
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax
	import jax.numpy as jnp
	import numpy as np

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
	import torch





@require_flax
class 		__lowerCamelCase     :



							def __init__(     self	,				__snake_case	,				__snake_case=1_4	,				__snake_case=7	,				__snake_case=True	,				__snake_case=True	,				__snake_case=True	,				__snake_case=9_9	,				__snake_case=3_2	,				__snake_case=5	,				__snake_case=4	,				__snake_case=3_7	,				__snake_case="gelu"	,				__snake_case=0.1	,				__snake_case=0.1	,				__snake_case=5_1_2	,				__snake_case=0.02	,				__snake_case=None	,				)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     parent
								UpperCAmelCase:					Optional[Any] 						=     batch_size
								UpperCAmelCase:					Tuple 						=     seq_length
								UpperCAmelCase:					Any 						=     is_training
								UpperCAmelCase:					Tuple 						=     use_input_mask
								UpperCAmelCase:					Tuple 						=     use_labels
								UpperCAmelCase:					Any 						=     vocab_size
								UpperCAmelCase:					int 						=     d_model
								UpperCAmelCase:					List[Any] 						=     num_hidden_layers
								UpperCAmelCase:					Dict 						=     num_attention_heads
								UpperCAmelCase:					Optional[int] 						=     ffn_dim
								UpperCAmelCase:					Tuple 						=     activation_function
								UpperCAmelCase:					str 						=     activation_dropout
								UpperCAmelCase:					List[str] 						=     attention_dropout
								UpperCAmelCase:					Any 						=     max_position_embeddings
								UpperCAmelCase:					str 						=     initializer_range
								UpperCAmelCase:					Union[str, Any] 						=     None
								UpperCAmelCase:					Optional[Any] 						=     0
								UpperCAmelCase:					Optional[int] 						=     2
								UpperCAmelCase:					Any 						=     1



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     np.clip(ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size							)	,				3	,				self.vocab_size							)

								UpperCAmelCase:					str 						=     None
								if self.use_input_mask:
									UpperCAmelCase:					List[Any] 						=     random_attention_mask([self.batch_size, self.seq_length]							)

								UpperCAmelCase:					str 						=     XGLMConfig(
								    vocab_size=self.vocab_size	,				d_model=self.hidden_size	,				num_layers=self.num_hidden_layers	,				attention_heads=self.num_attention_heads	,				ffn_dim=self.ffn_dim	,				activation_function=self.activation_function	,				activation_dropout=self.activation_dropout	,				attention_dropout=self.attention_dropout	,				max_position_embeddings=self.max_position_embeddings	,				initializer_range=self.initializer_range	,				use_cache=__snake_case	,				bos_token_id=self.bos_token_id	,				eos_token_id=self.eos_token_id	,				pad_token_id=self.pad_token_id	,				)

								return (config, input_ids, input_mask)



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






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     self.prepare_config_and_inputs()
								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					int 						=     config_and_inputs
								UpperCAmelCase:					int 						=     {"input_ids": input_ids, "attention_mask": attention_mask}
								return config, inputs_dict



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Any 						=     self.prepare_config_and_inputs()

								UpperCAmelCase:					Tuple 						=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
								UpperCAmelCase:					int 						=     ids_tensor([self.batch_size, self.seq_length]	,				vocab_size=2							)

								return (
								    config,
								    input_ids,
								    attention_mask,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     2_0
								UpperCAmelCase:					Optional[int] 						=     model_class_name(__snake_case							)

								UpperCAmelCase:					List[Any] 						=     model.init_cache(input_ids.shape[0]	,				__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     jnp.ones((input_ids.shape[0], max_decoder_length)	,				dtype="i4"							)

								UpperCAmelCase:					int 						=     jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1							)[None, :]	,				(input_ids.shape[0], input_ids.shape[-1] - 1)							)
								UpperCAmelCase:					Any 						=     model(
								    input_ids[:, :-1]	,				attention_mask=__snake_case	,				past_key_values=__snake_case	,				position_ids=__snake_case	,				)

								UpperCAmelCase:					Dict 						=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,				dtype="i4"							)
								UpperCAmelCase:					List[Any] 						=     model(
								    input_ids[:, -1:]	,				attention_mask=__snake_case	,				past_key_values=outputs_cache.past_key_values	,				position_ids=__snake_case	,				)

