





'''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       a_   :





     def __init__( self		:					Any	,      lowercase		:					Optional[int]	,      lowercase		:					List[Any]=13	,      lowercase		:					int=10	,      lowercase		:					str=3	,      lowercase		:					List[Any]=2	,      lowercase		:					Dict=2	,      lowercase		:					List[str]=2	,      lowercase		:					int=True	,      lowercase		:					List[Any]=True	,      lowercase		:					Union[str, Any]=32	,      lowercase		:					Optional[int]=5	,      lowercase		:					List[Any]=4	,      lowercase		:					List[str]=37	,      lowercase		:					Union[str, Any]="gelu"	,      lowercase		:					List[Any]=0.1	,      lowercase		:					Any=0.1	,      lowercase		:					Optional[Any]=10	,      lowercase		:					Union[str, Any]=0.02	,      lowercase		:					Optional[int]=0.9	,      lowercase		:					List[str]=None	,      ):





        """simple docstring"""




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

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

        # use this variable to define bool_masked_pos
        lowercase_			:Optional[Any]    			= int(mask_ratio * self.seq_length						)





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




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

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

        lowercase_			:Optional[int]    			= self.get_config()

        return config, pixel_values, labels





     def 							lowercase__      ( self		:					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=lowercase	,      initializer_range=self.initializer_range	,      )





     def 							lowercase__      ( self		:					Optional[int]	,      lowercase		:					Dict	,      lowercase		:					Dict	,      lowercase		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:int    			= VideoMAEModel(config=lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_			:Optional[int]    			= model(lowercase						)
        self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str	,      lowercase		:					List[str]	,      lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= VideoMAEForPreTraining(lowercase						)
        model.to(lowercase						)
        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
        lowercase_			:Optional[int]    			= torch.ones((self.num_masks,)						)
        lowercase_			:List[str]    			= torch.cat([mask, torch.zeros(self.seq_length - mask.size(0						)						)]						)
        lowercase_			:Dict    			= mask.expand(self.batch_size	,      -1						).bool()

        lowercase_			:str    			= model(lowercase	,      lowercase						)
        # model only returns predictions for masked patches
        lowercase_			:Any    			= mask.sum().item()
        lowercase_			: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 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:int    			= self.prepare_config_and_inputs()
        lowercase_			,							lowercase_			,							lowercase_			:Dict    			= config_and_inputs
        lowercase_			:Any    			= {"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class       a_   (				_lowerCAmelCase	,						_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			(
         (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
     )
     __A      =			(
         {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
         if is_torch_available()
         else {}
     )

     __A      =			False
     __A      =			False
     __A      =			False
     __A      =			False





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:List[Any]    			= VideoMAEModelTester(self						)
        lowercase_			:Dict    			= ConfigTester(self	,      config_class=lowercase	,      has_text_modality=lowercase	,      hidden_size=37						)





     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					List[str]	,      lowercase		:					List[str]	,      lowercase		:					List[str]=False						):





        """simple docstring"""




        lowercase_			:Tuple    			= copy.deepcopy(lowercase						)

        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
           lowercase_			:Tuple    			= torch.ones((self.model_tester.num_masks,)						)
           lowercase_			:Tuple    			= torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0						)						)]						)
           lowercase_			:Optional[int]    			= mask.expand(self.model_tester.batch_size	,      -1						).bool()
           lowercase_			:Dict    			= bool_masked_pos.to(lowercase						)

        if return_labels:
           if model_class in [
               *get_values(lowercase						),
           ]:
              lowercase_			:Union[str, Any]    			= torch.zeros(
                  self.model_tester.batch_size	,      dtype=torch.long	,      device=lowercase						)

        return inputs_dict





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        self.config_tester.run_common_tests()





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





        """simple docstring"""




        pass





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




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

        for model_class in self.all_model_classes:
           lowercase_			:Dict    			= model_class(lowercase						)
           self.assertIsInstance(model.get_input_embeddings()	,      (nn.Module)						)
           lowercase_			:List[Any]    			= model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(lowercase	,      nn.Linear						)						)





