
"""simple docstring"""




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     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=13     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=4     , SCREAMING_SNAKE_CASE__=37     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=0.9     , SCREAMING_SNAKE_CASE__=None     , )      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     parent
											SCREAMING_SNAKE_CASE__		: Dict     =     batch_size
											SCREAMING_SNAKE_CASE__		: int     =     image_size
											SCREAMING_SNAKE_CASE__		: Any     =     num_channels
											SCREAMING_SNAKE_CASE__		: Any     =     patch_size
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tubelet_size
											SCREAMING_SNAKE_CASE__		: List[Any]     =     num_frames
											SCREAMING_SNAKE_CASE__		: Dict     =     is_training
											SCREAMING_SNAKE_CASE__		: str     =     use_labels
											SCREAMING_SNAKE_CASE__		: Dict     =     hidden_size
											SCREAMING_SNAKE_CASE__		: str     =     num_hidden_layers
											SCREAMING_SNAKE_CASE__		: str     =     num_attention_heads
											SCREAMING_SNAKE_CASE__		: Tuple     =     intermediate_size
											SCREAMING_SNAKE_CASE__		: List[Any]     =     hidden_act
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     hidden_dropout_prob
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__		: str     =     type_sequence_label_size
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     initializer_range
											SCREAMING_SNAKE_CASE__		: Tuple     =     mask_ratio
											SCREAMING_SNAKE_CASE__		: Dict     =     scope

											# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
											SCREAMING_SNAKE_CASE__		: List[Any]     =     (image_size // patch_size) ** 2
											SCREAMING_SNAKE_CASE__		: str     =     (num_frames // tubelet_size) * self.num_patches_per_frame

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



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     floats_tensor(
											    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]					)

											SCREAMING_SNAKE_CASE__		: Any     =     None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     ids_tensor([self.batch_size]     , self.type_sequence_label_size					)

											SCREAMING_SNAKE_CASE__		: str     =     self.get_config()

											return config, pixel_values, labels



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



											"""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=SCREAMING_SNAKE_CASE__     , initializer_range=self.initializer_range     , )



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     VideoMAEModel(config=SCREAMING_SNAKE_CASE__					)
											model.to(SCREAMING_SNAKE_CASE__					)
											model.eval()
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     model(SCREAMING_SNAKE_CASE__					)
											self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     VideoMAEForPreTraining(SCREAMING_SNAKE_CASE__					)
											model.to(SCREAMING_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
											SCREAMING_SNAKE_CASE__		: Tuple     =     torch.ones((self.num_masks,)					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     torch.cat([mask, torch.zeros(self.seq_length - mask.size(0					)					)]					)
											SCREAMING_SNAKE_CASE__		: Dict     =     mask.expand(self.batch_size     , -1					).bool()

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     model(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											# model only returns predictions for masked patches
											SCREAMING_SNAKE_CASE__		: List[str]     =     mask.sum().item()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     3 * self.tubelet_size * self.patch_size**2
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, num_masked_patches, decoder_num_labels)					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




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






@require_torch
class 			lowerCAmelCase_		(a__				,      a__				,      unittest.TestCase						):


				"""simple docstring"""



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

				__UpperCamelCase   :		Union[str, Any]       =     False
				__UpperCamelCase   :		int       =     False
				__UpperCamelCase   :		Optional[Any]       =     False
				__UpperCamelCase   :		Any       =     False



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     VideoMAEModelTester(self					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     ConfigTester(self     , config_class=SCREAMING_SNAKE_CASE__     , has_text_modality=SCREAMING_SNAKE_CASE__     , hidden_size=37					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=False					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     copy.deepcopy(SCREAMING_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
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.ones((self.model_tester.num_masks,)					)
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0					)					)]					)
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     mask.expand(self.model_tester.batch_size     , -1					).bool()
																		SCREAMING_SNAKE_CASE__		: str     =     bool_masked_pos.to(SCREAMING_SNAKE_CASE__					)

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

											return inputs_dict



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											self.config_tester.run_common_tests()



				@unittest.skip(reason="""VideoMAE does not use inputs_embeds"""					)
				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[Any]     =     self.model_tester.prepare_config_and_inputs_for_common()

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



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Any     =     self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE__		: List[Any]     =     ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE__					)



				@slow
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     VideoMAEModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											if not self.has_attentions:
																		pass

											else:
																		SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: int     =     self.model_tester.prepare_config_and_inputs_for_common()
																		SCREAMING_SNAKE_CASE__		: List[str]     =     True

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

																									SCREAMING_SNAKE_CASE__		: str     =     True
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     False
																									SCREAMING_SNAKE_CASE__		: int     =     True
																									SCREAMING_SNAKE_CASE__		: int     =     model_class(SCREAMING_SNAKE_CASE__					)
																									model.to(SCREAMING_SNAKE_CASE__					)
																									model.eval()
																									with torch.no_grad():
																																SCREAMING_SNAKE_CASE__		: Dict     =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     outputs.attentions
																									self.assertEqual(len(SCREAMING_SNAKE_CASE__					)     , self.model_tester.num_hidden_layers					)

																									# check that output_attentions also work using config
																									del inputs_dict["output_attentions"]
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     True
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     model_class(SCREAMING_SNAKE_CASE__					)
																									model.to(SCREAMING_SNAKE_CASE__					)
																									model.eval()
																									with torch.no_grad():
																																SCREAMING_SNAKE_CASE__		: List[Any]     =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     outputs.attentions
																									self.assertEqual(len(SCREAMING_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]     , )
																									SCREAMING_SNAKE_CASE__		: Tuple     =     len(SCREAMING_SNAKE_CASE__					)

