


'''simple docstring'''





# 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


UpperCAmelCase__  			=				float('''nan''')
class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			List[Any]			,    lowerCAmelCase_							:			Any	)      ->		List[Any]:
						__A=	sys.stdout
						__A=	open(lowerCAmelCase_			,    'a'	)
			def __getattr__(  self							:			Dict			,    lowerCAmelCase_							:			str	)      ->		List[Any]:
						return getattr(self.stdout			,    lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			Any	)      ->		Dict:
						self.stdout.write(lowerCAmelCase_	)
						# strip tqdm codes
						self.file.write(re.sub(r'^.*\r'			,    ''			,    lowerCAmelCase_			,    0			,    re.M	)	)






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[str]=80,_SCREAMING_SNAKE_CASE					: str=False ):






			"""simple docstring"""




			__A=	[]

			# deal with critical env vars
			__A=	['CUDA_VISIBLE_DEVICES']
			for key in env_keys:
						__A=	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)
			__A=	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
			__A=	[]
			__A=	''
			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 )
									__A=	''
			return "\\\n".join(_SCREAMING_SNAKE_CASE )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: Optional[int] ):






			"""simple docstring"""




			__A=	re.sub(r'[\\\n]+',' ',args.base_cmd )

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

			# ensure we have --overwrite_output_dir
			__A=	re.sub('--overwrite_output_dir\s+','',args.base_cmd )
			args.base_cmd += " --overwrite_output_dir"

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






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Dict,_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




			if 0:
						import random
						from time import sleep

						sleep(0 )
						return dict(
						    {k: random.uniform(0,100 ) for k in metric_keys},**{target_metric_key: random.choice([nan, 10.31, 1_00.2, 55.66_66, 2_22.22_22_22_22] )},)

			__A=	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
			__A=	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:
						__A=	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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: Any,_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: Optional[Any],):






			"""simple docstring"""




			__A=	[]
			__A=	[]
			__A=	f"""{id}: {variation:<{longest_variation_len}}"""
			__A=	f"""{preamble}: """
			__A=	set(report_metric_keys + [target_metric_key] )
			for i in tqdm(range(_SCREAMING_SNAKE_CASE ),desc=_SCREAMING_SNAKE_CASE,leave=_SCREAMING_SNAKE_CASE ):
						__A=	process_run_single(
						    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
						__A=	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 += "✘"
			__A=	f"""\33[2K\r{outcome}"""
			if len(_SCREAMING_SNAKE_CASE ) > 0:
						__A=	{k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
						__A=	round(mean_metrics[target_metric_key],2 )
						__A=	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 )
						__A=	variation
						return mean_metrics
			else:
						print(_SCREAMING_SNAKE_CASE )
						return {variation_key: variation, target_metric_key: nan}






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	pd.DataFrame(_SCREAMING_SNAKE_CASE )
			__A=	'variation'
			__A=	'diff_%'

			__A=	nan
			if base_variation is not None and len(df[df[variation_key] == base_variation] ):
						# this may still return nan
						__A=	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
						__A=	df.loc[df[target_metric_key] != nan][target_metric_key].min()

			# create diff column if possible
			if not math.isnan(_SCREAMING_SNAKE_CASE ):
						__A=	df.apply(
						    lambda _SCREAMING_SNAKE_CASE : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value )
						    if not math.isnan(r[target_metric_key] )
						    else 0,axis='columns',)

			# re-order columns
			__A=	[variation_key, target_metric_key, diff_key, *report_metric_keys]
			__A=	df.reindex(_SCREAMING_SNAKE_CASE,axis='columns' )  # reorder cols

			# capitalize
			__A=	df.rename(str.capitalize,axis='columns' )

			# make the cols as narrow as possible
			__A=	df.rename(lambda _SCREAMING_SNAKE_CASE : c.replace('_','<br>' ),axis='columns' )
			__A=	df.rename(lambda _SCREAMING_SNAKE_CASE : c.replace('_','\n' ),axis='columns' )

			__A=	['', '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 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	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',)
			__A=	parser.parse_args()

			__A=	args.output_dir
			Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
			__A=	get_base_command(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

			# split each dimension into its --foo variations
			__A=	[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
			__A=	list(map(str.strip,map(' '.join,itertools.product(*_SCREAMING_SNAKE_CASE ) ) ) )
			__A=	max(len(_SCREAMING_SNAKE_CASE ) for x in variations )

			# split wanted keys
			__A=	args.report_metric_keys.split()

			# capture prints into a log file for convenience
			__A=	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}""" )

			__A=	Tee(_SCREAMING_SNAKE_CASE )

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

			__A=	'variation'
			__A=	[]
			for id, variation in enumerate(tqdm(_SCREAMING_SNAKE_CASE,desc='Total completion: ',leave=_SCREAMING_SNAKE_CASE ) ):
						__A=	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()







'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BridgeTowerImageProcessor
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Dict[str, int] = None			,    lowerCAmelCase_							:			int = 32			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Union[int, float] = 1 / 255			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			str=7			,    lowerCAmelCase_							:			Union[str, Any]=30			,    lowerCAmelCase_							:			str=400			,    lowerCAmelCase_							:			int=3			,    )      ->		str:
						__A=	parent
						__A=	do_resize
						__A=	size if size is not None else {'shortest_edge': 288}
						__A=	size_divisor
						__A=	do_rescale
						__A=	rescale_factor
						__A=	do_normalize
						__A=	do_center_crop
						__A=	image_mean
						__A=	image_std
						__A=	do_pad
						__A=	batch_size
						__A=	num_channels
						__A=	min_resolution
						__A=	max_resolution
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}

			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			str=False	)      ->		str:
						if not batched:
									__A=	self.size['shortest_edge']
									__A=	image_inputs[0]
									if isinstance(lowerCAmelCase_			,    Image.Image	):
												__A,	__A=	image.size
									else:
												__A,	__A=	image.shape[1], image.shape[2]
									__A=	size / min(lowerCAmelCase_			,    lowerCAmelCase_	)
									if h < w:
												__A,	__A=	size, scale * w
									else:
												__A,	__A=	scale * h, size

									__A=	int((1_333 / 800) * size	)
									if max(lowerCAmelCase_			,    lowerCAmelCase_	) > max_size:
												__A=	max_size / max(lowerCAmelCase_			,    lowerCAmelCase_	)
												__A=	newh * scale
												__A=	neww * scale

									__A,	__A=	int(newh + 0.5	), int(neww + 0.5	)
									__A,	__A=	(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

						else:
									__A=	[]
									for image in image_inputs:
												__A,	__A=	self.get_expected_values([image]	)
												expected_values.append((expected_height, expected_width)	)
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[0]	)[0]
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[1]	)[1]

						return expected_height, expected_width


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





			'''simple docstring'''

			A						:				int					      =			BridgeTowerImageProcessor if is_vision_available() else None
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[int]:
						__A=	BridgeTowerImageProcessingTester(self	)
			@property
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						return self.image_processor_tester.prepare_image_processor_dict()
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[Any]:
						__A=	self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_mean'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_std'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_normalize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_resize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size_divisor'	)	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						pass
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    Image.Image	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    numpify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    np.ndarray	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )

			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Tuple:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    torchify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    torch.Tensor	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )



