


import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
   from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_=13					,   A_=32					,   A_=3					,   A_=4					,   A_=[10, 20, 30, 40]					,   A_=[2, 2, 3, 2]					,   A_=True					,   A_=True					,   A_=37					,   A_="gelu"					,   A_=10					,   A_=0.02					,   A_=["stage2", "stage3", "stage4"]					,   A_=[2, 3, 4]					,   A_=None					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=parent
         __UpperCamelCase    							=batch_size
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=num_stages
         __UpperCamelCase    							=hidden_sizes
         __UpperCamelCase    							=depths
         __UpperCamelCase    							=is_training
         __UpperCamelCase    							=use_labels
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=num_labels
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=out_features
         __UpperCamelCase    							=out_indices
         __UpperCamelCase    							=scope






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         __UpperCamelCase    							=None
         if self.use_labels:
              __UpperCamelCase    							=ids_tensor([self.batch_size]					,   self.num_labels    )

         __UpperCamelCase    							=self.get_config()
         return config, pixel_values, labels






    def _a					(			self    )  ->       Optional[int]:
         return ConvNextConfig(
             num_channels=self.num_channels					,   hidden_sizes=self.hidden_sizes					,   depths=self.depths					,   num_stages=self.num_stages					,   hidden_act=self.hidden_act					,   is_decoder=A_					,   initializer_range=self.initializer_range					,   out_features=self.out_features					,   out_indices=self.out_indices					,   num_labels=self.num_labels					,   )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=ConvNextModel(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_    )
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape					,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,   )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       Optional[Any]:
         __UpperCamelCase    							=ConvNextForImageClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   labels=A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)    )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       Tuple:
         __UpperCamelCase    							=ConvNextBackbone(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_    )

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps    )					,   len(config.out_features    )    )
         self.parent.assertListEqual(list(result.feature_maps[0].shape    )					,   [self.batch_size, self.hidden_sizes[1], 4, 4]    )

         # verify channels
         self.parent.assertEqual(len(model.channels    )					,   len(config.out_features    )    )
         self.parent.assertListEqual(model.channels					,   config.hidden_sizes[1:]    )

         # verify backbone works with out_features=None
         __UpperCamelCase    							=None
         __UpperCamelCase    							=ConvNextBackbone(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_    )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps    )					,   1    )
         self.parent.assertListEqual(list(result.feature_maps[0].shape    )					,   [self.batch_size, self.hidden_sizes[-1], 1, 1]    )

         # verify channels
         self.parent.assertEqual(len(model.channels    )					,   1    )
         self.parent.assertListEqual(model.channels					,   [config.hidden_sizes[-1]]    )






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=self.prepare_config_and_inputs()
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=config_and_inputs
         __UpperCamelCase    							={'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class     UpperCAmelCase__       (					A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]      =  (
        (
            ConvNextModel,
            ConvNextForImageClassification,
            ConvNextBackbone,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__						:		Dict      =  (
        {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__						:		Dict      =  True
    UpperCAmelCase__						:		Tuple      =  False
    UpperCAmelCase__						:		Optional[Any]      =  False
    UpperCAmelCase__						:		List[Any]      =  False
    UpperCAmelCase__						:		str      =  False






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=ConvNextModelTester(self    )
         __UpperCamelCase    							=ConfigTester(self					,   config_class=A_					,   has_text_modality=A_					,   hidden_size=37    )






    def _a					(			self    )  ->       Union[str, Any]:
         self.create_and_test_config_common_properties()
         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    )  ->       str:
         return






    @unittest.skip(reason='ConvNext does not use inputs_embeds'    )
    def _a					(			self    )  ->       Optional[int]:
         pass






    @unittest.skip(reason='ConvNext does not support input and output embeddings'    )
    def _a					(			self    )  ->       Optional[Any]:
         pass






    @unittest.skip(reason='ConvNext does not use feedforward chunking'    )
    def _a					(			self    )  ->       List[Any]:
         pass






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()

