






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

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

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]     )    ->   int:





    """simple docstring"""

    __A       =						numpy.dtype(numpy.uintaa     ).newbyteorder(""">"""     )
    return numpy.frombuffer(bytestream.read(4     )      ,					dtype=A__     )[0]



@deprecated(A__      ,					"""Please use tf.data to implement this functionality."""     )
def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]     )    ->   Union[str, Any]:





    """simple docstring"""

    print("""Extracting"""      ,					f.name     )
    with gzip.GzipFile(fileobj=A__     ) as bytestream:
        __A       =						_readaa(A__     )
        if magic != 2_0_5_1:
            raise ValueError(
                """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)     )
        __A       =						_readaa(A__     )
        __A       =						_readaa(A__     )
        __A       =						_readaa(A__     )
        __A       =						bytestream.read(rows * cols * num_images     )
        __A       =						numpy.frombuffer(A__      ,					dtype=numpy.uinta     )
        __A       =						data.reshape(A__      ,					A__      ,					A__      ,					1     )
        return data



@deprecated(A__      ,					"""Please use tf.one_hot on tensors."""     )
def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      Tuple     )    ->   Dict:





    """simple docstring"""

    __A       =						labels_dense.shape[0]
    __A       =						numpy.arange(A__     ) * num_classes
    __A       =						numpy.zeros((num_labels, num_classes)     )
    __A       =						1
    return labels_one_hot



@deprecated(A__      ,					"""Please use tf.data to implement this functionality."""     )
def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]      ,					__lowercase :      List[str]=False      ,					__lowercase :      Optional[int]=1_0     )    ->   Any:





    """simple docstring"""

    print("""Extracting"""      ,					f.name     )
    with gzip.GzipFile(fileobj=A__     ) as bytestream:
        __A       =						_readaa(A__     )
        if magic != 2_0_4_9:
            raise ValueError(
                """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)     )
        __A       =						_readaa(A__     )
        __A       =						bytestream.read(A__     )
        __A       =						numpy.frombuffer(A__      ,					dtype=numpy.uinta     )
        if one_hot:
            return _dense_to_one_hot(A__      ,					A__     )
        return labels


class    __lowercase   :







     '''simple docstring'''




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


         """simple docstring"""



         __A       =						random_seed.get_seed(__a					)
         # If op level seed is not set, use whatever graph level seed is returned
         numpy.random.seed(seeda if seed is None else seeda					)
         __A       =						dtypes.as_dtype(__a					).base_dtype
         if dtype not in (dtypes.uinta, dtypes.floataa):
             raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype					)
         if fake_data:
             __A       =						10_000
             __A       =						one_hot
         else:
             assert (
                 images.shape[0] == labels.shape[0]
             ), F"images.shape: {images.shape} labels.shape: {labels.shape}"
             __A       =						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
                 __A       =						images.reshape(
                     images.shape[0]			,						images.shape[1] * images.shape[2]					)
             if dtype == dtypes.floataa:
                 # Convert from [0, 255] -> [0.0, 1.0].
                 __A       =						images.astype(numpy.floataa					)
                 __A       =						numpy.multiply(__a			,						1.0 / 255.0					)
         __A       =						images
         __A       =						labels
         __A       =						0
         __A       =						0




     @property
     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         return self._images




     @property
     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         return self._labels




     @property
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         return self._num_examples




     @property
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         return self._epochs_completed





     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]=False			,						UpperCamelCase_							:      Optional[int]=True					):


         """simple docstring"""



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



@deprecated(A__      ,					"""Please write your own downloading logic."""     )
def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]      ,					__lowercase :      int      ,					__lowercase :      int     )    ->   Tuple:





    """simple docstring"""

