

import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
					from tests.trainer.test_trainer import (  # noqa
					    RegressionModelConfig,
					    RegressionPreTrainedModel,
					    get_regression_trainer,
					)


set_seed(42)

SCREAMING_SNAKE_CASE_						=					get_tests_dir('fixtures')
SCREAMING_SNAKE_CASE_						=					os.path.join(dirname(get_tests_dir()))
SCREAMING_SNAKE_CASE_						=					dirname(os.path.abspath(__file__))

# default torch.distributed port
SCREAMING_SNAKE_CASE_						=					'''10999'''

SCREAMING_SNAKE_CASE_						=					'''t5-small'''

# *** Working Models ***
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-albert'''
SCREAMING_SNAKE_CASE_						=					'''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-bert'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-bigbird_pegasus'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-big_bird'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-blenderbot'''
SCREAMING_SNAKE_CASE_						=					'''bigscience/bigscience-small-testing'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-deberta'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-deberta-v2'''
SCREAMING_SNAKE_CASE_						=					'''sshleifer/tiny-distilbert-base-cased'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-electra'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-flaubert'''
SCREAMING_SNAKE_CASE_						=					'''stas/tiny-wmt19-en-de'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-funnel'''
SCREAMING_SNAKE_CASE_						=					'''sshleifer/tiny-gpt2'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-gptj'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-gpt_neo'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-layoutlm'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-led'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-longformer'''
SCREAMING_SNAKE_CASE_						=					'''stas/tiny-m2m_100'''  # hf tiny model is unsuitable
SCREAMING_SNAKE_CASE_						=					'''sshleifer/tiny-marian-en-de'''
SCREAMING_SNAKE_CASE_						=					'''sshleifer/tiny-mbart'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-mobilebert'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-mpnet'''
SCREAMING_SNAKE_CASE_						=					'''stas/pegasus-cnn_dailymail-tiny-random'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-prophetnet'''
SCREAMING_SNAKE_CASE_						=					'''sshleifer/tiny-distilroberta-base'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-squeezebert'''
SCREAMING_SNAKE_CASE_						=					'''patrickvonplaten/t5-tiny-random'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-t5-v1.1'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-vit'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-xlm-roberta'''
SCREAMING_SNAKE_CASE_						=					'''sshleifer/tiny-xlnet-base-cased'''


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-mt5'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-camembert'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-convbert'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-layoutlmv2'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-ctrl'''
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-dpr'''
# - "dpr"  examples/research_projects/rag-end2end-retriever/
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-rag'''
# - "rag" research_projects
SCREAMING_SNAKE_CASE_						=					''''''
# - "luke" Entities classes - no plan to make such example
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-lxmert'''
# - "lxmert" doesn't work with run_qa.py
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-clip'''
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-speech_to_text'''
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
SCREAMING_SNAKE_CASE_						=					'''hf-internal-testing/tiny-random-tapas'''

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Optional[int]=False       )      ->				Optional[int]:
				# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
				# - it won't be able to handle that
				# 2. for now testing with just 2 gpus max (since some quality tests may give different
				# results with mode gpus because we use very little data)
				_UpperCAmelCase				:						str       						=			min(2     ,					get_gpu_count()       ) if distributed else 1
				_UpperCAmelCase				:						Optional[Any]       						=			os.environ.get("DS_TEST_PORT"     ,					lowercase_       )
				return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()

def 							__SCREAMING_SNAKE_CASE							( )      ->				int:
				_UpperCAmelCase				:						Union[str, Any]       						=			F'{FIXTURE_DIRECTORY}/tests_samples'
				_UpperCAmelCase				:						Tuple       						=			F'{data_dir_samples}/wmt_en_ro'
				_UpperCAmelCase				:						Tuple       						=			F'{data_dir_samples}/xsum'
				_UpperCAmelCase				:						str       						=			"\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".split()

