



'''simple docstring'''

# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
UpperCAmelCase			=    1e-10
UpperCAmelCase			=    'This is incredible! I love it, this is the best chicken I have ever had.'
UpperCAmelCase			=    100

class __snake_case(      nn.Module					):


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_="gpt2-medium"	,       A_=False	,       A_="cpu"      )	->	Dict:
							super().__init__()
							lowerCAmelCase    =	GPTaTokenizer.from_pretrained(lowercase_      )
							lowerCAmelCase    =	GPTaLMHeadModel.from_pretrained(lowercase_      )
							lowerCAmelCase    =	self.encoder.transformer.config.hidden_size
							lowerCAmelCase    =	ClassificationHead(class_size=lowercase_	,       embed_size=self.embed_size      )
							lowerCAmelCase    =	cached_mode
							lowerCAmelCase    =	device


						def 				__snake_case    (					self      )	->	Dict:
							return self.classifier_head


						def 				__snake_case    (					self      )	->	str:







							for param in self.encoder.parameters():
								lowerCAmelCase    =	False
							self.classifier_head.train()


						def 				__snake_case    (					self	,       A_      )	->	str:
							lowerCAmelCase    =	x.ne(0      ).unsqueeze(2      ).repeat(1	,       1	,       self.embed_size      ).float().to(self.device      ).detach()
							lowerCAmelCase    =	self.encoder.transformer(lowercase_      )["""last_hidden_state"""]
							lowerCAmelCase    =	hidden * mask
							lowerCAmelCase    =	torch.sum(lowercase_	,       dim=1      ) / (torch.sum(lowercase_	,       dim=1      ).detach() + EPSILON)
							return avg_hidden




						def 				__snake_case    (					self	,       A_      )	->	List[Any]:







							if self.cached_mode:
								lowerCAmelCase    =	x.to(self.device      )
							else:
								lowerCAmelCase    =	self.avg_representation(x.to(self.device      )      )

							lowerCAmelCase    =	self.classifier_head(lowercase_      )
							lowerCAmelCase    =	nn.functional.log_softmax(lowercase_	,       dim=-1      )

							return probs







class __snake_case(      data.Dataset					):


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_      )	->	Any:
							lowerCAmelCase    =	X
							lowerCAmelCase    =	y


						def __len__(					self      )	->	str:
							return len(self.X      )




						def __getitem__(					self	,       A_      )	->	int:
							lowerCAmelCase    =	{}
							lowerCAmelCase    =	self.X[index]
							lowerCAmelCase    =	self.y[index]
							return data







def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							)			->						Tuple:







	"""simple docstring"""

	def pad_sequences(_SCREAMING_SNAKE_CASE     :						Union[str, Any]							):
		lowerCAmelCase    =	[len(a__							) for seq in sequences]

		lowerCAmelCase    =	torch.zeros(len(a__							)							,		max(a__							)							).long()  # padding value = 0

		for i, seq in enumerate(a__							):
			lowerCAmelCase    =	lengths[i]
			lowerCAmelCase    =	seq[:end]

		return padded_sequences, lengths

	lowerCAmelCase    =	{}
	for key in data[0].keys():
		lowerCAmelCase    =	[d[key] for d in data]

	lowerCAmelCase,   lowerCAmelCase    =	pad_sequences(item_info["""X"""]							)
	lowerCAmelCase    =	torch.tensor(item_info["""y"""]							,		dtype=torch.long							)

	return x_batch, y_batch






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Dict							)			->						Optional[int]:







	"""simple docstring"""
	lowerCAmelCase    =	{}
	for key in data[0].keys():
		lowerCAmelCase    =	[d[key] for d in data]

	lowerCAmelCase    =	torch.cat(item_info["""X"""]							,		0							)
	lowerCAmelCase    =	torch.tensor(item_info["""y"""]							,		dtype=torch.long							)

	return x_batch, y_batch






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						List[str]							,		_SCREAMING_SNAKE_CASE     :						List[str]							,		_SCREAMING_SNAKE_CASE     :						Tuple=0							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]=10							,		_SCREAMING_SNAKE_CASE     :						List[Any]="cpu"							)			->						Optional[int]:







	"""simple docstring"""
	lowerCAmelCase    =	0
	discriminator.train_custom()
	for batch_idx, (input_t, target_t) in enumerate(a__							):
		lowerCAmelCase,   lowerCAmelCase    =	input_t.to(a__							), target_t.to(a__							)

		optimizer.zero_grad()

		lowerCAmelCase    =	discriminator(a__							)
		lowerCAmelCase    =	nn.functional.nll_loss(a__							,		a__							)
		loss.backward(retain_graph=a__							)
		optimizer.step()

		samples_so_far += len(a__							)

		if batch_idx % log_interval == 0:
			print(
			    """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
			        epoch + 1							,		a__							,		len(data_loader.dataset							)							,		100 * samples_so_far / len(data_loader.dataset							)							,		loss.item()							,		)							)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						Optional[int]="cpu"							)			->						List[Any]:







	"""simple docstring"""
	discriminator.eval()
	lowerCAmelCase    =	0
	lowerCAmelCase    =	0
	with torch.no_grad():
		for input_t, target_t in data_loader:
			lowerCAmelCase,   lowerCAmelCase    =	input_t.to(a__							), target_t.to(a__							)
			lowerCAmelCase    =	discriminator(a__							)
			# sum up batch loss
			test_loss += nn.functional.nll_loss(a__							,		a__							,		reduction="""sum"""							).item()
			# get the index of the max log-probability
			lowerCAmelCase    =	output_t.argmax(dim=1							,		keepdim=a__							)
			correct += pred_t.eq(target_t.view_as(a__							)							).sum().item()

	test_loss /= len(data_loader.dataset							)

	print(
	    """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
	        a__							,		a__							,		len(data_loader.dataset							)							,		100.0 * correct / len(data_loader.dataset							)							)							)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						List[str]							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						Any							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]=False							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]="cpu"							)			->						List[str]:







	"""simple docstring"""
	lowerCAmelCase    =	model.tokenizer.encode(a__							)
	lowerCAmelCase    =	torch.tensor([input_t]							,		dtype=torch.long							,		device=a__							)
	if cached:
		lowerCAmelCase    =	model.avg_representation(a__							)

	lowerCAmelCase    =	model(a__							).data.cpu().numpy().flatten().tolist()
	print("""Input sentence:"""							,		a__							)
	print(
	    """Predictions:"""							,		""", """.join("""{}: {:.4f}""".format(a__							,		math.exp(a__							)							) for c, log_prob in zip(a__							,		a__							)							)							,		)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						List[Any]							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						str=False							,		_SCREAMING_SNAKE_CASE     :						Any="cpu"							)			->						List[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	torch.utils.data.DataLoader(dataset=a__							,		batch_size=a__							,		collate_fn=a__							)

	lowerCAmelCase    =	[]
	lowerCAmelCase    =	[]
	for batch_idx, (x, y) in enumerate(tqdm(a__							,		ascii=a__							)							):
		with torch.no_grad():
			lowerCAmelCase    =	x.to(a__							)
			lowerCAmelCase    =	discriminator.avg_representation(a__							).cpu().detach()
			lowerCAmelCase    =	torch.unbind(avg_rep.unsqueeze(1							)							)
			xs += avg_rep_list
			ys += y.cpu().numpy().tolist()

	lowerCAmelCase    =	torch.utils.data.DataLoader(
	    dataset=Dataset(a__							,		a__							)							,		batch_size=a__							,		shuffle=a__							,		collate_fn=a__							)

	return data_loader






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						List[Any]=None							,		_SCREAMING_SNAKE_CASE     :						Tuple="gpt2-medium"							,		_SCREAMING_SNAKE_CASE     :						Tuple=10							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]=64							,		_SCREAMING_SNAKE_CASE     :						Tuple=10							,		_SCREAMING_SNAKE_CASE     :						str=False							,		_SCREAMING_SNAKE_CASE     :						str=False							,		_SCREAMING_SNAKE_CASE     :						int=False							,		)			->						Optional[int]:







	"""simple docstring"""
	lowerCAmelCase    =	"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

	print("""Preprocessing {} dataset...""".format(a__							)							)
	lowerCAmelCase    =	time.time()

	if dataset == "SST":
		lowerCAmelCase    =	["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
		lowerCAmelCase    =	{c: i for i, c in enumerate(a__							)}

		lowerCAmelCase    =	Discriminator(
		    class_size=len(a__							)							,		pretrained_model=a__							,		cached_mode=a__							,		device=a__							).to(a__							)

		lowerCAmelCase    =	torchtext_data.Field()
		lowerCAmelCase    =	torchtext_data.Field(sequential=a__							)
		lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	datasets.SST.splits(
		    a__							,		a__							,		fine_grained=a__							,		train_subtrees=a__							,		)

		lowerCAmelCase    =	[]
		lowerCAmelCase    =	[]
		for i in trange(len(a__							)							,		ascii=a__							):
			lowerCAmelCase    =	TreebankWordDetokenizer().detokenize(vars(train_data[i]							)["""text"""]							)
			lowerCAmelCase    =	discriminator.tokenizer.encode(a__							)
			lowerCAmelCase    =	torch.tensor([50_256] + seq							,		device=a__							,		dtype=torch.long							)
			x.append(a__							)
			y.append(classaidx[vars(train_data[i]							)["""label"""]]							)
		lowerCAmelCase    =	Dataset(a__							,		a__							)

		lowerCAmelCase    =	[]
		lowerCAmelCase    =	[]
		for i in trange(len(a__							)							,		ascii=a__							):
			lowerCAmelCase    =	TreebankWordDetokenizer().detokenize(vars(test_data[i]							)["""text"""]							)
			lowerCAmelCase    =	discriminator.tokenizer.encode(a__							)
			lowerCAmelCase    =	torch.tensor([50_256] + seq							,		device=a__							,		dtype=torch.long							)
			test_x.append(a__							)
			test_y.append(classaidx[vars(test_data[i]							)["""label"""]]							)
		lowerCAmelCase    =	Dataset(a__							,		a__							)

		lowerCAmelCase    =	{
		    """class_size""": len(a__							),
		    """embed_size""": discriminator.embed_size,
		    """pretrained_model""": pretrained_model,
		    """class_vocab""": classaidx,
		    """default_class""": 2,
		}

	elif dataset == "clickbait":
		lowerCAmelCase    =	["""non_clickbait""", """clickbait"""]
		lowerCAmelCase    =	{c: i for i, c in enumerate(a__							)}

		lowerCAmelCase    =	Discriminator(
		    class_size=len(a__							)							,		pretrained_model=a__							,		cached_mode=a__							,		device=a__							).to(a__							)

		with open("""datasets/clickbait/clickbait_train_prefix.txt"""							) as f:
			lowerCAmelCase    =	[]
			for i, line in enumerate(a__							):
				try:
					data.append(eval(a__							)							)
				except Exception:
					print("""Error evaluating line {}: {}""".format(a__							,		a__							)							)
					continue
		lowerCAmelCase    =	[]
		lowerCAmelCase    =	[]
		with open("""datasets/clickbait/clickbait_train_prefix.txt"""							) as f:
			for i, line in enumerate(tqdm(a__							,		ascii=a__							)							):
				try:
					lowerCAmelCase    =	eval(a__							)
					lowerCAmelCase    =	discriminator.tokenizer.encode(d["""text"""]							)

					if len(a__							) < max_length_seq:
						lowerCAmelCase    =	torch.tensor([50_256] + seq							,		device=a__							,		dtype=torch.long							)
					else:
						print("""Line {} is longer than maximum length {}""".format(a__							,		a__							)							)
						continue
					x.append(a__							)
					y.append(d["""label"""]							)
				except Exception:
					print("""Error evaluating / tokenizing line {}, skipping it""".format(a__							)							)
					pass

		lowerCAmelCase    =	Dataset(a__							,		a__							)
		lowerCAmelCase    =	int(0.9 * len(a__							)							)
		lowerCAmelCase    =	len(a__							) - train_size
		lowerCAmelCase,   lowerCAmelCase    =	torch.utils.data.random_split(a__							,		[train_size, test_size]							)

		lowerCAmelCase    =	{
		    """class_size""": len(a__							),
		    """embed_size""": discriminator.embed_size,
		    """pretrained_model""": pretrained_model,
		    """class_vocab""": classaidx,
		    """default_class""": 1,
		}

	elif dataset == "toxic":
		lowerCAmelCase    =	["""non_toxic""", """toxic"""]
		lowerCAmelCase    =	{c: i for i, c in enumerate(a__							)}

