


import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

__lowerCamelCase       :       int				   =			logging.getLogger()
__lowerCamelCase       :       Dict				   =			torch.cuda.is_available()
__lowerCamelCase       :       int				   =			{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Any:
				A__     :							Union[str, Any]   				='''\n'''.join(snake_case_	)
				Path(snake_case_	).open('''w'''	).writelines(snake_case_	)


__lowerCamelCase       :       Any				   =			[" Sam ate lunch today.", "Sams lunch ingredients."]
__lowerCamelCase       :       List[str]				   =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__lowerCamelCase       :       Optional[Any]				   =			"patrickvonplaten/t5-tiny-random"
__lowerCamelCase       :       int				   =			"sshleifer/t5-tinier-random"
__lowerCamelCase       :       Any				   =			"sshleifer/bart-tiny-random"
__lowerCamelCase       :       List[Any]				   =			"sshleifer/tiny-mbart"
__lowerCamelCase       :       Optional[Any]				   =			"sshleifer/tiny-marian-en-de"
__lowerCamelCase       :       Tuple				   =			"stas/tiny-wmt19-en-de"


__lowerCamelCase       :       Dict				   =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							List[str]:
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(snake_case_,	f'{split}.source'	),	snake_case_	)
								_dump_articles(os.path.join(snake_case_,	f'{split}.target'	),	snake_case_	)
				return tmp_dir



class 			a      (      UpperCamelCase_   ):






		@classmethod
		def       lowerCAmelCase_  (  cls							)->			int:
						'''simple docstring'''


						logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
						return cls






		@slow
		@require_torch_gpu
		def       lowerCAmelCase_  (  self							)->			List[Any]:
						'''simple docstring'''


						A__     :							Union[str, Any]   				=list_models()
						A__     :							List[Any]   				='''sshleifer'''
						A__     :							Tuple   				=[x.modelId for x in model_list if x.modelId.startswith(__UpperCamelCase							)]
						A__     :							str   				=['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
						A__     :							Any   				=[]
						for m in model_ids:
										if m in allowed_to_be_broken:
														continue
										try:
														AutoConfig.from_pretrained(__UpperCamelCase							)
										except Exception:
														failures.append(__UpperCamelCase							)
						assert not failures, F'The following models could not be loaded through AutoConfig: {failures}'






		def       lowerCAmelCase_  (  self							)->			Dict:
						'''simple docstring'''


						A__     :							Optional[int]   				={'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
						self._test_distiller_cli(__UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Any:
						'''simple docstring'''


						A__     :							Optional[Any]   				={
						    '''student_encoder_layers''': 2,
						    '''student_decoder_layers''': 1,
						    '''max_epochs''': 4,
						    '''val_check_interval''': 0.25,
						    '''alpha_hid''': 2.0,
						    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
						}
						A__     :							int   				=self._test_distiller_cli(__UpperCamelCase	,     check_contents=__UpperCamelCase							)

						A__     :							Any   				=list(Path(model.output_dir							).glob('''*.ckpt'''							)							)
						self.assertEqual(1	,     len(__UpperCamelCase							)							)
						A__     :							int   				=list(Path(model.output_dir							).glob('''**/*.bin'''							)							)
						self.assertEqual(len(__UpperCamelCase							)	,     2							)
						A__     :							Dict   				=lmap(str.strip	,     Path(model.hparams.data_dir							).joinpath('''test.source'''							).open().readlines()							)
						A__     :							int   				=tempfile.mktemp()  # XXX: not being cleaned up
						generate_summaries_or_translations(__UpperCamelCase	,     __UpperCamelCase	,     str(model.output_dir / '''best_tfmr'''							)							)
						self.assertTrue(Path(__UpperCamelCase							).exists()							)

						A__     :							int   				=self.get_auto_remove_tmp_dir()
						convert_pl_to_hf(ckpts[0]	,     transformer_ckpts[0].parent	,     __UpperCamelCase							)
						assert os.path.exists(os.path.join(__UpperCamelCase	,     '''pytorch_model.bin'''							)							)






		def       lowerCAmelCase_  (  self							)->			List[str]:
						'''simple docstring'''