				# try to cover as many models as possible once (it's enough to run on one task per model)
				# but need a tiny model for each
				#
				# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
				_UpperCAmelCase				:						Optional[Any]       						=			{
				    "trans": [
				        "bart",
				        "fsmt",
				        "m2m_100",
				        "marian",
				        "mbart",
				        "t5",
				        "t5_v1",
				        # "mt5", missing model files
				    ],
				    "sum": [
				        "pegasus",
				    ],
				    "clm": [
				        "big_bird",
				        "bigbird_pegasus",
				        "blenderbot",
				        "bloom",
				        "gpt2",
				        "gpt_neo",
				        "gptj",
				        "xlm-roberta",
				        "prophetnet",
				        # "camembert", missing model files
				    ],
				    "mlm": [
				        "albert",
				        "deberta",
				        "deberta-v2",
				        "distilbert",
				        "electra",
				        "flaubert",
				        "funnel",
				        "layoutlm",
				        # "reformer", # multiple issues with either mlm/qa/clas
				    ],
				    "qa": [
				        "led",
				        "longformer",
				        "mobilebert",
				        "mpnet",
				        "roberta",
				        "squeezebert",
				        # "convbert", # missing tokenizer files
				        # "layoutlmv2", missing model files
				    ],
				    "clas": [
				        "bert",
				        "xlnet",
				        # "hubert", # missing tokenizer files
				        # "ibert", # multiple issues with either mlm/qa/clas
				        # "transfo-xl", # tokenizer issues as ctrl
				        # "ctrl", # tokenizer issues
				        # "openai-gpt", missing model files
				        # "tapas", multiple issues
				    ],
				    "img_clas": [
				        "vit",
				    ],
				}

				_UpperCAmelCase				:						Union[str, Any]       						=			F'{ROOT_DIRECTORY}/examples/pytorch'

				_UpperCAmelCase				:						List[str]       						=			{
				    "trans": F'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
				    "sum": F'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
				    "clm": F'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
				    "mlm": F'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
				    "qa": F'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
				    "clas": F'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
				    "img_clas": F'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
				}

				_UpperCAmelCase				:						Dict       						=			get_launcher(distributed=lowercase_       )

				_UpperCAmelCase				:						Tuple       						=			{}
				for task, args in tasks.items():
								_UpperCAmelCase				:						Union[str, Any]       						=			args.split()
								for model in tasksamodels[task]:
												_UpperCAmelCase				:						Optional[int]       						=			globals()[F'{model.upper().replace("-"     ,					"_"       )}_TINY']
												_UpperCAmelCase				:						Optional[int]       						=			F'--model_name_or_path {model_name}'.split()
												_UpperCAmelCase				:						List[str]       						=			launcher + args + args_model + args_main

												# # generation special case
												# if task == "gen":
												#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
												#     args_model += f"--model_type {model}".split()
												#     cmds[f"{task}_{model}"] = launcher + args + args_model
												# else:

				return cmds


SCREAMING_SNAKE_CASE_						=					make_task_cmds()

SCREAMING_SNAKE_CASE_						=					'''zero2'''
SCREAMING_SNAKE_CASE_						=					'''zero3'''

SCREAMING_SNAKE_CASE_						=					[ZEROa, ZEROa]

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Any     ,					lowerCAmelCase:     int     ,					lowerCAmelCase:     Tuple       )      ->				List[str]:
				# customize the test name generator function as we want both params to appear in the sub-test
				# name, as by default it shows only the first param
				_UpperCAmelCase				:						Optional[int]       						=			parameterized.to_safe_name("_".join(str(lowercase_       ) for x in param.args       )       )
				return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE_						=					list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class  a     (		A__ ):


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										if task not in task_cmds:
														raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'		)

										_UpperCAmelCase				:						int       						=			task_cmds[task]
										_UpperCAmelCase				:						List[str]       						=			f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

										_UpperCAmelCase				:						str       						=			self.get_auto_remove_tmp_dir()
										_UpperCAmelCase				:						Union[str, Any]       						=			f'--output_dir {output_dir}'.split()

										cmd += args_ds + args_out

										return cmd, output_dir





						@parameterized.expand(lowerCamelCase__  ,    name_func=lowerCamelCase__		)
						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[int]       						=			self.get_task_cmd(lowerCamelCase__  ,    lowerCamelCase__		)

