
import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__A				='''bert-base-cased'''
__A				='''google/pegasus-xsum'''
__A				=[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__A				=['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__A				='''patrickvonplaten/t5-tiny-random'''
__A				='''sshleifer/bart-tiny-random'''
__A				='''sshleifer/tiny-mbart'''
__A				='''sshleifer/tiny-marian-en-de'''





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			"\n".join(lowerCamelCase__    )
				Path(lowerCamelCase__    ).open("w"    ).writelines(lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(lowerCamelCase__	, F'{split}.source'    )	, lowerCamelCase__    )
								_dump_articles(os.path.join(lowerCamelCase__	, F'{split}.target'    )	, lowerCamelCase__    )
				return tmp_dir





class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]						,  )
	@slow
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[Any]:
					lowerCamelCase_						=			AutoTokenizer.from_pretrained(lowercase )
					lowerCamelCase_						=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
					lowerCamelCase_						=			max(len(tokenizer.encode(lowercase ) ) for a in ARTICLES )
					lowerCamelCase_						=			max(len(tokenizer.encode(lowercase ) ) for a in SUMMARIES )
					lowerCamelCase_						=			4
					lowerCamelCase_						=			8
					assert max_len_target > max_src_len  # Will be truncated
					assert max_len_source > max_src_len  # Will be truncated
					lowerCamelCase_	,    lowerCamelCase_						=			"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
					lowerCamelCase_						=			SeqaSeqDataset(
					    lowercase						,  data_dir=lowercase						,  type_path="train"						,  max_source_length=lowercase						,  max_target_length=lowercase						,  src_lang=lowercase						,  tgt_lang=lowercase						,  )
					lowerCamelCase_						=			DataLoader(lowercase						,  batch_size=2						,  collate_fn=train_dataset.collate_fn )
					for batch in dataloader:
									assert isinstance(lowercase						,  lowercase )
									assert batch["attention_mask"].shape == batch["input_ids"].shape
									# show that articles were trimmed.
									assert batch["input_ids"].shape[1] == max_src_len
									# show that targets are the same len
									assert batch["labels"].shape[1] == max_tgt_len
									if tok_name != MBART_TINY:
													continue
									# check language codes in correct place
									lowerCamelCase_						=			shift_tokens_right(batch["labels"]						,  tokenizer.pad_token_id )
									assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
									assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
									assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
									assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

									break  # No need to test every batch



	@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       str:
					lowerCamelCase_						=			AutoTokenizer.from_pretrained(lowercase )
					lowerCamelCase_						=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
					lowerCamelCase_						=			max(len(tokenizer.encode(lowercase ) ) for a in ARTICLES )
					lowerCamelCase_						=			max(len(tokenizer.encode(lowercase ) ) for a in SUMMARIES )
					lowerCamelCase_						=			4
					lowerCamelCase_						=			LegacySeqaSeqDataset(
					    lowercase						,  data_dir=lowercase						,  type_path="train"						,  max_source_length=20						,  max_target_length=lowercase						,  )
					lowerCamelCase_						=			DataLoader(lowercase						,  batch_size=2						,  collate_fn=train_dataset.collate_fn )
					for batch in dataloader:
									assert batch["attention_mask"].shape == batch["input_ids"].shape
									# show that articles were trimmed.
									assert batch["input_ids"].shape[1] == max_len_source
									assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
									# show that targets were truncated
									assert batch["labels"].shape[1] == trunc_target  # Truncated
									assert max_len_target > trunc_target  # Truncated
									break  # No need to test every batch



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )

					lowerCamelCase_						=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
					lowerCamelCase_						=			tmp_dir.joinpath("train.source" ).open().readlines()
					lowerCamelCase_						=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
					pack_data_dir(lowercase						,  lowercase						,  128						,  lowercase )
					lowerCamelCase_						=			{x.name for x in tmp_dir.iterdir()}
					lowerCamelCase_						=			{x.name for x in save_dir.iterdir()}
					lowerCamelCase_						=			save_dir.joinpath("train.source" ).open().readlines()
					# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
					# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
					assert len(lowercase ) < len(lowercase )
					assert len(lowercase ) == 1
					assert len(packed_examples[0] ) == sum(len(lowercase ) for x in orig_examples )
					assert orig_paths == new_paths