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






@deprecated(
    A__      ,					"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""     )
def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]      ,					__lowercase :      List[Any]=False      ,					__lowercase :      Optional[Any]=False      ,					__lowercase :      List[Any]=dtypes.floataa      ,					__lowercase :      Union[str, Any]=True      ,					__lowercase :      Optional[Any]=5_0_0_0      ,					__lowercase :      Optional[int]=None      ,					__lowercase :      Union[str, Any]=DEFAULT_SOURCE_URL      ,					)    ->   Union[str, Any]:





    """simple docstring"""

    if fake_data:

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

        __A       =						fake()
        __A       =						fake()
        __A       =						fake()
        return _Datasets(train=A__      ,					validation=A__      ,					test=A__     )

    if not source_url:  # empty string check
        __A       =						DEFAULT_SOURCE_URL

    __A       =						"""train-images-idx3-ubyte.gz"""
    __A       =						"""train-labels-idx1-ubyte.gz"""
    __A       =						"""t10k-images-idx3-ubyte.gz"""
    __A       =						"""t10k-labels-idx1-ubyte.gz"""

    __A       =						_maybe_download(
        A__      ,					A__      ,					source_url + train_images_file     )
    with gfile.Open(A__      ,					"""rb"""     ) as f:
        __A       =						_extract_images(A__     )

    __A       =						_maybe_download(
        A__      ,					A__      ,					source_url + train_labels_file     )
    with gfile.Open(A__      ,					"""rb"""     ) as f:
        __A       =						_extract_labels(A__      ,					one_hot=A__     )

    __A       =						_maybe_download(
        A__      ,					A__      ,					source_url + test_images_file     )
    with gfile.Open(A__      ,					"""rb"""     ) as f:
        __A       =						_extract_images(A__     )

    __A       =						_maybe_download(
        A__      ,					A__      ,					source_url + test_labels_file     )
    with gfile.Open(A__      ,					"""rb"""     ) as f:
        __A       =						_extract_labels(A__      ,					one_hot=A__     )

    if not 0 <= validation_size <= len(A__     ):
        __A       =						(
            """Validation size should be between 0 and """
            f"{len(A__     )}. Received: {validation_size}."
        )
        raise ValueError(A__     )

    __A       =						train_images[:validation_size]
    __A       =						train_labels[:validation_size]
    __A       =						train_images[validation_size:]
    __A       =						train_labels[validation_size:]

    __A       =						{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

    __A       =						_DataSet(A__      ,					A__      ,					**A__     )
    __A       =						_DataSet(A__      ,					A__      ,					**A__     )
    __A       =						_DataSet(A__      ,					A__      ,					**A__     )

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








# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
      import torch


snake_case__				:	int					       =					logging.get_logger(__name__)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				int		,				__a				:				Optional[Any]=False		,				__a				:				Tuple=False		,				__a				:				Tuple=6.0		,				__a				:				str=None		,				__a				:				List[str]=False		,				__a				:				str=False		,				__a				:				Optional[Any]=None		,				__a				:				Dict="fp4"		,				__a				:				Optional[int]=False		,				**__a				:				Dict		,				)					->str:
          lowerCamelCase_						:   str		= load_in_abit
          lowerCamelCase_						:   Union[str, Any]		= load_in_abit
          lowerCamelCase_						:   Tuple		= llm_inta_threshold
          lowerCamelCase_						:   int		= llm_inta_skip_modules
          lowerCamelCase_						:   List[str]		= llm_inta_enable_fpaa_cpu_offload
          lowerCamelCase_						:   Optional[Any]		= llm_inta_has_fpaa_weight
          lowerCamelCase_						:   Dict		= bnb_abit_quant_type
          lowerCamelCase_						:   int		= bnb_abit_use_double_quant

          if bnb_abit_compute_dtype is None:
             lowerCamelCase_						:   int		= torch.floataa
          elif isinstance(__a		,				__a							):
             lowerCamelCase_						:   Tuple		= getattr(__a		,				__a							)
          elif isinstance(__a		,				torch.dtype							):
             lowerCamelCase_						:   List[str]		= bnb_abit_compute_dtype
          else:
             raise ValueError("""bnb_4bit_compute_dtype must be a string or a torch.dtype"""							)

