



"""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


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

class    _A	:



       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a							=    sys.stdout
           __a							=    open(lowerCamelCase__					,		'''a''')



       def __getattr__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           return getattr(self.stdout					,		lowerCamelCase__)





       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           self.stdout.write(lowerCamelCase__)
           # strip tqdm codes
           self.file.write(re.sub(r'''^.*\r'''					,		''''''					,		lowerCamelCase__					,		0					,		re.M))





def 					__snake_case     (      _UpperCAmelCase=80       ,			_UpperCAmelCase=False    ):
    __a							=    []

    # deal with critical env vars
    __a							=    ['''CUDA_VISIBLE_DEVICES''']
    for key in env_keys:
        __a							=    os.environ.get(_UpperCAmelCase       ,			_UpperCAmelCase    )
        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(_UpperCAmelCase    )

    # 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(_UpperCAmelCase    ) > 0:
        current_line += f'{cmd.pop(0    )} '
        if len(_UpperCAmelCase    ) == 0 or len(_UpperCAmelCase    ) + len(cmd[0]    ) + 1 > max_width - 1:
            lines.append(_UpperCAmelCase    )
            __a							=    ''''''
    return "\\\n".join(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):



    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(_UpperCAmelCase       ,			capture_output=_UpperCAmelCase       ,			text=_UpperCAmelCase    )

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

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

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





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			):
    __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(_UpperCAmelCase    )       ,			desc=_UpperCAmelCase       ,			leave=_UpperCAmelCase    ):
        __a							=    process_run_single(
            _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
        __a							=    single_run_metrics[target_metric_key]
        if not math.isnan(_UpperCAmelCase    ):
            metrics.append(_UpperCAmelCase    )
            results.append(_UpperCAmelCase    )
            outcome += "✓"
        else:
            outcome += "✘"
    __a							=    f'\33[2K\r{outcome}'
    if len(_UpperCAmelCase    ) > 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(_UpperCAmelCase    ) > 1:
            results_str += f' {tuple(round(_UpperCAmelCase       ,			2    ) for x in results    )}'
        print(_UpperCAmelCase    )
        __a							=    variation
        return mean_metrics
    else:
        print(_UpperCAmelCase    )
        return {variation_key: variation, target_metric_key: nan}





def 					__snake_case     (      ):
    __a							=    torch.cuda.get_device_properties(torch.device('''cuda'''    )    )
    return f'\nDatetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"    )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    pd.DataFrame(_UpperCAmelCase    )
    __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(_UpperCAmelCase    ):
        # 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(_UpperCAmelCase    ):
        __a							=    df.apply(
            lambda _UpperCAmelCase    : 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(_UpperCAmelCase       ,			axis='''columns'''    )  # reorder cols

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

    # make the cols as narrow as possible
    __a							=    df.rename(lambda _UpperCAmelCase    : c.replace('''_'''       ,			'''<br>'''    )       ,			axis='''columns'''    )
    __a							=    df.rename(lambda _UpperCAmelCase    : 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=_UpperCAmelCase       ,			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=_UpperCAmelCase       ,			floatfmt='''.2f'''    )]

    print('''\n\n'''.join(_UpperCAmelCase    )    )





def 					__snake_case     (      ):
    __a							=    argparse.ArgumentParser()
    parser.add_argument(
        '''--base-cmd'''       ,			default=_UpperCAmelCase       ,			type=_UpperCAmelCase       ,			required=_UpperCAmelCase       ,			help='''Base cmd'''       ,			)
    parser.add_argument(
        '''--variations'''       ,			default=_UpperCAmelCase       ,			type=_UpperCAmelCase       ,			nargs='''+'''       ,			required=_UpperCAmelCase       ,			help='''Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''''       ,			)
    parser.add_argument(
        '''--base-variation'''       ,			default=_UpperCAmelCase       ,			type=_UpperCAmelCase       ,			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=_UpperCAmelCase       ,			type=_UpperCAmelCase       ,			required=_UpperCAmelCase       ,			help='''Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'''       ,			)
    parser.add_argument(
        '''--report-metric-keys'''       ,			default=''''''       ,			type=_UpperCAmelCase       ,			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=_UpperCAmelCase       ,			help='''How many times to re-run each variation - an average will be reported'''       ,			)
    parser.add_argument(
        '''--output_dir'''       ,			default='''output_benchmark'''       ,			type=_UpperCAmelCase       ,			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=_UpperCAmelCase       ,			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(_UpperCAmelCase    ).mkdir(exist_ok=_UpperCAmelCase    )
    __a							=    get_base_command(_UpperCAmelCase       ,			_UpperCAmelCase    )

