

import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCamelCase      				=     logging.getLogger(__name__)


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							np.argmax(UpperCAmelCase__		,					axis=1      )
					return np.sum(outputs == labels      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,					encoding='utf_8'      ) as f:
										__lowerCAmelCase					=							csv.reader(UpperCAmelCase__      )
										__lowerCAmelCase					=							[]
										next(UpperCAmelCase__      )  # skip the first line
										for line in tqdm(UpperCAmelCase__      ):
															output.append((' '.join(line[1:5]      ), line[5], line[6], int(line[-1]      ) - 1)      )
					return output


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]
					for dataset in encoded_datasets:
										__lowerCAmelCase					=							len(UpperCAmelCase__      )
										__lowerCAmelCase					=							np.zeros((n_batch, 2, input_len)		,					dtype=np.intaa      )
										__lowerCAmelCase					=							np.zeros((n_batch, 2)		,					dtype=np.intaa      )
										__lowerCAmelCase					=							np.full((n_batch, 2, input_len)		,					fill_value=-100		,					dtype=np.intaa      )
										__lowerCAmelCase					=							np.zeros((n_batch,)		,					dtype=np.intaa      )
										for (
										    i,
										    (story, conta, conta, mc_label),
										) in enumerate(UpperCAmelCase__      ):
															__lowerCAmelCase					=							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
															__lowerCAmelCase					=							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
															__lowerCAmelCase					=							with_conta
															__lowerCAmelCase					=							with_conta
															__lowerCAmelCase					=							len(UpperCAmelCase__      ) - 1
															__lowerCAmelCase					=							len(UpperCAmelCase__      ) - 1
															__lowerCAmelCase					=							with_conta
															__lowerCAmelCase					=							with_conta
															__lowerCAmelCase					=							mc_label
										__lowerCAmelCase					=							(input_ids, mc_token_ids, lm_labels, mc_labels)
										tensor_datasets.append(tuple(torch.tensor(UpperCAmelCase__      ) for t in all_inputs      )      )
					return tensor_datasets


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							argparse.ArgumentParser()
					parser.add_argument('--model_name'		,					type=UpperCAmelCase__		,					default='openai-gpt'		,					help='pretrained model name'      )
					parser.add_argument('--do_train'		,					action='store_true'		,					help='Whether to run training.'      )
					parser.add_argument('--do_eval'		,					action='store_true'		,					help='Whether to run eval on the dev set.'      )
					parser.add_argument(
					    '--output_dir'		,					default=UpperCAmelCase__		,					type=UpperCAmelCase__		,					required=UpperCAmelCase__		,					help='The output directory where the model predictions and checkpoints will be written.'		,					)
					parser.add_argument('--train_dataset'		,					type=UpperCAmelCase__		,					default=''      )
					parser.add_argument('--eval_dataset'		,					type=UpperCAmelCase__		,					default=''      )
					parser.add_argument('--seed'		,					type=UpperCAmelCase__		,					default=42      )
					parser.add_argument('--num_train_epochs'		,					type=UpperCAmelCase__		,					default=3      )
					parser.add_argument('--train_batch_size'		,					type=UpperCAmelCase__		,					default=8      )
					parser.add_argument('--eval_batch_size'		,					type=UpperCAmelCase__		,					default=16      )
					parser.add_argument('--adam_epsilon'		,					default=1E-8		,					type=UpperCAmelCase__		,					help='Epsilon for Adam optimizer.'      )
					parser.add_argument('--max_grad_norm'		,					type=UpperCAmelCase__		,					default=1      )
					parser.add_argument(
					    '--max_steps'		,					default=-1		,					type=UpperCAmelCase__		,					help=(
					        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
					    )		,					)
					parser.add_argument(
					    '--gradient_accumulation_steps'		,					type=UpperCAmelCase__		,					default=1		,					help='Number of updates steps to accumulate before                        performing a backward/update pass.'		,					)
					parser.add_argument('--learning_rate'		,					type=UpperCAmelCase__		,					default=6.25E-5      )
					parser.add_argument('--warmup_steps'		,					default=0		,					type=UpperCAmelCase__		,					help='Linear warmup over warmup_steps.'      )
					parser.add_argument('--lr_schedule'		,					type=UpperCAmelCase__		,					default='warmup_linear'      )
					parser.add_argument('--weight_decay'		,					type=UpperCAmelCase__		,					default=0.01      )
					parser.add_argument('--lm_coef'		,					type=UpperCAmelCase__		,					default=0.9      )
					parser.add_argument('--n_valid'		,					type=UpperCAmelCase__		,					default=374      )

