import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


_lowerCamelCase   :				str     			=						16
_lowerCamelCase   :				Union[str, Any]     			=						32





def 		__lowerCamelCase							(UpperCAmelCase__       :							Accelerator ,	UpperCAmelCase__       :							int = 1_6 ,	UpperCAmelCase__       :							str = "bert-base-cased"					):
					SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    load_dataset("glue" ,	"mrpc"					)

					def tokenize_function(UpperCAmelCase__       :							Any					):
										# max_length=None => use the model max length (it's actually the default)
										SCREAMING_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
					SCREAMING_SNAKE_CASE           =    datasets.map(
					    UpperCAmelCase__ ,	batched=UpperCAmelCase__ ,	remove_columns=["idx", "sentence1", "sentence2"] ,	load_from_cache_file=UpperCAmelCase__					)

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

					def collate_fn(UpperCAmelCase__       :							Tuple					):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(UpperCAmelCase__ ,	padding="max_length" ,	max_length=1_2_8 ,	return_tensors="pt"					)
										return tokenizer.pad(UpperCAmelCase__ ,	padding="longest" ,	return_tensors="pt"					)

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

					return train_dataloader, eval_dataloader





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Optional[int]					):
					model.eval()
					SCREAMING_SNAKE_CASE           =    0
					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():
															SCREAMING_SNAKE_CASE           =    model(**UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    outputs.logits.argmax(dim=-1					)
										# It is slightly faster to call this once, than multiple times
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    accelerator.gather(
										    (predictions, batch["labels"])					)  # If we are in a multiprocess environment, the last batch has duplicates
										if accelerator.use_distributed:
															if step == len(UpperCAmelCase__					) - 1:
																				SCREAMING_SNAKE_CASE           =    predictions[: len(eval_dataloader.dataset					) - samples_seen]
																				SCREAMING_SNAKE_CASE           =    references[: len(eval_dataloader.dataset					) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=UpperCAmelCase__ ,	references=UpperCAmelCase__ ,	)

					SCREAMING_SNAKE_CASE           =    metric.compute()
					return eval_metric["accuracy"]





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							List[str]					):
					# Initialize accelerator
					SCREAMING_SNAKE_CASE           =    Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					SCREAMING_SNAKE_CASE           =    config["lr"]
					SCREAMING_SNAKE_CASE           =    int(config["num_epochs"]					)
					SCREAMING_SNAKE_CASE           =    int(config["seed"]					)
					SCREAMING_SNAKE_CASE           =    int(config["batch_size"]					)
					SCREAMING_SNAKE_CASE           =    args.model_name_or_path

					set_seed(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    get_dataloaders(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					SCREAMING_SNAKE_CASE           =    AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__ ,	return_dict=UpperCAmelCase__					)

					# Instantiate optimizer
					SCREAMING_SNAKE_CASE           =    (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					SCREAMING_SNAKE_CASE           =    optimizer_cls(params=model.parameters() ,	lr=UpperCAmelCase__					)

					if accelerator.state.deepspeed_plugin is not None:
										SCREAMING_SNAKE_CASE           =    accelerator.state.deepspeed_plugin.deepspeed_config[
										    "gradient_accumulation_steps"
										]
					else:
										SCREAMING_SNAKE_CASE           =    1
					SCREAMING_SNAKE_CASE           =    (len(UpperCAmelCase__					) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										SCREAMING_SNAKE_CASE           =    get_linear_schedule_with_warmup(
										    optimizer=UpperCAmelCase__ ,	num_warmup_steps=0 ,	num_training_steps=UpperCAmelCase__ ,	)
					else:
										SCREAMING_SNAKE_CASE           =    DummyScheduler(UpperCAmelCase__ ,	total_num_steps=UpperCAmelCase__ ,	warmup_num_steps=0					)

					# 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.
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    accelerator.prepare(
					    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					# We need to keep track of how many total steps we have iterated over
					SCREAMING_SNAKE_CASE           =    0
					# We also need to keep track of the stating epoch so files are named properly
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    evaluate.load("glue" ,	"mrpc"					)
					SCREAMING_SNAKE_CASE           =    num_epochs

					if args.partial_train_epoch is not None:
										SCREAMING_SNAKE_CASE           =    args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint					)
										SCREAMING_SNAKE_CASE           =    args.resume_from_checkpoint.split("epoch_"					)[1]
										SCREAMING_SNAKE_CASE           =    ""
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										SCREAMING_SNAKE_CASE           =    int(UpperCAmelCase__					) + 1
										SCREAMING_SNAKE_CASE           =    evaluation_loop(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										accelerator.print("resumed checkpoint performance:" ,	UpperCAmelCase__					)
										accelerator.print("resumed checkpoint's scheduler's lr:" ,	lr_scheduler.get_lr()[0]					)
										accelerator.print("resumed optimizers's lr:" ,	optimizer.param_groups[0]["lr"]					)
										with open(os.path.join(args.output_dir ,	F"state_{starting_epoch-1}.json"					) ,	"r"					) as f:
															SCREAMING_SNAKE_CASE           =    json.load(UpperCAmelCase__					)
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					SCREAMING_SNAKE_CASE           =    {}
					for epoch in range(UpperCAmelCase__ ,	UpperCAmelCase__					):
										model.train()
										for step, batch in enumerate(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    model(**UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    outputs.loss
															SCREAMING_SNAKE_CASE           =    loss / gradient_accumulation_steps
															accelerator.backward(UpperCAmelCase__					)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										SCREAMING_SNAKE_CASE           =    F"epoch_{epoch}"
										SCREAMING_SNAKE_CASE           =    os.path.join(args.output_dir ,	UpperCAmelCase__					)
										accelerator.save_state(UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    evaluation_loop(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    accuracy
										SCREAMING_SNAKE_CASE           =    lr_scheduler.get_lr()[0]
										SCREAMING_SNAKE_CASE           =    optimizer.param_groups[0]["lr"]
										SCREAMING_SNAKE_CASE           =    epoch
										SCREAMING_SNAKE_CASE           =    overall_step
										accelerator.print(F"epoch {epoch}:" ,	UpperCAmelCase__					)

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir ,	F"state_{epoch}.json"					) ,	"w"					) as f:
																				json.dump(UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."					)
					parser.add_argument(
					    "--model_name_or_path" ,	type=UpperCAmelCase__ ,	default="bert-base-cased" ,	help="Path to pretrained model or model identifier from huggingface.co/models." ,	required=UpperCAmelCase__ ,	)
					parser.add_argument(
					    "--output_dir" ,	type=UpperCAmelCase__ ,	default="." ,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,	)
					parser.add_argument(
					    "--resume_from_checkpoint" ,	type=UpperCAmelCase__ ,	default=UpperCAmelCase__ ,	help="If the training should continue from a checkpoint folder." ,	)
					parser.add_argument(
					    "--partial_train_epoch" ,	type=UpperCAmelCase__ ,	default=UpperCAmelCase__ ,	help="If passed, the training will stop after this number of epochs." ,	)
					parser.add_argument(
					    "--num_epochs" ,	type=UpperCAmelCase__ ,	default=2 ,	help="Number of train epochs." ,	)
					SCREAMING_SNAKE_CASE           =    parser.parse_args()
					SCREAMING_SNAKE_CASE           =    {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}

					training_function(UpperCAmelCase__ ,	UpperCAmelCase__					)


if __name__ == "__main__":
							main()





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor


@require_vision
class   lowercase      (       unittest.TestCase       ):




							def 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE           =    [
												    "[UNK]",
												    "[CLS]",
												    "[SEP]",
												    "[PAD]",
												    "[MASK]",
												    "的",
												    "价",
												    "格",
												    "是",
												    "15",
												    "便",
												    "alex",
												    "##andra",
												    "，",
												    "。",
												    "-",
												    "t",
												    "shirt",
												]
												SCREAMING_SNAKE_CASE           =    os.path.join(self.tmpdirname		,							VOCAB_FILES_NAMES["vocab_file"] )
												with open(self.vocab_file		,							"w"		,							encoding="utf-8" ) as vocab_writer:
																	vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