		lowerCAmelCase    =	Discriminator(
		    class_size=len(a__							)							,		pretrained_model=a__							,		cached_mode=a__							,		device=a__							).to(a__							)

		lowerCAmelCase    =	[]
		lowerCAmelCase    =	[]
		with open("""datasets/toxic/toxic_train.txt"""							) as f:
			for i, line in enumerate(tqdm(a__							,		ascii=a__							)							):
				try:
					lowerCAmelCase    =	eval(a__							)
					lowerCAmelCase    =	discriminator.tokenizer.encode(d["""text"""]							)

					if len(a__							) < max_length_seq:
						lowerCAmelCase    =	torch.tensor([50_256] + seq							,		device=a__							,		dtype=torch.long							)
					else:
						print("""Line {} is longer than maximum length {}""".format(a__							,		a__							)							)
						continue
					x.append(a__							)
					y.append(int(np.sum(d["""label"""]							) > 0							)							)
				except Exception:
					print("""Error evaluating / tokenizing line {}, skipping it""".format(a__							)							)
					pass

		lowerCAmelCase    =	Dataset(a__							,		a__							)
		lowerCAmelCase    =	int(0.9 * len(a__							)							)
		lowerCAmelCase    =	len(a__							) - train_size
		lowerCAmelCase,   lowerCAmelCase    =	torch.utils.data.random_split(a__							,		[train_size, test_size]							)

		lowerCAmelCase    =	{
		    """class_size""": len(a__							),
		    """embed_size""": discriminator.embed_size,
		    """pretrained_model""": pretrained_model,
		    """class_vocab""": classaidx,
		    """default_class""": 0,
		}

	else:  # if dataset == "generic":
		# This assumes the input dataset is a TSV with the following structure:
		# class \t text

		if dataset_fp is None:
			raise ValueError("""When generic dataset is selected, dataset_fp needs to be specified aswell."""							)

		lowerCAmelCase    =	set()
		with open(a__							) as f:
			lowerCAmelCase    =	csv.reader(a__							,		delimiter="""\t"""							)
			for row in tqdm(a__							,		ascii=a__							):
				if row:
					classes.add(row[0]							)

		lowerCAmelCase    =	sorted(a__							)
		lowerCAmelCase    =	{c: i for i, c in enumerate(a__							)}

		lowerCAmelCase    =	Discriminator(
		    class_size=len(a__							)							,		pretrained_model=a__							,		cached_mode=a__							,		device=a__							).to(a__							)

		lowerCAmelCase    =	[]
		lowerCAmelCase    =	[]
		with open(a__							) as f:
			lowerCAmelCase    =	csv.reader(a__							,		delimiter="""\t"""							)
			for i, row in enumerate(tqdm(a__							,		ascii=a__							)							):
				if row:
					lowerCAmelCase    =	row[0]
					lowerCAmelCase    =	row[1]

					try:
						lowerCAmelCase    =	discriminator.tokenizer.encode(a__							)
						if len(a__							) < max_length_seq:
							lowerCAmelCase    =	torch.tensor([50_256] + seq							,		device=a__							,		dtype=torch.long							)

						else:
							print("""Line {} is longer than maximum length {}""".format(a__							,		a__							)							)
							continue

						x.append(a__							)
						y.append(classaidx[label]							)

					except Exception:
						print("""Error tokenizing line {}, skipping it""".format(a__							)							)
						pass

		lowerCAmelCase    =	Dataset(a__							,		a__							)
		lowerCAmelCase    =	int(0.9 * len(a__							)							)
		lowerCAmelCase    =	len(a__							) - train_size
		lowerCAmelCase,   lowerCAmelCase    =	torch.utils.data.random_split(a__							,		[train_size, test_size]							)

		lowerCAmelCase    =	{
		    """class_size""": len(a__							),
		    """embed_size""": discriminator.embed_size,
		    """pretrained_model""": pretrained_model,
		    """class_vocab""": classaidx,
		    """default_class""": 0,
		}

	lowerCAmelCase    =	time.time()
	print("""Preprocessed {} data points""".format(len(a__							) + len(a__							)							)							)
	print("""Data preprocessing took: {:.3f}s""".format(end - start							)							)

	if cached:
		print("""Building representation cache..."""							)

