


'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class 	__a   (       snake_case_    ):
		_a			:						List[Any]    			=			42



class 	__a   (       snake_case_					,   snake_case_    ):
		@register_to_config
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = 3    ,					_SCREAMING_SNAKE_CASE = 3    ,					_SCREAMING_SNAKE_CASE = False    ,					_SCREAMING_SNAKE_CASE = "positional"    ,					_SCREAMING_SNAKE_CASE = 0    ,					_SCREAMING_SNAKE_CASE = True    ,					_SCREAMING_SNAKE_CASE = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")    ,					_SCREAMING_SNAKE_CASE = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")    ,					_SCREAMING_SNAKE_CASE = (224, 448, 672, 896)    ,					_SCREAMING_SNAKE_CASE = 2    ,					_SCREAMING_SNAKE_CASE = 1    ,					_SCREAMING_SNAKE_CASE = 1    ,					_SCREAMING_SNAKE_CASE = "conv"    ,					_SCREAMING_SNAKE_CASE = "conv"    ,					_SCREAMING_SNAKE_CASE = "silu"    ,					_SCREAMING_SNAKE_CASE = 8    ,					_SCREAMING_SNAKE_CASE = 32    ,					_SCREAMING_SNAKE_CASE = 1e-5    ,					_SCREAMING_SNAKE_CASE = "default"    ,					_SCREAMING_SNAKE_CASE = True    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					)				-> Optional[Any]:




									"""simple docstring"""





									super().__init__()

									_UpperCAmelCase					=    sample_size
									_UpperCAmelCase					=    block_out_channels[0] * 4

									# Check inputs
									if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
																raise ValueError(
																    f'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''		)

									if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
																raise ValueError(
																    f'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''		)

									# input
									_UpperCAmelCase					=    nn.Convad(_SCREAMING_SNAKE_CASE    ,					block_out_channels[0]    ,					kernel_size=3    ,					padding=(1, 1)		)

									# time
									if time_embedding_type == "fourier":
																_UpperCAmelCase					=    GaussianFourierProjection(embedding_size=block_out_channels[0]    ,					scale=16		)
																_UpperCAmelCase					=    2 * block_out_channels[0]
									elif time_embedding_type == "positional":
																_UpperCAmelCase					=    Timesteps(block_out_channels[0]    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    block_out_channels[0]

									_UpperCAmelCase					=    TimestepEmbedding(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

									# class embedding
									if class_embed_type is None and num_class_embeds is not None:
																_UpperCAmelCase					=    nn.Embedding(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									elif class_embed_type == "timestep":
																_UpperCAmelCase					=    TimestepEmbedding(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									elif class_embed_type == "identity":
																_UpperCAmelCase					=    nn.Identity(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase					=    None

									_UpperCAmelCase					=    nn.ModuleList([]		)
									_UpperCAmelCase					=    None
									_UpperCAmelCase					=    nn.ModuleList([]		)

									# down
									_UpperCAmelCase					=    block_out_channels[0]
									for i, down_block_type in enumerate(_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    output_channel
																_UpperCAmelCase					=    block_out_channels[i]
																_UpperCAmelCase					=    i == len(_SCREAMING_SNAKE_CASE		) - 1

																_UpperCAmelCase					=    get_down_block(
																    _SCREAMING_SNAKE_CASE    ,					num_layers=_SCREAMING_SNAKE_CASE    ,					in_channels=_SCREAMING_SNAKE_CASE    ,					out_channels=_SCREAMING_SNAKE_CASE    ,					temb_channels=_SCREAMING_SNAKE_CASE    ,					add_downsample=not is_final_block    ,					resnet_eps=_SCREAMING_SNAKE_CASE    ,					resnet_act_fn=_SCREAMING_SNAKE_CASE    ,					resnet_groups=_SCREAMING_SNAKE_CASE    ,					attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel    ,					downsample_padding=_SCREAMING_SNAKE_CASE    ,					resnet_time_scale_shift=_SCREAMING_SNAKE_CASE    ,					downsample_type=_SCREAMING_SNAKE_CASE    ,					)
																self.down_blocks.append(_SCREAMING_SNAKE_CASE		)

									# mid
									_UpperCAmelCase					=    UNetMidBlockaD(
									    in_channels=block_out_channels[-1]    ,					temb_channels=_SCREAMING_SNAKE_CASE    ,					resnet_eps=_SCREAMING_SNAKE_CASE    ,					resnet_act_fn=_SCREAMING_SNAKE_CASE    ,					output_scale_factor=_SCREAMING_SNAKE_CASE    ,					resnet_time_scale_shift=_SCREAMING_SNAKE_CASE    ,					attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]    ,					resnet_groups=_SCREAMING_SNAKE_CASE    ,					add_attention=_SCREAMING_SNAKE_CASE    ,					)

