






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCamelCase      				=     '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase      				=     direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCamelCase      				=     re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCamelCase      				=     re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCamelCase      				=     re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase      				=     [
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'		,					UpperCAmelCase__      )
					return [m.group(0      ) for m in matches]


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					__lowerCAmelCase					=							{
					    config.replace('Config'		,					''      ): model_type for model_type, config in config_maping_names.items()
					}

					# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
					__lowerCAmelCase					=							collections.defaultdict(UpperCAmelCase__      )
					__lowerCAmelCase					=							collections.defaultdict(UpperCAmelCase__      )
					__lowerCAmelCase					=							collections.defaultdict(UpperCAmelCase__      )

					# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
					for attr_name in dir(UpperCAmelCase__      ):
										__lowerCAmelCase					=							None
										if _re_tf_models.match(UpperCAmelCase__      ) is not None:
															__lowerCAmelCase					=							tf_models
															__lowerCAmelCase					=							_re_tf_models.match(UpperCAmelCase__      ).groups()[0]
										elif _re_flax_models.match(UpperCAmelCase__      ) is not None:
															__lowerCAmelCase					=							flax_models
															__lowerCAmelCase					=							_re_flax_models.match(UpperCAmelCase__      ).groups()[0]
										elif _re_pt_models.match(UpperCAmelCase__      ) is not None:
															__lowerCAmelCase					=							pt_models
															__lowerCAmelCase					=							_re_pt_models.match(UpperCAmelCase__      ).groups()[0]

										if lookup_dict is not None:
															while len(UpperCAmelCase__      ) > 0:
																				if attr_name in model_prefix_to_model_type:
																									__lowerCAmelCase					=							True
																									break
																				# Try again after removing the last word in the name
																				__lowerCAmelCase					=							''.join(camel_case_split(UpperCAmelCase__      )[:-1]      )

					__lowerCAmelCase					=							set(list(pt_models.keys()      ) + list(tf_models.keys()      ) + list(flax_models.keys()      )      )
					__lowerCAmelCase					=							list(UpperCAmelCase__      )
					all_models.sort()

					__lowerCAmelCase					=							{'model_type': all_models}
					__lowerCAmelCase					=							[pt_models[t] for t in all_models]
					__lowerCAmelCase					=							[tf_models[t] for t in all_models]
					__lowerCAmelCase					=							[flax_models[t] for t in all_models]

					# Now let's use the auto-mapping names to make sure
					__lowerCAmelCase					=							{}
					for t in all_models:
										if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
															__lowerCAmelCase					=							'AutoProcessor'
										elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
															__lowerCAmelCase					=							'AutoTokenizer'
										elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
															__lowerCAmelCase					=							'AutoFeatureExtractor'
										else:
															# Default to AutoTokenizer if a model has nothing, for backward compatibility.
															__lowerCAmelCase					=							'AutoTokenizer'

					__lowerCAmelCase					=							[processors[t] for t in all_models]

					return pd.DataFrame(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[
					    transformers_module.models.auto.modeling_auto,
					    transformers_module.models.auto.modeling_tf_auto,
					    transformers_module.models.auto.modeling_flax_auto,
					]
					for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
										__lowerCAmelCase					=							[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
										__lowerCAmelCase					=							[auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
										# Loop through all three frameworks
										for module, cls, mapping in zip(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):
															# The type of pipeline may not exist in this framework
															if not hasattr(UpperCAmelCase__		,					UpperCAmelCase__      ):
																				continue
															# First extract all model_names
															__lowerCAmelCase					=							[]
															for name in getattr(UpperCAmelCase__		,					UpperCAmelCase__      ).values():
																				if isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
																									model_names.append(UpperCAmelCase__      )
																				else:
																									model_names.extend(list(UpperCAmelCase__      )      )

            # Add pipeline tag and auto model class for those models
															table.update({model_name: (pipeline_tag, cls) for model_name in model_names}      )

					return table


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							get_frameworks_table()
					__lowerCAmelCase					=							Dataset.from_pandas(UpperCAmelCase__      )

					__lowerCAmelCase					=							hf_hub_download(
					    'huggingface/transformers-metadata'		,					'pipeline_tags.json'		,					repo_type='dataset'		,					token=UpperCAmelCase__      )
					__lowerCAmelCase					=							Dataset.from_json(UpperCAmelCase__      )
					__lowerCAmelCase					=							{
					    tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
					    for i in range(len(UpperCAmelCase__      )      )
					}
					__lowerCAmelCase					=							update_pipeline_and_auto_class_table(UpperCAmelCase__      )

					# Sort the model classes to avoid some nondeterministic updates to create false update commits.
					__lowerCAmelCase					=							sorted(table.keys()      )
					__lowerCAmelCase					=							pd.DataFrame(
					    {
					        'model_class': model_classes,
					        'pipeline_tag': [table[m][0] for m in model_classes],
					        'auto_class': [table[m][1] for m in model_classes],
					    }      )
					__lowerCAmelCase					=							Dataset.from_pandas(UpperCAmelCase__      )

					with tempfile.TemporaryDirectory() as tmp_dir:
										frameworks_dataset.to_json(os.path.join(UpperCAmelCase__		,					'frameworks.json'      )      )
										tags_dataset.to_json(os.path.join(UpperCAmelCase__		,					'pipeline_tags.json'      )      )

										if commit_sha is not None:
															__lowerCAmelCase					=							(
															    F"""Update with commit {commit_sha}\n\nSee: """
															    F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
															)
										else:
															__lowerCAmelCase					=							'Update'

										upload_folder(
										    repo_id='huggingface/transformers-metadata'		,					folder_path=UpperCAmelCase__		,					repo_type='dataset'		,					token=UpperCAmelCase__		,					commit_message=UpperCAmelCase__		,					)


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
					__lowerCAmelCase					=							transformers_module.pipelines.SUPPORTED_TASKS
					__lowerCAmelCase					=							[]
					for key in pipeline_tasks:
										if key not in in_table:
															__lowerCAmelCase					=							pipeline_tasks[key]['pt']
															if isinstance(UpperCAmelCase__		,					(list, tuple)      ):
																				__lowerCAmelCase					=							model[0]
															__lowerCAmelCase					=							model.__name__
															if model not in in_table.values():
																				missing.append(UpperCAmelCase__      )

					if len(UpperCAmelCase__      ) > 0:
										__lowerCAmelCase					=							', '.join(UpperCAmelCase__      )
										raise ValueError(
										    'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
										    F"""`utils/update_metadata.py`: {msg}. Please add them!"""      )


if __name__ == "__main__":
		lowerCamelCase      				=     argparse.ArgumentParser()
		parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
		parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
		parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
		lowerCamelCase      				=     parser.parse_args()

		if args.check_only:
				check_pipeline_tags()
		else:
				update_metadata(args.token, args.commit_sha)









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()