		lowerCAmelCase    =	time.time()

		lowerCAmelCase    =	get_cached_data_loader(a__							,		a__							,		a__							,		shuffle=a__							,		device=a__							)

		lowerCAmelCase    =	get_cached_data_loader(a__							,		a__							,		a__							,		device=a__							)

		lowerCAmelCase    =	time.time()
		print("""Building representation cache took: {:.3f}s""".format(end - start							)							)

	else:
		lowerCAmelCase    =	torch.utils.data.DataLoader(
		    dataset=a__							,		batch_size=a__							,		shuffle=a__							,		collate_fn=a__							)
		lowerCAmelCase    =	torch.utils.data.DataLoader(dataset=a__							,		batch_size=a__							,		collate_fn=a__							)

	if save_model:
		with open("""{}_classifier_head_meta.json""".format(a__							)							,		"""w"""							) as meta_file:
			json.dump(a__							,		a__							)

	lowerCAmelCase    =	optim.Adam(discriminator.parameters()							,		lr=0.0001							)

	for epoch in range(a__							):
		lowerCAmelCase    =	time.time()
		print("""\nEpoch"""							,		epoch + 1							)

		train_epoch(
		    discriminator=a__							,		data_loader=a__							,		optimizer=a__							,		epoch=a__							,		log_interval=a__							,		device=a__							,		)
		evaluate_performance(data_loader=a__							,		discriminator=a__							,		device=a__							)

		lowerCAmelCase    =	time.time()
		print("""Epoch took: {:.3f}s""".format(end - start							)							)

		print("""\nExample prediction"""							)
		predict(a__							,		a__							,		a__							,		cached=a__							,		device=a__							)

		if save_model:
			# torch.save(discriminator.state_dict(),
			#           "{}_discriminator_{}.pt".format(
			#               args.dataset, epoch + 1
			#               ))
			torch.save(
			    discriminator.get_classifier().state_dict()							,		"""{}_classifier_head_epoch_{}.pt""".format(a__							,		epoch + 1							)							,		)


if __name__ == "__main__":
				UpperCAmelCase			=    argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
				parser.add_argument(
				    '--dataset',
				    type=str,
				    default='SST',
				    choices=('SST', 'clickbait', 'toxic', 'generic'),
				    help=(
				        'dataset to train the discriminator on.'
				        'In case of generic, the dataset is expected'
				        'to be a TSBV file with structure: class \\t text'
				    ),
				)
				parser.add_argument(
				    '--dataset_fp',
				    type=str,
				    default='',
				    help='File path of the dataset to use. Needed only in case of generic datadset',
				)
				parser.add_argument(
				    '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
				)
				parser.add_argument('--epochs', type=int, default=10, metavar='N', help='Number of training epochs')
				parser.add_argument(
				    '--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)'
				)
				parser.add_argument(
				    '--log_interval',
				    type=int,
				    default=10,
				    metavar='N',
				    help='how many batches to wait before logging training status',
				)
				parser.add_argument('--save_model', action='store_true', help='whether to save the model')
				parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
				parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
				UpperCAmelCase			=    parser.parse_args()

				train_discriminator(**(vars(args)))
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__   = logging.get_logger()




@dataclass
class  _UpperCAmelCase       :



 '''simple docstring'''




 __A           = 42
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )
 __A           = field(default_factory=lowerCAmelCase   )







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					Union[str, Any]				,      lowercase_							:					Tensor				,      lowercase_							:					Tensor				,      lowercase_							:					str)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase				=  len(list(m.modules())) == 1 or isinstance(lowercase_				,      nn.Convad) or isinstance(lowercase_				,      nn.BatchNormad)
   if has_not_submodules:
     self.traced.append(lowercase_)
     _UpperCamelCase				=  m







 def __call__(						self							:					Optional[Any]				,      lowercase_							:					Tensor)		->     List[str]:


   """simple docstring"""







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







 @property
 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[Any]:


   """simple docstring"""







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




class  _UpperCAmelCase       (			nn.Module   ):



 '''simple docstring'''




 def __init__(						self							:					Tuple				,      lowercase_							:					nn.Module)		->     List[str]:


   """simple docstring"""







   super().__init__()