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




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

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

           lowercase_			:str    			= ["pixel_values"]
           self.assertListEqual(arg_names[:1]	,      lowercase						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:Tuple    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase						)





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowercase						)





     @slow
     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase_			:List[Any]    			= VideoMAEModel.from_pretrained(lowercase						)
           self.assertIsNotNone(lowercase						)





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        if not self.has_attentions:
           pass

        else:
           lowercase_			,							lowercase_			:Any    			= self.model_tester.prepare_config_and_inputs_for_common()
           lowercase_			:Union[str, Any]    			= True

           for model_class in self.all_model_classes:
              lowercase_			:Dict    			= self.model_tester.seq_length - self.model_tester.num_masks
              lowercase_			:Optional[Any]    			= (
                  num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
              )

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

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

              self.assertListEqual(
                  list(attentions[0].shape[-3:]						)	,      [self.model_tester.num_attention_heads, seq_len, seq_len]	,      )
              lowercase_			:List[str]    			= len(lowercase						)

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

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

              lowercase_			:int    			= outputs.attentions

              self.assertEqual(len(lowercase						)	,      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 							lowercase__      ( self		:					int						):





        """simple docstring"""




        def check_hidden_states_output(lowercase		:					Union[str, Any]	,      lowercase		:					Dict	,      lowercase		:					Any						):
           lowercase_			:Any    			= model_class(lowercase						)
           model.to(lowercase						)
           model.eval()

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

           lowercase_			:Optional[int]    			= outputs.hidden_states
           lowercase_			:Any    			= self.model_tester.num_hidden_layers + 1
           self.assertEqual(len(lowercase						)	,      lowercase						)

           lowercase_			:List[str]    			= self.model_tester.seq_length - self.model_tester.num_masks
           lowercase_			:List[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]	,      )

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

        for model_class in self.all_model_classes:
           lowercase_			:Optional[int]    			= True
           check_hidden_states_output(lowercase	,      lowercase	,      lowercase						)

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

           check_hidden_states_output(lowercase	,      lowercase	,      lowercase						)







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





        """simple docstring"""




        pass







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


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





     @cached_property
     def 							lowercase__      ( 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 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"						).to(
            lowercase						)

        lowercase_			:List[str]    			= self.default_image_processor
        lowercase_			:List[str]    			= prepare_video()
        lowercase_			:int    			= image_processor(lowercase	,      return_tensors="pt"						).to(lowercase						)

        # forward pass
        with torch.no_grad():
           lowercase_			:Dict    			= model(**lowercase						)

        # verify the logits
        lowercase_			:Dict    			= torch.Size((1, 400)						)
        self.assertEqual(outputs.logits.shape	,      lowercase						)

        lowercase_			:int    			= torch.tensor([0.36_69, -0.06_88, -0.24_21]						).to(lowercase						)

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







     @slow
     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        lowercase_			:List[Any]    			= VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"						).to(lowercase						)

        lowercase_			:Dict    			= self.default_image_processor
        lowercase_			:Union[str, Any]    			= prepare_video()
        lowercase_			:List[str]    			= image_processor(lowercase	,      return_tensors="pt"						).to(lowercase						)

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

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

        # verify the logits
        lowercase_			:Union[str, Any]    			= torch.Size([1, 1_408, 1_536]						)
        lowercase_			:List[Any]    			= torch.tensor(
            [[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]	,      device=lowercase						)
        self.assertEqual(outputs.logits.shape	,      lowercase						)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,      lowercase	,      atol=1e-4						)						)

        # verify the loss (`config.norm_pix_loss` = `True`)
        lowercase_			:Any    			= torch.tensor([0.51_42]	,      device=lowercase						)
        self.assertTrue(torch.allclose(outputs.loss	,      lowercase	,      atol=1e-4						)						)

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

        with torch.no_grad():
           lowercase_			:Tuple    			= model(**lowercase						)

        lowercase_			:Optional[Any]    			= torch.tensor(torch.tensor([0.64_69]						)	,      device=lowercase						)
        self.assertTrue(torch.allclose(outputs.loss	,      lowercase	,      atol=1e-4						)						)