									# up
									_UpperCAmelCase					=    list(reversed(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    reversed_block_out_channels[0]
									for i, up_block_type in enumerate(_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    output_channel
																_UpperCAmelCase					=    reversed_block_out_channels[i]
																_UpperCAmelCase					=    reversed_block_out_channels[min(i + 1    ,					len(_SCREAMING_SNAKE_CASE		) - 1		)]

																_UpperCAmelCase					=    i == len(_SCREAMING_SNAKE_CASE		) - 1

																_UpperCAmelCase					=    get_up_block(
																    _SCREAMING_SNAKE_CASE    ,					num_layers=layers_per_block + 1    ,					in_channels=_SCREAMING_SNAKE_CASE    ,					out_channels=_SCREAMING_SNAKE_CASE    ,					prev_output_channel=_SCREAMING_SNAKE_CASE    ,					temb_channels=_SCREAMING_SNAKE_CASE    ,					add_upsample=not is_final_block    ,					resnet_eps=_SCREAMING_SNAKE_CASE    ,					resnet_act_fn=_SCREAMING_SNAKE_CASE    ,					resnet_groups=_SCREAMING_SNAKE_CASE    ,					attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel    ,					resnet_time_scale_shift=_SCREAMING_SNAKE_CASE    ,					upsample_type=_SCREAMING_SNAKE_CASE    ,					)
																self.up_blocks.append(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    output_channel

									# out
									_UpperCAmelCase					=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,					32		)
									_UpperCAmelCase					=    nn.GroupNorm(num_channels=block_out_channels[0]    ,					num_groups=_SCREAMING_SNAKE_CASE    ,					eps=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    nn.SiLU()
									_UpperCAmelCase					=    nn.Convad(block_out_channels[0]    ,					_SCREAMING_SNAKE_CASE    ,					kernel_size=3    ,					padding=1		)






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = True    ,					)				-> Union[UNetaDOutput, Tuple]:




									"""simple docstring"""





									if self.config.center_input_sample:
																_UpperCAmelCase					=    2 * sample - 1.0

									# 1. time
									_UpperCAmelCase					=    timestep
									if not torch.is_tensor(_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    torch.tensor([timesteps]    ,					dtype=torch.long    ,					device=sample.device		)
									elif torch.is_tensor(_SCREAMING_SNAKE_CASE		) and len(timesteps.shape		) == 0:
																_UpperCAmelCase					=    timesteps[None].to(sample.device		)

									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									_UpperCAmelCase					=    timesteps * torch.ones(sample.shape[0]    ,					dtype=timesteps.dtype    ,					device=timesteps.device		)

									_UpperCAmelCase					=    self.time_proj(_SCREAMING_SNAKE_CASE		)

									# timesteps does not contain any weights and will always return f32 tensors
									# but time_embedding might actually be running in fp16. so we need to cast here.
									# there might be better ways to encapsulate this.
									_UpperCAmelCase					=    t_emb.to(dtype=self.dtype		)
									_UpperCAmelCase					=    self.time_embedding(_SCREAMING_SNAKE_CASE		)

									if self.class_embedding is not None:
																if class_labels is None:
																							raise ValueError('class_labels should be provided when doing class conditioning'		)

																if self.config.class_embed_type == "timestep":
																							_UpperCAmelCase					=    self.time_proj(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    self.class_embedding(_SCREAMING_SNAKE_CASE		).to(dtype=self.dtype		)
																_UpperCAmelCase					=    emb + class_emb

									# 2. pre-process
									_UpperCAmelCase					=    sample
									_UpperCAmelCase					=    self.conv_in(_SCREAMING_SNAKE_CASE		)

									# 3. down
									_UpperCAmelCase					=    (sample,)
									for downsample_block in self.down_blocks:
																if hasattr(_SCREAMING_SNAKE_CASE    ,					'skip_conv'		):
																							_UpperCAmelCase	, _UpperCAmelCase	, _UpperCAmelCase					=    downsample_block(
																							    hidden_states=_SCREAMING_SNAKE_CASE    ,					temb=_SCREAMING_SNAKE_CASE    ,					skip_sample=_SCREAMING_SNAKE_CASE		)
																else:
																							_UpperCAmelCase	, _UpperCAmelCase					=    downsample_block(hidden_states=_SCREAMING_SNAKE_CASE    ,					temb=_SCREAMING_SNAKE_CASE		)

																down_block_res_samples += res_samples

									# 4. mid
									_UpperCAmelCase					=    self.mid_block(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

									# 5. up
									_UpperCAmelCase					=    None
									for upsample_block in self.up_blocks:
																_UpperCAmelCase					=    down_block_res_samples[-len(upsample_block.resnets		) :]
																_UpperCAmelCase					=    down_block_res_samples[: -len(upsample_block.resnets		)]

																if hasattr(_SCREAMING_SNAKE_CASE    ,					'skip_conv'		):
																							_UpperCAmelCase	, _UpperCAmelCase					=    upsample_block(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																else:
																							_UpperCAmelCase					=    upsample_block(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

