





'''simple docstring'''


import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple ):
   if model.config.model_type == "gpt2":
      return model.transformer.h[0].mlp.c_fc
   return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
 import torch
 import torch.nn as nn
 class       a_   (				nn.Module							):





      def __init__( self		:					List[str]	,      lowercase		:					nn.Module	,      lowercase		:					int						):





         """simple docstring"""




         super().__init__()
         lowercase_			:Tuple    			= module
         lowercase_			:List[str]    			= nn.Sequential(
             nn.Linear(module.in_features	,      lowercase	,      bias=lowercase						)	,      nn.Linear(lowercase	,      module.out_features	,      bias=lowercase						)	,      )
         lowercase_			:Dict    			= (2.0 / (5 * min(module.in_features	,      module.out_features						))) ** 0.5
         nn.init.normal_(self.adapter[0].weight	,      std=lowercase						)
         nn.init.zeros_(self.adapter[1].weight						)
         self.adapter.to(module.weight.device						)







      def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Optional[int]	,      *lowercase		:					str	,      **lowercase		:					Union[str, Any]						):





         """simple docstring"""




         return self.module(lowercase	,      *lowercase	,      **lowercase						) + self.adapter(lowercase						)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class       a_   (				unittest.TestCase							):
     # We keep the constants inside the init function and model loading inside setUp function

     # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
     # Therefore here we use only bloom-1b3 to test our module
     __A      =			"bigscience/bloom-1b7"

     # Constant values
     __A      =			2.109659552692574

     __A      =			"Hello my name is"
     __A      =			set()
     EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"							)
     EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"							)
     EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"							)
     __A      =			10





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





        """simple docstring"""




        lowercase_			:int    			= AutoTokenizer.from_pretrained(self.model_name						)







class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        super().setUp()

        # Models and tokenizer
        lowercase_			:str    			= AutoModelForCausalLM.from_pretrained(
            self.model_name	,      torch_dtype=torch.floataa	,      device_map="auto"						)
        lowercase_			:Optional[Any]    			= AutoModelForCausalLM.from_pretrained(self.model_name	,      load_in_abit=lowercase	,      device_map="auto"						)





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        del self.model_fpaa
        del self.model_abit

        gc.collect()
        torch.cuda.empty_cache()





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





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.model_abit.config

        self.assertTrue(hasattr(lowercase	,      "quantization_config"						)						)

        lowercase_			:Tuple    			= config.to_dict()
        lowercase_			:Union[str, Any]    			= config.to_diff_dict()

        lowercase_			:Optional[int]    			= config.to_json_string()





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





        """simple docstring"""




        from bitsandbytes.nn import Paramsabit

        lowercase_			:Dict    			= self.model_fpaa.get_memory_footprint()
        lowercase_			:Union[str, Any]    			= self.model_abit.get_memory_footprint()

        self.assertAlmostEqual(mem_fpaa / mem_abit	,      self.EXPECTED_RELATIVE_DIFFERENCE						)
        lowercase_			:Optional[Any]    			= get_some_linear_layer(self.model_abit						)
        self.assertTrue(linear.weight.__class__ == Paramsabit						)





     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        from transformers import TaPreTrainedModel

        self.model_fpaa.get_memory_footprint()
        self.model_abit.get_memory_footprint()

        for name, module in self.model_abit.named_modules():
           if isinstance(lowercase	,      torch.nn.Linear						):
              if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                 # 4-bit parameters are packed in uint8 variables
                 self.assertTrue(module.weight.dtype == torch.uinta						)





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:Dict    			= self.tokenizer(self.input_text	,      return_tensors="pt"						)
        lowercase_			:str    			= self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0						)	,      max_new_tokens=10						)

        self.assertIn(self.tokenizer.decode(output_sequences[0]	,      skip_special_tokens=lowercase						)	,      self.EXPECTED_OUTPUTS						)





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Dict    			= BitsAndBytesConfig()
        lowercase_			:List[str]    			= True

        lowercase_			:Union[str, Any]    			= AutoModelForCausalLM.from_pretrained(
            self.model_name	,      quantization_config=lowercase	,      device_map="auto"						)

        lowercase_			:Optional[Any]    			= self.tokenizer(self.input_text	,      return_tensors="pt"						)
        lowercase_			:int    			= model_abit_from_config.generate(
            input_ids=encoded_input["input_ids"].to(0						)	,      max_new_tokens=10						)

        self.assertIn(self.tokenizer.decode(output_sequences[0]	,      skip_special_tokens=lowercase						)	,      self.EXPECTED_OUTPUTS						)