												SCREAMING_SNAKE_CASE           =    {
												    "do_resize": True,
												    "size": {"height": 224, "width": 224},
												    "do_center_crop": True,
												    "crop_size": {"height": 18, "width": 18},
												    "do_normalize": True,
												    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
												    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
												    "do_convert_rgb": True,
												}
												SCREAMING_SNAKE_CASE           =    os.path.join(self.tmpdirname		,							_UpperCamelCase )
												with open(self.image_processor_file		,							"w"		,							encoding="utf-8" ) as fp:
																	json.dump(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							**_UpperCamelCase				:   str )   ->			int:



												'''simple docstring'''

												return BertTokenizer.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			str:



												'''simple docstring'''

												return BertTokenizerFast.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							**_UpperCamelCase				:   Optional[Any] )   ->			Any:



												'''simple docstring'''

												return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												shutil.rmtree(self.tmpdirname )




							def 		__snake_case(  self				:   Dict )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [np.random.randint(255		,							size=(3, 30, 400)		,							dtype=np.uinta )]

												SCREAMING_SNAKE_CASE           =    [Image.fromarray(np.moveaxis(_UpperCamelCase		,							0		,							-1 ) ) for x in image_inputs]

												return image_inputs




							def 		__snake_case(  self				:   List[str] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()
												SCREAMING_SNAKE_CASE           =    self.get_rust_tokenizer()
												SCREAMING_SNAKE_CASE           =    self.get_image_processor()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )
												processor_slow.save_pretrained(self.tmpdirname )
												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,							use_fast=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )
												processor_fast.save_pretrained(self.tmpdirname )
												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname )

												self.assertEqual(processor_slow.tokenizer.get_vocab()		,							tokenizer_slow.get_vocab() )
												self.assertEqual(processor_fast.tokenizer.get_vocab()		,							tokenizer_fast.get_vocab() )
												self.assertEqual(tokenizer_slow.get_vocab()		,							tokenizer_fast.get_vocab() )
												self.assertIsInstance(processor_slow.tokenizer		,							_UpperCamelCase )
												self.assertIsInstance(processor_fast.tokenizer		,							_UpperCamelCase )

												self.assertEqual(processor_slow.image_processor.to_json_string()		,							image_processor.to_json_string() )
												self.assertEqual(processor_fast.image_processor.to_json_string()		,							image_processor.to_json_string() )
												self.assertIsInstance(processor_slow.image_processor		,							_UpperCamelCase )
												self.assertIsInstance(processor_fast.image_processor		,							_UpperCamelCase )




							def 		__snake_case(  self				:   str )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,							image_processor=self.get_image_processor() )
												processor.save_pretrained(self.tmpdirname )

												SCREAMING_SNAKE_CASE           =    self.get_tokenizer(cls_token="(CLS)"		,							sep_token="(SEP)" )
												SCREAMING_SNAKE_CASE           =    self.get_image_processor(do_normalize=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(
												    self.tmpdirname		,							cls_token="(CLS)"		,							sep_token="(SEP)"		,							do_normalize=_UpperCamelCase )

												self.assertEqual(processor.tokenizer.get_vocab()		,							tokenizer_add_kwargs.get_vocab() )
												self.assertIsInstance(processor.tokenizer		,							_UpperCamelCase )

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




							def 		__snake_case(  self				:   List[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    image_processor(_UpperCamelCase		,							return_tensors="np" )
												SCREAMING_SNAKE_CASE           =    processor(images=_UpperCamelCase		,							return_tensors="np" )

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




							def 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase )

												for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]		,							encoded_processor[key] )




							def 		__snake_case(  self				:   Optional[Any] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"
												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase		,							images=_UpperCamelCase )

												self.assertListEqual(list(inputs.keys() )		,							["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )

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




							def 		__snake_case(  self				:   Optional[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

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

												SCREAMING_SNAKE_CASE           =    processor.batch_decode(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase )

												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase )





							def 		__snake_case(  self				:   int )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"
												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase		,							images=_UpperCamelCase )

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




