




'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 		a							(  unittest.TestCase	):
				snake_case_           =						ViTImageProcessor if is_vision_available() else None


				@property
				def     A_					(						self       :   List[str]   ):
									return self.image_processor_tester.prepare_image_processor_dict()


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							(3, 32, 128)
									snake_case_		     =							tempfile.mkdtemp()

									# fmt: off
									snake_case_		     =							['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
									# fmt: on
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )

									snake_case_		     =							os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''vocab_file''']   )
									with open(self.vocab_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														fp.write(json.dumps(lowercase_   ) + '''\n'''   )

									snake_case_		     =							{
									    '''do_normalize''': False,
									    '''do_resize''': True,
									    '''image_processor_type''': '''ViTImageProcessor''',
									    '''resample''': 3,
									    '''size''': {'''height''': 32, '''width''': 128},
									}
									snake_case_		     =							os.path.join(self.tmpdirname			,							lowercase_   )
									with open(self.image_processor_file			,							'''w'''			,							encoding='''utf-8'''   ) as fp:
														json.dump(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any			,							**lowercase_       :   str   ):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   List[str]			,							**lowercase_       :   List[Any]   ):
									return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									shutil.rmtree(self.tmpdirname   )


				def     A_					(						self       :   int   ):
									snake_case_		     =							np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta   )

									snake_case_		     =							Image.fromarray(np.moveaxis(lowercase_			,							0			,							-1   )   )

									return image_input


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )
									snake_case_		     =							MgpstrProcessor.from_pretrained(self.tmpdirname			,							use_fast=lowercase_   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.get_tokenizer()
									snake_case_		     =							self.get_image_processor()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )
									processor.save_pretrained(self.tmpdirname   )

									snake_case_		     =							self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''   )
									snake_case_		     =							self.get_image_processor(do_normalize=lowercase_			,							padding_value=1.0   )

									snake_case_		     =							MgpstrProcessor.from_pretrained(
									    self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=lowercase_			,							padding_value=1.0   )

									self.assertEqual(processor.char_tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()   )
									self.assertIsInstance(processor.char_tokenizer			,							lowercase_   )

									self.assertEqual(processor.image_processor.to_json_string()			,							image_processor_add_kwargs.to_json_string()   )
									self.assertIsInstance(processor.image_processor			,							lowercase_   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							image_processor(lowercase_			,							return_tensors='''np'''   )
									snake_case_		     =							processor(images=lowercase_			,							return_tensors='''np'''   )

									for key in input_image_proc.keys():
														self.assertAlmostEqual(input_image_proc[key].sum()			,							input_processor[key].sum()			,							delta=1e-2   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''

									snake_case_		     =							processor(text=lowercase_   )

									snake_case_		     =							tokenizer(lowercase_   )
									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							'''test'''
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							['''pixel_values''', '''labels''']   )

									# test if it raises when no input is passed
									with pytest.raises(lowercase_   ):
														processor()


				def     A_					(						self       :   int   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

									snake_case_		     =							processor.char_decode(lowercase_   )
									snake_case_		     =							tokenizer.batch_decode(lowercase_   )
									snake_case_		     =							[seq.replace(''' '''			,							''''''   ) for seq in decoded_tok]

									self.assertListEqual(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							None
									snake_case_		     =							self.prepare_image_inputs()

									snake_case_		     =							processor(text=lowercase_			,							images=lowercase_   )

									self.assertListEqual(list(inputs.keys()   )			,							processor.model_input_names   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.get_image_processor()
									snake_case_		     =							self.get_tokenizer()

									snake_case_		     =							MgpstrProcessor(tokenizer=lowercase_			,							image_processor=lowercase_   )

									snake_case_		     =							torch.randn(1			,							27			,							38   )
									snake_case_		     =							torch.randn(1			,							27			,							5_0257   )
									snake_case_		     =							torch.randn(1			,							27			,							3_0522   )

									snake_case_		     =							processor.batch_decode([char_input, bpe_input, wp_input]   )

									self.assertListEqual(list(results.keys()   )			,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']   )







'''simple docstring'''

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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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    :							str										=    16
a    :							Union[str, Any]										=    32