        # 6. post-process
									_UpperCAmelCase					=    self.conv_norm_out(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.conv_act(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.conv_out(_SCREAMING_SNAKE_CASE		)

									if skip_sample is not None:
																sample += skip_sample

									if self.config.time_embedding_type == "fourier":
																_UpperCAmelCase					=    timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]		)))		)
																_UpperCAmelCase					=    sample / timesteps

									if not return_dict:
																return (sample,)

									return UNetaDOutput(sample=_SCREAMING_SNAKE_CASE		)




import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


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

		from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor

class 	__a   (       unittest.TestCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=32    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=10    ,					_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]    ,					_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE="relu"    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=None    ,					)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    parent
									_UpperCAmelCase					=    batch_size
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    num_channels
									_UpperCAmelCase					=    embeddings_size
									_UpperCAmelCase					=    hidden_sizes
									_UpperCAmelCase					=    depths
									_UpperCAmelCase					=    is_training
									_UpperCAmelCase					=    use_labels
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    num_labels
									_UpperCAmelCase					=    scope
									_UpperCAmelCase					=    len(_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_UpperCAmelCase					=    self.get_config()

									return config, pixel_values
		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									return ResNetConfig(
									    num_channels=self.num_channels    ,					embeddings_size=self.embeddings_size    ,					hidden_sizes=self.hidden_sizes    ,					depths=self.depths    ,					hidden_act=self.hidden_act    ,					num_labels=self.num_labels    ,					image_size=self.image_size    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetModel(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE		)

									# Output shape (b, c, h, w)
									self.parent.assertEqual(
									    result.last_hidden_state.shape    ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,					)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    self.num_labels
									_UpperCAmelCase					=    FlaxResNetForImageClassification(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)		)






		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    self.prepare_config_and_inputs()
									_UpperCAmelCase	, _UpperCAmelCase					=    config_and_inputs
									_UpperCAmelCase					=    {'pixel_values': pixel_values}
									return config, inputs_dict



@require_flax
class 	__a   (       UpperCAmelCase					,   unittest.TestCase    ):
		_a			:						Any    			=			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		_a			:						Dict    			=			False
		_a			:						Optional[Any]    			=			False
		_a			:						Optional[Any]    			=			False
		def 			UpperCAmelCase__       (    self		)				-> None:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetModelTester(self		)
									_UpperCAmelCase					=    ConfigTester(self    ,					config_class=_SCREAMING_SNAKE_CASE    ,					has_text_modality=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									return
		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)
		@unittest.skip(reason='ResNet does not use inputs_embeds'		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									pass
		@unittest.skip(reason='ResNet does not support input and output embeddings'		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									pass
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase	, _UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    inspect.signature(model.__call__		)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCAmelCase					=    [*signature.parameters.keys()]

																_UpperCAmelCase					=    ['pixel_values']
																self.assertListEqual(arg_names[:1]    ,					_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)		)

																_UpperCAmelCase					=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																_UpperCAmelCase					=    self.model_tester.num_stages
																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					expected_num_stages + 1		)
		@unittest.skip(reason='ResNet does not use feedforward chunking'		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									pass






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




									"""simple docstring"""





									_UpperCAmelCase	, _UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__		):
																							_UpperCAmelCase					=    self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE		)

																							@jax.jit
																							def model_jitted(_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		):
																														return model(pixel_values=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

																							with self.subTest('JIT Enabled'		):
																														_UpperCAmelCase					=    model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							with self.subTest('JIT Disabled'		):
																														with jax.disable_jit():
																																					_UpperCAmelCase					=    model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							self.assertEqual(len(_SCREAMING_SNAKE_CASE		)    ,					len(_SCREAMING_SNAKE_CASE		)		)
																							for jitted_output, output in zip(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																														self.assertEqual(jitted_output.shape    ,					output.shape		)



def  lowerCAmelCase__				(    )						->	Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
							return image







@require_flax
class 	__a   (       unittest.TestCase    ):
		@cached_property
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'		) if is_vision_available() else None






		@slow
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'		)

									_UpperCAmelCase					=    self.default_image_processor
									_UpperCAmelCase					=    prepare_img()
									_UpperCAmelCase					=    image_processor(images=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'		)

									_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE		)

									# verify the logits
									_UpperCAmelCase					=    (1, 1000)
									self.assertEqual(outputs.logits.shape    ,					_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    jnp.array([-11.1069, -9.7877, -8.3777]		)

									self.assertTrue(jnp.allclose(outputs.logits[0, :3]    ,					_SCREAMING_SNAKE_CASE    ,					atol=1e-4		)		)