										# 1. generate the checkpoint
										cmd += "--save_steps 1".split()
										# keep for quick debug
										# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
										execute_subprocess_async(lowerCamelCase__  ,    env=self.get_env()		)

										# 2. test that the fp32 weights get reconsolidated
										_UpperCAmelCase				:						List[str]       						=			f'{output_dir}/checkpoint-1'
										_UpperCAmelCase				:						Optional[int]       						=			f'{chkpt_dir}/out.bin'
										_UpperCAmelCase				:						Dict       						=			f'{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'
										# keep for quick debug
										# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
										subprocess.check_call(lowerCamelCase__  ,    shell=lowerCamelCase__		)
										assert os.path.exists(lowerCamelCase__		), f'{recovered_model_path} was not found'

										# possibly could also test that the resulting saved model is usable but given that we use
										# random models we won't know if it's any good







"""simple docstring"""
import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     :  Tuple										=					logging.get_logger(__name__)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				List[nn.Module]        =       field(default_factory=A__			)
					lowercase__      :				list        =       field(default_factory=A__			)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = len(list(m.modules()      )      ) == 1 or isinstance(lowerCamelCase__ ,				nn.Convad      ) or isinstance(lowerCamelCase__ ,				nn.BatchNormad      )
									if has_not_submodules:
													if not isinstance(lowerCamelCase__ ,				lowerCamelCase__      ):
																	self.traced.append(lowerCamelCase__      )

					def __call__(			self ,				lowerCamelCase__      ):
									for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook      )      )
									self.module(lowerCamelCase__      )
									[x.remove() for x in self.handles]
									return self





					@property
					def 	snake_case__					(			self      ):
									# check the len of the state_dict keys to see if we have learnable params
									return list(filter(lambda lowerCamelCase__      : len(list(x.state_dict().keys()      )      ) > 0 ,				self.traced      )      )


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				nn.Module
					lowercase__      :				int        =       0
					lowercase__      :				List        =       field(default_factory=A__			)
					lowercase__      :				List        =       field(default_factory=A__			)

					def __call__(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = Tracker(self.dest      )(lowerCamelCase__      ).parametrized
									_lowerCamelCase					     = Tracker(self.src      )(lowerCamelCase__      ).parametrized

									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.src_skip ,				lowerCamelCase__      )      )
									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.dest_skip ,				lowerCamelCase__      )      )

									if len(lowerCamelCase__      ) != len(lowerCamelCase__      ):
													raise Exception(
													    F"""Numbers of operations are different. Source module has {len(lowerCamelCase__      )} operations while"""
													    F""" destination module has {len(lowerCamelCase__      )}."""      )

									for dest_m, src_m in zip(lowerCamelCase__ ,				lowerCamelCase__      ):
													dest_m.load_state_dict(src_m.state_dict()      )
													if self.verbose == 1:
																	print(F"""Transfered from={src_m} to={dest_m}"""      )



def 				lowerCAmelCase_(      lowercase_   :							nn.Module  ,      lowercase_   :							nn.Module							)		->				nn.Module:
				# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
				_lowerCamelCase					     = from_model.state_dict()
				_lowerCamelCase					     = our_model.state_dict()
				_lowerCamelCase					     = our_model.config
				_lowerCamelCase					     = []
				for stage_idx in range(len(config.hidden_sizes							)							):
								for block_id in range(config.depths[stage_idx]							):
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

												all_keys.append((from_key, to_key)							)
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

												all_keys.append((from_key, to_key)							)

				for from_key, to_key in all_keys:
								_lowerCamelCase					     = from_state_dict.pop(lowercase_							)

