states
             __A													=      1 if model_class.__name__ == 'SwinBackbone' else 2
             self.assertEqual(out_len + added_hidden_states						,					len(_A      )      )

             __A													=      outputs.attentions

             self.assertEqual(len(_A      )						,					_A      )

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






 def 						lowercase_    (      self	:int						,					_A	:int						,					_A	:Dict						,					_A	:Optional[Any]						,					_A	:Dict      )					->      Dict:







       '''simple docstring'''



       __A													=      model_class(_A      )
       model.to(_A      )
       model.eval()

       with torch.no_grad():
             __A													=      model(**self._prepare_for_class(_A						,					_A      )      )

       __A													=      outputs.hidden_states

       __A													=      getattr(
           self.model_tester						,					'expected_num_hidden_layers'						,					len(self.model_tester.depths      ) + 1      )
       self.assertEqual(len(_A      )						,					_A      )

       # Swin has a different seq_length
       __A													=      (
           config.patch_size
           if isinstance(config.patch_size						,					collections.abc.Iterable      )
           else (config.patch_size, config.patch_size)
       )

       __A													=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]      )						,					[num_patches, self.model_tester.embed_dim]						,					)

       if not model_class.__name__ == "SwinBackbone":
             __A													=      outputs.reshaped_hidden_states
             self.assertEqual(len(_A      )						,					_A      )

             __A						,   __A						,   __A						,   __A													=      reshaped_hidden_states[0].shape
             __A													=      (
                 reshaped_hidden_states[0].view(_A						,					_A						,					height * width      ).permute(0						,					2						,					1      )
             )
             self.assertListEqual(
                 list(reshaped_hidden_states.shape[-2:]      )						,					[num_patches, self.model_tester.embed_dim]						,					)






 def 						lowercase_    (      self	:int      )					->      Optional[Any]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       __A													=      (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size						,					collections.abc.Iterable      )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
             __A													=      True
             self.check_hidden_states_output(_A						,					_A						,					_A						,					_A      )

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

             self.check_hidden_states_output(_A						,					_A						,					_A						,					_A      )






 def 						lowercase_    (      self	:int      )					->      Union[str, Any]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()
       __A													=      3

       __A													=      (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size						,					collections.abc.Iterable      )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       __A													=      (
           config.patch_size
           if isinstance(config.patch_size						,					collections.abc.Iterable      )
           else (config.patch_size, config.patch_size)
       )

       __A													=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       __A													=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             __A													=      True
             self.check_hidden_states_output(_A						,					_A						,					_A						,					(padded_height, padded_width)      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __A													=      True
             self.check_hidden_states_output(_A						,					_A						,					_A						,					(padded_height, padded_width)      )






 @slow
 def 						lowercase_    (      self	:Optional[Any]      )					->      Dict:







       '''simple docstring'''



       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A													=      SwinModel.from_pretrained(_A      )
             self.assertIsNotNone(_A      )






 def 						lowercase_    (      self	:str      )					->      List[str]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       __A													=      _config_zero_init(_A      )
       for model_class in self.all_model_classes:
             __A													=      model_class(config=_A      )
             for name, param in model.named_parameters():
                   if "embeddings" not in name and 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'						,					)


@require_vision
@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @cached_property
 def 						lowercase_    (      self	:Any      )					->      Any:







       '''simple docstring'''



       return (
           AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
           if is_vision_available()
           else None
       )






 @slow
 def 						lowercase_    (      self	:List[Any]      )					->      Any:







       '''simple docstring'''



       __A													=      SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      ).to(_A      )
       __A													=      self.default_image_processor

       __A													=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
       __A													=      image_processor(images=_A						,					return_tensors='pt'      ).to(_A      )

       # forward pass
       with torch.no_grad():
             __A													=      model(**_A      )

       # verify the logits
       __A													=      torch.Size((1, 1_000)      )
       self.assertEqual(outputs.logits.shape						,					_A      )
       __A													=      torch.tensor([-0.0_948, -0.6_454, -0.0_921]      ).to(_A      )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]						,					_A						,					atol=1E-4      )      )


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase   ,  SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     int    					=       (SwinBackbone,) if is_torch_available() else ()
 UpperCAmelCase__     :     Optional[int]    					=       SwinConfig






 def 						lowercase_    (      self	:str      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      SwinModelTester(self      )










'''simple docstring'''