																									# Check attention is always last and order is fine
																									SCREAMING_SNAKE_CASE__		: int     =     True
																									SCREAMING_SNAKE_CASE__		: List[str]     =     True
																									SCREAMING_SNAKE_CASE__		: str     =     model_class(SCREAMING_SNAKE_CASE__					)
																									model.to(SCREAMING_SNAKE_CASE__					)
																									model.eval()
																									with torch.no_grad():
																																SCREAMING_SNAKE_CASE__		: List[Any]     =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)

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

																									SCREAMING_SNAKE_CASE__		: Tuple     =     outputs.attentions

																									self.assertEqual(len(SCREAMING_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 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											def check_hidden_states_output(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: int     =     model_class(SCREAMING_SNAKE_CASE__					)
																		model.to(SCREAMING_SNAKE_CASE__					)
																		model.eval()

																		with torch.no_grad():
																									SCREAMING_SNAKE_CASE__		: int     =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)

																		SCREAMING_SNAKE_CASE__		: Dict     =     outputs.hidden_states
																		SCREAMING_SNAKE_CASE__		: Dict     =     self.model_tester.num_hidden_layers + 1
																		self.assertEqual(len(SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__					)

																		SCREAMING_SNAKE_CASE__		: Any     =     self.model_tester.seq_length - self.model_tester.num_masks
																		SCREAMING_SNAKE_CASE__		: Any     =     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]     , )

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     True
																		check_hidden_states_output(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

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

																		check_hidden_states_output(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											pass






def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: List[Any]     =     hf_hub_download(
							    repo_id="""hf-internal-testing/spaghetti-video"""     ,filename="""eating_spaghetti.npy"""     ,repo_type="""dataset"""		)
							SCREAMING_SNAKE_CASE__		: str     =     np.load(_snake_case		)
							return list(_snake_case		)



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


				"""simple docstring"""



				@cached_property
				def 			__magic_name__					(self					)      ->	Any:



											"""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 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics"""					).to(
											    SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: int     =     self.default_image_processor
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     prepare_video()
											SCREAMING_SNAKE_CASE__		: Tuple     =     image_processor(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE__					)

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__		: int     =     model(**SCREAMING_SNAKE_CASE__					)

											# verify the logits
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     torch.Size((1, 4_00)					)
											self.assertEqual(outputs.logits.shape     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     torch.tensor([0.3669, -0.0688, -0.2421]					).to(SCREAMING_SNAKE_CASE__					)

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



				@slow
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""					).to(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Any     =     self.default_image_processor
											SCREAMING_SNAKE_CASE__		: str     =     prepare_video()
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     image_processor(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE__					)

											# add boolean mask, indicating which patches to mask
											SCREAMING_SNAKE_CASE__		: Tuple     =     hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""     , filename="""bool_masked_pos.pt"""					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     torch.load(SCREAMING_SNAKE_CASE__					)

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__		: int     =     model(**SCREAMING_SNAKE_CASE__					)

											# verify the logits
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     torch.Size([1, 14_08, 15_36]					)
											SCREAMING_SNAKE_CASE__		: Dict     =     torch.tensor(
											    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]     , device=SCREAMING_SNAKE_CASE__					)
											self.assertEqual(outputs.logits.shape     , SCREAMING_SNAKE_CASE__					)
											self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]     , SCREAMING_SNAKE_CASE__     , atol=1E-4					)					)

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

											# verify the loss (`config.norm_pix_loss` = `False`)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short"""     , norm_pix_loss=SCREAMING_SNAKE_CASE__					).to(
											    SCREAMING_SNAKE_CASE__					)

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     model(**SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     torch.tensor(torch.tensor([0.6469]					)     , device=SCREAMING_SNAKE_CASE__					)
											self.assertTrue(torch.allclose(outputs.loss     , SCREAMING_SNAKE_CASE__     , atol=1E-4					)					)






"""simple docstring"""




import math
import os
import sys

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     """"""
							try:
														with open(_snake_case     ,"""rb"""		) as binary_file:
																					SCREAMING_SNAKE_CASE__		: str     =     binary_file.read()
														for dat in data:
																					SCREAMING_SNAKE_CASE__		: int     =     f'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							lexicon.pop(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     last_match_id

							if math.loga(_snake_case		).is_integer():
														for curr_key in lexicon:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     """0""" + lexicon[curr_key]

							SCREAMING_SNAKE_CASE__		: Tuple     =     bin(_snake_case		)[2:]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     {"""0""": """0""", """1""": """1"""}
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: str     =     """""", """"""
							SCREAMING_SNAKE_CASE__		: int     =     len(_snake_case		)

							for i in range(len(_snake_case		)		):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														SCREAMING_SNAKE_CASE__		: Dict     =     lexicon[curr_string]
														result += last_match_id
														add_key_to_lexicon(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
														index += 1
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """"""

							while curr_string != "" and curr_string not in lexicon:
														curr_string += "0"

							if curr_string != "":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     lexicon[curr_string]
														result += last_match_id

							return result

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     os.path.getsize(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     bin(_snake_case		)[2:]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     len(_snake_case		)

							return "0" * (length_length - 1) + file_length_binary + compressed

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     8
							try:
														with open(_snake_case     ,"""wb"""		) as opened_file:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     [
																					    to_write[i : i + byte_length]
																					    for i in range(0     ,len(_snake_case		)     ,_snake_case		)
																					]

																					if len(result_byte_array[-1]		) % byte_length == 0:
																												result_byte_array.append("""10000000"""		)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]		) - 1
																												)

																					for elem in result_byte_array:
																												opened_file.write(int(_snake_case     ,2		).to_bytes(1     ,byteorder="""big"""		)		)
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     read_file_binary(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     compress_data(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     add_file_length(_snake_case     ,_snake_case		)
							write_file_binary(_snake_case     ,_snake_case		)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])




