


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


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

__UpperCamelCase    		=       '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n'


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


__UpperCamelCase    		=       '\n################################################################################\n                                  !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n'

__UpperCamelCase    		=       'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.'







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






			"""simple docstring"""






			def 				snake_case__				(							self     ):

						'''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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case=[1, 10, 100]      ,						snake_case=4      ,						snake_case=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=snake_case     ) as executor:
									UpperCamelCase__					=       []
									UpperCamelCase__					=       Counter()
									UpperCamelCase__					=       0
									UpperCamelCase__					=       defaultdict(snake_case     )

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

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

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

						UpperCamelCase__					=       k
						UpperCamelCase__					=       {F'''pass@{k}''': estimate_pass_at_k(snake_case      ,						snake_case      ,						snake_case     ).mean() for k in ks if (total >= k).all()}

						return pass_at_k, results



def        UpperCamelCase_(				_A   :Optional[int]				,     _A   :List[str]				,     _A   :int  )->		Tuple:

			def estimator(_A   :int				,     _A   :int				,     _A   :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(_A				,     _A  ):
						UpperCamelCase__					=       itertools.repeat(_A				,     len(_A  )  )
			else:
						assert len(_A  ) == len(_A  )
						UpperCamelCase__					=       iter(_A  )

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






import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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 torch import nn

						from transformers import (
						    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
						    VideoMAEForPreTraining,
						    VideoMAEForVideoClassification,
						    VideoMAEModel,
						)
						from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from transformers import VideoMAEImageProcessor







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=10      ,						snake_case=3      ,						snake_case=2      ,						snake_case=2      ,						snake_case=2      ,						snake_case=True      ,						snake_case=True      ,						snake_case=32      ,						snake_case=5      ,						snake_case=4      ,						snake_case=37      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=10      ,						snake_case=0.02      ,						snake_case=0.9      ,						snake_case=None      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       patch_size
						UpperCamelCase__					=       tubelet_size
						UpperCamelCase__					=       num_frames
						UpperCamelCase__					=       is_training
						UpperCamelCase__					=       use_labels
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       type_sequence_label_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       mask_ratio
						UpperCamelCase__					=       scope

						# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
						UpperCamelCase__					=       (image_size // patch_size) ** 2
						UpperCamelCase__					=       (num_frames // tubelet_size) * self.num_patches_per_frame

						# use this variable to define bool_masked_pos
						UpperCamelCase__					=       int(mask_ratio * self.seq_length     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


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

						UpperCamelCase__					=       None
						if self.use_labels:
									UpperCamelCase__					=       ids_tensor([self.batch_size]      ,						self.type_sequence_label_size     )

						UpperCamelCase__					=       self.get_config()

						return config, pixel_values, labels




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return VideoMAEConfig(
						    image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						num_frames=self.num_frames      ,						tubelet_size=self.tubelet_size      ,						hidden_size=self.hidden_size      ,						num_hidden_layers=self.num_hidden_layers      ,						num_attention_heads=self.num_attention_heads      ,						intermediate_size=self.intermediate_size      ,						hidden_act=self.hidden_act      ,						hidden_dropout_prob=self.hidden_dropout_prob      ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,						is_decoder=snake_case      ,						initializer_range=self.initializer_range      ,						)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForPreTraining(snake_case     )
						model.to(snake_case     )
						model.eval()
						# important: each video needs to have the same number of masked patches
						# hence we define a single mask, which we then repeat for each example in the batch
						UpperCamelCase__					=       torch.ones((self.num_masks,)     )
						UpperCamelCase__					=       torch.cat([mask, torch.zeros(self.seq_length - mask.size(0     )     )]     )
						UpperCamelCase__					=       mask.expand(self.batch_size      ,						-1     ).bool()

						UpperCamelCase__					=       model(snake_case      ,						snake_case     )
						# model only returns predictions for masked patches
						UpperCamelCase__					=       mask.sum().item()
						UpperCamelCase__					=       3 * self.tubelet_size * self.patch_size**2
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, num_masked_patches, decoder_num_labels)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()
						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       config_and_inputs
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[Any]    =		(
			    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
			)
			_UpperCamelCase		:					Union[str, Any]    =		(
			    {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:					Optional[int]    =		False
			_UpperCamelCase		:					Tuple    =		False
			_UpperCamelCase		:					int    =		False
			_UpperCamelCase		:					Any    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case      ,						hidden_size=37     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case=False     ):

						'''simple docstring'''


