

"""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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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  :					Tuple												= 16
__A  :					List[str]												= 32
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					# Initialize accelerator
					if args.with_tracking:
										A    =      Accelerator(
										    cpu=args.cpu    ,							mixed_precision=args.mixed_precision    ,							log_with="all"    ,							project_dir=args.project_dir   )
					else:
										A    =      Accelerator(cpu=args.cpu    ,							mixed_precision=args.mixed_precision   )

					if hasattr(args.checkpointing_steps    ,							"isdigit"   ):
										if args.checkpointing_steps == "epoch":
															A    =      args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															A    =      int(args.checkpointing_steps   )
										else:
															raise ValueError(
															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
					else:
										A    =      None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					A    =      config["lr"]
					A    =      int(config["num_epochs"]   )
					A    =      int(config["seed"]   )
					A    =      int(config["batch_size"]   )

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										A    =      os.path.split(lowercase__   )[-1].split("."   )[0]
										accelerator.init_trackers(lowercase__    ,							lowercase__   )

					A    =      AutoTokenizer.from_pretrained("bert-base-cased"   )
					A    =      load_dataset("glue"    ,							"mrpc"   )
					A    =      evaluate.load("glue"    ,							"mrpc"   )

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

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

					def collate_fn(lowercase__   ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										A    =      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":
															A    =      16
										elif accelerator.mixed_precision != "no":
															A    =      8
										else:
															A    =      None

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

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

					set_seed(lowercase__   )

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

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

					# Instantiate optimizer
					A    =      AdamW(params=model.parameters()    ,							lr=lowercase__   )

					# Instantiate scheduler
					A    =      get_linear_schedule_with_warmup(
					    optimizer=lowercase__    ,							num_warmup_steps=100    ,							num_training_steps=(len(lowercase__   ) * 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.
					A    ,					A    ,					A    ,					A    ,					A    =      accelerator.prepare(
					    lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   )

					# We need to keep track of how many total steps we have iterated over
					A    =      0
					# We also need to keep track of the stating epoch so files are named properly
					A    =      0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""   )
															accelerator.load_state(args.resume_from_checkpoint   )
															A    =      os.path.basename(args.resume_from_checkpoint   )
										else:
															# Get the most recent checkpoint
															A    =      [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
															dirs.sort(key=os.path.getctime   )
															A    =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										A    =      os.path.splitext(lowercase__   )[0]

										if "epoch" in training_difference:
															A    =      int(training_difference.replace("epoch_"    ,							""   )   ) + 1
															A    =      None
										else:
															A    =      int(training_difference.replace("step_"    ,							""   )   )
															A    =      resume_step // len(lowercase__   )
															resume_step -= starting_epoch * len(lowercase__   )

    # Now we train the model
					for epoch in range(lowercase__    ,							lowercase__   ):
										model.train()
										if args.with_tracking:
															A    =      0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															A    =      accelerator.skip_first_batches(lowercase__    ,							lowercase__   )
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															A    =      train_dataloader
										for step, batch in enumerate(lowercase__   ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device   )
															A    =      model(**lowercase__   )
															A    =      outputs.loss
															A    =      loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(lowercase__   )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(lowercase__    ,							lowercase__   ):
																				A    =      F"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														A    =      os.path.join(args.output_dir    ,							lowercase__   )
																									accelerator.save_state(lowercase__   )

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

										A    =      metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""    ,							lowercase__   )
										if args.with_tracking:
															accelerator.log(
															    {
															        "accuracy": eval_metric["accuracy"],
															        "f1": eval_metric["f1"],
															        "train_loss": total_loss.item() / len(lowercase__   ),
															        "epoch": epoch,
															    }    ,							step=lowercase__    ,							)

										if checkpointing_steps == "epoch":
															A    =      F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				A    =      os.path.join(args.output_dir    ,							lowercase__   )
															accelerator.save_state(lowercase__   )