    # split each dimension into its --foo variations
    __a							=    [list(map(str.strip       ,			re.split(R'''\|'''       ,			_UpperCAmelCase    )    )    ) 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(*_UpperCAmelCase    )    )    )    )
    __a							=    max(len(_UpperCAmelCase    ) 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(_UpperCAmelCase    )

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

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

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


if __name__ == "__main__":
 main()










import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class    _A	:



       def __init__(		self							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple=13					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=32					,		__SCREAMING_SNAKE_CASE							:					List[Any]=2					,		__SCREAMING_SNAKE_CASE							:					Dict=3					,		__SCREAMING_SNAKE_CASE							:					Any=16					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=[1, 2, 1]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=[2, 2, 4]					,		__SCREAMING_SNAKE_CASE							:					Any=2					,		__SCREAMING_SNAKE_CASE							:					Tuple=2.0					,		__SCREAMING_SNAKE_CASE							:					List[str]=True					,		__SCREAMING_SNAKE_CASE							:					Dict=0.0					,		__SCREAMING_SNAKE_CASE							:					List[str]=0.0					,		__SCREAMING_SNAKE_CASE							:					str=0.1					,		__SCREAMING_SNAKE_CASE							:					int="gelu"					,		__SCREAMING_SNAKE_CASE							:					Tuple=False					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		__SCREAMING_SNAKE_CASE							:					Dict=0.02					,		__SCREAMING_SNAKE_CASE							:					Dict=1E-5					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=True					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					str=True					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=10					,		__SCREAMING_SNAKE_CASE							:					int=8					,		):


           '''simple docstring'''

           __a							=    parent
           __a							=    batch_size
           __a							=    image_size
           __a							=    patch_size
           __a							=    num_channels
           __a							=    embed_dim
           __a							=    depths
           __a							=    num_heads
           __a							=    window_size
           __a							=    mlp_ratio
           __a							=    qkv_bias
           __a							=    hidden_dropout_prob
           __a							=    attention_probs_dropout_prob
           __a							=    drop_path_rate
           __a							=    hidden_act
           __a							=    use_absolute_embeddings
           __a							=    patch_norm
           __a							=    layer_norm_eps
           __a							=    initializer_range
           __a							=    is_training
           __a							=    scope
           __a							=    use_labels
           __a							=    type_sequence_label_size
           __a							=    encoder_stride



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           __a							=    None
           if self.use_labels:
               __a							=    ids_tensor([self.batch_size]					,		self.type_sequence_label_size)

           __a							=    self.get_config()

           return config, pixel_values, labels



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return SwinvaConfig(
               image_size=self.image_size					,		patch_size=self.patch_size					,		num_channels=self.num_channels					,		embed_dim=self.embed_dim					,		depths=self.depths					,		num_heads=self.num_heads					,		window_size=self.window_size					,		mlp_ratio=self.mlp_ratio					,		qkv_bias=self.qkv_bias					,		hidden_dropout_prob=self.hidden_dropout_prob					,		attention_probs_dropout_prob=self.attention_probs_dropout_prob					,		drop_path_rate=self.drop_path_rate					,		hidden_act=self.hidden_act					,		use_absolute_embeddings=self.use_absolute_embeddings					,		path_norm=self.patch_norm					,		layer_norm_eps=self.layer_norm_eps					,		initializer_range=self.initializer_range					,		encoder_stride=self.encoder_stride					,		)



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           __a							=    SwinvaModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE)

           __a							=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
           __a							=    int(config.embed_dim * 2 ** (len(config.depths) - 1))

           self.parent.assertEqual(result.last_hidden_state.shape					,		(self.batch_size, expected_seq_len, expected_dim))



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.logits.shape					,		(self.batch_size, self.num_channels, self.image_size, self.image_size))

           # test greyscale images
           __a							=    1
           __a							=    SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           __a							=    model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, 1, self.image_size, self.image_size))



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    self.type_sequence_label_size
           __a							=    SwinvaForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.type_sequence_label_size))