   _UpperCamelCase				=  []
   # - 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}'
     _UpperCamelCase				=  len(lowercase_) + 1
     feature_blocks.append((f'res{block_index}', v))

   _UpperCamelCase				=  nn.ModuleDict(lowercase_)







 def 						__UpperCAmelCase							(						self							:					int				,      lowercase_							:					Tensor)		->     int:


   """simple docstring"""







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




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[Any]:


   """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 							lowerCAmelCase__    (       a__      )	->Dict[str, str]:




  '''simple docstring'''

  _UpperCamelCase				=  RegNetConfig(depths=[2, 7, 17, 1]  ,			hidden_sizes=[8, 8, 8, 8]  ,			groups_width=8      )
  if "in1k" in model_name:
    _UpperCamelCase				=  RegNetForImageClassification(a__      )
  else:
    _UpperCamelCase				=  RegNetModel(a__      )
  # create from model (with small weights)
  _UpperCamelCase				=  FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27  ,			group_width=1_010  ,			w_a=1_744  ,			w_a=620.83  ,			w_m=2.52      )      )      )

  with torch.no_grad():
    _UpperCamelCase				=  from_model.eval()
    _UpperCamelCase				=  our_model.eval()

    _UpperCamelCase				=  torch.randn((1, 3, 32, 32)      )
    # trace both
    _UpperCamelCase				=  Tracker(a__      )
    _UpperCamelCase				=  dest_tracker(a__      ).parametrized

    pprint(dest_tracker.nameamodule      )
    _UpperCamelCase				=  Tracker(a__      )
    _UpperCamelCase				=  src_tracker(a__      ).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(a__      ):
    _UpperCamelCase				=  OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        _UpperCamelCase				=  param
    return params_dict

  _UpperCamelCase				=  {}

  _UpperCamelCase				=  to_params_dict(a__      )
  _UpperCamelCase				=  to_params_dict(a__      )

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()  ,			dst_state_dict.items()      ):
    _UpperCamelCase				=  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:
    _UpperCamelCase				=  "classifier.1.weight"
    _UpperCamelCase				=  "classifier.1.bias"

  return from_to_ours_keys


def 							lowerCAmelCase__    (       a__  ,			a__ = None  ,			a__ = True      )	->Dict:




  '''simple docstring'''

  _UpperCamelCase				=  "imagenet-1k-id2label.json"
  _UpperCamelCase				=  1_000

  _UpperCamelCase				=  "huggingface/label-files"
  _UpperCamelCase				=  num_labels
  _UpperCamelCase				=  json.load(open(cached_download(hf_hub_url(a__  ,			a__  ,			repo_type="dataset"      )      )  ,			"r"      )      )
  _UpperCamelCase				=  {int(a__      ): v for k, v in idalabel.items()}

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

  _UpperCamelCase				=  partial(a__  ,			num_labels=a__  ,			idalabel=a__  ,			labelaid=a__      )

  _UpperCamelCase				=  {
      "regnet-y-10b-seer": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
      # finetuned on imagenet
      "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010      ),
  }

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

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

  _UpperCamelCase				=  get_from_to_our_keys(a__      )

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

    _UpperCamelCase				=  {}

    _UpperCamelCase				=  list(from_state_dict.keys()      )
    _UpperCamelCase				=  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
      _UpperCamelCase				=  re.sub(a__  ,			""  ,			a__      )
      # 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
      _UpperCamelCase				=  from_to_ours_keys[src_key]
      # store the parameter with our key
      _UpperCamelCase				=  from_state_dict[key]
      not_used_keys.remove(a__      )
    # check that all keys have been updated
    assert len(a__      ) == 0, f'Some keys where not used {",".join(a__      )}'

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

    # save our state dict to disk
    torch.save(a__  ,			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
    _UpperCamelCase				=  names_to_config[model_name]
    _UpperCamelCase				=  RegNetModel
    if "in1k" in model_name:
      _UpperCamelCase				=  RegNetForImageClassification
    _UpperCamelCase				=  our_model_func(a__      )
    # 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
    _UpperCamelCase				=  our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        a__  ,			a__  ,			[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  ,			)
    _UpperCamelCase				=  384
    # we can use the convnext one
    _UpperCamelCase				=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  ,			size=a__      )
    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__   = 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__   = parser.parse_args()

   lowerCamelCase__   = 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)




