


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
)
_snake_case     :		Union[str, Any]						=				logging.getLogger(__name__)







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			str							 =   np.argmax(UpperCamelCase__							,	axis=1	)
							return np.sum(outputs == labels	)







def 				lowerCAmelCase_     (					__lowerCamelCase	):






							with open(UpperCamelCase__							,	encoding="utf_8"	) as f:
														__snake_case		:			List[str]							 =   csv.reader(UpperCamelCase__	)
														__snake_case		:			Dict							 =   []
														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_     (					__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			List[Any]							 =   []
							for dataset in encoded_datasets:
														__snake_case		:			int							 =   len(UpperCamelCase__	)
														__snake_case		:			List[str]							 =   np.zeros((n_batch, 2, input_len)							,	dtype=np.intaa	)
														__snake_case		:			Any							 =   np.zeros((n_batch, 2)							,	dtype=np.intaa	)
														__snake_case		:			int							 =   np.full((n_batch, 2, input_len)							,	fill_value=-1_0_0							,	dtype=np.intaa	)
														__snake_case		:			str							 =   np.zeros((n_batch,)							,	dtype=np.intaa	)
														for (
														    i,
														    (story, conta, conta, mc_label),
														) in enumerate(UpperCamelCase__	):
																					__snake_case		:			str							 =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																					__snake_case		:			List[Any]							 =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																					__snake_case		:			List[str]							 =   with_conta
																					__snake_case		:			List[Any]							 =   with_conta
																					__snake_case		:			Optional[int]							 =   len(UpperCamelCase__	) - 1
																					__snake_case		:			List[str]							 =   len(UpperCamelCase__	) - 1
																					__snake_case		:			Optional[int]							 =   with_conta
																					__snake_case		:			Optional[int]							 =   with_conta
																					__snake_case		:			Optional[Any]							 =   mc_label
														__snake_case		:			List[str]							 =   (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_     (					):
							__snake_case		:			Optional[Any]							 =   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=4_2	)
							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=1_6	)
							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=0.0_1	)
							parser.add_argument("--lm_coef"							,	type=UpperCamelCase__							,	default=0.9	)
							parser.add_argument("--n_valid"							,	type=UpperCamelCase__							,	default=3_7_4	)

							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."	)
							__snake_case		:			Tuple							 =   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	)

							__snake_case		:			Dict							 =   torch.device("cuda" if torch.cuda.is_available() else "cpu"	)
							__snake_case		:			int							 =   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
							__snake_case		:			Optional[Any]							 =   ["_start_", "_delimiter_", "_classify_"]
							__snake_case		:			Optional[int]							 =   OpenAIGPTTokenizer.from_pretrained(args.model_name	)
							tokenizer.add_tokens(UpperCamelCase__	)
							__snake_case		:			List[Any]							 =   tokenizer.convert_tokens_to_ids(UpperCamelCase__	)
							__snake_case		:			List[Any]							 =   OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name	)
							model.resize_token_embeddings(len(UpperCamelCase__	)	)
							model.to(UpperCamelCase__	)

							# Load and encode the datasets
							def tokenize_and_encode(__lowerCamelCase	):
														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..."	)
							__snake_case		:			Dict							 =   load_rocstories_dataset(args.train_dataset	)
							__snake_case		:			Optional[Any]							 =   load_rocstories_dataset(args.eval_dataset	)
							__snake_case		:			Dict							 =   (train_dataset, eval_dataset)
							__snake_case		:			str							 =   tokenize_and_encode(UpperCamelCase__	)

							# Compute the max input length for the Transformer
							__snake_case		:			Dict							 =   model.config.n_positions // 2 - 2
							__snake_case		:			Optional[int]							 =   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	)
							__snake_case		:			Any							 =   min(UpperCamelCase__							,	model.config.n_positions	)  # Max size of input for the pre-trained model

							# Prepare inputs tensors and dataloaders
							__snake_case		:			List[str]							 =   pre_process_datasets(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	*UpperCamelCase__	)
							__snake_case ,			__snake_case		:			Tuple							 =   tensor_datasets[0], tensor_datasets[1]

							__snake_case		:			str							 =   TensorDataset(*UpperCamelCase__	)
							__snake_case		:			Dict							 =   RandomSampler(UpperCamelCase__	)
							__snake_case		:			Optional[Any]							 =   DataLoader(UpperCamelCase__							,	sampler=UpperCamelCase__							,	batch_size=args.train_batch_size	)

							__snake_case		:			int							 =   TensorDataset(*UpperCamelCase__	)
							__snake_case		:			List[str]							 =   SequentialSampler(UpperCamelCase__	)
							__snake_case		:			str							 =   DataLoader(UpperCamelCase__							,	sampler=UpperCamelCase__							,	batch_size=args.eval_batch_size	)

							# Prepare optimizer
							if args.do_train:
														if args.max_steps > 0:
																					__snake_case		:			Dict							 =   args.max_steps
																					__snake_case		:			Optional[int]							 =   args.max_steps // (len(UpperCamelCase__	) // args.gradient_accumulation_steps) + 1
														else:
																					__snake_case		:			Dict							 =   len(UpperCamelCase__	) // args.gradient_accumulation_steps * args.num_train_epochs