						A__     :							int   				=AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase							)
						A__				,  A__     :							int   				=model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
						A__     :							Optional[int]   				=torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]	,     dtype=torch.long	,     device=model.device							)
						A__     :							int   				=target_ids[:, :-1].contiguous()  # Why this line?
						A__     :							Optional[int]   				=target_ids[:, 1:].clone()  # why clone?
						A__     :							str   				=model(
						    __UpperCamelCase	,     attention_mask=__UpperCamelCase	,     decoder_input_ids=__UpperCamelCase	,     labels=__UpperCamelCase	,     use_cache=__UpperCamelCase							).loss

						A__     :							int   				=model(__UpperCamelCase	,     attention_mask=__UpperCamelCase	,     decoder_input_ids=__UpperCamelCase	,     use_cache=__UpperCamelCase							).logits

						A__     :							Optional[Any]   				=nn.functional.log_softmax(__UpperCamelCase	,     dim=-1							)
						A__				,  A__     :							Dict   				=label_smoothed_nll_loss(
						    __UpperCamelCase	,     __UpperCamelCase	,     0.1	,     ignore_index=model.config.pad_token_id							)
						with self.assertRaises(__UpperCamelCase							):
										# TODO: understand why this breaks
										self.assertEqual(__UpperCamelCase	,     __UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Tuple:
						'''simple docstring'''


						A__     :							Tuple   				={
						    '''student_encoder_layers''': 2,
						    '''student_decoder_layers''': 1,
						    '''num_train_epochs''': 4,
						    '''val_check_interval''': 0.25,
						    '''alpha_hid''': 2.0,
						    '''task''': '''translation''',
						    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
						    '''tokenizer_name''': MBART_TINY,
						    '''teacher''': MBART_TINY,
						    '''src_lang''': '''en_XX''',
						    '''tgt_lang''': '''ro_RO''',
						}
						A__     :							int   				=self._test_distiller_cli(__UpperCamelCase	,     check_contents=__UpperCamelCase							)
						assert model.model.config.model_type == "mbart"

						A__     :							List[Any]   				=list(Path(model.output_dir							).glob('''*.ckpt'''							)							)
						self.assertEqual(1	,     len(__UpperCamelCase							)							)
						A__     :							List[Any]   				=list(Path(model.output_dir							).glob('''**/*.bin'''							)							)
						A__     :							int   				=list(Path(model.output_dir							).glob('''best_tfmr/*'''							)							)
						assert len(__UpperCamelCase							) > 2
						self.assertEqual(len(__UpperCamelCase							)	,     2							)






		def       lowerCAmelCase_  (  self							)->			Tuple:
						'''simple docstring'''


						A__     :							str   				={
						    '''student_encoder_layers''': 1,
						    '''student_decoder_layers''': 1,
						    '''alpha_hid''': 2.0,
						    '''teacher''': T5_TINY,
						    '''model_name_or_path''': T5_TINY,
						    '''tokenizer_name''': T5_TINY,
						}
						self._test_distiller_cli(__UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							int   				={
						    '''teacher''': T5_TINY,
						    '''student''': T5_TINIER,
						    '''model_name_or_path''': T5_TINIER,
						    '''tokenizer_name''': T5_TINIER,
						}
						self._test_distiller_cli(__UpperCamelCase							)






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=True							)->			List[str]:
						'''simple docstring'''


						A__     :							Tuple   				={
						    '''label_smoothing''': 0.0,
						    '''early_stopping_patience''': -1,
						    '''train_batch_size''': 1,
						    '''eval_batch_size''': 2,
						    '''max_epochs''': 2,
						    '''alpha_mlm''': 0.2,
						    '''alpha_ce''': 0.8,
						    '''do_predict''': True,
						    '''model_name_or_path''': '''sshleifer/tinier_bart''',
						    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
						    '''val_check_interval''': 0.5,
						}
						default_updates.update(__UpperCamelCase							)
						A__     :							dict   				=CHEAP_ARGS.copy()
						A__     :							str   				=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
						A__     :							Any   				=self.get_auto_remove_tmp_dir()

						args_d.update(data_dir=__UpperCamelCase	,     output_dir=__UpperCamelCase	,     **__UpperCamelCase							)
						A__     :							List[str]   				=distill_main(argparse.Namespace(**__UpperCamelCase							)							)
						if not check_contents:
										return model
						A__     :							Optional[int]   				=os.listdir(__UpperCamelCase							)
						A__     :							Union[str, Any]   				={os.path.basename(__UpperCamelCase							) for p in contents}
						A__     :							int   				=[p for p in contents if p.endswith('''ckpt'''							)]
						assert len(__UpperCamelCase							) > 0

						self.assertIn('''test_generations.txt'''	,     __UpperCamelCase							)
						self.assertIn('''test_results.txt'''	,     __UpperCamelCase							)