'''simple docstring'''


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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase					:     str												='''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   lowercase_			:Tuple    			= numpy.dtype(numpy.uintaa ).newbyteorder(">" )
   return numpy.frombuffer(bytestream.read(4 )	,dtype=__lowerCamelCase )[0]
@deprecated(__lowerCamelCase	,"Please use tf.data to implement this functionality." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   print("Extracting"	,f.name )
   with gzip.GzipFile(fileobj=__lowerCamelCase ) as bytestream:
      lowercase_			:Union[str, Any]    			= _readaa(__lowerCamelCase )
      if magic != 20_51:
         raise ValueError(
             "Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
      lowercase_			:int    			= _readaa(__lowerCamelCase )
      lowercase_			:int    			= _readaa(__lowerCamelCase )
      lowercase_			:Tuple    			= _readaa(__lowerCamelCase )
      lowercase_			:Optional[Any]    			= bytestream.read(rows * cols * num_images )
      lowercase_			:List[str]    			= numpy.frombuffer(__lowerCamelCase	,dtype=numpy.uinta )
      lowercase_			:Dict    			= data.reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,1 )
      return data
@deprecated(__lowerCamelCase	,"Please use tf.one_hot on tensors." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Tuple ):
   lowercase_			:int    			= labels_dense.shape[0]
   lowercase_			:Any    			= numpy.arange(__lowerCamelCase ) * num_classes
   lowercase_			:Optional[int]    			= numpy.zeros((num_labels, num_classes) )
   lowercase_			:List[Any]    			= 1
   return labels_one_hot
@deprecated(__lowerCamelCase	,"Please use tf.data to implement this functionality." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					Optional[Any]=False	,__lowerCamelCase					:					List[str]=10 ):
   print("Extracting"	,f.name )
   with gzip.GzipFile(fileobj=__lowerCamelCase ) as bytestream:
      lowercase_			:List[Any]    			= _readaa(__lowerCamelCase )
      if magic != 20_49:
         raise ValueError(
             "Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
      lowercase_			:List[str]    			= _readaa(__lowerCamelCase )
      lowercase_			:Tuple    			= bytestream.read(__lowerCamelCase )
      lowercase_			:int    			= numpy.frombuffer(__lowerCamelCase	,dtype=numpy.uinta )
      if one_hot:
         return _dense_to_one_hot(__lowerCamelCase	,__lowerCamelCase )
      return labels
class       a_   :





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





        """simple docstring"""




        lowercase_			,							lowercase_			:Union[str, Any]    			= random_seed.get_seed(lowercase						)
        # If op level seed is not set, use whatever graph level seed is returned
        numpy.random.seed(seeda if seed is None else seeda						)
        lowercase_			:Optional[int]    			= dtypes.as_dtype(lowercase						).base_dtype
        if dtype not in (dtypes.uinta, dtypes.floataa):
           raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype						)
        if fake_data:
           lowercase_			:List[str]    			= 10_000
           lowercase_			:int    			= one_hot
        else:
           assert (
               images.shape[0] == labels.shape[0]
           ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
           lowercase_			: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
              lowercase_			:Optional[int]    			= images.reshape(
                  images.shape[0]	,      images.shape[1] * images.shape[2]						)
           if dtype == dtypes.floataa:
              # Convert from [0, 255] -> [0.0, 1.0].
              lowercase_			:Any    			= images.astype(numpy.floataa						)
              lowercase_			:Optional[int]    			= numpy.multiply(lowercase	,      1.0 / 2_55.0						)
        lowercase_			:Tuple    			= images
        lowercase_			:Any    			= labels
        lowercase_			:Dict    			= 0
        lowercase_			:Optional[Any]    			= 0





     @property
     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        return self._images





     @property
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        return self._labels





     @property
     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        return self._num_examples





     @property
     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        return self._epochs_completed







     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					int	,      lowercase		:					List[Any]=False	,      lowercase		:					int=True						):