								UpperCAmelCase:					List[Any] 						=     model(__snake_case							)

								UpperCAmelCase:					List[str] 						=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
								self.parent.assertTrue(diff < 1e-3	,				msg=F'Max diff is {diff}'							)




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     2_0
								UpperCAmelCase:					List[Any] 						=     model_class_name(__snake_case							)

								UpperCAmelCase:					str 						=     jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])							)]	,				axis=-1	,				)

								UpperCAmelCase:					Optional[int] 						=     model.init_cache(input_ids.shape[0]	,				__snake_case							)
								UpperCAmelCase:					Union[str, Any] 						=     jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1							)[None, :]	,				(input_ids.shape[0], input_ids.shape[-1] - 1)							)

								UpperCAmelCase:					Union[str, Any] 						=     model(
								    input_ids[:, :-1]	,				attention_mask=__snake_case	,				past_key_values=__snake_case	,				position_ids=__snake_case	,				)
								UpperCAmelCase:					Optional[Any] 						=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,				dtype="i4"							)
								UpperCAmelCase:					Optional[int] 						=     model(
								    input_ids[:, -1:]	,				past_key_values=outputs_cache.past_key_values	,				attention_mask=__snake_case	,				position_ids=__snake_case	,				)

								UpperCAmelCase:					List[Any] 						=     model(__snake_case	,				attention_mask=__snake_case							)
								UpperCAmelCase:					Any 						=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
								self.parent.assertTrue(diff < 1e-3	,				msg=F'Max diff is {diff}'							)





@require_sentencepiece
@require_flax
class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Dict      =							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
							lowerCamelCase__:				Tuple      =							(FlaxXGLMForCausalLM,) if is_flax_available() else ()



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     FlaxXGLMModelTester(self							)



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								for model_class_name in self.all_model_classes:
									UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					List[str] 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								for model_class_name in self.all_model_classes:
									UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)



							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     XGLMTokenizer.from_pretrained("XGLM"	,				padding_side="left"							)
								UpperCAmelCase:					Any 						=     tokenizer(["Hello this is a long string", "Hey"]	,				return_tensors="np"	,				padding=__snake_case	,				truncation=__snake_case							)

								UpperCAmelCase:					Dict 						=     FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"							)
								UpperCAmelCase:					Optional[Any] 						=     1
								UpperCAmelCase:					Dict 						=     False

								UpperCAmelCase:					Tuple 						=     jax.jit(model.generate							)

								UpperCAmelCase:					Dict 						=     jit_generate(inputs["input_ids"]	,				attention_mask=inputs["attention_mask"]							).sequences

								UpperCAmelCase:					str 						=     tokenizer.batch_decode(__snake_case	,				skip_special_tokens=__snake_case							)

								UpperCAmelCase:					List[Any] 						=     [
								    "Hello this is a long string of questions, but I'm not sure if I'm",
								    "Hey, I'm a newbie to the forum and I'",
								]

								self.assertListEqual(__snake_case	,				__snake_case							)



							@is_pt_flax_cross_test
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					str 						=     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
										# prepare inputs
										UpperCAmelCase:					Union[str, Any] 						=     self._prepare_for_class(__snake_case	,				__snake_case							)
										UpperCAmelCase:					Tuple 						=     {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										UpperCAmelCase:					Optional[Any] 						=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
										UpperCAmelCase:					List[str] 						=     getattr(__snake_case	,				__snake_case							)

										UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     pt_inputs["input_ids"].shape
										UpperCAmelCase:					Any 						=     np.random.randint(0	,				seq_length - 1	,				size=(batch_size,)							)
										for batch_idx, start_index in enumerate(__snake_case							):
											UpperCAmelCase:					Dict 						=     0
											UpperCAmelCase:					Union[str, Any] 						=     1
											UpperCAmelCase:					Tuple 						=     0
											UpperCAmelCase:					Tuple 						=     1
										UpperCAmelCase:					List[Any] 						=     pt_model_class(__snake_case							).eval()
										# Flax models don't use the `use_cache` option and cache is not returned as a default.
										# So we disable `use_cache` here for PyTorch model.
										UpperCAmelCase:					List[Any] 						=     False
										UpperCAmelCase:					Any 						=     model_class(__snake_case	,				dtype=jnp.floataa							)
										UpperCAmelCase:					List[Any] 						=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,				__snake_case							)
										UpperCAmelCase:					str 						=     fx_state

										with torch.no_grad():
											UpperCAmelCase:					List[Any] 						=     pt_model(**__snake_case							).to_tuple()