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 16      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							AutoTokenizer.from_pretrained('''bert-base-cased'''      )
					snake_case_		     =							load_dataset('''glue''', '''mrpc'''      )

					def tokenize_function(__UpperCAmelCase      ):
										# max_length=None => use the model max length (it's actually the default)
										snake_case_		     =							tokenizer(examples['''sentence1'''], examples['''sentence2'''], truncation=__UpperCAmelCase, max_length=__UpperCAmelCase      )
										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():
										snake_case_		     =							datasets.map(
										    __UpperCAmelCase, batched=__UpperCAmelCase, 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
					snake_case_		     =							tokenized_datasets.rename_column('''label''', '''labels'''      )

					def collate_fn(__UpperCAmelCase      ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										snake_case_		     =							128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															snake_case_		     =							16
										elif accelerator.mixed_precision != "no":
															snake_case_		     =							8
										else:
															snake_case_		     =							None

										return tokenizer.pad(
										    __UpperCAmelCase, padding='''longest''', max_length=__UpperCAmelCase, pad_to_multiple_of=__UpperCAmelCase, return_tensors='''pt''', )

					# Instantiate dataloaders.
					snake_case_		     =							DataLoader(
					    tokenized_datasets['''train'''], shuffle=__UpperCAmelCase, collate_fn=__UpperCAmelCase, batch_size=__UpperCAmelCase      )
					snake_case_		     =							DataLoader(
					    tokenized_datasets['''validation'''], shuffle=__UpperCAmelCase, collate_fn=__UpperCAmelCase, batch_size=__UpperCAmelCase      )

					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    :							Optional[Any]										=    mocked_dataloaders  # noqa: F811


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					if os.environ.get('''TESTING_MOCKED_DATALOADERS''', __UpperCAmelCase      ) == "1":
										snake_case_		     =							2
					# New Code #
					snake_case_		     =							int(args.gradient_accumulation_steps      )
					snake_case_		     =							int(args.local_sgd_steps      )
					# Initialize accelerator
					snake_case_		     =							Accelerator(
					    cpu=args.cpu, mixed_precision=args.mixed_precision, gradient_accumulation_steps=__UpperCAmelCase      )
					if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
										raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''      )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_		     =							config['''lr''']
					snake_case_		     =							int(config['''num_epochs''']      )
					snake_case_		     =							int(config['''seed''']      )
					snake_case_		     =							int(config['''batch_size''']      )

					snake_case_		     =							evaluate.load('''glue''', '''mrpc'''      )

					set_seed(__UpperCAmelCase      )
					snake_case_     ,snake_case_		     =							get_dataloaders(__UpperCAmelCase, __UpperCAmelCase      )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					snake_case_		     =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''', return_dict=__UpperCAmelCase      )

					# 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).
					snake_case_		     =							model.to(accelerator.device      )

					# Instantiate optimizer
					snake_case_		     =							AdamW(params=model.parameters(), lr=__UpperCAmelCase      )

					# Instantiate scheduler
					snake_case_		     =							get_linear_schedule_with_warmup(
					    optimizer=__UpperCAmelCase, num_warmup_steps=100, num_training_steps=(len(__UpperCAmelCase      ) * num_epochs), )

					# 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.
					snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							accelerator.prepare(
					    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

					# Now we train the model
					for epoch in range(__UpperCAmelCase      ):
										model.train()
										with LocalSGD(
										    accelerator=__UpperCAmelCase, model=__UpperCAmelCase, local_sgd_steps=__UpperCAmelCase, enabled=local_sgd_steps is not None      ) as local_sgd:
															for step, batch in enumerate(__UpperCAmelCase      ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device      )
																				# New code #
																				# We use the new `accumulate` context manager to perform gradient accumulation
																				# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																				with accelerator.accumulate(__UpperCAmelCase      ):
																									snake_case_		     =							model(**__UpperCAmelCase      )
																									snake_case_		     =							output.loss
																									accelerator.backward(__UpperCAmelCase      )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

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

										snake_case_		     =							metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:", __UpperCAmelCase      )


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							argparse.ArgumentParser(description='''Simple example of training script.'''      )
					parser.add_argument(
					    '''--mixed_precision''', type=__UpperCAmelCase, default=__UpperCAmelCase, 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.''', )
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps''', type=__UpperCAmelCase, default=1, help='''The number of minibatches to be ran before gradients are accumulated.''', )
					parser.add_argument(
					    '''--local_sgd_steps''', type=__UpperCAmelCase, default=8, help='''Number of local SGD steps or None to disable local SGD'''      )
					parser.add_argument('''--cpu''', action='''store_true''', help='''If passed, will train on the CPU.'''      )
					snake_case_		     =							parser.parse_args()
					snake_case_		     =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__UpperCAmelCase, __UpperCAmelCase      )


if __name__ == "__main__":
							main()

