code: 
  
  
  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'
  
  
  
  
style_context: 
  
  
  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)
  
  
  
  