          self.post_init()



       def 				_lowerCAmelCase							(						self				:				Any							)					->Union[str, Any]:
          if not isinstance(self.llm_inta_threshold		,				__a							):
             raise ValueError("""llm_int8_threshold must be a float"""							)

          if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules		,				__a							):
             raise ValueError("""llm_int8_skip_modules must be a list of strings"""							)
          if not isinstance(self.llm_inta_enable_fpaa_cpu_offload		,				__a							):
             raise ValueError("""llm_int8_enable_fp32_cpu_offload must be a boolean"""							)

          if not isinstance(self.llm_inta_has_fpaa_weight		,				__a							):
             raise ValueError("""llm_int8_has_fp16_weight must be a boolean"""							)

          if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype		,				torch.dtype							):
             raise ValueError("""bnb_4bit_compute_dtype must be torch.dtype"""							)

          if not isinstance(self.bnb_abit_quant_type		,				__a							):
             raise ValueError("""bnb_4bit_quant_type must be a string"""							)

          if not isinstance(self.bnb_abit_use_double_quant		,				__a							):
             raise ValueError("""bnb_4bit_use_double_quant must be a boolean"""							)

          if self.load_in_abit and not version.parse(importlib.metadata.version("""bitsandbytes"""							)							) >= version.parse(
              """0.39.0"""							):
             raise ValueError(
                 """4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"""							)



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->int:
          return self.load_in_abit or self.load_in_abit



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->List[Any]:
          if self.load_in_abit:
             return "llm_int8"
          elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
             return "fp4"
          elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
             return "nf4"
          else:
             return None



       @classmethod
       def 				_lowerCAmelCase							(						cls				:				int		,				__a				:				Dict		,				__a				:				str		,				**__a				:				Any							)					->Optional[Any]:
          lowerCamelCase_						:   Optional[Any]		= cls(**__a							)

          lowerCamelCase_						:   List[Any]		= []
          for key, value in kwargs.items():
             if hasattr(__a		,				__a							):
                setattr(__a		,				__a		,				__a							)
                to_remove.append(__a							)
          for key in to_remove:
             kwargs.pop(__a		,				__a							)

          if return_unused_kwargs:
             return config, kwargs
          else:
             return config



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				Union[str, os.PathLike]							)					->Any:
          with open(__a		,				"""w"""		,				encoding="""utf-8"""							) as writer:
             lowerCamelCase_						:   List[Any]		= self.to_dict()
             lowerCamelCase_						:   Dict		= json.dumps(__a		,				indent=2		,				sort_keys=__a							) + """\n"""

             writer.write(__a							)



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Dict[str, Any]:
          lowerCamelCase_						:   Dict		= copy.deepcopy(self.__dict__							)
          lowerCamelCase_						:   Tuple		= str(output["""bnb_4bit_compute_dtype"""]							).split("""."""							)[1]

          return output



       def __repr__(						self				:				List[str]							)					->Any:
          return F'''{self.__class__.__name__} {self.to_json_string()}'''



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				bool = True							)					->str:
          if use_diff is True:
             lowerCamelCase_						:   Dict		= self.to_diff_dict()
          else:
             lowerCamelCase_						:   Any		= self.to_dict()
          return json.dumps(__a		,				indent=2		,				sort_keys=__a							) + "\n"




       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Dict[str, Any]:
          lowerCamelCase_						:   Any		= self.to_dict()

          # get the default config dict
          lowerCamelCase_						:   List[str]		= BitsAndBytesConfig().to_dict()

          lowerCamelCase_						:   str		= {}

          # only serialize values that differ from the default config
          for key, value in config_dict.items():
             if value != default_config_dict[key]:
                lowerCamelCase_						:   List[Any]		= value

          return serializable_config_dict