	@pytest.mark.skipif(not FAIRSEQ_AVAILABLE						,  reason="This test requires fairseq" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					if not FAIRSEQ_AVAILABLE:
									return
					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			self._get_dataset(max_len=64 )
					lowerCamelCase_						=			64
					lowerCamelCase_						=			ds.make_dynamic_sampler(lowercase						,  required_batch_size_multiple=lowercase )
					lowerCamelCase_						=			[len(lowercase ) for x in batch_sampler]
					assert len(set(lowercase ) ) > 1  # it's not dynamic batch size if every batch is the same length
					assert sum(lowercase ) == len(lowercase )  # no dropped or added examples
					lowerCamelCase_						=			DataLoader(lowercase						,  batch_sampler=lowercase						,  collate_fn=ds.collate_fn						,  num_workers=2 )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[]
					for batch in data_loader:
									lowerCamelCase_						=			batch["input_ids"].shape
									lowerCamelCase_						=			src_shape[0]
									assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
									lowerCamelCase_						=			np.product(batch["input_ids"].shape )
									num_src_per_batch.append(lowercase )
									if num_src_tokens > (max_tokens * 1.1):
													failures.append(lowercase )
					assert num_src_per_batch[0] == max(lowercase )
					if failures:
									raise AssertionError(f'too many tokens in {len(lowercase )} batches' )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			self._get_dataset(max_len=512 )
					lowerCamelCase_						=			2
					lowerCamelCase_						=			ds.make_sortish_sampler(lowercase						,  shuffle=lowercase )

					lowerCamelCase_						=			DataLoader(lowercase						,  batch_size=lowercase						,  collate_fn=ds.collate_fn						,  num_workers=2 )
					lowerCamelCase_						=			DataLoader(lowercase						,  batch_size=lowercase						,  collate_fn=ds.collate_fn						,  num_workers=2						,  sampler=lowercase )

					lowerCamelCase_						=			tokenizer.pad_token_id

					def count_pad_tokens(lowercase						,  lowercase="input_ids" ):
									return [batch[k].eq(lowercase ).sum().item() for batch in data_loader]

					assert sum(count_pad_tokens(lowercase						,  k="labels" ) ) < sum(count_pad_tokens(lowercase						,  k="labels" ) )
					assert sum(count_pad_tokens(lowercase ) ) < sum(count_pad_tokens(lowercase ) )
					assert len(lowercase ) == len(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase=1000						,  lowercase=128 )				->       str:
					if os.getenv("USE_REAL_DATA"						,  lowercase ):
									lowerCamelCase_						=			"examples/seq2seq/wmt_en_ro"
									lowerCamelCase_						=			max_len * 2 * 64
									if not Path(lowercase ).joinpath("train.len" ).exists():
													save_len_file(lowercase						,  lowercase )
					else:
									lowerCamelCase_						=			"examples/seq2seq/test_data/wmt_en_ro"
									lowerCamelCase_						=			max_len * 4
									save_len_file(lowercase						,  lowercase )

					lowerCamelCase_						=			AutoTokenizer.from_pretrained(lowercase )
					lowerCamelCase_						=			SeqaSeqDataset(
					    lowercase						,  data_dir=lowercase						,  type_path="train"						,  max_source_length=lowercase						,  max_target_length=lowercase						,  n_obs=lowercase						,  )
					return ds, max_tokens, tokenizer



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			self._get_dataset()
					lowerCamelCase_						=			set(DistributedSortishSampler(lowercase						,  256						,  num_replicas=2						,  rank=0						,  add_extra_examples=lowercase ) )
					lowerCamelCase_						=			set(DistributedSortishSampler(lowercase						,  256						,  num_replicas=2						,  rank=1						,  add_extra_examples=lowercase ) )
					assert idsa.intersection(lowercase ) == set()



	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]						,  )
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			AutoTokenizer.from_pretrained(lowercase						,  use_fast=lowercase )
					if tok_name == MBART_TINY:
									lowerCamelCase_						=			SeqaSeqDataset(
									    lowercase						,  data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )						,  type_path="train"						,  max_source_length=4						,  max_target_length=8						,  src_lang="EN"						,  tgt_lang="FR"						,  )
									lowerCamelCase_						=			train_dataset.dataset_kwargs
									assert "src_lang" in kwargs and "tgt_lang" in kwargs
					else:
									lowerCamelCase_						=			SeqaSeqDataset(
									    lowercase						,  data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )						,  type_path="train"						,  max_source_length=4						,  max_target_length=8						,  )
									lowerCamelCase_						=			train_dataset.dataset_kwargs
									assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
									assert len(lowercase ) == 1 if tok_name == BART_TINY else len(lowercase ) == 0