						A__     :							Optional[int]   				=load_json(model.metrics_save_path							)
						A__     :							Optional[int]   				=metrics['''val'''][-1]
						self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']	,     0.01							)
						self.assertGreaterEqual(1.0	,     last_step_stats['''val_avg_gen_time''']							)
						self.assertIsInstance(last_step_stats[F'val_avg_{model.val_metric}']	,     __UpperCamelCase							)
						A__     :							Optional[Any]   				=int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1							)
						self.assertEqual(len(metrics['''val''']							)	,     __UpperCamelCase							)
						self.assertEqual(len(metrics['''test''']							)	,     1							)
						return model







class 			a      (      UpperCamelCase_   ):






		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]	,     )
		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			str:
						'''simple docstring'''


						A__     :							dict   				=CHEAP_ARGS.copy()
						A__     :							List[Any]   				='''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
						A__     :							List[Any]   				=0.1 if task == '''translation''' else 0

						A__     :							Union[str, Any]   				=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
						A__     :							Optional[int]   				=self.get_auto_remove_tmp_dir()
						args_d.update(
						    data_dir=__UpperCamelCase	,     model_name_or_path=__UpperCamelCase	,     tokenizer_name=__UpperCamelCase	,     train_batch_size=2	,     eval_batch_size=2	,     output_dir=__UpperCamelCase	,     do_predict=__UpperCamelCase	,     task=__UpperCamelCase	,     src_lang='''en_XX'''	,     tgt_lang='''ro_RO'''	,     freeze_encoder=__UpperCamelCase	,     freeze_embeds=__UpperCamelCase	,     )
						assert "n_train" in args_d
						A__     :							Tuple   				=argparse.Namespace(**__UpperCamelCase							)
						A__     :							Union[str, Any]   				=main(__UpperCamelCase							)

						A__     :							List[str]   				=module.model.get_input_embeddings()
						assert not input_embeds.weight.requires_grad
						if model == T5_TINY:
										A__     :							Optional[int]   				=module.model.lm_head
										assert not lm_head.weight.requires_grad
										assert (lm_head.weight == input_embeds.weight).all().item()
						elif model == FSMT_TINY:
										A__     :							str   				=module.model.model
										A__     :							Optional[int]   				=fsmt.decoder.embed_positions
										assert not embed_pos.weight.requires_grad
										assert not fsmt.decoder.embed_tokens.weight.requires_grad
										# check that embeds are not the same
										assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
						else:
										A__     :							Union[str, Any]   				=module.model.model
										A__     :							List[Any]   				=bart.decoder.embed_positions
										assert not embed_pos.weight.requires_grad
										assert not bart.shared.weight.requires_grad
										# check that embeds are the same
										assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
										assert bart.decoder.embed_tokens == bart.shared

						A__     :							Optional[Any]   				=load_json(module.output_dir / '''text_batch.json'''							)
						assert isinstance(__UpperCamelCase	,     __UpperCamelCase							)
						assert len(__UpperCamelCase							) >= 4






		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							dict   				=CHEAP_ARGS.copy()

						A__     :							Optional[Any]   				='''summarization'''
						A__     :							int   				=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

						args_d.update(
						    data_dir=__UpperCamelCase	,     tokenizer_name=__UpperCamelCase	,     train_batch_size=2	,     eval_batch_size=2	,     do_predict=__UpperCamelCase	,     task=__UpperCamelCase	,     src_lang='''en_XX'''	,     tgt_lang='''ro_RO'''	,     freeze_encoder=__UpperCamelCase	,     freeze_embeds=__UpperCamelCase	,     )

						# test models whose config includes the extra_model_args
						A__     :							int   				=BART_TINY
						A__     :							Tuple   				=self.get_auto_remove_tmp_dir()
						A__     :							Optional[Any]   				=args_d.copy()
						args_da.update(
						    model_name_or_path=__UpperCamelCase	,     output_dir=__UpperCamelCase	,     )
						A__     :							str   				=('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
						for p in extra_model_params:
										A__     :							Any   				=0.5
						A__     :							Union[str, Any]   				=argparse.Namespace(**__UpperCamelCase							)
						A__     :							Optional[Any]   				=main(__UpperCamelCase							)
						for p in extra_model_params:
										assert getattr(model.config	,     __UpperCamelCase							) == 0.5, F'failed to override the model config for param {p}'