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

              __UpperCamelCase    							=['pixel_values']
              self.assertListEqual(arg_names[:1]					,   A_    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*A_    )






    def _a					(			self    )  ->       Optional[Any]:
         def check_hidden_states_output(A_					,   A_					,   A_    ):
              __UpperCamelCase    							=model_class(A_    )
              model.to(A_    )
              model.eval()

              with torch.no_grad():
                   __UpperCamelCase    							=model(**self._prepare_for_class(A_					,   A_    )    )

              __UpperCamelCase    							=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              __UpperCamelCase    							=self.model_tester.num_stages
              self.assertEqual(len(A_    )					,   expected_num_stages + 1    )

              # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]    )					,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,   )

         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __UpperCamelCase    							=True
              check_hidden_states_output(A_					,   A_					,   A_    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __UpperCamelCase    							=True

              check_hidden_states_output(A_					,   A_					,   A_    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_    )






    @slow
    def _a					(			self    )  ->       Optional[Any]:
         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase    							=ConvNextModel.from_pretrained(A_    )
              self.assertIsNotNone(A_    )

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image






@require_torch
@require_vision
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    @cached_property
    def _a					(			self    )  ->       Optional[int]:
         return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224'    ) if is_vision_available() else None






    @slow
    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'    ).to(A_    )

         __UpperCamelCase    							=self.default_image_processor
         __UpperCamelCase    							=prepare_img()
         __UpperCamelCase    							=image_processor(images=A_					,   return_tensors='pt'    ).to(A_    )

         # forward pass
         with torch.no_grad():
              __UpperCamelCase    							=model(**A_    )

         # verify the logits
         __UpperCamelCase    							=torch.Size((1, 1000)    )
         self.assertEqual(outputs.logits.shape					,   A_    )

         __UpperCamelCase    							=torch.tensor([-0.0260, -0.4739, 0.1911]    ).to(A_    )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]					,   A_					,   atol=1E-4    )    )






@require_torch
class     UpperCAmelCase__       (					unittest.TestCase   ,  A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]      =  (ConvNextBackbone,) if is_torch_available() else ()
    UpperCAmelCase__						:		Optional[Any]      =  ConvNextConfig

    UpperCAmelCase__						:		Any      =  False






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=ConvNextModelTester(self    )









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
)
_A		       =						logging.getLogger(__name__)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=np.argmax(SCREAMING_SNAKE_CASE__			,     axis=1				)
     return np.sum(outputs == labels				)

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Dict				):
     __UpperCamelCase    							=[]
     for dataset in encoded_datasets:
          __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=np.zeros((n_batch, 2, input_len)			,     dtype=np.intaa				)
          __UpperCamelCase    							=np.zeros((n_batch, 2)			,     dtype=np.intaa				)
          __UpperCamelCase    							=np.full((n_batch, 2, input_len)			,     fill_value=-1_00			,     dtype=np.intaa				)
          __UpperCamelCase    							=np.zeros((n_batch,)			,     dtype=np.intaa				)
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __UpperCamelCase    							=[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __UpperCamelCase    							=with_conta
               __UpperCamelCase    							=with_conta
               __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) - 1
               __UpperCamelCase    							=len(SCREAMING_SNAKE_CASE__				) - 1
               __UpperCamelCase    							=with_conta
               __UpperCamelCase    							=with_conta
               __UpperCamelCase    							=mc_label
          __UpperCamelCase    							=(input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE__				) for t in all_inputs				)				)
     return tensor_datasets

