


'''simple docstring'''


import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__     						=  get_tests_dir('fixtures/spiece.model')






@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							unittest.TestCase						):
			lowerCAmelCase			:						List[Any]							 =  ErnieMTokenizer
			lowerCAmelCase			:						str							 =  False
			lowerCAmelCase			:						List[Any]							 =  True
			lowerCAmelCase			:						Any							 =  False
			lowerCAmelCase			:						Union[str, Any]							 =  False




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



					'''simple docstring'''







					super().setUp()

					# We have a SentencePiece fixture for testing
					_UpperCAmelCase		:							Union[str, Any]             =   ErnieMTokenizer(lowerCamelCase__						,	unk_token="<unk>"						,	pad_token="<pad>"			)
					tokenizer.save_pretrained(self.tmpdirname			)




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



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   "this is a test"
					_UpperCAmelCase		:							str             =   "this is a test"
					return input_text, output_text




			def 				lowerCAmelCase__     (		self	:  Dict			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Tuple             =   "<pad>"
					_UpperCAmelCase		:							Tuple             =   0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__			)						,	lowerCamelCase__			)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__			)						,	lowerCamelCase__			)




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



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   list(self.get_tokenizer().get_vocab().keys()			)

					self.assertEqual(vocab_keys[0]						,	"<pad>"			)
					self.assertEqual(vocab_keys[1]						,	"<unk>"			)
					self.assertEqual(vocab_keys[-1]						,	"▁eloquent"			)
					self.assertEqual(len(lowerCamelCase__			)						,	3_00_00			)




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



					'''simple docstring'''







					self.assertEqual(self.get_tokenizer().vocab_size						,	3_00_00			)




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



					'''simple docstring'''







					if not self.test_rust_tokenizer:
							return

					_UpperCAmelCase		:							List[Any]             =   self.get_tokenizer()
					_UpperCAmelCase		:							Tuple             =   self.get_rust_tokenizer()

					_UpperCAmelCase		:							Any             =   "I was born in 92000, and this is falsé."

					_UpperCAmelCase		:							Any             =   tokenizer.tokenize(lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   rust_tokenizer.tokenize(lowerCamelCase__			)
					self.assertListEqual(lowerCamelCase__						,	lowerCamelCase__			)

					_UpperCAmelCase		:							int             =   tokenizer.encode(lowerCamelCase__						,	add_special_tokens=lowerCamelCase__			)
					_UpperCAmelCase		:							List[Any]             =   rust_tokenizer.encode(lowerCamelCase__						,	add_special_tokens=lowerCamelCase__			)
					self.assertListEqual(lowerCamelCase__						,	lowerCamelCase__			)

					_UpperCAmelCase		:							Optional[Any]             =   self.get_rust_tokenizer()
					_UpperCAmelCase		:							Any             =   tokenizer.encode(lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[Any]             =   rust_tokenizer.encode(lowerCamelCase__			)
					self.assertListEqual(lowerCamelCase__						,	lowerCamelCase__			)




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



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   ErnieMTokenizer(lowerCamelCase__						,	do_lower_case=lowerCamelCase__						,	unk_token="<unk>"						,	pad_token="<pad>"			)

					_UpperCAmelCase		:							Any             =   tokenizer.tokenize("This is a test"			)
					self.assertListEqual(lowerCamelCase__						,	["▁this", "▁is", "▁a", "▁test"]			)

					self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__			)						,	[48, 25, 21, 12_89]			)

					_UpperCAmelCase		:							Tuple             =   tokenizer.tokenize("I was born in 92000, and this is falsé."			)
					# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
					self.assertListEqual(
					    lowerCamelCase__						,	["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]			)
					_UpperCAmelCase		:							Optional[Any]             =   tokenizer.convert_tokens_to_ids(lowerCamelCase__			)
					self.assertListEqual(lowerCamelCase__						,	[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]			)

					_UpperCAmelCase		:							Optional[Any]             =   tokenizer.convert_ids_to_tokens(lowerCamelCase__			)
					self.assertListEqual(
					    lowerCamelCase__						,	["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]						,	)




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



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   ErnieMTokenizer(lowerCamelCase__						,	unk_token="<unk>"						,	pad_token="<pad>"			)

					_UpperCAmelCase		:							Tuple             =   tokenizer.encode("sequence builders"			)
					_UpperCAmelCase		:							Union[str, Any]             =   tokenizer.encode("multi-sequence build"			)

					_UpperCAmelCase		:							Tuple             =   tokenizer.build_inputs_with_special_tokens(lowerCamelCase__			)
					_UpperCAmelCase		:							List[str]             =   tokenizer.build_inputs_with_special_tokens(lowerCamelCase__						,	lowerCamelCase__			)

					assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
					assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
					    tokenizer.sep_token_id
					] + text_a + [tokenizer.sep_token_id]






			@slow
			def 				lowerCAmelCase__     (		self	:  int			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=lowerCamelCase__						,	model_name="susnato/ernie-m-base_pytorch"						,	sequences=[
					        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
					        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
					        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
					        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
					        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
					        "conditioning on both left and right context in all layers.",
					        "The quick brown fox jumps over the lazy dog.",
					    ]						,	)







'''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



