code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  import json
  import os
  import re
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from pprint import pprint
  from typing import Dict, List, Tuple
  
  import torch
  import torch.nn as nn
  from classy_vision.models.regnet import RegNet, RegNetParams
  from huggingface_hub import cached_download, hf_hub_url
  from torch import Tensor
  from vissl.models.model_helpers import get_trunk_forward_outputs
  
  from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  __lowercase			:		Optional[Any] =			logging.get_logger()
  
  
  
  
  
  @dataclass
  class 							__UpperCamelCase       :
  	A_			    =					42
  	A_			    =					field(default_factory=lowerCAmelCase_      )
  	A_			    =					field(default_factory=lowerCAmelCase_      )
  	A_			    =					field(default_factory=lowerCAmelCase_      )
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						len(list(m.modules()							)							) == 1 or isinstance(__a		,       nn.Convad							) or isinstance(__a		,       nn.BatchNormad							)
  						if has_not_submodules:
  											self.traced.append(__a							)
  											__a    : Optional[Any]      					=						m
  
  
  
  
  	def __call__(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for name, m in self.module.named_modules():
  											self.handles.append(m.register_forward_hook(partial(self._forward_hook		,       name=__a							)							)							)
  						self.module(__a							)
  						[x.remove() for x in self.handles]
  						return self
  
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}
  
  
  
  
  
  class 							__UpperCamelCase       (	nn.Module      ):
  
  
  
  
  	def __init__(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().__init__()
  
  						__a    : List[Tuple[str, nn.Module]]      					=						[]
  						# - get the stem
  						feature_blocks.append(('conv1', model.stem)							)
  						# - get all the feature blocks
  						for k, v in model.trunk_output.named_children():
  											assert k.startswith('block'							), f"""Unexpected layer name {k}"""
  											__a    : List[Any]      					=						len(__a							) + 1
  											feature_blocks.append((f"""res{block_index}""", v)							)
  
  						__a    : str      					=						nn.ModuleDict(__a							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return get_trunk_forward_outputs(
  						    __a		,       out_feat_keys=__a		,       feature_blocks=self._feature_blocks		,       )
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str     ):
  					__a    : Dict      					=						RegNetConfig(depths=[2, 7, 17, 1]		,   hidden_sizes=[8, 8, 8, 8]		,   groups_width=8     )
  					if "in1k" in model_name:
  										__a    : Tuple      					=						RegNetForImageClassification(_SCREAMING_SNAKE_CASE     )
  					else:
  										__a    : Optional[int]      					=						RegNetModel(_SCREAMING_SNAKE_CASE     )
  					# create from model (with small weights)
  					__a    : Tuple      					=						FakeRegNetVisslWrapper(
  					    RegNet(FakeRegNetParams(depth=27		,   group_width=1_010		,   w_a=1_744		,   w_a=6_2_0.8_3		,   w_m=2.5_2     )     )     )
  
  					with torch.no_grad():
  										__a    : int      					=						from_model.eval()
  										__a    : List[Any]      					=						our_model.eval()
  
  										__a    : Tuple      					=						torch.randn((1, 3, 32, 32)     )
  										# trace both
  										__a    : List[Any]      					=						Tracker(_SCREAMING_SNAKE_CASE     )
  										__a    : List[str]      					=						dest_tracker(_SCREAMING_SNAKE_CASE     ).parametrized
  
  										pprint(dest_tracker.nameamodule     )
  										__a    : str      					=						Tracker(_SCREAMING_SNAKE_CASE     )
  										__a    : int      					=						src_tracker(_SCREAMING_SNAKE_CASE     ).parametrized
  
  					# convert the keys -> module dict to keys -> params
  					def to_params_dict(_SCREAMING_SNAKE_CASE   :     List[Any]     ):
  										__a    : List[str]      					=						OrderedDict()
  										for name, module in dict_with_modules.items():
  															for param_name, param in module.state_dict().items():
  																				__a    : Dict      					=						param
  										return params_dict
  