						# test models whose config doesn't include the extra_model_args
						A__     :							Dict   				=T5_TINY
						A__     :							Any   				=self.get_auto_remove_tmp_dir()
						A__     :							Dict   				=args_d.copy()
						args_da.update(
						    model_name_or_path=__UpperCamelCase	,     output_dir=__UpperCamelCase	,     )
						A__     :							Dict   				='''encoder_layerdrop'''
						A__     :							Any   				=0.5
						A__     :							str   				=argparse.Namespace(**__UpperCamelCase							)
						with pytest.raises(__UpperCamelCase							) as excinfo:
										A__     :							List[Any]   				=main(__UpperCamelCase							)
						assert str(excinfo.value							) == F'model config doesn\'t have a `{unsupported_param}` attribute'






		def       lowerCAmelCase_  (  self							)->			Optional[int]:
						'''simple docstring'''


						A__     :							dict   				=CHEAP_ARGS.copy()

						A__     :							Tuple   				='''summarization'''
						A__     :							Optional[int]   				=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

						A__     :							List[str]   				=BART_TINY
						A__     :							Tuple   				=self.get_auto_remove_tmp_dir()

						args_d.update(
						    data_dir=__UpperCamelCase	,     model_name_or_path=__UpperCamelCase	,     output_dir=__UpperCamelCase	,     tokenizer_name=__UpperCamelCase	,     train_batch_size=2	,     eval_batch_size=2	,     do_predict=__UpperCamelCase	,     task=__UpperCamelCase	,     src_lang='''en_XX'''	,     tgt_lang='''ro_RO'''	,     freeze_encoder=__UpperCamelCase	,     freeze_embeds=__UpperCamelCase	,     )

						# emulate finetune.py
						A__     :							str   				=argparse.ArgumentParser()
						A__     :							List[Any]   				=pl.Trainer.add_argparse_args(__UpperCamelCase							)
						A__     :							Any   				=SummarizationModule.add_model_specific_args(__UpperCamelCase	,     os.getcwd()							)
						A__     :							Optional[int]   				={'''--help''': True}

						# --help test
						with pytest.raises(__UpperCamelCase							) as excinfo:
										with CaptureStdout() as cs:
														A__     :							str   				=parser.parse_args(__UpperCamelCase							)
										assert False, "--help is expected to sys.exit"
						assert excinfo.type == SystemExit
						A__     :							List[Any]   				=lightning_base.arg_to_scheduler_metavar
						assert expected in cs.out, "--help is expected to list the supported schedulers"

						# --lr_scheduler=non_existing_scheduler test
						A__     :							int   				='''non_existing_scheduler'''
						A__     :							int   				={F'--lr_scheduler={unsupported_param}'}
						with pytest.raises(__UpperCamelCase							) as excinfo:
										with CaptureStderr() as cs:
														A__     :							int   				=parser.parse_args(__UpperCamelCase							)
										assert False, "invalid argument is expected to sys.exit"
						assert excinfo.type == SystemExit
						A__     :							Dict   				=F'invalid choice: \'{unsupported_param}\''
						assert expected in cs.err, F'should have bailed on invalid choice of scheduler {unsupported_param}'

						# --lr_scheduler=existing_scheduler test
						A__     :							Any   				='''cosine'''
						A__     :							Union[str, Any]   				=args_d.copy()
						A__     :							Union[str, Any]   				=supported_param
						A__     :							str   				=argparse.Namespace(**__UpperCamelCase							)
						A__     :							Union[str, Any]   				=main(__UpperCamelCase							)
						assert (
						    getattr(model.hparams	,     '''lr_scheduler'''							) == supported_param
						), F'lr_scheduler={supported_param} shouldn\'t fail'







import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase       :       Union[str, Any]				   =			logging.get_logger()



@dataclass
class 			a      :
		__lowercase													=		42
		__lowercase													=		field(default_factory=UpperCamelCase_   )
		__lowercase													=		field(default_factory=UpperCamelCase_   )
		__lowercase													=		field(default_factory=UpperCamelCase_   )






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			List[Any]:
						'''simple docstring'''