					parser.add_argument('--server_ip'		,					type=UpperCAmelCase__		,					default=''		,					help='Can be used for distant debugging.'      )
					parser.add_argument('--server_port'		,					type=UpperCAmelCase__		,					default=''		,					help='Can be used for distant debugging.'      )
					__lowerCAmelCase					=							parser.parse_args()
					print(UpperCAmelCase__      )

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print('Waiting for debugger attach'      )
										ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,					redirect_output=UpperCAmelCase__      )
										ptvsd.wait_for_attach()

					random.seed(args.seed      )
					np.random.seed(args.seed      )
					torch.manual_seed(args.seed      )
					torch.cuda.manual_seed_all(args.seed      )

					__lowerCAmelCase					=							torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
					__lowerCAmelCase					=							torch.cuda.device_count()
					logger.info('device: {}, n_gpu {}'.format(UpperCAmelCase__		,					UpperCAmelCase__      )      )

					if not args.do_train and not args.do_eval:
										raise ValueError('At least one of `do_train` or `do_eval` must be True.'      )

					if not os.path.exists(args.output_dir      ):
										os.makedirs(args.output_dir      )

					# Load tokenizer and model
					# This loading functions also add new tokens and embeddings called `special tokens`
					# These new embeddings will be fine-tuned on the RocStories dataset
					__lowerCAmelCase					=							['_start_', '_delimiter_', '_classify_']
					__lowerCAmelCase					=							OpenAIGPTTokenizer.from_pretrained(args.model_name      )
					tokenizer.add_tokens(UpperCAmelCase__      )
					__lowerCAmelCase					=							tokenizer.convert_tokens_to_ids(UpperCAmelCase__      )
					__lowerCAmelCase					=							OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name      )
					model.resize_token_embeddings(len(UpperCAmelCase__      )      )
					model.to(UpperCAmelCase__      )

					# Load and encode the datasets
					def tokenize_and_encode(UpperCAmelCase__      ):
										if isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
															return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(UpperCAmelCase__      )      )
										elif isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
															return obj
										return [tokenize_and_encode(UpperCAmelCase__      ) for o in obj]

					logger.info('Encoding dataset...'      )
					__lowerCAmelCase					=							load_rocstories_dataset(args.train_dataset      )
					__lowerCAmelCase					=							load_rocstories_dataset(args.eval_dataset      )
					__lowerCAmelCase					=							(train_dataset, eval_dataset)
					__lowerCAmelCase					=							tokenize_and_encode(UpperCAmelCase__      )

					# Compute the max input length for the Transformer
					__lowerCAmelCase					=							model.config.n_positions // 2 - 2
					__lowerCAmelCase					=							max(
					    len(story[:max_length]      ) + max(len(conta[:max_length]      )		,					len(conta[:max_length]      )      ) + 3
					    for dataset in encoded_datasets
					    for story, conta, conta, _ in dataset      )
					__lowerCAmelCase					=							min(UpperCAmelCase__		,					model.config.n_positions      )  # Max size of input for the pre-trained model

					# Prepare inputs tensors and dataloaders
					__lowerCAmelCase					=							pre_process_datasets(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					*UpperCAmelCase__      )
					__lowerCAmelCase,	__lowerCAmelCase					=							tensor_datasets[0], tensor_datasets[1]

					__lowerCAmelCase					=							TensorDataset(*UpperCAmelCase__      )
					__lowerCAmelCase					=							RandomSampler(UpperCAmelCase__      )
					__lowerCAmelCase					=							DataLoader(UpperCAmelCase__		,					sampler=UpperCAmelCase__		,					batch_size=args.train_batch_size      )

					__lowerCAmelCase					=							TensorDataset(*UpperCAmelCase__      )
					__lowerCAmelCase					=							SequentialSampler(UpperCAmelCase__      )
					__lowerCAmelCase					=							DataLoader(UpperCAmelCase__		,					sampler=UpperCAmelCase__		,					batch_size=args.eval_batch_size      )

