





# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


_lowerCamelCase :    Union[str, Any]  							= float('''nan''')


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[str],			_UpperCAmelCase     :					Tuple						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						sys.stdout
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						open(_UpperCAmelCase,			"a"						)






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





														"""simple docstring"""
														return getattr(self.stdout,			_UpperCAmelCase						)






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					Tuple						)				->			Optional[int]:





														"""simple docstring"""
														self.stdout.write(_UpperCAmelCase						)
														# strip tqdm codes
														self.file.write(re.sub(r"^.*\r",			"",			_UpperCAmelCase,			0,			re.M						)						)




def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]=80			,				SCREAMING_SNAKE_CASE__      :  List[str]=False       )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						[]

							# deal with critical env vars
							SCREAMING_SNAKE_CASE__       :      Any     =						["CUDA_VISIBLE_DEVICES"]
							for key in env_keys:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						os.environ.get(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
														if val is not None:
																					cmd.append(f'''{key}={val}'''       )

    # python executable (not always needed if the script is executable)
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						sys.executable if full_python_path else sys.executable.split("/"       )[-1]
							cmd.append(SCREAMING_SNAKE_CASE__       )

							# now the normal args
							cmd += list(map(shlex.quote			,				sys.argv       )       )

							# split up into up to MAX_WIDTH lines with shell multi-line escapes
							SCREAMING_SNAKE_CASE__       :      Tuple     =						[]
							SCREAMING_SNAKE_CASE__       :      str     =						""
							while len(SCREAMING_SNAKE_CASE__       ) > 0:
														current_line += f'''{cmd.pop(0       )} '''
														if len(SCREAMING_SNAKE_CASE__       ) == 0 or len(SCREAMING_SNAKE_CASE__       ) + len(cmd[0]       ) + 1 > max_width - 1:
																					lines.append(SCREAMING_SNAKE_CASE__       )
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						""
							return "\\\n".join(SCREAMING_SNAKE_CASE__       )




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

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[str]     =						re.sub(R"[\\\n]+"			,				" "			,				args.base_cmd       )

							# remove --output_dir if any and set our own
							SCREAMING_SNAKE_CASE__       :      Dict     =						re.sub("--output_dir\s+[^\s]+"			,				""			,				args.base_cmd       )
							args.base_cmd += f''' --output_dir {output_dir}'''

							# ensure we have --overwrite_output_dir
							SCREAMING_SNAKE_CASE__       :      Dict     =						re.sub("--overwrite_output_dir\s+"			,				""			,				args.base_cmd       )
							args.base_cmd += " --overwrite_output_dir"

							return [sys.executable] + shlex.split(args.base_cmd       )




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Optional[int]			,				SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  str       )	->    Dict:

							'''simple docstring'''






							if 0:
														import random
														from time import sleep

														sleep(0       )
														return dict(
														    {k: random.uniform(0			,				1_00       ) for k in metric_keys}			,				**{target_metric_key: random.choice([nan, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2]       )}			,				)

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						subprocess.run(SCREAMING_SNAKE_CASE__			,				capture_output=SCREAMING_SNAKE_CASE__			,				text=SCREAMING_SNAKE_CASE__       )

							if verbose:
														print("STDOUT"			,				result.stdout       )
														print("STDERR"			,				result.stderr       )

							# save the streams
							SCREAMING_SNAKE_CASE__       :      str     =						variation.replace(" "			,				"-"       )
							with open(Path(SCREAMING_SNAKE_CASE__       ) / f'''log.{prefix}.stdout.txt'''			,				"w"       ) as f:
														f.write(result.stdout       )
							with open(Path(SCREAMING_SNAKE_CASE__       ) / f'''log.{prefix}.stderr.txt'''			,				"w"       ) as f:
														f.write(result.stderr       )

							if result.returncode != 0:
														if verbose:
																					print("failed"       )
														return {target_metric_key: nan}

							with io.open(f'''{output_dir}/all_results.json'''			,				"r"			,				encoding="utf-8"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						json.load(SCREAMING_SNAKE_CASE__       )

							# filter out just the keys we want
							return {k: v for k, v in metrics.items() if k in metric_keys}




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  Dict			,				SCREAMING_SNAKE_CASE__      :  Dict			,				SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				)	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						[]
							SCREAMING_SNAKE_CASE__       :      List[str]     =						[]
							SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''{id}: {variation:<{longest_variation_len}}'''
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						f'''{preamble}: '''
							SCREAMING_SNAKE_CASE__       :      int     =						set(report_metric_keys + [target_metric_key]       )
							for i in tqdm(range(SCREAMING_SNAKE_CASE__       )			,				desc=SCREAMING_SNAKE_CASE__			,				leave=SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						process_run_single(
														    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						single_run_metrics[target_metric_key]
														if not math.isnan(SCREAMING_SNAKE_CASE__       ):
																					metrics.append(SCREAMING_SNAKE_CASE__       )
																					results.append(SCREAMING_SNAKE_CASE__       )
																					outcome += "✓"
														else:
																					outcome += "✘"
							SCREAMING_SNAKE_CASE__       :      int     =						f'''\33[2K\r{outcome}'''
							if len(SCREAMING_SNAKE_CASE__       ) > 0:
														SCREAMING_SNAKE_CASE__       :      Any     =						{k: fmean([x[k] for x in metrics]       ) for k in metrics[0].keys()}
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						round(mean_metrics[target_metric_key]			,				2       )
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						f'''{outcome} {mean_target}'''
														if len(SCREAMING_SNAKE_CASE__       ) > 1:
																					results_str += f''' {tuple(round(SCREAMING_SNAKE_CASE__			,				2       ) for x in results       )}'''
														print(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[str]     =						variation
														return mean_metrics
							else:
														print(SCREAMING_SNAKE_CASE__       )
														return {variation_key: variation, target_metric_key: nan}




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

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.cuda.get_device_properties(torch.device("cuda"       )       )
							return f'''
Datetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"       )}

Software:
transformers: {transformers.__version__}
torch       : {torch.__version__}
cuda        : {torch.version.cuda}
python      : {platform.python_version()}

Hardware:
{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB
'''




def _a (  SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  Optional[int]			,				SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						pd.DataFrame(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						"variation"
							SCREAMING_SNAKE_CASE__       :      int     =						"diff_%"

							SCREAMING_SNAKE_CASE__       :      int     =						nan
							if base_variation is not None and len(df[df[variation_key] == base_variation]       ):
														# this may still return nan
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						df.loc[df[variation_key] == base_variation][target_metric_key].item()
							if math.isnan(SCREAMING_SNAKE_CASE__       ):
														# as a fallback, use the minimal value as the sentinel
														SCREAMING_SNAKE_CASE__       :      int     =						df.loc[df[target_metric_key] != nan][target_metric_key].min()

							# create diff column if possible
							if not math.isnan(SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Tuple     =						df.apply(
														    lambda SCREAMING_SNAKE_CASE__       : round(1_00 * (r[target_metric_key] - sentinel_value) / sentinel_value       )
														    if not math.isnan(r[target_metric_key]       )
														    else 0			,				axis="columns"			,				)

							# re-order columns
							SCREAMING_SNAKE_CASE__       :      List[str]     =						[variation_key, target_metric_key, diff_key, *report_metric_keys]
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						df.reindex(SCREAMING_SNAKE_CASE__			,				axis="columns"       )  # reorder cols

							# capitalize
							SCREAMING_SNAKE_CASE__       :      Dict     =						df.rename(str.capitalize			,				axis="columns"       )

							# make the cols as narrow as possible
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						df.rename(lambda SCREAMING_SNAKE_CASE__       : c.replace("_"			,				"<br>"       )			,				axis="columns"       )
							SCREAMING_SNAKE_CASE__       :      Dict     =						df.rename(lambda SCREAMING_SNAKE_CASE__       : c.replace("_"			,				"\n"       )			,				axis="columns"       )