														__snake_case		:			List[str]							 =   list(model.named_parameters()	)
														__snake_case		:			Optional[Any]							 =   ["bias", "LayerNorm.bias", "LayerNorm.weight"]
														__snake_case		:			List[Any]							 =   [
														    {
														        "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},
														]
														__snake_case		:			Union[str, Any]							 =   AdamW(UpperCamelCase__							,	lr=args.learning_rate							,	eps=args.adam_epsilon	)
														__snake_case		:			List[str]							 =   get_linear_schedule_with_warmup(
														    UpperCamelCase__							,	num_warmup_steps=args.warmup_steps							,	num_training_steps=UpperCamelCase__	)

							if args.do_train:
														__snake_case ,			__snake_case ,			__snake_case		:			Tuple							 =   0, 0, None
														model.train()
														for _ in trange(int(args.num_train_epochs	)							,	desc="Epoch"	):
																					__snake_case		:			Any							 =   0
																					__snake_case		:			List[str]							 =   0
																					__snake_case		:			Dict							 =   tqdm(UpperCamelCase__							,	desc="Training"	)
																					for step, batch in enumerate(UpperCamelCase__	):
																												__snake_case		:			Any							 =   tuple(t.to(UpperCamelCase__	) for t in batch	)
																												__snake_case ,			__snake_case ,			__snake_case ,			__snake_case		:			Union[str, Any]							 =   batch
																												__snake_case		:			Any							 =   model(UpperCamelCase__							,	mc_token_ids=UpperCamelCase__							,	lm_labels=UpperCamelCase__							,	mc_labels=UpperCamelCase__	)
																												__snake_case		:			int							 =   args.lm_coef * losses[0] + losses[1]
																												loss.backward()
																												optimizer.step()
																												scheduler.step()
																												optimizer.zero_grad()
																												tr_loss += loss.item()
																												__snake_case		:			Dict							 =   (
																												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																												)
																												nb_tr_steps += 1
																												__snake_case		:			Any							 =   "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
														__snake_case		:			Tuple							 =   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`
														__snake_case		:			List[Any]							 =   os.path.join(args.output_dir							,	UpperCamelCase__	)
														__snake_case		:			str							 =   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
														__snake_case		:			List[str]							 =   OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir	)
														__snake_case		:			Tuple							 =   OpenAIGPTTokenizer.from_pretrained(args.output_dir	)
														model.to(UpperCamelCase__	)

							if args.do_eval:
														model.eval()
														__snake_case ,			__snake_case		:			List[str]							 =   0, 0
														__snake_case ,			__snake_case		:			Any							 =   0, 0
														for batch in tqdm(UpperCamelCase__							,	desc="Evaluating"	):
																					__snake_case		:			str							 =   tuple(t.to(UpperCamelCase__	) for t in batch	)
																					__snake_case ,			__snake_case ,			__snake_case ,			__snake_case		:			str							 =   batch
																					with torch.no_grad():
																												__snake_case ,			__snake_case ,			__snake_case ,			__snake_case		:			Dict							 =   model(
																												    UpperCamelCase__							,	mc_token_ids=UpperCamelCase__							,	lm_labels=UpperCamelCase__							,	mc_labels=UpperCamelCase__	)

																					__snake_case		:			Dict							 =   mc_logits.detach().cpu().numpy()
																					__snake_case		:			str							 =   mc_labels.to("cpu"	).numpy()
																					__snake_case		:			Optional[int]							 =   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

														__snake_case		:			Optional[int]							 =   eval_loss / nb_eval_steps
														__snake_case		:			Optional[Any]							 =   eval_accuracy / nb_eval_examples
														__snake_case		:			Any							 =   tr_loss / nb_tr_steps if args.do_train else None
														__snake_case		:			Any							 =   {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

														__snake_case		:			str							 =   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 unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			None
				__SCREAMING_SNAKE_CASE					: Any       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: int       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			True
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''tokenizer_file'''
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def     a							(					self       ):
											super().setUp()
											snake_case_							=    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
											tokenizer.save_pretrained(self.tmpdirname       )




				def     a							(					self      ,   **snake_case       ):
											kwargs.update(self.special_tokens_map       )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()

											snake_case_							=    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											snake_case_							=    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

											snake_case_							=    tokenizer.batch_encode_plus(snake_case       )['input_ids']
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    tokenizer.batch_decode(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self      ,   snake_case=6       ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									snake_case_							=    self.rust_tokenizer_class.from_pretrained(snake_case      ,   **snake_case       )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									snake_case_							=    'This is a simple input'
																									snake_case_							=    ['This is a simple input 1', 'This is a simple input 2']
																									snake_case_							=    ('This is a simple input', 'This is a pair')
																									snake_case_							=    [
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode_plus(snake_case      ,   max_length=snake_case       )

																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'       )

																									snake_case_							=    None  # Hotfixing padding = None
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    load_dataset('xnli'      ,   'all_languages'      ,   split='test'      ,   streaming=snake_case       )

											snake_case_							=    next(iter(snake_case       )       )['premise']  # pick up one data
											snake_case_							=    list(sample_data.values()       )

											snake_case_							=    list(map(tokenizer.encode      ,   snake_case       )       )
											snake_case_							=    [tokenizer.decode(snake_case      ,   clean_up_tokenization_spaces=snake_case       ) for x in output_tokens]
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
											# any sequence length constraints. This test of the parent class will fail since it relies on the
											# maximum sequence length of the positoonal embeddings.
											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )      ,   1       )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )      ,   1       )