  					__a    : Optional[int]      					=						{}
  
  					__a    : Any      					=						to_params_dict(_SCREAMING_SNAKE_CASE     )
  					__a    : int      					=						to_params_dict(_SCREAMING_SNAKE_CASE     )
  
  					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()		,   dst_state_dict.items()     ):
  										__a    : List[Any]      					=						dest_key
  										logger.info(F"""{src_key} -> {dest_key}"""     )
  					# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
  					if "in1k" in model_name:
  										__a    : str      					=						'classifier.1.weight'
  										__a    : Dict      					=						'classifier.1.bias'
  
  					return from_to_ours_keys
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Path		,   _SCREAMING_SNAKE_CASE   :     str = None		,   _SCREAMING_SNAKE_CASE   :     bool = True     ):
  					__a    : List[Any]      					=						'imagenet-1k-id2label.json'
  					__a    : str      					=						1_000
  
  					__a    : List[str]      					=						'huggingface/label-files'
  					__a    : Tuple      					=						num_labels
  					__a    : Tuple      					=						json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   repo_type='dataset'     )     )		,   'r'     )     )
  					__a    : Union[str, Any]      					=						{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
  
  					__a    : List[str]      					=						idalabel
  					__a    : str      					=						{v: k for k, v in idalabel.items()}
  
  					__a    : Dict      					=						partial(_SCREAMING_SNAKE_CASE		,   num_labels=_SCREAMING_SNAKE_CASE		,   idalabel=_SCREAMING_SNAKE_CASE		,   labelaid=_SCREAMING_SNAKE_CASE     )
  
  					__a    : List[str]      					=						{
  					    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
  					        depths=[2, 7, 17, 1]		,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]		,   groups_width=1_010     ),
  					    # finetuned on imagenet
  					    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
  					        depths=[2, 7, 17, 1]		,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]		,   groups_width=1_010     ),
  					}
  
  					# add seer weights logic
  					def load_using_classy_vision(_SCREAMING_SNAKE_CASE   :     str     ) -> Tuple[Dict, Dict]:
  										__a    : List[Any]      					=						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE		,   model_dir=str(_SCREAMING_SNAKE_CASE     )		,   map_location='cpu'     )
  										# check if we have a head, if yes add it
  										__a    : Optional[Any]      					=						files['classy_state_dict']['base_model']['model']
  										return model_state_dict["trunk"], model_state_dict["heads"]
  
  					__a    : List[Any]      					=						{
  					    'regnet-y-10b-seer': partial(
  					        _SCREAMING_SNAKE_CASE		,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'		,   ),
  					    'regnet-y-10b-seer-in1k': partial(
  					        _SCREAMING_SNAKE_CASE		,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'		,   ),
  					}
  
  					__a    : Dict      					=						get_from_to_our_keys(_SCREAMING_SNAKE_CASE     )
  
  					if not (save_directory / F"""{model_name}.pth""").exists():
  										logger.info('Loading original state_dict.'     )
  										__a					,       __a    : Union[str, Any]      					=						names_to_from_model[model_name]()
  										__a    : List[str]      					=						from_state_dict_trunk
  										if "in1k" in model_name:
  															# add the head
  															__a    : Dict      					=						{**from_state_dict_trunk, **from_state_dict_head}
  										logger.info('Done!'     )
  
  										__a    : Optional[Any]      					=						{}
  
  										__a    : Optional[int]      					=						list(from_state_dict.keys()     )
  										__a    : int      					=						r'\.block.-part.'
  										# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
  										for key in from_state_dict.keys():
  															# remove the weird "block[0,1]-part" from the key
  															__a    : Any      					=						re.sub(_SCREAMING_SNAKE_CASE		,   ''		,   _SCREAMING_SNAKE_CASE     )
  															# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
  															__a    : Union[str, Any]      					=						from_to_ours_keys[src_key]
  															# store the parameter with our key
  															__a    : List[str]      					=						from_state_dict[key]
  															not_used_keys.remove(_SCREAMING_SNAKE_CASE     )
  										# check that all keys have been updated
  										assert len(_SCREAMING_SNAKE_CASE     ) == 0, F"""Some keys where not used {",".join(_SCREAMING_SNAKE_CASE     )}"""
  
  										logger.info(F"""The following keys were not used: {",".join(_SCREAMING_SNAKE_CASE     )}"""     )
  