       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           __a	,       __a	,       __a							=    config_and_inputs
           __a							=    {'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_torch
class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   Dict						 =						(
           (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
       )
       UpperCamelCase__  :   Optional[int]						 =						(
           {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase__  :   int						 =						False
       UpperCamelCase__  :   Tuple						 =						False
       UpperCamelCase__  :   Optional[Any]						 =						False
       UpperCamelCase__  :   Optional[Any]						 =						False



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    SwinvaModelTester(self)
           __a							=    ConfigTester(self					,		config_class=__SCREAMING_SNAKE_CASE					,		embed_dim=37)



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''')
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           pass



       @unittest.skip(reason='''Swinv2 does not use inputs_embeds''')
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a							=    model_class(__SCREAMING_SNAKE_CASE)
               self.assertIsInstance(model.get_input_embeddings()					,		(nn.Module))
               __a							=    model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE					,		nn.Linear))



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

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

               __a							=    ['''pixel_values''']
               self.assertListEqual(arg_names[:1]					,		__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()
           __a							=    True

           for model_class in self.all_model_classes:
               __a							=    True
               __a							=    False
               __a							=    True
               __a							=    model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               with torch.no_grad():
                   __a							=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))
               __a							=    outputs.attentions
               __a							=    len(self.model_tester.depths)
               self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               __a							=    True
               __a							=    config.window_size**2
               __a							=    model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               with torch.no_grad():
                   __a							=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))
               __a							=    outputs.attentions
               self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

               self.assertListEqual(
                   list(attentions[0].shape[-3:])					,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,		)
               __a							=    len(__SCREAMING_SNAKE_CASE)

               # Check attention is always last and order is fine
               __a							=    True
               __a							=    True
               __a							=    model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               with torch.no_grad():
                   __a							=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))

               if hasattr(self.model_tester					,		'''num_hidden_states_types'''):
                   __a							=    self.model_tester.num_hidden_states_types
               else:
                   # also another +1 for reshaped_hidden_states
                   __a							=    2
               self.assertEqual(out_len + added_hidden_states					,		len(__SCREAMING_SNAKE_CASE))

               __a							=    outputs.attentions

               self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:])					,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,		)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           __a							=    model_class(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
               __a							=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))

           __a							=    outputs.hidden_states

           __a							=    getattr(
               self.model_tester					,		'''expected_num_hidden_layers'''					,		len(self.model_tester.depths) + 1)
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

           # Swinv2 has a different seq_length
           __a							=    (
               config.patch_size
               if isinstance(config.patch_size					,		collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

           __a							=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:])					,		[num_patches, self.model_tester.embed_dim]					,		)

           __a							=    outputs.reshaped_hidden_states
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

           __a	,       __a	,       __a	,       __a							=    reshaped_hidden_states[0].shape
           __a							=    (
               reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		height * width).permute(0					,		2					,		1)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:])					,		[num_patches, self.model_tester.embed_dim]					,		)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           __a							=    (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size					,		collections.abc.Iterable)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
               __a							=    True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a							=    True

               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()
           __a							=    3

           __a							=    (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size					,		collections.abc.Iterable)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           __a							=    (
               config.patch_size
               if isinstance(config.patch_size					,		collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

           __a							=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           __a							=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
               __a							=    True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		(padded_height, padded_width))

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a							=    True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		(padded_height, padded_width))



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


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)



       @slow
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a							=    SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           __a							=    _config_zero_init(__SCREAMING_SNAKE_CASE)
           for model_class in self.all_model_classes:
               __a							=    model_class(config=__SCREAMING_SNAKE_CASE)
               for name, param in model.named_parameters():
                   if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1E9).round() / 1E9).item()					,		[0.0, 1.0]					,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,		)


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



       @cached_property
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''')
               if is_vision_available()
               else None
           )





       @slow
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''').to(
               __SCREAMING_SNAKE_CASE)
           __a							=    self.default_image_processor

           __a							=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           __a							=    image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
               __a							=    model(**__SCREAMING_SNAKE_CASE)

           # verify the logits
           __a							=    torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape					,		__SCREAMING_SNAKE_CASE)
           __a							=    torch.tensor([-0.39_47, -0.43_06, 0.00_26]).to(__SCREAMING_SNAKE_CASE)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]					,		__SCREAMING_SNAKE_CASE					,		atol=1E-4))