							SCREAMING_SNAKE_CASE__       :      int     =						["", "Copy between the cut-here-lines and paste as is to github or a forum"]
							report += ["----------8<-----------------8<--------"]
							report += ["*** Results:", df_github.to_markdown(index=SCREAMING_SNAKE_CASE__			,				floatfmt=".2f"       )]
							report += ["```"]
							report += ["*** Setup:", get_versions()]
							report += ["*** The benchmark command line was:", get_original_command()]
							report += ["```"]
							report += ["----------8<-----------------8<--------"]
							report += ["*** Results (console):", df_console.to_markdown(index=SCREAMING_SNAKE_CASE__			,				floatfmt=".2f"       )]

							print("\n\n".join(SCREAMING_SNAKE_CASE__       )       )




def _a (  )	->    Tuple:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Dict     =						argparse.ArgumentParser()
							parser.add_argument(
							    "--base-cmd"			,				default=SCREAMING_SNAKE_CASE__			,				type=SCREAMING_SNAKE_CASE__			,				required=SCREAMING_SNAKE_CASE__			,				help="Base cmd"			,				)
							parser.add_argument(
							    "--variations"			,				default=SCREAMING_SNAKE_CASE__			,				type=SCREAMING_SNAKE_CASE__			,				nargs="+"			,				required=SCREAMING_SNAKE_CASE__			,				help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'"			,				)
							parser.add_argument(
							    "--base-variation"			,				default=SCREAMING_SNAKE_CASE__			,				type=SCREAMING_SNAKE_CASE__			,				help="Baseline variation to compare to. if None the minimal target value will be used to compare against"			,				)
							parser.add_argument(
							    "--target-metric-key"			,				default=SCREAMING_SNAKE_CASE__			,				type=SCREAMING_SNAKE_CASE__			,				required=SCREAMING_SNAKE_CASE__			,				help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second"			,				)
							parser.add_argument(
							    "--report-metric-keys"			,				default=""			,				type=SCREAMING_SNAKE_CASE__			,				help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples"			,				)
							parser.add_argument(
							    "--repeat-times"			,				default=1			,				type=SCREAMING_SNAKE_CASE__			,				help="How many times to re-run each variation - an average will be reported"			,				)
							parser.add_argument(
							    "--output_dir"			,				default="output_benchmark"			,				type=SCREAMING_SNAKE_CASE__			,				help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked"			,				)
							parser.add_argument(
							    "--verbose"			,				default=SCREAMING_SNAKE_CASE__			,				action="store_true"			,				help="Whether to show the outputs of each run or just the benchmark progress"			,				)
							SCREAMING_SNAKE_CASE__       :      List[str]     =						parser.parse_args()

							SCREAMING_SNAKE_CASE__       :      Any     =						args.output_dir
							Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Any     =						get_base_command(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							# split each dimension into its --foo variations
							SCREAMING_SNAKE_CASE__       :      List[str]     =						[list(map(str.strip			,				re.split(R"\|"			,				SCREAMING_SNAKE_CASE__       )       )       ) for x in args.variations]
							# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
							# while stripping white space for inputs that were empty
							SCREAMING_SNAKE_CASE__       :      List[str]     =						list(map(str.strip			,				map(" ".join			,				itertools.product(*SCREAMING_SNAKE_CASE__       )       )       )       )
							SCREAMING_SNAKE_CASE__       :      str     =						max(len(SCREAMING_SNAKE_CASE__       ) for x in variations       )

							# split wanted keys
							SCREAMING_SNAKE_CASE__       :      Any     =						args.report_metric_keys.split()

							# capture prints into a log file for convenience
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						f'''benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"       )}.txt'''
							print(f'''\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt'''       )
							print(f'''and this script\'s output is also piped into {report_fn}'''       )

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						Tee(SCREAMING_SNAKE_CASE__       )

							print(f'''\n*** Running {len(SCREAMING_SNAKE_CASE__       )} benchmarks:'''       )
							print(f'''Base command: {" ".join(SCREAMING_SNAKE_CASE__       )}'''       )

							SCREAMING_SNAKE_CASE__       :      str     =						"variation"
							SCREAMING_SNAKE_CASE__       :      str     =						[]
							for id, variation in enumerate(tqdm(SCREAMING_SNAKE_CASE__			,				desc="Total completion: "			,				leave=SCREAMING_SNAKE_CASE__       )       ):
														SCREAMING_SNAKE_CASE__       :      Any     =						base_cmd + variation.split()
														results.append(
														    process_run(
														        id + 1			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				args.target_metric_key			,				SCREAMING_SNAKE_CASE__			,				args.repeat_times			,				SCREAMING_SNAKE_CASE__			,				args.verbose			,				)       )

							process_results(SCREAMING_SNAKE_CASE__			,				args.target_metric_key			,				SCREAMING_SNAKE_CASE__			,				args.base_variation			,				SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
		main()













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__       )






