code: 
  
  
  '''simple docstring'''
  
  
  
  import ast
  import difflib
  from collections.abc import Mapping
  from typing import Any, Callable, Dict
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
   pass
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Dict[str, Callable]		,   _SCREAMING_SNAKE_CASE   :     Dict=None		,   _SCREAMING_SNAKE_CASE   :     Any=False     ):
       try:
            __a    : int      					=						ast.parse(_SCREAMING_SNAKE_CASE     )
       except SyntaxError as e:
            print('The code generated by the agent is not valid.\n'		,   _SCREAMING_SNAKE_CASE     )
            return
       if state is None:
            __a    : Optional[Any]      					=						{}
       __a    : List[Any]      					=						None
       for idx, node in enumerate(expression.body     ):
            try:
                 __a    : List[str]      					=						evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
            except InterpretorError as e:
                 __a    : str      					=						F"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
                 if chat_mode:
                      msg += (
                          F""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
                      )
                 else:
                      msg += F""":\n{e}"""
                 print(_SCREAMING_SNAKE_CASE     )
                 break
            if line_result is not None:
                 __a    : Union[str, Any]      					=						line_result
  
       return result
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     ast.AST		,   _SCREAMING_SNAKE_CASE   :     Dict[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Dict[str, Callable]     ):
       if isinstance(_SCREAMING_SNAKE_CASE		,   ast.Assign     ):
            # Assignement -> we evaluate the assignement which should update the state
            # We return the variable assigned as it may be used to determine the final result.
            return evaluate_assign(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Call     ):
            # Function call -> we return the value of the function call
            return evaluate_call(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Constant     ):
            # Constant -> just return the value
            return expression.value
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Dict     ):
            # Dict -> evaluate all keys and values
            __a    : Optional[Any]      					=						[evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ) for k in expression.keys]
            __a    : List[str]      					=						[evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ) for v in expression.values]
            return dict(zip(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Expr     ):
            # Expression -> evaluate the content
            return evaluate_ast(expression.value		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.For     ):
            # For loop -> execute the loop
            return evaluate_for(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.FormattedValue     ):
            # Formatted value (part of f-string) -> evaluate the content and return
            return evaluate_ast(expression.value		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.If     ):
            # If -> execute the right branch
            return evaluate_if(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       elif hasattr(_SCREAMING_SNAKE_CASE		,   'Index'     ) and isinstance(_SCREAMING_SNAKE_CASE		,   ast.Index     ):
            return evaluate_ast(expression.value		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.JoinedStr     ):
            return "".join([str(evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )     ) for v in expression.values]     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.List     ):
            # List -> evaluate all elements
            return [evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ) for elt in expression.elts]
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Name     ):
            # Name -> pick up the value in the state
            return evaluate_name(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Subscript     ):
            # Subscript -> return the value of the indexing
            return evaluate_subscript(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       else:
            # For now we refuse anything else. Let's add things as we need them.
            raise InterpretorError(F"""{expression.__class__.__name__} is not supported."""     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]     ):
       __a    : Any      					=						assign.targets
       __a    : int      					=						evaluate_ast(assign.value		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
       if len(_SCREAMING_SNAKE_CASE     ) == 1:
            __a    : Optional[Any]      					=						result
       else:
            if len(_SCREAMING_SNAKE_CASE     ) != len(_SCREAMING_SNAKE_CASE     ):
                 raise InterpretorError(F"""Expected {len(_SCREAMING_SNAKE_CASE     )} values but got {len(_SCREAMING_SNAKE_CASE     )}."""     )
            for var_name, r in zip(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
                 __a    : Optional[int]      					=						r
       return result
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     List[Any]		,   _SCREAMING_SNAKE_CASE   :     List[Any]     ):
       if not isinstance(call.func		,   ast.Name     ):
            raise InterpretorError(
                F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
                F"""type {type(call.func     )}."""     )
       __a    : Any      					=						call.func.id
       if func_name not in tools:
            raise InterpretorError(
                F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."""     )
  
       __a    : str      					=						tools[func_name]
       # Todo deal with args
       __a    : List[Any]      					=						[evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ) for arg in call.args]
       __a    : int      					=						{keyword.arg: evaluate_ast(keyword.value		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ) for keyword in call.keywords}
       return func(*_SCREAMING_SNAKE_CASE		,   **_SCREAMING_SNAKE_CASE     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
       __a    : Optional[int]      					=						evaluate_ast(subscript.slice		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       __a    : str      					=						evaluate_ast(subscript.value		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       if isinstance(_SCREAMING_SNAKE_CASE		,   (list, tuple)     ):
            return value[int(_SCREAMING_SNAKE_CASE     )]
       if index in value:
            return value[index]
       if isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ) and isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
            __a    : str      					=						difflib.get_close_matches(_SCREAMING_SNAKE_CASE		,   list(value.keys()     )     )
            if len(_SCREAMING_SNAKE_CASE     ) > 0:
                 return value[close_matches[0]]
  
       raise InterpretorError(F"""Could not index {value} with '{index}'."""     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
       if name.id in state:
            return state[name.id]
       __a    : Tuple      					=						difflib.get_close_matches(name.id		,   list(state.keys()     )     )
       if len(_SCREAMING_SNAKE_CASE     ) > 0:
            return state[close_matches[0]]
       raise InterpretorError(F"""The variable `{name.id}` is not defined."""     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Dict     ):
       if len(condition.ops     ) > 1:
            raise InterpretorError('Cannot evaluate conditions with multiple operators'     )
  
       __a    : Union[str, Any]      					=						evaluate_ast(condition.left		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       __a    : Optional[int]      					=						condition.ops[0]
       __a    : Dict      					=						evaluate_ast(condition.comparators[0]		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
       if isinstance(_SCREAMING_SNAKE_CASE		,   ast.Eq     ):
            return left == right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.NotEq     ):
            return left != right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Lt     ):
            return left < right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.LtE     ):
            return left <= right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Gt     ):
            return left > right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.GtE     ):
            return left >= right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.Is     ):
            return left is right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.IsNot     ):
            return left is not right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.In     ):
            return left in right
       elif isinstance(_SCREAMING_SNAKE_CASE		,   ast.NotIn     ):
            return left not in right
       else:
            raise InterpretorError(F"""Operator not supported: {comparator}"""     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Any     ):
       __a    : Optional[int]      					=						None
       if evaluate_condition(if_statement.test		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ):
            for line in if_statement.body:
                 __a    : List[Any]      					=						evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
                 if line_result is not None:
                      __a    : Dict      					=						line_result
       else:
            for line in if_statement.orelse:
                 __a    : Dict      					=						evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
                 if line_result is not None:
                      __a    : Optional[Any]      					=						line_result
       return result
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[Any]		,   _SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
       __a    : int      					=						None
       __a    : Any      					=						evaluate_ast(for_loop.iter		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
       for counter in iterator:
            __a    : List[Any]      					=						counter
            for expression in for_loop.body:
                 __a    : List[str]      					=						evaluate_ast(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
                 if line_result is not None:
                      __a    : Tuple      					=						line_result
       return result
  
  
  
  
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
  
  
  
  