  										# save our state dict to disk
  										torch.save(_SCREAMING_SNAKE_CASE		,   save_directory / F"""{model_name}.pth"""     )
  
  										del converted_state_dict
  					else:
  										logger.info('The state_dict was already stored on disk.'     )
  					if push_to_hub:
  										logger.info(F"""Token is {os.environ["HF_TOKEN"]}"""     )
  										logger.info('Loading our model.'     )
  										# create our model
  										__a    : Dict      					=						names_to_config[model_name]
  										__a    : str      					=						RegNetModel
  										if "in1k" in model_name:
  															__a    : Any      					=						RegNetForImageClassification
  										__a    : List[str]      					=						our_model_func(_SCREAMING_SNAKE_CASE     )
  										# place our model to the meta device (so remove all the weights)
  										our_model.to(torch.device('meta'     )     )
  										logger.info('Loading state_dict in our model.'     )
  										# load state dict
  										__a    : str      					=						our_model.state_dict().keys()
  										PreTrainedModel._load_pretrained_model_low_mem(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   [save_directory / F"""{model_name}.pth"""]     )
  										logger.info('Finally, pushing!'     )
  										# push it to hub
  										our_model.push_to_hub(
  										    repo_path_or_name=save_directory / model_name		,   commit_message='Add model'		,   output_dir=save_directory / model_name		,   )
  										__a    : Tuple      					=						384
  										# we can use the convnext one
  										__a    : Any      					=						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'		,   size=_SCREAMING_SNAKE_CASE     )
  										image_processor.push_to_hub(
  										    repo_path_or_name=save_directory / model_name		,   commit_message='Add image processor'		,   output_dir=save_directory / model_name		,   )
  
  
  if __name__ == "__main__":
  	__lowercase			:		List[str] =			argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    '--model_name',
  	    default=None,
  	    type=str,
  	    help=(
  	        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
  	        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
  	    ),
  	)
  	parser.add_argument(
  	    '--pytorch_dump_folder_path',
  	    default=None,
  	    type=Path,
  	    required=True,
  	    help='Path to the output PyTorch model directory.',
  	)
  	parser.add_argument(
  	    '--push_to_hub',
  	    default=True,
  	    type=bool,
  	    required=False,
  	    help='If True, push model and image processor to the hub.',
  	)
  
  	__lowercase			:		int =			parser.parse_args()
  
  	__lowercase			:		Path =			args.pytorch_dump_folder_path
  	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
  
  
  
  
style_context: 
  
  
  '''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 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					UNetaDModel
  	A_			    =					"sample"
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						4
  						__a    : str      					=						14
  						__a    : int      					=						16
  
  						__a    : List[str]      					=						floats_tensor((batch_size, num_features, seq_len)							).to(__a							)
  						__a    : Optional[Any]      					=						torch.tensor([10] * batch_size							).to(__a							)
  
  						return {"sample": noise, "timestep": time_step}
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_determinism()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_outputs_equivalence()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained_variant()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_model_from_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_output()
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Union[str, Any]      					=						{
  						    'block_out_channels': (32, 64, 128, 256),
  						    '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',
  						}
  						__a    : str      					=						self.dummy_input
  						return init_dict, inputs_dict
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='unet'							)
  						self.assertIsNotNone(__a							)
  						self.assertEqual(len(loading_info['missing_keys']							)		,       0							)
  
  						model.to(__a							)
  						__a    : Optional[int]      					=						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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						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							)
  