						UpperCamelCase__					=       copy.deepcopy(snake_case     )

						if model_class == VideoMAEForPreTraining:
									# important: each video needs to have the same number of masked patches
									# hence we define a single mask, which we then repeat for each example in the batch
									UpperCamelCase__					=       torch.ones((self.model_tester.num_masks,)     )
									UpperCamelCase__					=       torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0     )     )]     )
									UpperCamelCase__					=       mask.expand(self.model_tester.batch_size      ,						-1     ).bool()
									UpperCamelCase__					=       bool_masked_pos.to(snake_case     )

						if return_labels:
									if model_class in [
									    *get_values(snake_case     ),
									]:
												UpperCamelCase__					=       torch.zeros(
												    self.model_tester.batch_size      ,						dtype=torch.long      ,						device=snake_case     )

						return inputs_dict




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			@unittest.skip(reason="VideoMAE does not use inputs_embeds"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       model_class(snake_case     )
									self.assertIsInstance(model.get_input_embeddings()      ,						(nn.Module)     )
									UpperCamelCase__					=       model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case      ,						nn.Linear     )     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCamelCase__					=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*snake_case     )




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__					=       VideoMAEModel.from_pretrained(snake_case     )
									self.assertIsNotNone(snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if not self.has_attentions:
									pass

						else:
									UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase__					=       True

									for model_class in self.all_model_classes:
												UpperCamelCase__					=       self.model_tester.seq_length - self.model_tester.num_masks
												UpperCamelCase__					=       (
												    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
												)

												UpperCamelCase__					=       True
												UpperCamelCase__					=       False
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )
												UpperCamelCase__					=       outputs.attentions
												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )
												UpperCamelCase__					=       outputs.attentions
												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )

												self.assertListEqual(
												    list(attentions[0].shape[-3:]     )      ,						[self.model_tester.num_attention_heads, seq_len, seq_len]      ,						)
												UpperCamelCase__					=       len(snake_case     )

												# Check attention is always last and order is fine
												UpperCamelCase__					=       True
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

												self.assertEqual(out_len + 1      ,						len(snake_case     )     )

												UpperCamelCase__					=       outputs.attentions

												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]     )      ,						[self.model_tester.num_attention_heads, seq_len, seq_len]      ,						)




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     ):
									UpperCamelCase__					=       model_class(snake_case     )
									model.to(snake_case     )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

									UpperCamelCase__					=       outputs.hidden_states
									UpperCamelCase__					=       self.model_tester.num_hidden_layers + 1
									self.assertEqual(len(snake_case     )      ,						snake_case     )

									UpperCamelCase__					=       self.model_tester.seq_length - self.model_tester.num_masks
									UpperCamelCase__					=       num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]     )      ,						[seq_length, self.model_tester.hidden_size]      ,						)

						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       True
									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCamelCase__					=       True

									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass



def        UpperCamelCase_(				)->		Union[str, Any]:
			UpperCamelCase__					=       hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"				,     filename="eating_spaghetti.npy"				,     repo_type="dataset"  )
			UpperCamelCase__					=       np.load(_A  )
			return list(_A  )







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






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return (
						    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,						image_std=[0.5, 0.5, 0.5]     )
						    if is_vision_available()
						    else None
						)




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"     ).to(
						    snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_video()
						UpperCamelCase__					=       image_processor(snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size((1, 400)     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )

						UpperCamelCase__					=       torch.tensor([0.3669, -0.0688, -0.2421]     ).to(snake_case     )

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







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"     ).to(snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_video()
						UpperCamelCase__					=       image_processor(snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# add boolean mask, indicating which patches to mask
						UpperCamelCase__					=       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"      ,						filename="bool_masked_pos.pt"     )
						UpperCamelCase__					=       torch.load(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size([1, 1408, 1536]     )
						UpperCamelCase__					=       torch.tensor(
						    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]      ,						device=snake_case     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )
						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,						snake_case      ,						atol=1E-4     )     )

						# verify the loss (`config.norm_pix_loss` = `True`)
						UpperCamelCase__					=       torch.tensor([0.5142]      ,						device=snake_case     )
						self.assertTrue(torch.allclose(outputs.loss      ,						snake_case      ,						atol=1E-4     )     )

						# verify the loss (`config.norm_pix_loss` = `False`)
						UpperCamelCase__					=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"      ,						norm_pix_loss=snake_case     ).to(
						    snake_case     )

						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						UpperCamelCase__					=       torch.tensor(torch.tensor([0.6469]     )      ,						device=snake_case     )
						self.assertTrue(torch.allclose(outputs.loss      ,						snake_case      ,						atol=1E-4     )     )


