




'''simple docstring'''

import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed

import numpy as np

import datasets

from .execute import check_correctness


a       :						Tuple								=		'''\
@misc{chen2021evaluating,
      title={Evaluating Large Language Models Trained on Code},
      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
      year={2021},
      eprint={2107.03374},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
'''

a       :						Optional[int]								=		'''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''


a       :						Tuple								=		'''
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of candidates to evaluate. Each candidates should be a list
        of strings with several code candidates to solve the problem.
    references: a list with a test for each prediction. Each test should evaluate the
        correctness of a code candidate.
    k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
    num_workers: number of workers used to evaluate the canidate programs (Default: 4).
    timeout:
Returns:
    pass_at_k: dict with pass rates for each k
    results: dict with granular results of each unittest
Examples:
    >>> code_eval = datasets.load_metric("code_eval")
    >>> test_cases = ["assert add(2,3)==5"]
    >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
    >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
    >>> print(pass_at_k)
    {\'pass@1\': 0.5, \'pass@2\': 1.0}
'''


a       :						Optional[int]								=		'''
################################################################################
                                  !!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).

Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:

>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"

################################################################################\
'''

a       :						Optional[int]								=		'''The MIT License

Copyright (c) OpenAI (https://openai.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,     _KWARGS_DESCRIPTION	)
class 		SCREAMING_SNAKE_CASE__  (			datasets.Metric	):





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						return datasets.MetricInfo(
						    # This is the description that will appear on the metrics page.
						    description=_DESCRIPTION      ,		citation=_CITATION      ,		inputs_description=_KWARGS_DESCRIPTION      ,		features=datasets.Features(
						        {
						            "predictions": datasets.Sequence(datasets.Value("string"							)							),
						            "references": datasets.Value("string"							),
						        }							)      ,		homepage="https://github.com/openai/human-eval"      ,		codebase_urls=["https://github.com/openai/human-eval"]      ,		reference_urls=["https://github.com/openai/human-eval"]      ,		license=_LICENSE      ,		)






			def 							A (      self						:    str      ,		a_						:    List[Any]      ,		a_						:    Optional[int]      ,		a_						:    Any=[1, 10, 100]      ,		a_						:    Optional[int]=4      ,		a_						:    Optional[int]=3.0							):



						"""simple docstring"""




						if os.getenv("HF_ALLOW_CODE_EVAL"      ,		0							) != "1":
									raise ValueError(_WARNING							)

						if os.name == "nt":
									raise NotImplementedError("This metric is currently not supported on Windows."							)

						with ThreadPoolExecutor(max_workers=a_							) as executor:
									__snake_case			=		[]
									__snake_case			=		Counter()
									__snake_case			=		0
									__snake_case			=		defaultdict(a_							)

									for task_id, (candidates, test_case) in enumerate(zip(a_      ,		a_							)							):
												for candidate in candidates:
															__snake_case			=		candidate + "\n" + test_case
															__snake_case			=		(test_program, timeout, task_id, completion_id[task_id])
															__snake_case			=		executor.submit(a_      ,		*a_							)
															futures.append(a_							)
															completion_id[task_id] += 1
															n_samples += 1

									for future in as_completed(a_							):
												__snake_case			=		future.result()
												results[result["task_id"]].append((result["completion_id"], result)							)

						__snake_case		,     __snake_case			=		[], []
						for result in results.values():
									result.sort()
									__snake_case			=		[r[1]["passed"] for r in result]
									total.append(len(a_							)							)
									correct.append(sum(a_							)							)
						__snake_case			=		np.array(a_							)
						__snake_case			=		np.array(a_							)

						__snake_case			=		k
						__snake_case			=		{f'''pass@{k}''': estimate_pass_at_k(a_      ,		a_      ,		a_							).mean() for k in ks if (total >= k).all()}

						return pass_at_k, results







def __UpperCAmelCase	(   _UpperCAmelCase		:   str		,				_UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   Any						)      ->      List[Any]:

			def estimator(_UpperCAmelCase		:   int		,				_UpperCAmelCase		:   int		,				_UpperCAmelCase		:   int						) -> float:
						if n - c < k:
									return 1.0
						return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1		,				n + 1						)						)

			if isinstance(_UpperCAmelCase		,				_UpperCAmelCase						):
						__snake_case			=		itertools.repeat(_UpperCAmelCase		,				len(_UpperCAmelCase						)						)
			else:
						assert len(_UpperCAmelCase						) == len(_UpperCAmelCase						)
						__snake_case			=		iter(_UpperCAmelCase						)

			return np.array([estimator(int(_UpperCAmelCase						)		,				int(_UpperCAmelCase						)		,				_UpperCAmelCase						) for n, c in zip(_UpperCAmelCase		,				_UpperCAmelCase						)]						)