					# Prepare optimizer
					if args.do_train:
										if args.max_steps > 0:
															__lowerCAmelCase					=							args.max_steps
															__lowerCAmelCase					=							args.max_steps // (len(UpperCAmelCase__      ) // args.gradient_accumulation_steps) + 1
										else:
															__lowerCAmelCase					=							len(UpperCAmelCase__      ) // args.gradient_accumulation_steps * args.num_train_epochs

										__lowerCAmelCase					=							list(model.named_parameters()      )
										__lowerCAmelCase					=							['bias', 'LayerNorm.bias', 'LayerNorm.weight']
										__lowerCAmelCase					=							[
										    {
										        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay      )],
										        'weight_decay': args.weight_decay,
										    },
										    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay      )], 'weight_decay': 0.0},
										]
										__lowerCAmelCase					=							AdamW(UpperCAmelCase__		,					lr=args.learning_rate		,					eps=args.adam_epsilon      )
										__lowerCAmelCase					=							get_linear_schedule_with_warmup(
										    UpperCAmelCase__		,					num_warmup_steps=args.warmup_steps		,					num_training_steps=UpperCAmelCase__      )

					if args.do_train:
										__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							0, 0, None
										model.train()
										for _ in trange(int(args.num_train_epochs      )		,					desc='Epoch'      ):
															__lowerCAmelCase					=							0
															__lowerCAmelCase					=							0
															__lowerCAmelCase					=							tqdm(UpperCAmelCase__		,					desc='Training'      )
															for step, batch in enumerate(UpperCAmelCase__      ):
																				__lowerCAmelCase					=							tuple(t.to(UpperCAmelCase__      ) for t in batch      )
																				__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							batch
																				__lowerCAmelCase					=							model(UpperCAmelCase__		,					mc_token_ids=UpperCAmelCase__		,					lm_labels=UpperCAmelCase__		,					mc_labels=UpperCAmelCase__      )
																				__lowerCAmelCase					=							args.lm_coef * losses[0] + losses[1]
																				loss.backward()
																				optimizer.step()
																				scheduler.step()
																				optimizer.zero_grad()
																				tr_loss += loss.item()
																				__lowerCAmelCase					=							(
																				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																				)
																				nb_tr_steps += 1
																				__lowerCAmelCase					=							'Training loss: {:.2e} lr: {:.2e}'.format(UpperCAmelCase__		,					scheduler.get_lr()[0]      )

    # Save a trained model
					if args.do_train:
										# Save a trained model, configuration and tokenizer
										__lowerCAmelCase					=							model.module if hasattr(UpperCAmelCase__		,					'module'      ) else model  # Only save the model itself

										# If we save using the predefined names, we can load using `from_pretrained`
										__lowerCAmelCase					=							os.path.join(args.output_dir		,					UpperCAmelCase__      )
										__lowerCAmelCase					=							os.path.join(args.output_dir		,					UpperCAmelCase__      )

										torch.save(model_to_save.state_dict()		,					UpperCAmelCase__      )
										model_to_save.config.to_json_file(UpperCAmelCase__      )
										tokenizer.save_vocabulary(args.output_dir      )

										# Load a trained model and vocabulary that you have fine-tuned
										__lowerCAmelCase					=							OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir      )
										__lowerCAmelCase					=							OpenAIGPTTokenizer.from_pretrained(args.output_dir      )
										model.to(UpperCAmelCase__      )

					if args.do_eval:
										model.eval()
										__lowerCAmelCase,	__lowerCAmelCase					=							0, 0
										__lowerCAmelCase,	__lowerCAmelCase					=							0, 0
										for batch in tqdm(UpperCAmelCase__		,					desc='Evaluating'      ):
															__lowerCAmelCase					=							tuple(t.to(UpperCAmelCase__      ) for t in batch      )
															__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							batch
															with torch.no_grad():
																				__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							model(
																				    UpperCAmelCase__		,					mc_token_ids=UpperCAmelCase__		,					lm_labels=UpperCAmelCase__		,					mc_labels=UpperCAmelCase__      )