										UpperCAmelCase:					str 						=     fx_model(**__snake_case							).to_tuple()
										self.assertEqual(len(__snake_case							)	,				len(__snake_case							)	,				"Output lengths differ between Flax and PyTorch"							)
										for fx_output, pt_output in zip(__snake_case	,				__snake_case							):
											self.assert_almost_equals(fx_output[:, -1]	,				pt_output[:, -1].numpy()	,				4e-2							)

										with tempfile.TemporaryDirectory() as tmpdirname:
											pt_model.save_pretrained(__snake_case							)
											UpperCAmelCase:					str 						=     model_class.from_pretrained(__snake_case	,				from_pt=__snake_case							)

										UpperCAmelCase:					str 						=     fx_model_loaded(**__snake_case							).to_tuple()
										self.assertEqual(
										    len(__snake_case							)	,				len(__snake_case							)	,				"Output lengths differ between Flax and PyTorch"							)
										for fx_output_loaded, pt_output in zip(__snake_case	,				__snake_case							):
											self.assert_almost_equals(fx_output_loaded[:, -1]	,				pt_output[:, -1].numpy()	,				4e-2							)



							@is_pt_flax_cross_test
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
										# prepare inputs
										UpperCAmelCase:					Optional[Any] 						=     self._prepare_for_class(__snake_case	,				__snake_case							)
										UpperCAmelCase:					str 						=     {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										UpperCAmelCase:					Optional[int] 						=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
										UpperCAmelCase:					Optional[int] 						=     getattr(__snake_case	,				__snake_case							)

										UpperCAmelCase:					List[str] 						=     pt_model_class(__snake_case							).eval()
										UpperCAmelCase:					Tuple 						=     False
										UpperCAmelCase:					str 						=     model_class(__snake_case	,				dtype=jnp.floataa							)

										UpperCAmelCase:					Optional[Any] 						=     load_flax_weights_in_pytorch_model(__snake_case	,				fx_model.params							)
										UpperCAmelCase						,   UpperCAmelCase:					str 						=     pt_inputs["input_ids"].shape
										UpperCAmelCase:					Optional[Any] 						=     np.random.randint(0	,				seq_length - 1	,				size=(batch_size,)							)
										for batch_idx, start_index in enumerate(__snake_case							):
											UpperCAmelCase:					int 						=     0
											UpperCAmelCase:					Dict 						=     1
											UpperCAmelCase:					List[Any] 						=     0
											UpperCAmelCase:					List[Any] 						=     1

										# make sure weights are tied in PyTorch
										pt_model.tie_weights()

										with torch.no_grad():
											UpperCAmelCase:					str 						=     pt_model(**__snake_case							).to_tuple()

										UpperCAmelCase:					Optional[int] 						=     fx_model(**__snake_case							).to_tuple()
										self.assertEqual(len(__snake_case							)	,				len(__snake_case							)	,				"Output lengths differ between Flax and PyTorch"							)
										for fx_output, pt_output in zip(__snake_case	,				__snake_case							):
											self.assert_almost_equals(fx_output[:, -1]	,				pt_output[:, -1].numpy()	,				4e-2							)

										with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(__snake_case							)
											UpperCAmelCase:					Optional[int] 						=     pt_model_class.from_pretrained(__snake_case	,				from_flax=__snake_case							)

										with torch.no_grad():
											UpperCAmelCase:					Any 						=     pt_model_loaded(**__snake_case							).to_tuple()

										self.assertEqual(
										    len(__snake_case							)	,				len(__snake_case							)	,				"Output lengths differ between Flax and PyTorch"							)
										for fx_output, pt_output in zip(__snake_case	,				__snake_case							):
											self.assert_almost_equals(fx_output[:, -1]	,				pt_output[:, -1].numpy()	,				4e-2							)