						A__     :							Tuple   				=len(list(m.modules()							)							) == 1 or isinstance(__UpperCamelCase	,     nn.Convad							) or isinstance(__UpperCamelCase	,     nn.BatchNormad							)
						if has_not_submodules:
										self.traced.append(__UpperCamelCase							)
										A__     :							Union[str, Any]   				=m






		def __call__(  self	,     __UpperCamelCase							)->			Any:
						'''simple docstring'''


						for name, m in self.module.named_modules():
										self.handles.append(m.register_forward_hook(partial(self._forward_hook	,     name=__UpperCamelCase							)							)							)
						self.module(__UpperCamelCase							)
						[x.remove() for x in self.handles]
						return self






		@property
		def       lowerCAmelCase_  (  self							)->			str:
						'''simple docstring'''


						return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}







class 			a      (      nn.Module   ):






		def __init__(  self	,     __UpperCamelCase							)->			Optional[Any]:
						'''simple docstring'''


						super().__init__()

						A__     :							List[Tuple[str, nn.Module]]   				=[]
						# - get the stem
						feature_blocks.append(('''conv1''', model.stem)							)
						# - get all the feature blocks
						for k, v in model.trunk_output.named_children():
										assert k.startswith('''block'''							), F'Unexpected layer name {k}'
										A__     :							Any   				=len(__UpperCamelCase							) + 1
										feature_blocks.append((F'res{block_index}', v)							)

						A__     :							Tuple   				=nn.ModuleDict(__UpperCamelCase							)






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			List[str]:
						'''simple docstring'''


						return get_trunk_forward_outputs(
						    __UpperCamelCase	,     out_feat_keys=__UpperCamelCase	,     feature_blocks=self._feature_blocks	,     )







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			int:
						'''simple docstring'''


						return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict[str, str]:
				A__     :							Any   				=RegNetConfig(depths=[2, 7, 1_7, 1],	hidden_sizes=[8, 8, 8, 8],	groups_width=8	)
				if "in1k" in model_name:
								A__     :							Any   				=RegNetForImageClassification(snake_case_	)
				else:
								A__     :							Tuple   				=RegNetModel(snake_case_	)
				# create from model (with small weights)
				A__     :							List[str]   				=FakeRegNetVisslWrapper(
				    RegNet(FakeRegNetParams(depth=2_7,	group_width=1_0_1_0,	w_a=1_7_4_4,	w_a=6_2_0.8_3,	w_m=2.5_2	)	)	)

				with torch.no_grad():
								A__     :							str   				=from_model.eval()
								A__     :							int   				=our_model.eval()

								A__     :							Optional[int]   				=torch.randn((1, 3, 3_2, 3_2)	)
								# trace both
								A__     :							int   				=Tracker(snake_case_	)
								A__     :							Optional[int]   				=dest_tracker(snake_case_	).parametrized

								pprint(dest_tracker.nameamodule	)
								A__     :							Optional[int]   				=Tracker(snake_case_	)
								A__     :							Any   				=src_tracker(snake_case_	).parametrized

				# convert the keys -> module dict to keys -> params
				def to_params_dict(snake_case_	):
								A__     :							Optional[Any]   				=OrderedDict()
								for name, module in dict_with_modules.items():
												for param_name, param in module.state_dict().items():
																A__     :							Union[str, Any]   				=param
								return params_dict

				A__     :							Optional[int]   				={}

				A__     :							int   				=to_params_dict(snake_case_	)
				A__     :							int   				=to_params_dict(snake_case_	)

				for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),	dst_state_dict.items()	):
								A__     :							int   				=dest_key
								logger.info(f'{src_key} -> {dest_key}'	)
				# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
				if "in1k" in model_name:
								A__     :							str   				='''classifier.1.weight'''
								A__     :							Union[str, Any]   				='''classifier.1.bias'''

				return from_to_ours_keys



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_ = None,	snake_case_ = True	)			->							int:
				A__     :							Tuple   				='''imagenet-1k-id2label.json'''
				A__     :							Any   				=1_0_0_0

				A__     :							int   				='''huggingface/label-files'''
				A__     :							Tuple   				=num_labels
				A__     :							Optional[int]   				=json.load(open(cached_download(hf_hub_url(snake_case_,	snake_case_,	repo_type='''dataset'''	)	),	'''r'''	)	)
				A__     :							str   				={int(snake_case_	): v for k, v in idalabel.items()}

