






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_		:						Optional[Any]             =    logging.getLogger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= np.argmax(_UpperCAmelCase							,       axis=1)
			return np.sum(outputs == labels)






def 				lowerCamelCase__				(_UpperCAmelCase):
			with open(_UpperCAmelCase							,       encoding='utf_8') as f:
						SCREAMING_SNAKE_CASE						= csv.reader(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= []
						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 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= []
			for dataset in encoded_datasets:
						SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= np.zeros((n_batch, 2, input_len)							,       dtype=np.intaa)
						SCREAMING_SNAKE_CASE						= np.zeros((n_batch, 2)							,       dtype=np.intaa)
						SCREAMING_SNAKE_CASE						= np.full((n_batch, 2, input_len)							,       fill_value=-100							,       dtype=np.intaa)
						SCREAMING_SNAKE_CASE						= np.zeros((n_batch,)							,       dtype=np.intaa)
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(_UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE						= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE						= with_conta
									SCREAMING_SNAKE_CASE						= with_conta
									SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase) - 1
									SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase) - 1
									SCREAMING_SNAKE_CASE						= with_conta
									SCREAMING_SNAKE_CASE						= with_conta
									SCREAMING_SNAKE_CASE						= mc_label
						SCREAMING_SNAKE_CASE						= (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 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 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.')
			SCREAMING_SNAKE_CASE						= 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)

			SCREAMING_SNAKE_CASE						= torch.device('cuda' if torch.cuda.is_available() else 'cpu')
			SCREAMING_SNAKE_CASE						= 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
			SCREAMING_SNAKE_CASE						= ['_start_', '_delimiter_', '_classify_']
			SCREAMING_SNAKE_CASE						= OpenAIGPTTokenizer.from_pretrained(args.model_name)
			tokenizer.add_tokens(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= tokenizer.convert_tokens_to_ids(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= 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...')
			SCREAMING_SNAKE_CASE						= load_rocstories_dataset(args.train_dataset)
			SCREAMING_SNAKE_CASE						= load_rocstories_dataset(args.eval_dataset)
			SCREAMING_SNAKE_CASE						= (train_dataset, eval_dataset)
			SCREAMING_SNAKE_CASE						= tokenize_and_encode(_UpperCAmelCase)

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

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

			SCREAMING_SNAKE_CASE						= TensorDataset(*_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= RandomSampler(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= DataLoader(_UpperCAmelCase							,       sampler=_UpperCAmelCase							,       batch_size=args.train_batch_size)

			SCREAMING_SNAKE_CASE						= TensorDataset(*_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= SequentialSampler(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= DataLoader(_UpperCAmelCase							,       sampler=_UpperCAmelCase							,       batch_size=args.eval_batch_size)

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

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

			if args.do_train:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs)							,       desc='Epoch'):
									SCREAMING_SNAKE_CASE						= 0
									SCREAMING_SNAKE_CASE						= 0
									SCREAMING_SNAKE_CASE						= tqdm(_UpperCAmelCase							,       desc='Training')
									for step, batch in enumerate(_UpperCAmelCase):
												SCREAMING_SNAKE_CASE						= tuple(t.to(_UpperCAmelCase) for t in batch)
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= batch
												SCREAMING_SNAKE_CASE						= model(_UpperCAmelCase							,       mc_token_ids=_UpperCAmelCase							,       lm_labels=_UpperCAmelCase							,       mc_labels=_UpperCAmelCase)
												SCREAMING_SNAKE_CASE						= args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												SCREAMING_SNAKE_CASE						= (
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												SCREAMING_SNAKE_CASE						= '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
						SCREAMING_SNAKE_CASE						= 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`
						SCREAMING_SNAKE_CASE						= os.path.join(args.output_dir							,       _UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
						SCREAMING_SNAKE_CASE						= OpenAIGPTTokenizer.from_pretrained(args.output_dir)
						model.to(_UpperCAmelCase)

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

									SCREAMING_SNAKE_CASE						= mc_logits.detach().cpu().numpy()
									SCREAMING_SNAKE_CASE						= mc_labels.to('cpu').numpy()
									SCREAMING_SNAKE_CASE						= 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

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

						SCREAMING_SNAKE_CASE						= 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()












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# customize the test name generator function as we want both params to appear in the sub-test
			# name, as by default it shows only the first param
			SCREAMING_SNAKE_CASE						= parameterized.to_safe_name('_'.join(str(_UpperCAmelCase) for x in param.args))
			return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
a_		:						List[Any]             =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
						# - it won't be able to handle that
						# 2. for now testing with just 2 gpus max (since some quality tests may give different
						# results with mode gpus because we use very little data)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




