





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     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Tuple=1_3,			_UpperCAmelCase     :					Tuple=1_0,			_UpperCAmelCase     :					Optional[Any]=3,			_UpperCAmelCase     :					int=2,			_UpperCAmelCase     :					List[Any]=2,			_UpperCAmelCase     :					str=2,			_UpperCAmelCase     :					Union[str, Any]=True,			_UpperCAmelCase     :					int=True,			_UpperCAmelCase     :					Any=3_2,			_UpperCAmelCase     :					str=5,			_UpperCAmelCase     :					int=4,			_UpperCAmelCase     :					Optional[int]=3_7,			_UpperCAmelCase     :					int="gelu",			_UpperCAmelCase     :					List[Any]=0.1,			_UpperCAmelCase     :					List[Any]=0.1,			_UpperCAmelCase     :					List[Any]=1_0,			_UpperCAmelCase     :					Tuple=0.02,			_UpperCAmelCase     :					Tuple=0.9,			_UpperCAmelCase     :					List[str]=None,			)				->			Union[str, Any]:





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

														# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
														SCREAMING_SNAKE_CASE__       :      Tuple     =						(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__       :      Optional[Any]     =						int(mask_ratio * self.seq_length						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_tensor(
														    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

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

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.get_config()

														return config, pixel_values, labels






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





														"""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=_UpperCAmelCase,			initializer_range=self.initializer_range,			)






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Dict						)				->			Any:





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






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Dict						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						VideoMAEForPreTraining(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														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__       :      Optional[Any]     =						torch.ones((self.num_masks,)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						torch.cat([mask, torch.zeros(self.seq_length - mask.size(0						)						)]						)
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						mask.expand(self.batch_size,			-1						).bool()

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






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      int     =						{"pixel_values": pixel_values}
														return config, inputs_dict


@require_torch
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   (
							    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
							)
							UpperCAmelCase_      						=   (
							    {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False






							def  A_     ( self     :					Dict						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						VideoMAEModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase,			hidden_size=3_7						)






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[str]=False						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						copy.deepcopy(_UpperCAmelCase						)

														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__       :      Tuple     =						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__       :      Optional[int]     =						bool_masked_pos.to(_UpperCAmelCase						)

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

														return inputs_dict






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





														"""simple docstring"""
														self.config_tester.run_common_tests()






							@unittest.skip(reason="VideoMAE does not use inputs_embeds"						)
							def  A_     ( self     :					List[str]						)				->			str:





														"""simple docstring"""
														pass






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						self.model_tester.prepare_config_and_inputs_for_common()

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






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																					SCREAMING_SNAKE_CASE__       :      int     =						["pixel_values"]
																					self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase						)






							@slow
							def  A_     ( self     :					Tuple						)				->			Union[str, Any]:





														"""simple docstring"""
														for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					SCREAMING_SNAKE_CASE__       :      str     =						VideoMAEModel.from_pretrained(_UpperCAmelCase						)
																					self.assertIsNotNone(_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any]						)				->			str:





														"""simple docstring"""
														if not self.has_attentions:
																					pass

														else:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.model_tester.prepare_config_and_inputs_for_common()
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						True

																					for model_class in self.all_model_classes:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						self.model_tester.seq_length - self.model_tester.num_masks
																												SCREAMING_SNAKE_CASE__       :      int     =						(
																												    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
																												)

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

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

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

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

																												SCREAMING_SNAKE_CASE__       :      Any     =						outputs.attentions

																												self.assertEqual(len(_UpperCAmelCase						),			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  A_     ( self     :					Any						)				->			List[Any]:





														"""simple docstring"""
														def check_hidden_states_output(_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Any						):
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						model_class(_UpperCAmelCase						)
																					model.to(_UpperCAmelCase						)
																					model.eval()

																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						model(**self._prepare_for_class(_UpperCAmelCase,			_UpperCAmelCase						)						)

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						outputs.hidden_states
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.num_hidden_layers + 1
																					self.assertEqual(len(_UpperCAmelCase						),			_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      str     =						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__       :      Any     =						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(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)

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

																					check_hidden_states_output(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)






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





														"""simple docstring"""
														pass





def _a (  )	->    int:

							'''simple docstring'''






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







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







							"""simple docstring"""




							@cached_property
							def  A_     ( self     :					Dict						)				->			Optional[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  A_     ( self     :					List[str]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"						).to(
														    _UpperCAmelCase						)

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

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

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size((1, 4_0_0)						)
														self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

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

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






							@slow
							def  A_     ( self     :					Optional[int]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"						).to(_UpperCAmelCase						)

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

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

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						model(**_UpperCAmelCase						)