				A__     :							int   				=idalabel
				A__     :							Tuple   				={v: k for k, v in idalabel.items()}

				A__     :							str   				=partial(snake_case_,	num_labels=snake_case_,	idalabel=snake_case_,	labelaid=snake_case_	)

				A__     :							Tuple   				={
				    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 1_7, 1],	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],	groups_width=1_0_1_0	),
				    # finetuned on imagenet
				    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 1_7, 1],	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],	groups_width=1_0_1_0	),
				}

				# add seer weights logic
				def load_using_classy_vision(snake_case_	) -> Tuple[Dict, Dict]:
								A__     :							Optional[int]   				=torch.hub.load_state_dict_from_url(snake_case_,	model_dir=str(snake_case_	),	map_location='''cpu'''	)
								# check if we have a head, if yes add it
								A__     :							Dict   				=files['''classy_state_dict''']['''base_model''']['''model''']
								return model_state_dict["trunk"], model_state_dict["heads"]

				A__     :							Any   				={
				    '''regnet-y-10b-seer''': partial(
				        snake_case_,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''',	),
				    '''regnet-y-10b-seer-in1k''': partial(
				        snake_case_,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''',	),
				}

				A__     :							Tuple   				=get_from_to_our_keys(snake_case_	)

				if not (save_directory / f'{model_name}.pth').exists():
								logger.info('''Loading original state_dict.'''	)
								A__				,  A__     :							Any   				=names_to_from_model[model_name]()
								A__     :							int   				=from_state_dict_trunk
								if "in1k" in model_name:
												# add the head
												A__     :							Union[str, Any]   				={**from_state_dict_trunk, **from_state_dict_head}
								logger.info('''Done!'''	)

								A__     :							List[str]   				={}

								A__     :							int   				=list(from_state_dict.keys()	)
								A__     :							Optional[Any]   				=R'''\.block.-part.'''
								# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
								for key in from_state_dict.keys():
												# remove the weird "block[0,1]-part" from the key
												A__     :							Tuple   				=re.sub(snake_case_,	'''''',	snake_case_	)
												# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
												A__     :							Any   				=from_to_ours_keys[src_key]
												# store the parameter with our key
												A__     :							str   				=from_state_dict[key]
												not_used_keys.remove(snake_case_	)
								# check that all keys have been updated
								assert len(snake_case_	) == 0, f'Some keys where not used {",".join(snake_case_	)}'

								logger.info(f'The following keys were not used: {",".join(snake_case_	)}'	)

								# save our state dict to disk
								torch.save(snake_case_,	save_directory / f'{model_name}.pth'	)

								del converted_state_dict
				else:
								logger.info('''The state_dict was already stored on disk.'''	)
				if push_to_hub:
								logger.info(f'Token is {os.environ["HF_TOKEN"]}'	)
								logger.info('''Loading our model.'''	)
								# create our model
								A__     :							Optional[int]   				=names_to_config[model_name]
								A__     :							Any   				=RegNetModel
								if "in1k" in model_name:
												A__     :							Optional[int]   				=RegNetForImageClassification
								A__     :							Optional[int]   				=our_model_func(snake_case_	)
								# place our model to the meta device (so remove all the weights)
								our_model.to(torch.device('''meta'''	)	)
								logger.info('''Loading state_dict in our model.'''	)
								# load state dict
								A__     :							Any   				=our_model.state_dict().keys()
								PreTrainedModel._load_pretrained_model_low_mem(
								    snake_case_,	snake_case_,	[save_directory / f'{model_name}.pth']	)
								logger.info('''Finally, pushing!'''	)
								# push it to hub
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / model_name,	commit_message='''Add model''',	output_dir=save_directory / model_name,	)
								A__     :							List[Any]   				=3_8_4
								# we can use the convnext one
								A__     :							List[Any]   				=AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''',	size=snake_case_	)
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / model_name,	commit_message='''Add image processor''',	output_dir=save_directory / model_name,	)


if __name__ == "__main__":
						__lowerCamelCase       :       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 regnet* architecture,"
						        " currently: regnetx-*, regnety-*. 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(
						    "--push_to_hub",
						    default=True,
						    type=bool,
						    required=False,
						    help="If True, push model and image processor to the hub.",
						)

						__lowerCamelCase       :       List[str]				   =			parser.parse_args()

						__lowerCamelCase       :       Path				   =			args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