				our_model.load_state_dict(lowercase_							)
				return our_model



def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							VanConfig  ,      lowercase_   :							str  ,      lowercase_   :							nn.Module  ,      lowercase_   :							Path  ,      lowercase_   :							bool = True  ,      )		->				Union[str, Any]:
				print(F"""Downloading weights for {name}..."""							)
				_lowerCamelCase					     = cached_download(lowercase_							)
				print(F"""Converting {name}..."""							)
				_lowerCamelCase					     = torch.load(lowercase_							)['''state_dict''']
				from_model.load_state_dict(lowercase_							)
				from_model.eval()
				with torch.no_grad():
								_lowerCamelCase					     = VanForImageClassification(lowercase_							).eval()
								_lowerCamelCase					     = ModuleTransfer(src=lowercase_  ,      dest=lowercase_							)
								_lowerCamelCase					     = torch.randn((1, 3, 2_24, 2_24)							)
								module_transfer(lowercase_							)
								_lowerCamelCase					     = copy_parameters(lowercase_  ,      lowercase_							)

				if not torch.allclose(from_model(lowercase_							)  ,      our_model(lowercase_							).logits							):
								raise ValueError('''The model logits don\'t match the original one.'''							)

				_lowerCamelCase					     = name
				print(lowercase_							)

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name  ,      commit_message='''Add model'''  ,      use_temp_dir=lowercase_  ,      )

								# we can use the convnext one
								_lowerCamelCase					     = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							)
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name  ,      commit_message='''Add image processor'''  ,      use_temp_dir=lowercase_  ,      )

								print(F"""Pushed {checkpoint_name}"""							)



def 				lowerCAmelCase_(      lowercase_   :							Path  ,      lowercase_   :							str = None  ,      lowercase_   :							bool = True							)		->				List[str]:
				_lowerCamelCase					     = '''imagenet-1k-id2label.json'''
				_lowerCamelCase					     = 10_00

				_lowerCamelCase					     = '''huggingface/label-files'''
				_lowerCamelCase					     = num_labels
				_lowerCamelCase					     = json.load(open(hf_hub_download(lowercase_  ,      lowercase_  ,      repo_type='''dataset'''							)  ,      '''r'''							)							)
				_lowerCamelCase					     = {int(lowercase_							): v for k, v in idalabel.items()}

				_lowerCamelCase					     = idalabel
				_lowerCamelCase					     = {v: k for k, v in idalabel.items()}

				_lowerCamelCase					     = partial(lowercase_  ,      num_labels=lowercase_  ,      idalabel=lowercase_  ,      labelaid=lowercase_							)

				_lowerCamelCase					     = {
				    '''van-tiny''': ImageNetPreTrainedConfig(
				        hidden_sizes=[32, 64, 1_60, 2_56]  ,      depths=[3, 3, 5, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-small''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[2, 2, 4, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-base''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 3, 12, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-large''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 5, 27, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': van_tiny,
				    '''van-small''': van_small,
				    '''van-base''': van_base,
				    '''van-large''': van_large,
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
				    ),
				    '''van-small''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
				    ),
				    '''van-base''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
				    ),
				    '''van-large''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
				    ),
				}

				if model_name:
								convert_weight_and_push(
								    lowercase_  ,      names_to_config[model_name]  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    lowercase_  ,      lowercase_  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  Any										=					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model-name''',
			    default=None,
			    type=str,
			    help=(
			        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
			        ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=Path,
			    required=True,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--van_dir''',
			    required=True,
			    type=Path,
			    help=(
			        '''A path to VAN\'s original implementation directory. You can download from here:'''
			        ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
			    ),
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    default=True,
			    type=bool,
			    required=False,
			    help='''If True, push model and image processor to the hub.''',
			)

			__SCREAMING_SNAKE_CASE     :  int										=					parser.parse_args()
			__SCREAMING_SNAKE_CASE     :  Path										=					args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			__SCREAMING_SNAKE_CASE     :  List[str]										=					args.van_dir
			# append the path to the parents to maskformer dir
			sys.path.append(str(van_dir.parent))
			from van.models.van import van_base, van_large, van_small, van_tiny

			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