'''simple docstring'''






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
      from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileNetVaImageProcessor






class        lowercase_      (						lowerCAmelCase_							):





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Union[str, Any]	      =    self.config_class(**self.inputs_dict			)
           self.parent.assertTrue(hasattr(__lowerCamelCase     ,							'tf_padding'			)			)
           self.parent.assertTrue(hasattr(__lowerCamelCase     ,							'depth_multiplier'			)			)






class        lowercase_      :





     def __init__(     self     :					Optional[int]     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Dict=13     ,							__lowerCamelCase     :					Optional[Any]=3     ,							__lowerCamelCase     :					str=32     ,							__lowerCamelCase     :					str=0.2_5     ,							__lowerCamelCase     :					List[Any]=8     ,							__lowerCamelCase     :					List[Any]=8     ,							__lowerCamelCase     :					Union[str, Any]=6     ,							__lowerCamelCase     :					Optional[Any]=32     ,							__lowerCamelCase     :					Dict=True     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					int=True     ,							__lowerCamelCase     :					Tuple="relu6"     ,							__lowerCamelCase     :					List[str]=1280     ,							__lowerCamelCase     :					Optional[Any]=0.1     ,							__lowerCamelCase     :					Tuple=0.0_2     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					int=True     ,							__lowerCamelCase     :					int=10     ,							__lowerCamelCase     :					int=None     ,							):
           snake_case__       :   Dict	      =    parent
           snake_case__       :   Union[str, Any]	      =    batch_size
           snake_case__       :   List[Any]	      =    num_channels
           snake_case__       :   List[str]	      =    image_size
           snake_case__       :   Optional[int]	      =    depth_multiplier
           snake_case__       :   int	      =    depth_divisible_by
           snake_case__       :   List[Any]	      =    min_depth
           snake_case__       :   Dict	      =    expand_ratio
           snake_case__       :   Optional[Any]	      =    tf_padding
           snake_case__       :   List[Any]	      =    output_stride
           snake_case__       :   List[str]	      =    first_layer_is_expansion
           snake_case__       :   Optional[Any]	      =    finegrained_output
           snake_case__       :   int	      =    hidden_act
           snake_case__       :   List[Any]	      =    last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier			)
           snake_case__       :   List[str]	      =    classifier_dropout_prob
           snake_case__       :   List[Any]	      =    use_labels
           snake_case__       :   Tuple	      =    is_training
           snake_case__       :   Dict	      =    num_labels
           snake_case__       :   Any	      =    initializer_range
           snake_case__       :   int	      =    scope





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   int	      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

           snake_case__       :   Optional[int]	      =    None
           snake_case__       :   Optional[int]	      =    None
           if self.use_labels:
                 snake_case__       :   Dict	      =    ids_tensor([self.batch_size]     ,							self.num_labels			)
                 snake_case__       :   List[str]	      =    ids_tensor([self.batch_size, self.image_size, self.image_size]     ,							self.num_labels			)

           snake_case__       :   List[Any]	      =    self.get_config()

           return config, pixel_values, labels, pixel_labels





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           return MobileNetVaConfig(
               num_channels=self.num_channels     ,							image_size=self.image_size     ,							depth_multiplier=self.depth_multiplier     ,							depth_divisible_by=self.depth_divisible_by     ,							min_depth=self.min_depth     ,							expand_ratio=self.expand_ratio     ,							output_stride=self.output_stride     ,							first_layer_is_expansion=self.first_layer_is_expansion     ,							finegrained_output=self.finegrained_output     ,							hidden_act=self.hidden_act     ,							tf_padding=self.tf_padding     ,							classifier_dropout_prob=self.classifier_dropout_prob     ,							initializer_range=self.initializer_range     ,							)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Union[str, Any]			):
           snake_case__       :   Optional[Any]	      =    MobileNetVaModel(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(
               result.last_hidden_state.shape     ,							(
                   self.batch_size,
                   self.last_hidden_size,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )     ,							)
           self.parent.assertEqual(
               result.pooler_output.shape     ,							(self.batch_size, self.last_hidden_size)     ,							)