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size([1, 1_4_0_8, 1_5_3_6]						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						torch.tensor(
														    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]],			device=_UpperCAmelCase						)
														self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)
														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3],			_UpperCAmelCase,			atol=1E-4						)						)

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

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

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

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













import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_lowerCamelCase :    Tuple  							= collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowerCamelCase :    Optional[Any]  							= '''https://storage.googleapis.com/cvdf-datasets/mnist/'''




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						numpy.dtype(numpy.uintaa       ).newbyteorder(">"       )
							return numpy.frombuffer(bytestream.read(4       )			,				dtype=SCREAMING_SNAKE_CASE__       )[0]




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.data to implement this functionality."       )
def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    List[str]:

							'''simple docstring'''






							print("Extracting"			,				f.name       )
							with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
														SCREAMING_SNAKE_CASE__       :      int     =						_readaa(SCREAMING_SNAKE_CASE__       )
														if magic != 20_51:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      str     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      str     =						bytestream.read(rows * cols * num_images       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						numpy.frombuffer(SCREAMING_SNAKE_CASE__			,				dtype=numpy.uinta       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						data.reshape(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				1       )
														return data




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.one_hot on tensors."       )
def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						labels_dense.shape[0]
							SCREAMING_SNAKE_CASE__       :      List[str]     =						numpy.arange(SCREAMING_SNAKE_CASE__       ) * num_classes
							SCREAMING_SNAKE_CASE__       :      Dict     =						numpy.zeros((num_labels, num_classes)       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						1
							return labels_one_hot




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.data to implement this functionality."       )
def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]=False			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=10       )	->    Optional[int]:

							'''simple docstring'''






							print("Extracting"			,				f.name       )
							with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_readaa(SCREAMING_SNAKE_CASE__       )
														if magic != 20_49:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)       )
														SCREAMING_SNAKE_CASE__       :      Tuple     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						bytestream.read(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						numpy.frombuffer(SCREAMING_SNAKE_CASE__			,				dtype=numpy.uinta       )
														if one_hot:
																					return _dense_to_one_hot(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
														return labels


class 						lowerCamelCase       :







							"""simple docstring"""




							@deprecated(
							    _UpperCAmelCase,			"Please use alternatives such as official/mnist/_DataSet.py"
							    " from tensorflow/models.",			)
							def __init__( self     :					int,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					List[str]=False,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					Union[str, Any]=dtypes.floataa,			_UpperCAmelCase     :					Optional[Any]=True,			_UpperCAmelCase     :					Optional[int]=None,			)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						random_seed.get_seed(_UpperCAmelCase						)
														# If op level seed is not set, use whatever graph level seed is returned
														numpy.random.seed(seeda if seed is None else seeda						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						dtypes.as_dtype(_UpperCAmelCase						).base_dtype
														if dtype not in (dtypes.uinta, dtypes.floataa):
																					raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype						)
														if fake_data:
																					SCREAMING_SNAKE_CASE__       :      str     =						1_0_0_0_0
																					SCREAMING_SNAKE_CASE__       :      str     =						one_hot
														else:
																					assert (
																					    images.shape[0] == labels.shape[0]
																					), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						images.shape[0]

																					# Convert shape from [num examples, rows, columns, depth]
																					# to [num examples, rows*columns] (assuming depth == 1)
																					if reshape:
																												assert images.shape[3] == 1
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						images.reshape(
																												    images.shape[0],			images.shape[1] * images.shape[2]						)
																					if dtype == dtypes.floataa:
																												# Convert from [0, 255] -> [0.0, 1.0].
																												SCREAMING_SNAKE_CASE__       :      Any     =						images.astype(numpy.floataa						)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						numpy.multiply(_UpperCAmelCase,			1.0 / 255.0						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						images
														SCREAMING_SNAKE_CASE__       :      str     =						labels
														SCREAMING_SNAKE_CASE__       :      Any     =						0
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0






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





														"""simple docstring"""
														return self._images






							@property
							def  A_     ( self     :					List[str]						)				->			Optional[int]:





														"""simple docstring"""
														return self._labels






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





														"""simple docstring"""
														return self._num_examples






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





														"""simple docstring"""
														return self._epochs_completed






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any]=False,			_UpperCAmelCase     :					str=True						)				->			Any:





														"""simple docstring"""
														if fake_data:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[1] * 7_8_4
																					SCREAMING_SNAKE_CASE__       :      Dict     =						[1] + [0] * 9 if self.one_hot else 0
																					return (
																					    [fake_image for _ in range(_UpperCAmelCase						)],
																					    [fake_label for _ in range(_UpperCAmelCase						)],
																					)
														SCREAMING_SNAKE_CASE__       :      str     =						self._index_in_epoch
														# Shuffle for the first epoch
														if self._epochs_completed == 0 and start == 0 and shuffle:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						numpy.arange(self._num_examples						)
																					numpy.random.shuffle(_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.images[perma]
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.labels[perma]
														# Go to the next epoch
														if start + batch_size > self._num_examples:
																					# Finished epoch
																					self._epochs_completed += 1
																					# Get the rest examples in this epoch
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self._num_examples - start
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self._images[start : self._num_examples]
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self._labels[start : self._num_examples]
																					# Shuffle the data
																					if shuffle:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						numpy.arange(self._num_examples						)
																												numpy.random.shuffle(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Dict     =						self.images[perm]
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.labels[perm]
																					# Start next epoch
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						batch_size - rest_num_examples
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self._index_in_epoch
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self._images[start:end]
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self._labels[start:end]
																					return (
																					    numpy.concatenate((images_rest_part, images_new_part),			axis=0						),
																					    numpy.concatenate((labels_rest_part, labels_new_part),			axis=0						),
																					)
														else:
																					self._index_in_epoch += batch_size
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self._index_in_epoch
																					return self._images[start:end], self._labels[start:end]




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please write your own downloading logic."       )
def _a (  SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]       )	->    str:

							'''simple docstring'''






							if not gfile.Exists(SCREAMING_SNAKE_CASE__       ):
														gfile.MakeDirs(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						os.path.join(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							if not gfile.Exists(SCREAMING_SNAKE_CASE__       ):
														urllib.request.urlretrieve(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )  # noqa: S310
														with gfile.GFile(SCREAMING_SNAKE_CASE__       ) as f:
																					SCREAMING_SNAKE_CASE__       :      Any     =						f.size()
														print("Successfully downloaded"			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				"bytes."       )
							return filepath







@deprecated(
    SCREAMING_SNAKE_CASE__			,				"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"       )
def _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  str=False			,				SCREAMING_SNAKE_CASE__      :  Any=False			,				SCREAMING_SNAKE_CASE__      :  List[Any]=dtypes.floataa			,				SCREAMING_SNAKE_CASE__      :  str=True			,				SCREAMING_SNAKE_CASE__      :  Dict=50_00			,				SCREAMING_SNAKE_CASE__      :  str=None			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=DEFAULT_SOURCE_URL			,				)	->    Any:

							'''simple docstring'''






							if fake_data:

														def fake():
																					return _DataSet(
																					    []			,				[]			,				fake_data=SCREAMING_SNAKE_CASE__			,				one_hot=SCREAMING_SNAKE_CASE__			,				dtype=SCREAMING_SNAKE_CASE__			,				seed=SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__       :      int     =						fake()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						fake()
														SCREAMING_SNAKE_CASE__       :      Any     =						fake()
														return _Datasets(train=SCREAMING_SNAKE_CASE__			,				validation=SCREAMING_SNAKE_CASE__			,				test=SCREAMING_SNAKE_CASE__       )

							if not source_url:  # empty string check
														SCREAMING_SNAKE_CASE__       :      str     =						DEFAULT_SOURCE_URL

							SCREAMING_SNAKE_CASE__       :      Dict     =						"train-images-idx3-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"train-labels-idx1-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"t10k-images-idx3-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      str     =						"t10k-labels-idx1-ubyte.gz"

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + train_images_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Dict     =						_extract_images(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + train_labels_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						_extract_labels(SCREAMING_SNAKE_CASE__			,				one_hot=SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + test_images_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_extract_images(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + test_labels_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Any     =						_extract_labels(SCREAMING_SNAKE_CASE__			,				one_hot=SCREAMING_SNAKE_CASE__       )

							if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						(
														    "Validation size should be between 0 and "
														    f'''{len(SCREAMING_SNAKE_CASE__       )}. Received: {validation_size}.'''
														)
														raise ValueError(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      str     =						train_images[:validation_size]
							SCREAMING_SNAKE_CASE__       :      str     =						train_labels[:validation_size]
							SCREAMING_SNAKE_CASE__       :      Any     =						train_images[validation_size:]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						train_labels[validation_size:]

							SCREAMING_SNAKE_CASE__       :      Any     =						{"dtype": dtype, "reshape": reshape, "seed": seed}

							SCREAMING_SNAKE_CASE__       :      Tuple     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )

							return _Datasets(train=SCREAMING_SNAKE_CASE__			,				validation=SCREAMING_SNAKE_CASE__			,				test=SCREAMING_SNAKE_CASE__       )