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        with self.assertRaises(lowercase						), tempfile.TemporaryDirectory() as tmpdirname:
           self.model_abit.save_pretrained(lowercase						)





     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= BitsAndBytesConfig()

        with self.assertRaises(lowercase						):
           lowercase_			:Tuple    			= AutoModelForCausalLM.from_pretrained(
               self.model_name	,      quantization_config=lowercase	,      load_in_abit=lowercase	,      device_map="auto"	,      bnb_abit_quant_type="nf4"	,      )





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        with self.assertRaises(lowercase						):
           # Tries with `str`
           self.model_abit.to("cpu"						)

        with self.assertRaises(lowercase						):
           # Tries with a `dtype``
           self.model_abit.to(torch.floataa						)

        with self.assertRaises(lowercase						):
           # Tries with a `device`
           self.model_abit.to(torch.device("cuda:0"						)						)

        with self.assertRaises(lowercase						):
           # Tries with a `device`
           self.model_abit.float()

        with self.assertRaises(lowercase						):
           # Tries with a `device`
           self.model_abit.half()

        # Test if we did not break anything
        lowercase_			:int    			= self.tokenizer(self.input_text	,      return_tensors="pt"						)

        lowercase_			:int    			= self.model_fpaa.to(torch.floataa						)
        lowercase_			:Tuple    			= self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0						)	,      max_new_tokens=10						)

        # Check this does not throw an error
        lowercase_			:int    			= self.model_fpaa.to("cpu"						)

        # Check this does not throw an error
        lowercase_			:Dict    			= self.model_fpaa.half()

        # Check this does not throw an error
        lowercase_			:List[str]    			= self.model_fpaa.float()







     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= AutoModelForSeqaSeqLM.from_pretrained("t5-small"	,      load_in_abit=lowercase	,      device_map="auto"						)
        self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa						)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class       a_   (				unittest.TestCase							):





     @classmethod
     def 							lowercase__      ( cls		:					Any						):





        """simple docstring"""




        lowercase_			:List[str]    			= "t5-small"
        lowercase_			:Optional[Any]    			= "google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
        lowercase_			:str    			= AutoTokenizer.from_pretrained(cls.model_name						)
        lowercase_			:int    			= "Translate in German: Hello, my dog is cute"





     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        gc.collect()
        torch.cuda.empty_cache()





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        from transformers import TaForConditionalGeneration

        lowercase_			:List[Any]    			= TaForConditionalGeneration._keep_in_fpaa_modules
        lowercase_			:Optional[int]    			= None

        # test with `t5-small`
        lowercase_			:List[Any]    			= TaForConditionalGeneration.from_pretrained(self.model_name	,      load_in_abit=lowercase	,      device_map="auto"						)
        lowercase_			:List[Any]    			= self.tokenizer(self.input_text	,      return_tensors="pt"						).to(0						)
        lowercase_			:str    			= model.generate(**lowercase						)

        # test with `flan-t5-small`
        lowercase_			:Optional[Any]    			= TaForConditionalGeneration.from_pretrained(
            self.dense_act_model_name	,      load_in_abit=lowercase	,      device_map="auto"						)
        lowercase_			:str    			= self.tokenizer(self.input_text	,      return_tensors="pt"						).to(0						)
        lowercase_			:int    			= model.generate(**lowercase						)
        lowercase_			:List[str]    			= modules







     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        import bitsandbytes as bnb

        from transformers import TaForConditionalGeneration

        # test with `t5-small`
        lowercase_			:Union[str, Any]    			= TaForConditionalGeneration.from_pretrained(self.model_name	,      load_in_abit=lowercase	,      device_map="auto"						)

        # there was a bug with decoders - this test checks that it is fixed
        self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q	,      bnb.nn.Linearabit						)						)

        lowercase_			:int    			= self.tokenizer(self.input_text	,      return_tensors="pt"						).to(0						)
        lowercase_			:Tuple    			= model.generate(**lowercase						)

        # test with `flan-t5-small`
        lowercase_			:int    			= TaForConditionalGeneration.from_pretrained(
            self.dense_act_model_name	,      load_in_abit=lowercase	,      device_map="auto"						)
        lowercase_			:Dict    			= self.tokenizer(self.input_text	,      return_tensors="pt"						).to(0						)
        lowercase_			:Dict    			= model.generate(**lowercase						)







class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        super().setUp()
        # model_name
        lowercase_			:List[Any]    			= "bigscience/bloom-560m"
        lowercase_			:str    			= "t5-small"

        # Different types of model

        lowercase_			:Optional[Any]    			= AutoModel.from_pretrained(self.model_name	,      load_in_abit=lowercase	,      device_map="auto"						)
        # Sequence classification model
        lowercase_			:List[str]    			= AutoModelForSequenceClassification.from_pretrained(
            self.model_name	,      load_in_abit=lowercase	,      device_map="auto"						)
        # CausalLM model
        lowercase_			:Optional[Any]    			= AutoModelForCausalLM.from_pretrained(self.model_name	,      load_in_abit=lowercase	,      device_map="auto"						)
        # Seq2seq model
        lowercase_			:Tuple    			= AutoModelForSeqaSeqLM.from_pretrained(
            self.seq_to_seq_name	,      load_in_abit=lowercase	,      device_map="auto"						)





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        del self.base_model
        del self.sequence_model
        del self.model_abit
        del self.seq_to_seq_model

        gc.collect()
        torch.cuda.empty_cache()







     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        from bitsandbytes.nn import Paramsabit

        self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit						)

        # Other heads should be nn.Parameter
        self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter						)
        self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter						)
        self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter						)







class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        super().setUp()





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        del self.pipe

        gc.collect()
        torch.cuda.empty_cache()







     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        lowercase_			:Any    			= pipeline(
            "text-generation"	,      model=self.model_name	,      model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}	,      max_new_tokens=self.MAX_NEW_TOKENS	,      )

        # Real second forward pass
        lowercase_			:str    			= self.pipe(self.input_text						)
        self.assertIn(pipeline_output[0]["generated_text"]	,      self.EXPECTED_OUTPUTS						)







@require_torch_multi_gpu
class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        super().setUp()







     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= AutoModelForCausalLM.from_pretrained(
            self.model_name	,      load_in_abit=lowercase	,      device_map="balanced"						)

        # Check correct device map
        self.assertEqual(set(model_parallel.hf_device_map.values()						)	,      {0, 1}						)

        # Check that inference pass works on the model
        lowercase_			:List[Any]    			= self.tokenizer(self.input_text	,      return_tensors="pt"						)

        # Second real batch
        lowercase_			:Dict    			= model_parallel.generate(input_ids=encoded_input["input_ids"].to(0						)	,      max_new_tokens=10						)
        self.assertIn(self.tokenizer.decode(output_parallel[0]	,      skip_special_tokens=lowercase						)	,      self.EXPECTED_OUTPUTS						)







class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:Dict    			= "facebook/opt-350m"
        super().setUp()







     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        if version.parse(importlib.metadata.version("bitsandbytes"						)						) < version.parse("0.37.0"						):
           return

        # Step 1: freeze all parameters
        lowercase_			:Optional[int]    			= AutoModelForCausalLM.from_pretrained(self.model_name	,      load_in_abit=lowercase						)

        self.assertEqual(set(model.hf_device_map.values()						)	,      {torch.cuda.current_device()}						)

        for param in model.parameters():
           lowercase_			:Optional[int]    			= False  # freeze the model - train adapters later
           if param.ndim == 1:
              # cast the small parameters (e.g. layernorm) to fp32 for stability
              lowercase_			:Union[str, Any]    			= param.data.to(torch.floataa						)

        # Step 2: add adapters
        for _, module in model.named_modules():
           if "OPTAttention" in repr(type(lowercase						)						):
              lowercase_			:List[str]    			= LoRALayer(module.q_proj	,      rank=16						)
              lowercase_			:Optional[int]    			= LoRALayer(module.k_proj	,      rank=16						)
              lowercase_			:Optional[int]    			= LoRALayer(module.v_proj	,      rank=16						)

        # Step 3: dummy batch
        lowercase_			:Union[str, Any]    			= self.tokenizer("Test batch "	,      return_tensors="pt"						).to(0						)

        # Step 4: Check if the gradient is not None
        with torch.cuda.amp.autocast():
           lowercase_			:Union[str, Any]    			= model.forward(**lowercase						)
           out.logits.norm().backward()

        for module in model.modules():
           if isinstance(lowercase	,      lowercase						):
              self.assertTrue(module.adapter[1].weight.grad is not None						)
              self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0						)
           elif isinstance(lowercase	,      nn.Embedding						):
              self.assertTrue(module.weight.grad is None						)







class       a_   (				_lowerCAmelCase							):
     __A      =			"gpt2-xl"
     __A      =			3.3191854854152187









'''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 )