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							=argparse.ArgumentParser()
     parser.add_argument('--model_name'			,     type=SCREAMING_SNAKE_CASE__			,     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=SCREAMING_SNAKE_CASE__			,     type=SCREAMING_SNAKE_CASE__			,     required=SCREAMING_SNAKE_CASE__			,     help='The output directory where the model predictions and checkpoints will be written.'			,     )
     parser.add_argument('--train_dataset'			,     type=SCREAMING_SNAKE_CASE__			,     default=''				)
     parser.add_argument('--eval_dataset'			,     type=SCREAMING_SNAKE_CASE__			,     default=''				)
     parser.add_argument('--seed'			,     type=SCREAMING_SNAKE_CASE__			,     default=42				)
     parser.add_argument('--num_train_epochs'			,     type=SCREAMING_SNAKE_CASE__			,     default=3				)
     parser.add_argument('--train_batch_size'			,     type=SCREAMING_SNAKE_CASE__			,     default=8				)
     parser.add_argument('--eval_batch_size'			,     type=SCREAMING_SNAKE_CASE__			,     default=16				)
     parser.add_argument('--adam_epsilon'			,     default=1E-8			,     type=SCREAMING_SNAKE_CASE__			,     help='Epsilon for Adam optimizer.'				)
     parser.add_argument('--max_grad_norm'			,     type=SCREAMING_SNAKE_CASE__			,     default=1				)
     parser.add_argument(
         '--max_steps'			,     default=-1			,     type=SCREAMING_SNAKE_CASE__			,     help=(
             'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
         )			,     )
     parser.add_argument(
         '--gradient_accumulation_steps'			,     type=SCREAMING_SNAKE_CASE__			,     default=1			,     help='Number of updates steps to accumulate before                        performing a backward/update pass.'			,     )
     parser.add_argument('--learning_rate'			,     type=SCREAMING_SNAKE_CASE__			,     default=6.25E-5				)
     parser.add_argument('--warmup_steps'			,     default=0			,     type=SCREAMING_SNAKE_CASE__			,     help='Linear warmup over warmup_steps.'				)
     parser.add_argument('--lr_schedule'			,     type=SCREAMING_SNAKE_CASE__			,     default='warmup_linear'				)
     parser.add_argument('--weight_decay'			,     type=SCREAMING_SNAKE_CASE__			,     default=0.01				)
     parser.add_argument('--lm_coef'			,     type=SCREAMING_SNAKE_CASE__			,     default=0.9				)
     parser.add_argument('--n_valid'			,     type=SCREAMING_SNAKE_CASE__			,     default=3_74				)

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

     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=SCREAMING_SNAKE_CASE__				)
          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				)

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

     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
     __UpperCamelCase    							=['_start_', '_delimiter_', '_classify_']
     __UpperCamelCase    							=OpenAIGPTTokenizer.from_pretrained(args.model_name				)
     tokenizer.add_tokens(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
     model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE__				)				)
     model.to(SCREAMING_SNAKE_CASE__				)

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

     logger.info('Encoding dataset...'				)
     __UpperCamelCase    							=load_rocstories_dataset(args.train_dataset				)
     __UpperCamelCase    							=load_rocstories_dataset(args.eval_dataset				)
     __UpperCamelCase    							=(train_dataset, eval_dataset)
     __UpperCamelCase    							=tokenize_and_encode(SCREAMING_SNAKE_CASE__				)

     # Compute the max input length for the Transformer
     __UpperCamelCase    							=model.config.n_positions // 2 - 2
     __UpperCamelCase    							=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				)
     __UpperCamelCase    							=min(SCREAMING_SNAKE_CASE__			,     model.config.n_positions				)  # Max size of input for the pre-trained model

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

     __UpperCamelCase    							=TensorDataset(*SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=RandomSampler(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=DataLoader(SCREAMING_SNAKE_CASE__			,     sampler=SCREAMING_SNAKE_CASE__			,     batch_size=args.train_batch_size				)

     __UpperCamelCase    							=TensorDataset(*SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=SequentialSampler(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=DataLoader(SCREAMING_SNAKE_CASE__			,     sampler=SCREAMING_SNAKE_CASE__			,     batch_size=args.eval_batch_size				)

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

          __UpperCamelCase    							=list(model.named_parameters()				)
          __UpperCamelCase    							=['bias', 'LayerNorm.bias', 'LayerNorm.weight']
          __UpperCamelCase    							=[
              {
                  '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},
          ]
          __UpperCamelCase    							=AdamW(SCREAMING_SNAKE_CASE__			,     lr=args.learning_rate			,     eps=args.adam_epsilon				)
          __UpperCamelCase    							=get_linear_schedule_with_warmup(
              SCREAMING_SNAKE_CASE__			,     num_warmup_steps=args.warmup_steps			,     num_training_steps=SCREAMING_SNAKE_CASE__				)

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

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

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

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

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

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

               __UpperCamelCase    							=mc_logits.detach().cpu().numpy()
               __UpperCamelCase    							=mc_labels.to('cpu'				).numpy()
               __UpperCamelCase    							=accuracy(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

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

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

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

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


if __name__ == "__main__":
   main()