					if args.with_tracking:
										accelerator.end_training()
def      __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A    =      argparse.ArgumentParser(description="Simple example of training script."   )
					parser.add_argument(
					    "--mixed_precision"    ,							type=lowercase__    ,							default=lowercase__    ,							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(
					    "--checkpointing_steps"    ,							type=lowercase__    ,							default=lowercase__    ,							help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,							)
					parser.add_argument(
					    "--resume_from_checkpoint"    ,							type=lowercase__    ,							default=lowercase__    ,							help="If the training should continue from a checkpoint folder."    ,							)
					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(
					    "--output_dir"    ,							type=lowercase__    ,							default="."    ,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,							)
					parser.add_argument(
					    "--project_dir"    ,							type=lowercase__    ,							default="logs"    ,							help="Location on where to store experiment tracking logs` and relevent project information"    ,							)
					A    =      parser.parse_args()
					A    =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(lowercase__    ,							lowercase__   )


if __name__ == "__main__":
	main()








"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:





	class     __UpperCamelCase				:


			@staticmethod
			def       SCREAMING_SNAKE_CASE__     (*__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	Any):
								pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class     __UpperCamelCase				(     unittest.TestCase		):
		SCREAMING_SNAKE_CASE              =					MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
		SCREAMING_SNAKE_CASE              =					TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
							A    =      ImageClassificationPipeline(model=__SCREAMING_SNAKE_CASE  ,  image_processor=__SCREAMING_SNAKE_CASE  ,  top_k=2)
							A    =      [
							    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
							    "http://images.cocodataset.org/val2017/000000039769.jpg",
							]
							return image_classifier, examples


		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")

							self.assertEqual(
							    __SCREAMING_SNAKE_CASE  ,  [
							        {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							    ]  ,  )

							import datasets

							A    =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,  "image"  ,  split="test")

							# Accepts URL + PIL.Image + lists
							A    =      image_classifier(
							    [
							        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        # RGBA
							        dataset[0]["file"],
							        # LA
							        dataset[1]["file"],
							        # L
							        dataset[2]["file"],
							    ])
							self.assertEqual(
							    __SCREAMING_SNAKE_CASE  ,  [
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							        [
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							            {"score": ANY(__SCREAMING_SNAKE_CASE), "label": ANY(__SCREAMING_SNAKE_CASE)},
							        ],
							    ]  ,  )


		@require_torch
		def       SCREAMING_SNAKE_CASE__     (self				:	str):
							A    =      "hf-internal-testing/tiny-random-vit"
							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE)

							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )

							A    =      image_classifier(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]  ,  top_k=2  ,  )
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
							    ]  ,  )


		@require_tf
		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
							A    =      "hf-internal-testing/tiny-random-vit"
							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE  ,  framework="tf")

							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )

							A    =      image_classifier(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]  ,  top_k=2  ,  )
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
							    ]  ,  )


		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							A    =      PreTrainedTokenizer()

							# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
							A    =      pipeline(
							    "image-classification"  ,  model="hf-internal-testing/tiny-random-vit"  ,  tokenizer=__SCREAMING_SNAKE_CASE)

							self.assertIs(image_classifier.tokenizer  ,  __SCREAMING_SNAKE_CASE)


		@slow
		@require_torch
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							# Perceiver is not tested by `run_pipeline_test` properly.
							# That is because the type of feature_extractor and model preprocessor need to be kept
							# in sync, which is not the case in the current design
							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-conv")
							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
							        {"score": 0.3_2_1, "label": "tiger cat"},
							        {"score": 0.0_5_0_2, "label": "Egyptian cat"},
							        {"score": 0.0_1_3_7, "label": "crib, cot"},
							        {"score": 0.0_0_7, "label": "radiator"},
							    ]  ,  )

							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-fourier")
							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
							        {"score": 0.1_3_0_9, "label": "tiger cat"},
							        {"score": 0.0_7_2_2, "label": "Egyptian cat"},
							        {"score": 0.0_7_0_7, "label": "remote control, remote"},
							        {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
							    ]  ,  )

							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-learned")
							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
							self.assertEqual(
							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
							        {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
							        {"score": 0.2_3_6_2, "label": "Egyptian cat"},
							        {"score": 0.1_8_5_6, "label": "tiger cat"},
							        {"score": 0.0_3_2_4, "label": "remote control, remote"},
							        {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
							    ]  ,  )