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin






class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__     ,							unittest.TestCase						):
			lowerCAmelCase			:						Any							 =  UNetaDModel
			lowerCAmelCase			:						List[Any]							 =  "sample"




			@property
			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   4
					_UpperCAmelCase		:							Any             =   14
					_UpperCAmelCase		:							Any             =   16

					_UpperCAmelCase		:							Optional[int]             =   floats_tensor((batch_size, num_features, seq_len)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   torch.tensor([10] * batch_size			).to(lowerCamelCase__			)

					return {"sample": noise, "timestep": time_step}




			@property
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->List[Any]:



					'''simple docstring'''







					return (4, 14, 16)




			@property
			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Tuple:



					'''simple docstring'''







					return (4, 14, 16)




			def 				lowerCAmelCase__     (		self	:  int			) ->Tuple:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  Dict			) ->Tuple:



					'''simple docstring'''







					pass




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Any			) ->List[str]:



					'''simple docstring'''







					super().test_determinism()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					super().test_outputs_equivalence()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->List[str]:



					'''simple docstring'''







					super().test_from_save_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					super().test_from_save_pretrained_variant()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  str			) ->int:



					'''simple docstring'''







					super().test_model_from_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Union[str, Any]:



					'''simple docstring'''







					super().test_output()




			def 				lowerCAmelCase__     (		self	:  int			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   {
					    "block_out_channels": (32, 64, 1_28, 2_56),
					    "in_channels": 14,
					    "out_channels": 14,
					    "time_embedding_type": "positional",
					    "use_timestep_embedding": True,
					    "flip_sin_to_cos": False,
					    "freq_shift": 1.0,
					    "out_block_type": "OutConv1DBlock",
					    "mid_block_type": "MidResTemporalBlock1D",
					    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
					    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
					    "act_fn": "mish",
					}
					_UpperCAmelCase		:							List[Any]             =   self.dummy_input
					return init_dict, inputs_dict




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							int             =   UNetaDModel.from_pretrained(
					    "bglick13/hopper-medium-v2-value-function-hor32"						,	output_loading_info=lowerCamelCase__						,	subfolder="unet"			)
					self.assertIsNotNone(lowerCamelCase__			)
					self.assertEqual(len(loading_info["missing_keys"]			)						,	0			)

					model.to(lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   model(**self.dummy_input			)

					assert image is not None, "Make sure output is not None"




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"						,	subfolder="unet"			)
					torch.manual_seed(0			)
					if torch.cuda.is_available():
							torch.cuda.manual_seed_all(0			)

					_UpperCAmelCase		:							str             =   model.config.in_channels
					_UpperCAmelCase		:							Optional[int]             =   16
					_UpperCAmelCase		:							List[str]             =   torch.randn((1, seq_len, num_features)			).permute(
					    0						,	2						,	1			)  # match original, we can update values and remove
					_UpperCAmelCase		:							Tuple             =   torch.full((num_features,)						,	0			)

					with torch.no_grad():
							_UpperCAmelCase		:							List[Any]             =   model(lowerCamelCase__						,	lowerCamelCase__			).sample.permute(0						,	2						,	1			)

					_UpperCAmelCase		:							Optional[int]             =   output[0, -3:, -3:].flatten()
					# fmt: off
					_UpperCAmelCase		:							Dict             =   torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]			)
					# fmt: on
					self.assertTrue(torch.allclose(lowerCamelCase__						,	lowerCamelCase__						,	rtol=1E-3			)			)




			def 				lowerCAmelCase__     (		self	:  Any			) ->List[str]:



					'''simple docstring'''







					pass






			@slow
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   "harmonai/maestro-150k"
					_UpperCAmelCase		:							Tuple             =   UNetaDModel.from_pretrained(lowerCamelCase__						,	subfolder="unet"			)
					model.to(lowerCamelCase__			)

					_UpperCAmelCase		:							List[str]             =   6_55_36
					_UpperCAmelCase		:							Any             =   torch.sin(torch.arange(lowerCamelCase__			)[None, None, :].repeat(1						,	2						,	1			)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							List[Any]             =   torch.tensor([1]			).to(lowerCamelCase__			)

					with torch.no_grad():
							_UpperCAmelCase		:							List[str]             =   model(lowerCamelCase__						,	lowerCamelCase__			).sample