        """simple docstring"""




        if fake_data:
           lowercase_			:List[str]    			= [1] * 784
           lowercase_			:Dict    			= [1] + [0] * 9 if self.one_hot else 0
           return (
               [fake_image for _ in range(lowercase						)],
               [fake_label for _ in range(lowercase						)],
           )
        lowercase_			:Optional[Any]    			= self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
           lowercase_			:Dict    			= numpy.arange(self._num_examples						)
           numpy.random.shuffle(lowercase						)
           lowercase_			:int    			= self.images[perma]
           lowercase_			:Tuple    			= 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
           lowercase_			:Dict    			= self._num_examples - start
           lowercase_			:List[str]    			= self._images[start : self._num_examples]
           lowercase_			:Optional[Any]    			= self._labels[start : self._num_examples]
           # Shuffle the data
           if shuffle:
              lowercase_			:str    			= numpy.arange(self._num_examples						)
              numpy.random.shuffle(lowercase						)
              lowercase_			:Optional[int]    			= self.images[perm]
              lowercase_			:List[Any]    			= self.labels[perm]
           # Start next epoch
           lowercase_			:List[str]    			= 0
           lowercase_			:Any    			= batch_size - rest_num_examples
           lowercase_			:Optional[int]    			= self._index_in_epoch
           lowercase_			:Tuple    			= self._images[start:end]
           lowercase_			: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
           lowercase_			:Any    			= self._index_in_epoch
           return self._images[start:end], self._labels[start:end]







@deprecated(__lowerCamelCase	,"Please write your own downloading logic." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					Dict ):
   if not gfile.Exists(__lowerCamelCase ):
      gfile.MakeDirs(__lowerCamelCase )
   lowercase_			:Dict    			= os.path.join(__lowerCamelCase	,__lowerCamelCase )
   if not gfile.Exists(__lowerCamelCase ):
      urllib.request.urlretrieve(__lowerCamelCase	,__lowerCamelCase )  # noqa: S310
      with gfile.GFile(__lowerCamelCase ) as f:
         lowercase_			:List[str]    			= f.size()
      print("Successfully downloaded"	,__lowerCamelCase	,__lowerCamelCase	,"bytes." )
   return filepath


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

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

      lowercase_			:int    			= fake()
      lowercase_			:Optional[Any]    			= fake()
      lowercase_			:Tuple    			= fake()
      return _Datasets(train=__lowerCamelCase	,validation=__lowerCamelCase	,test=__lowerCamelCase )

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

   lowercase_			:Optional[int]    			= "train-images-idx3-ubyte.gz"
   lowercase_			:Optional[int]    			= "train-labels-idx1-ubyte.gz"
   lowercase_			:Optional[Any]    			= "t10k-images-idx3-ubyte.gz"
   lowercase_			:int    			= "t10k-labels-idx1-ubyte.gz"

   lowercase_			:Tuple    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + train_images_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Any    			= _extract_images(__lowerCamelCase )

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

   lowercase_			:Any    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + test_images_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Union[str, Any]    			= _extract_images(__lowerCamelCase )

   lowercase_			:int    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + test_labels_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Union[str, Any]    			= _extract_labels(__lowerCamelCase	,one_hot=__lowerCamelCase )

   if not 0 <= validation_size <= len(__lowerCamelCase ):
      lowercase_			:Union[str, Any]    			= (
          "Validation size should be between 0 and "
          F'{len(__lowerCamelCase )}. Received: {validation_size}.'
      )
      raise ValueError(__lowerCamelCase )

   lowercase_			:int    			= train_images[:validation_size]
   lowercase_			:Optional[int]    			= train_labels[:validation_size]
   lowercase_			:List[str]    			= train_images[validation_size:]
   lowercase_			:int    			= train_labels[validation_size:]

   lowercase_			:Dict    			= {"dtype": dtype, "reshape": reshape, "seed": seed}

   lowercase_			:List[str]    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )
   lowercase_			:str    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )
   lowercase_			:List[str]    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )

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