							@slow
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								for model_class_name in self.all_model_classes:
									UpperCAmelCase:					Union[str, Any] 						=     model_class_name.from_pretrained("facebook/xglm-564M"							)
									UpperCAmelCase:					Optional[int] 						=     model(np.ones((1, 1)							)							)
									self.assertIsNotNone(__snake_case							)


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     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				int      =							UNetaDModel
							lowerCamelCase__:				Any      =							'''sample'''



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     4
								UpperCAmelCase:					Optional[Any] 						=     1_4
								UpperCAmelCase:					str 						=     1_6

								UpperCAmelCase:					Optional[int] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

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



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								return (4, 1_4, 1_6)



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






								"""simple docstring"""



								return (4, 1_4, 1_6)



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								pass



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






								"""simple docstring"""



								super().test_determinism()



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






								"""simple docstring"""



								super().test_outputs_equivalence()



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






								"""simple docstring"""



								super().test_from_save_pretrained()



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






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



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






								"""simple docstring"""



								super().test_model_from_pretrained()



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






								"""simple docstring"""



								super().test_output()



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






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "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:					Tuple 						=     self.dummy_input
								return init_dict, inputs_dict



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






								"""simple docstring"""



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

								model.to(__snake_case							)
								UpperCAmelCase:					Tuple 						=     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     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, 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							)

								UpperCAmelCase:					int 						=     model.config.in_channels
								UpperCAmelCase:					List[str] 						=     1_6
								UpperCAmelCase:					Union[str, Any] 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					List[str] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(__snake_case	,				__snake_case							).sample.permute(0	,				2	,				1							)

								UpperCAmelCase:					str 						=     output[0, -3:, -3:].flatten()
								# fmt: off
								UpperCAmelCase:					Dict 						=     torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass




							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     "harmonai/maestro-150k"
								UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(__snake_case	,				subfolder="unet"							)
								model.to(__snake_case							)

								UpperCAmelCase:					Any 						=     6_5_5_3_6
								UpperCAmelCase:					str 						=     torch.sin(torch.arange(__snake_case							)[None, None, :].repeat(1	,				2	,				1							)							).to(__snake_case							)
								UpperCAmelCase:					Dict 						=     torch.tensor([1]							).to(__snake_case							)

								with torch.no_grad():
									UpperCAmelCase:					Optional[Any] 						=     model(__snake_case	,				__snake_case							).sample

								UpperCAmelCase:					List[Any] 						=     output.abs().sum()
								UpperCAmelCase:					Dict 						=     output.abs().max()

								assert (output_sum - 2_24.08_96).abs() < 0.5
								assert (output_max - 0.06_07).abs() < 4e-4





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Dict      =							UNetaDModel
							lowerCamelCase__:				int      =							'''sample'''



							@property
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     4
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					Optional[int] 						=     1_6

								UpperCAmelCase:					List[str] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

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



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






								"""simple docstring"""



								return (4, 1_4, 1_6)



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






								"""simple docstring"""



								return (4, 1_4, 1)



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






								"""simple docstring"""



								super().test_determinism()



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






								"""simple docstring"""



								super().test_outputs_equivalence()



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






								"""simple docstring"""



								super().test_from_save_pretrained()



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






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



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






								"""simple docstring"""



								super().test_model_from_pretrained()



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






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase:					Tuple 						=     self.model_class(**__snake_case							)
								model.to(__snake_case							)
								model.eval()

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(**__snake_case							)

									if isinstance(__snake_case	,				__snake_case							):
										UpperCAmelCase:					List[str] 						=     output.sample

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



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
								    "up_block_types": [],
								    "out_block_type": "ValueFunction",
								    "mid_block_type": "ValueFunctionMidBlock1D",
								    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
								    "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:					Union[str, Any] 						=     self.dummy_input
								return init_dict, inputs_dict



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






								"""simple docstring"""



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

								value_function.to(__snake_case							)
								UpperCAmelCase:					Union[str, 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     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					str 						=     value_function.config.in_channels
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					int 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					Union[str, Any] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Dict 						=     value_function(__snake_case	,				__snake_case							).sample

								# fmt: off
								UpperCAmelCase:					Tuple 						=     torch.tensor([1_65.25] * seq_len							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)




							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								pass