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__A				=1_6
__A				=3_2





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 1_6    ):
				lowerCamelCase_						=			AutoTokenizer.from_pretrained("bert-base-cased"    )
				lowerCamelCase_						=			load_dataset("glue"	, "mrpc"    )

				def tokenize_function(lowerCamelCase__    ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCamelCase_						=			tokenizer(examples["sentence1"]	, examples["sentence2"]	, truncation=lowerCamelCase__	, max_length=lowerCamelCase__    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCamelCase_						=			datasets.map(
								    lowerCamelCase__	, batched=lowerCamelCase__	, remove_columns=["idx", "sentence1", "sentence2"]	, )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCamelCase_						=			tokenized_datasets.rename_column("label"	, "labels"    )

				def collate_fn(lowerCamelCase__    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCamelCase_						=			1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCamelCase_						=			1_6
								elif accelerator.mixed_precision != "no":
												lowerCamelCase_						=			8
								else:
												lowerCamelCase_						=			None

								return tokenizer.pad(
								    lowerCamelCase__	, padding="longest"	, max_length=lowerCamelCase__	, pad_to_multiple_of=lowerCamelCase__	, return_tensors="pt"	, )

				# Instantiate dataloaders.
				lowerCamelCase_						=			DataLoader(
				    tokenized_datasets["train"]	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__    )
				lowerCamelCase_						=			DataLoader(
				    tokenized_datasets["validation"]	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__    )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			__A				=mocked_dataloaders  # noqa: F811





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"	, lowerCamelCase__    ) == "1":
								lowerCamelCase_						=			2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCamelCase_						=			Accelerator(
								    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with="all"	, project_dir=args.project_dir    )
				else:
								lowerCamelCase_						=			Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase_						=			config["lr"]
				lowerCamelCase_						=			int(config["num_epochs"]    )
				lowerCamelCase_						=			int(config["seed"]    )
				lowerCamelCase_						=			int(config["batch_size"]    )
				set_seed(lowerCamelCase__    )

				lowerCamelCase_	,    lowerCamelCase_						=			get_dataloaders(lowerCamelCase__	, lowerCamelCase__    )
				lowerCamelCase_						=			evaluate.load("glue"	, "mrpc"    )

				# If the batch size is too big we use gradient accumulation
				lowerCamelCase_						=			1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCamelCase_						=			batch_size // MAX_GPU_BATCH_SIZE
								lowerCamelCase_						=			MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCamelCase_						=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"	, return_dict=lowerCamelCase__    )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				lowerCamelCase_						=			model.to(accelerator.device    )

				# Instantiate optimizer
				lowerCamelCase_						=			AdamW(params=model.parameters()	, lr=lowerCamelCase__    )

				# Instantiate scheduler
				lowerCamelCase_						=			get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase__	, num_warmup_steps=1_0_0	, num_training_steps=(len(lowerCamelCase__    ) * num_epochs) // gradient_accumulation_steps	, )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			accelerator.prepare(
				    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCamelCase_						=			os.path.split(lowerCamelCase__    )[-1].split("."    )[0]
								accelerator.init_trackers(lowerCamelCase__	, lowerCamelCase__    )

				# Now we train the model
				for epoch in range(lowerCamelCase__    ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCamelCase_						=			0
								for step, batch in enumerate(lowerCamelCase__    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												lowerCamelCase_						=			model(**lowerCamelCase__    )
												lowerCamelCase_						=			outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCamelCase_						=			loss / gradient_accumulation_steps
												accelerator.backward(lowerCamelCase__    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(lowerCamelCase__    ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device    )
												with torch.no_grad():
																lowerCamelCase_						=			model(**lowerCamelCase__    )
												lowerCamelCase_						=			outputs.logits.argmax(dim=-1    )
												lowerCamelCase_	,    lowerCamelCase_						=			accelerator.gather_for_metrics((predictions, batch["labels"])    )
												metric.add_batch(
												    predictions=lowerCamelCase__	, references=lowerCamelCase__	, )

								lowerCamelCase_						=			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'	, lowerCamelCase__    )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(lowerCamelCase__    ),
												        "epoch": epoch,
												    }	, step=lowerCamelCase__	, )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			argparse.ArgumentParser(description="Simple example of training script."    )
				parser.add_argument(
				    "--mixed_precision"	, type=lowerCamelCase__	, default=lowerCamelCase__	, choices=["no", "fp16", "bf16", "fp8"]	, help="Whether to use mixed precision. Choose"
				    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
				    "and an Nvidia Ampere GPU."	, )
				parser.add_argument("--cpu"	, action="store_true"	, help="If passed, will train on the CPU."    )
				parser.add_argument(
				    "--with_tracking"	, action="store_true"	, help="Whether to load in all available experiment trackers from the environment and use them for logging."	, )
				parser.add_argument(
				    "--project_dir"	, type=lowerCamelCase__	, default="logs"	, help="Location on where to store experiment tracking logs` and relevent project information"	, )
				lowerCamelCase_						=			parser.parse_args()
				lowerCamelCase_						=			{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			main()