															__lowerCAmelCase					=							mc_logits.detach().cpu().numpy()
															__lowerCAmelCase					=							mc_labels.to('cpu'      ).numpy()
															__lowerCAmelCase					=							accuracy(UpperCAmelCase__		,					UpperCAmelCase__      )

															eval_loss += mc_loss.mean().item()
															eval_accuracy += tmp_eval_accuracy

															nb_eval_examples += input_ids.size(0      )
															nb_eval_steps += 1

										__lowerCAmelCase					=							eval_loss / nb_eval_steps
										__lowerCAmelCase					=							eval_accuracy / nb_eval_examples
										__lowerCAmelCase					=							tr_loss / nb_tr_steps if args.do_train else None
										__lowerCAmelCase					=							{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

										__lowerCAmelCase					=							os.path.join(args.output_dir		,					'eval_results.txt'      )
										with open(UpperCAmelCase__		,					'w'      ) as writer:
															logger.info('***** Eval results *****'      )
															for key in sorted(result.keys()      ):
																				logger.info('  %s = %s'		,					UpperCAmelCase__		,					str(result[key]      )      )
																				writer.write('%s = %s\n' % (key, str(result[key]      ))      )


if __name__ == "__main__":
		main()


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=None					,						_A=None					,						_A=None					,						_A="resnet50"					,						_A=3					,						_A=3_2					,						_A=3					,						_A=True					,						_A=True					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							out_indices if out_indices is not None else [4]
												__lowerCAmelCase					=							stage_names
												__lowerCAmelCase					=							out_features
												__lowerCAmelCase					=							backbone
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							use_pretrained_backbone
												__lowerCAmelCase					=							is_training




							def        A__  ( self							):
												__lowerCAmelCase					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
												__lowerCAmelCase					=							self.get_config()

												return config, pixel_values




							def        A__  ( self							):
												return TimmBackboneConfig(
												    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							TimmBackbone(config=_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(_A							)
												self.parent.assertEqual(
												    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 1_4, 1_4)					,						)




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





@require_torch
@require_timm
class     snake_case_			(				_a			, _a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TimmBackbone,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TimmBackboneModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()




							def        A__  ( self							):
												__lowerCAmelCase					=							'resnet18'
												__lowerCAmelCase					=							'microsoft/resnet-18'

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A							)

												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(len(timm_model.stage_names							)					,						len(transformers_model.stage_names							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)
												# Out indices are set to the last layer by default. For timm models, we don't know
												# the number of layers in advance, so we set it to (-1,), whereas for transformers
												# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
												self.assertEqual(timm_model.out_indices					,						(-1,)							)
												self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names							) - 1]							)

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A					,						out_indices=[1, 2, 3]							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						out_indices=[1, 2, 3]							)

												self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices							)
												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)




							@unittest.skip('TimmBackbone doesn\'t support feed forward chunking'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone initialization is managed on the timm side'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t support output_attentions.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Safetensors is not supported by timm.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	__lowerCAmelCase					=							['pixel_values']
																	self.assertListEqual(arg_names[:1]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							self.has_attentions

												# no need to test all models as different heads yield the same functionality
												__lowerCAmelCase					=							self.all_model_classes[0]
												__lowerCAmelCase					=							model_class(_A							)
												model.to(_A							)

												__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
												__lowerCAmelCase					=							model(**_A							)
												__lowerCAmelCase					=							outputs[0][-1]

												# Encoder-/Decoder-only models
												__lowerCAmelCase					=							outputs.hidden_states[0]
												hidden_states.retain_grad()

												if self.has_attentions:
																	__lowerCAmelCase					=							outputs.attentions[0]
																	attentions.retain_grad()

												output.flatten()[0].backward(retain_graph=_A							)

												self.assertIsNotNone(hidden_states.grad							)

												if self.has_attentions:
																	self.assertIsNotNone(attentions.grad							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						len(config.out_indices							)							)
																	self.assertEqual(len(model.channels							)					,						len(config.out_indices							)							)

																	# Check output of last stage is taken if out_features=None, out_indices=None
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							None
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						1							)
																	self.assertEqual(len(model.channels							)					,						1							)

																	# Check backbone can be initialized with fresh weights
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							False
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)