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Dict			):
           snake_case__       :   Optional[int]	      =    self.num_labels
           snake_case__       :   int	      =    MobileNetVaForImageClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   int	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Union[str, Any]			):
           snake_case__       :   Tuple	      =    self.num_labels
           snake_case__       :   Optional[Any]	      =    MobileNetVaForSemanticSegmentation(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Optional[Any]	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(
               result.logits.shape     ,							(
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )     ,							)
           snake_case__       :   Dict	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(
               result.logits.shape     ,							(
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )     ,							)





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   List[Any]	      =    self.prepare_config_and_inputs()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   str	      =    config_and_inputs
           snake_case__       :   Union[str, Any]	      =    {'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			(
         (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     A_											=			(
         {
             "feature-extraction": MobileNetVaModel,
             "image-classification": MobileNetVaForImageClassification,
             "image-segmentation": MobileNetVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     A_											=			False
     A_											=			False
     A_											=			False
     A_											=			False





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   List[Any]	      =    MobileNetVaModelTester(self			)
           snake_case__       :   str	      =    MobileNetVaConfigTester(self     ,							config_class=__lowerCamelCase     ,							has_text_modality=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           self.config_tester.run_common_tests()





     @unittest.skip(reason='MobileNetV2 does not use inputs_embeds'			)
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           pass





     @unittest.skip(reason='MobileNetV2 does not support input and output embeddings'			)
     def 			_lowerCAmelCase      (     self     :					int			):
           pass





     @unittest.skip(reason='MobileNetV2 does not output attentions'			)
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           pass





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__      ,							snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 snake_case__       :   str	      =    model_class(__lowerCamelCase			)
                 snake_case__       :   Optional[int]	      =    inspect.signature(model.forward			)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 snake_case__       :   Union[str, Any]	      =    [*signature.parameters.keys()]

                 snake_case__       :   int	      =    ['pixel_values']
                 self.assertListEqual(arg_names[:1]     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   str	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           def check_hidden_states_output(__lowerCamelCase     :					str     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					List[str]			):
                 snake_case__       :   Tuple	      =    model_class(__lowerCamelCase			)
                 model.to(__lowerCamelCase			)
                 model.eval()

                 with torch.no_grad():
                       snake_case__       :   List[str]	      =    model(**self._prepare_for_class(__lowerCamelCase     ,							__lowerCamelCase			)			)

                 snake_case__       :   List[str]	      =    outputs.hidden_states

                 snake_case__       :   List[Any]	      =    16
                 self.assertEqual(len(__lowerCamelCase			)     ,							__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Optional[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 snake_case__       :   List[Any]	      =    True
                 check_hidden_states_output(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 snake_case__       :   List[Any]	      =    True

                 check_hidden_states_output(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Union[str, Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Tuple	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Dict			):
           for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__       :   Any	      =    MobileNetVaModel.from_pretrained(__lowerCamelCase			)
                 self.assertIsNotNone(__lowerCamelCase			)






def 		UpperCamelCase__      (		)    ->   Dict:
      snake_case__       :   Tuple	      =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image






@require_torch
@require_vision
class        lowercase_      (						unittest.TestCase							):





     @cached_property
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           return (
               MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'			) if is_vision_available() else None
           )





     @slow
     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Dict	      =    MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'			).to(__lowerCamelCase			)

           snake_case__       :   int	      =    self.default_image_processor
           snake_case__       :   Optional[Any]	      =    prepare_img()
           snake_case__       :   str	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Any	      =    model(**__lowerCamelCase			)

           # verify the logits
           snake_case__       :   Tuple	      =    torch.Size((1, 1001)			)
           self.assertEqual(outputs.logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   int	      =    torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]			).to(__lowerCamelCase			)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,							__lowerCamelCase     ,							atol=1E-4			)			)





     @slow
     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   str	      =    MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'			)
           snake_case__       :   int	      =    model.to(__lowerCamelCase			)

           snake_case__       :   Any	      =    MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'			)

           snake_case__       :   Any	      =    prepare_img()
           snake_case__       :   Dict	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Any	      =    model(**__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    outputs.logits

           # verify the logits
           snake_case__       :   str	      =    torch.Size((1, 21, 65, 65)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    torch.tensor(
               [
                   [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                   [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                   [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
               ]     ,							device=__lowerCamelCase     ,							)

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,							__lowerCamelCase     ,							atol=1E-4			)			)