  						__a    : str      					=						model.config.in_channels
  						__a    : int      					=						16
  						__a    : List[str]      					=						torch.randn((1, seq_len, num_features)							).permute(
  						    0		,       2		,       1							)  # match original, we can update values and remove
  						__a    : List[Any]      					=						torch.full((num_features,)		,       0							)
  
  						with torch.no_grad():
  											__a    : Tuple      					=						model(__a		,       __a							).sample.permute(0		,       2		,       1							)
  
  						__a    : int      					=						output[0, -3:, -3:].flatten()
  						# fmt: off
  						__a    : List[str]      					=						torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]							)
  						# fmt: on
  						self.assertTrue(torch.allclose(__a		,       __a		,       rtol=1E-3							)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Tuple      					=						'harmonai/maestro-150k'
  						__a    : Any      					=						UNetaDModel.from_pretrained(__a		,       subfolder='unet'							)
  						model.to(__a							)
  
  						__a    : Optional[Any]      					=						6_5536
  						__a    : str      					=						torch.sin(torch.arange(__a							)[None, None, :].repeat(1		,       2		,       1							)							).to(__a							)
  						__a    : int      					=						torch.tensor([1]							).to(__a							)
  
  						with torch.no_grad():
  											__a    : Optional[int]      					=						model(__a		,       __a							).sample
  
  						__a    : Union[str, Any]      					=						output.abs().sum()
  						__a    : Dict      					=						output.abs().max()
  
  						assert (output_sum - 224.0896).abs() < 0.5
  						assert (output_max - 0.0607).abs() < 4E-4
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					UNetaDModel
  	A_			    =					"sample"
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						4
  						__a    : str      					=						14
  						__a    : Optional[int]      					=						16
  
  						__a    : str      					=						floats_tensor((batch_size, num_features, seq_len)							).to(__a							)
  						__a    : Optional[Any]      					=						torch.tensor([10] * batch_size							).to(__a							)
  
  						return {"sample": noise, "timestep": time_step}
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 1)
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_determinism()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_outputs_equivalence()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained_variant()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_model_from_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[Any]      					=						self.prepare_init_args_and_inputs_for_common()
  						__a    : int      					=						self.model_class(**__a							)
  						model.to(__a							)
  						model.eval()
  
  						with torch.no_grad():
  											__a    : Optional[Any]      					=						model(**__a							)
  
  											if isinstance(__a		,       __a							):
  																__a    : Any      					=						output.sample
  
  						self.assertIsNotNone(__a							)
  						__a    : Tuple      					=						torch.Size((inputs_dict['sample'].shape[0], 1)							)
  						self.assertEqual(output.shape		,       __a		,       'Input and output shapes do not match'							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : int      					=						{
  						    '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, 128, 256],
  						    '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',
  						}
  						__a    : Union[str, Any]      					=						self.dummy_input
  						return init_dict, inputs_dict
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='value_function'							)
  						self.assertIsNotNone(__a							)
  						self.assertEqual(len(vf_loading_info['missing_keys']							)		,       0							)
  
  						value_function.to(__a							)
  						__a    : Optional[Any]      					=						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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Dict      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='value_function'							)
  						torch.manual_seed(0							)
  						if torch.cuda.is_available():
  											torch.cuda.manual_seed_all(0							)
  
  						__a    : str      					=						value_function.config.in_channels
  						__a    : Dict      					=						14
  						__a    : List[str]      					=						torch.randn((1, seq_len, num_features)							).permute(
  						    0		,       2		,       1							)  # match original, we can update values and remove
  						__a    : List[Any]      					=						torch.full((num_features,)		,       0							)
  
  						with torch.no_grad():
  											__a    : Any      					=						value_function(__a		,       __a							).sample
  
  						# fmt: off
  						__a    : str      					=						torch.tensor([165.25] * seq_len							)
  						# fmt: on
  						self.assertTrue(torch.allclose(__a		,       __a		,       rtol=1E-3							)							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  