					_UpperCAmelCase		:							Union[str, Any]             =   output.abs().sum()
					_UpperCAmelCase		:							Any             =   output.abs().max()

					assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
					assert (output_max - 0.0_6_0_7).abs() < 4E-4






class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__     ,							unittest.TestCase						):
			lowerCAmelCase			:						Union[str, Any]							 =  UNetaDModel
			lowerCAmelCase			:						Union[str, Any]							 =  "sample"




			@property
			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							Tuple             =   4
					_UpperCAmelCase		:							Optional[int]             =   14
					_UpperCAmelCase		:							Optional[int]             =   16

					_UpperCAmelCase		:							Optional[int]             =   floats_tensor((batch_size, num_features, seq_len)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							Union[str, Any]             =   torch.tensor([10] * batch_size			).to(lowerCamelCase__			)

					return {"sample": noise, "timestep": time_step}




			@property
			def 				lowerCAmelCase__     (		self	:  Dict			) ->Optional[int]:



					'''simple docstring'''







					return (4, 14, 16)




			@property
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					return (4, 14, 1)




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Dict:



					'''simple docstring'''







					super().test_determinism()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Optional[Any]:



					'''simple docstring'''







					super().test_outputs_equivalence()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  str			) ->Optional[int]:



					'''simple docstring'''







					super().test_from_save_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Dict:



					'''simple docstring'''







					super().test_from_save_pretrained_variant()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Dict:



					'''simple docstring'''







					super().test_model_from_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   self.prepare_init_args_and_inputs_for_common()
					_UpperCAmelCase		:							Optional[int]             =   self.model_class(**lowerCamelCase__			)
					model.to(lowerCamelCase__			)
					model.eval()

					with torch.no_grad():
							_UpperCAmelCase		:							int             =   model(**lowerCamelCase__			)

							if isinstance(lowerCamelCase__						,	lowerCamelCase__			):
									_UpperCAmelCase		:							List[Any]             =   output.sample

					self.assertIsNotNone(lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   torch.Size((inputs_dict["sample"].shape[0], 1)			)
					self.assertEqual(output.shape						,	lowerCamelCase__						,	"Input and output shapes do not match"			)




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Dict:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->List[str]:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  Dict			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   {
					    "in_channels": 14,
					    "out_channels": 14,
					    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
					    "up_block_types": [],
					    "out_block_type": "ValueFunction",
					    "mid_block_type": "ValueFunctionMidBlock1D",
					    "block_out_channels": [32, 64, 1_28, 2_56],
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "use_timestep_embedding": True,
					    "freq_shift": 1.0,
					    "flip_sin_to_cos": False,
					    "time_embedding_type": "positional",
					    "act_fn": "mish",
					}
					_UpperCAmelCase		:							List[Any]             =   self.dummy_input
					return init_dict, inputs_dict




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							Any             =   UNetaDModel.from_pretrained(
					    "bglick13/hopper-medium-v2-value-function-hor32"						,	output_loading_info=lowerCamelCase__						,	subfolder="value_function"			)
					self.assertIsNotNone(lowerCamelCase__			)
					self.assertEqual(len(vf_loading_info["missing_keys"]			)						,	0			)

					value_function.to(lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   value_function(**self.dummy_input			)

					assert image is not None, "Make sure output is not None"




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   UNetaDModel.from_pretrained(
					    "bglick13/hopper-medium-v2-value-function-hor32"						,	output_loading_info=lowerCamelCase__						,	subfolder="value_function"			)
					torch.manual_seed(0			)
					if torch.cuda.is_available():
							torch.cuda.manual_seed_all(0			)

					_UpperCAmelCase		:							Tuple             =   value_function.config.in_channels
					_UpperCAmelCase		:							Tuple             =   14
					_UpperCAmelCase		:							int             =   torch.randn((1, seq_len, num_features)			).permute(
					    0						,	2						,	1			)  # match original, we can update values and remove
					_UpperCAmelCase		:							Tuple             =   torch.full((num_features,)						,	0			)

					with torch.no_grad():
							_UpperCAmelCase		:							int             =   value_function(lowerCamelCase__						,	lowerCamelCase__			).sample

					# fmt: off
					_UpperCAmelCase		:							Union[str, Any]             =   torch.tensor([1_6_5.2_5] * seq_len			)
					# fmt: on
					self.assertTrue(torch.allclose(lowerCamelCase__						,	lowerCamelCase__						,	rtol=1E-3			)			)






			def 				lowerCAmelCase__     (		self	:  Dict			) ->Union[str, Any]:



					'''simple docstring'''







					pass



