






'''simple docstring'''






from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
      from PIL import Image

      from transformers import ConvNextImageProcessor




class a_     :







   def __init__(self       , __a       , __a=1_3       , __a=3_2       , __a=3       , __a=4       , __a=[1_0, 2_0, 3_0, 4_0]       , __a=[2, 2, 3, 2]       , __a=True       , __a=True       , __a=3_7       , __a="gelu"       , __a=1_0       , __a=0.02       , __a=3       , __a=None       , )      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			parent
      __snake_case					:   int  				=			batch_size
      __snake_case					:   Any  				=			image_size
      __snake_case					:   Dict  				=			num_channels
      __snake_case					:   int  				=			num_stages
      __snake_case					:   Dict  				=			hidden_sizes
      __snake_case					:   int  				=			depths
      __snake_case					:   Union[str, Any]  				=			is_training
      __snake_case					:   str  				=			use_labels
      __snake_case					:   List[Any]  				=			intermediate_size
      __snake_case					:   str  				=			hidden_act
      __snake_case					:   str  				=			type_sequence_label_size
      __snake_case					:   Dict  				=			initializer_range
      __snake_case					:   Optional[int]  				=			scope







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      __snake_case					:   Union[str, Any]  				=			None
      if self.use_labels:
         __snake_case					:   Dict  				=			ids_tensor([self.batch_size]       , self.type_sequence_label_size)

      __snake_case					:   Optional[Any]  				=			self.get_config()

      return config, pixel_values, labels







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      return ConvNextConfig(
          num_channels=self.num_channels       , hidden_sizes=self.hidden_sizes       , depths=self.depths       , num_stages=self.num_stages       , hidden_act=self.hidden_act       , is_decoder=lowerCamelCase__       , initializer_range=self.initializer_range       , )







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   int  				=			TFConvNextModel(config=lowerCamelCase__)
      __snake_case					:   Union[str, Any]  				=			model(lowerCamelCase__       , training=lowerCamelCase__)
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape       , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       , )







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   str:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.type_sequence_label_size
      __snake_case					:   Optional[Any]  				=			TFConvNextForImageClassification(lowerCamelCase__)
      __snake_case					:   str  				=			model(lowerCamelCase__       , labels=lowerCamelCase__       , training=lowerCamelCase__)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.type_sequence_label_size))







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






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




@require_tf
class a_     (     UpperCAmelCase__		,   UpperCAmelCase__		,   unittest.TestCase				):
   _snake_case	   =				(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
   _snake_case	   =				(
       {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
       if is_tf_available()
       else {}
   )

   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			TFConvNextModelTester(self)
      __snake_case					:   int  				=			ConfigTester(
          self       , config_class=lowerCamelCase__       , has_text_modality=lowerCamelCase__       , hidden_size=3_7       , )







   @unittest.skip(reason='ConvNext does not use inputs_embeds')
   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      pass







   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0       , reason='TF does not support backprop for grouped convolutions on CPU.'       , )
   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      super().test_keras_fit()







   @unittest.skip(reason='ConvNext does not support input and output embeddings')
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      pass







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   int  				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __snake_case					:   Union[str, Any]  				=			model_class(lowerCamelCase__)
         __snake_case					:   Any  				=			inspect.signature(model.call)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __snake_case					:   List[Any]  				=			[*signature.parameters.keys()]

         __snake_case					:   List[str]  				=			["pixel_values"]
         self.assertListEqual(arg_names[:1]       , lowerCamelCase__)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase__)







   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0       , reason='TF does not support backprop for grouped convolutions on CPU.'       , )
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      super().test_dataset_conversion()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      def check_hidden_states_output(__a       , __a       , __a):
         __snake_case					:   List[Any]  				=			model_class(lowerCamelCase__)

         __snake_case					:   Union[str, Any]  				=			model(**self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__))
         __snake_case					:   List[Any]  				=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         __snake_case					:   int  				=			self.model_tester.num_stages
         self.assertEqual(len(lowerCamelCase__)       , expected_num_stages + 1)

         # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:])       , [self.model_tester.image_size // 4, self.model_tester.image_size // 4]       , )

      __snake_case					:   Union[str, Any]  				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __snake_case					:   str  				=			True
         check_hidden_states_output(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__)

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

         check_hidden_states_output(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Any  				=			self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(__a       , __a       , __a       , __a={}):
         __snake_case					:   Optional[int]  				=			model(lowerCamelCase__       , return_dict=lowerCamelCase__       , **lowerCamelCase__)
         __snake_case					:   Optional[int]  				=			model(lowerCamelCase__       , return_dict=lowerCamelCase__       , **lowerCamelCase__).to_tuple()

         def recursive_check(__a       , __a):
            if isinstance(lowerCamelCase__       , (List, Tuple)):
               for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase__       , lowerCamelCase__):
                  recursive_check(lowerCamelCase__       , lowerCamelCase__)
            elif tuple_object is None:
               return
            else:
               self.assertTrue(
                   all(tf.equal(lowerCamelCase__       , lowerCamelCase__))       , msg=(
                       'Tuple and dict output are not equal. Difference:'
                       F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"""
                   )       , )

            recursive_check(lowerCamelCase__       , lowerCamelCase__)

      for model_class in self.all_model_classes:
         __snake_case					:   List[Any]  				=			model_class(lowerCamelCase__)

         __snake_case					:   Any  				=			self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__)
         __snake_case					:   Optional[Any]  				=			self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__)
         check_equivalence(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__)

         __snake_case					:   str  				=			self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__       , return_labels=lowerCamelCase__)
         __snake_case					:   Optional[Any]  				=			self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__       , return_labels=lowerCamelCase__)
         check_equivalence(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__)

         __snake_case					:   Tuple  				=			self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__)
         __snake_case					:   List[Any]  				=			self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__)
         check_equivalence(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__       , {'output_hidden_states': True})

         __snake_case					:   List[Any]  				=			self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__       , return_labels=lowerCamelCase__)
         __snake_case					:   List[str]  				=			self._prepare_for_class(lowerCamelCase__       , lowerCamelCase__       , return_labels=lowerCamelCase__)
         check_equivalence(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__       , {'output_hidden_states': True})







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__)







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Any  				=			TFConvNextModel.from_pretrained('facebook/convnext-tiny-224')
      self.assertIsNotNone(lowerCamelCase__)




def 							_SCREAMING_SNAKE_CASE	(					)     ->			Dict:







   """simple docstring"""
   __snake_case					:   List[Any]  				=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image





@require_tf
@require_vision
class a_     (     unittest.TestCase				):







   @cached_property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224') if is_vision_available() else None







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   int  				=			TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224')

      __snake_case					:   Optional[Any]  				=			self.default_image_processor
      __snake_case					:   Dict  				=			prepare_img()
      __snake_case					:   str  				=			image_processor(images=lowerCamelCase__       , return_tensors='tf')

      # forward pass
      __snake_case					:   str  				=			model(**lowerCamelCase__)

      # verify the logits
      __snake_case					:   int  				=			tf.TensorShape((1, 1_0_0_0))
      self.assertEqual(outputs.logits.shape       , lowerCamelCase__)

      __snake_case					:   Dict  				=			tf.constant([-0.0_260, -0.4_739, 0.1_911])

      tf.debugging.assert_near(outputs.logits[0, :3]       , lowerCamelCase__       , atol=1E-4)



'''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



