




"""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,
# 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				=							16
A				=							32

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase = 16				)      ->      str:


			"""simple docstring"""
			__UpperCAmelCase						:			Union[str, Any]												=       AutoTokenizer.from_pretrained("bert-base-cased"				)
			__UpperCAmelCase						:			Any												=       load_dataset("glue"					,							"mrpc"				)

			def tokenize_function(UpperCamelCase				):
						# max_length=None => use the model max length (it's actually the default)
						__UpperCAmelCase						:			Optional[Any]												=       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():
						__UpperCAmelCase						:			Union[str, Any]												=       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
			__UpperCAmelCase						:			Tuple												=       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.
						__UpperCAmelCase						:			Dict												=       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":
									__UpperCAmelCase						:			List[Any]												=       16
						elif accelerator.mixed_precision != "no":
									__UpperCAmelCase						:			Optional[int]												=       8
						else:
									__UpperCAmelCase						:			Any												=       None

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

			# Instantiate dataloaders.
			__UpperCAmelCase						:			Optional[int]												=       DataLoader(
			    tokenized_datasets["train"]					,							shuffle=UpperCAmelCase_					,							collate_fn=UpperCAmelCase_					,							batch_size=UpperCAmelCase_				)
			__UpperCAmelCase						:			List[str]												=       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				=							mocked_dataloaders  # noqa: F811

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS"					,							UpperCAmelCase_				) == "1":
						__UpperCAmelCase						:			Tuple												=       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:
						__UpperCAmelCase						:			str												=       Accelerator(
						    cpu=args.cpu					,							mixed_precision=args.mixed_precision					,							log_with="all"					,							project_dir=args.project_dir				)
			else:
						__UpperCAmelCase						:			int												=       Accelerator(cpu=args.cpu					,							mixed_precision=args.mixed_precision				)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__UpperCAmelCase						:			Optional[Any]												=       config['lr']
			__UpperCAmelCase						:			int												=       int(config["num_epochs"]				)
			__UpperCAmelCase						:			Union[str, Any]												=       int(config["seed"]				)
			__UpperCAmelCase						:			List[str]												=       int(config["batch_size"]				)
			set_seed(UpperCAmelCase_				)

			__UpperCAmelCase						:			Optional[Any]												=       get_dataloaders(UpperCAmelCase_					,							UpperCAmelCase_				)
			__UpperCAmelCase						:			int												=       evaluate.load("glue"					,							"mrpc"				)

			# If the batch size is too big we use gradient accumulation
			__UpperCAmelCase						:			Optional[int]												=       1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						__UpperCAmelCase						:			Union[str, Any]												=       batch_size // MAX_GPU_BATCH_SIZE
						__UpperCAmelCase						:			Optional[int]												=       MAX_GPU_BATCH_SIZE

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__UpperCAmelCase						:			Optional[int]												=       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).
			__UpperCAmelCase						:			Dict												=       model.to(accelerator.device				)

			# Instantiate optimizer
			__UpperCAmelCase						:			int												=       AdamW(params=model.parameters()					,							lr=UpperCAmelCase_				)

			# Instantiate scheduler
			__UpperCAmelCase						:			str												=       get_linear_schedule_with_warmup(
			    optimizer=UpperCAmelCase_					,							num_warmup_steps=100					,							num_training_steps=(len(UpperCAmelCase_				) * 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.
			__UpperCAmelCase						:			Dict												=       accelerator.prepare(
			    UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_				)

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

			# Now we train the model
			for epoch in range(UpperCAmelCase_				):
						model.train()
						# New Code #
						# For our tracking example, we will log the total loss of each epoch
						if args.with_tracking:
									__UpperCAmelCase						:			Dict												=       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				)
									__UpperCAmelCase						:			Union[str, Any]												=       model(**UpperCAmelCase_				)
									__UpperCAmelCase						:			Dict												=       outputs.loss
									# New Code #
									if args.with_tracking:
												total_loss += loss.detach().float()
									__UpperCAmelCase						:			Union[str, Any]												=       loss / gradient_accumulation_steps
									accelerator.backward(UpperCAmelCase_				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

						__UpperCAmelCase						:			Optional[Any]												=       metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"epoch {epoch}:"					,							UpperCAmelCase_				)

						# 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(UpperCAmelCase_				),
									        "epoch": epoch,
									    }					,							step=UpperCAmelCase_					,							)

    # 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        _UpperCamelCase     (      )      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			Union[str, Any]												=       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."					,							)
			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=UpperCAmelCase_					,							default="logs"					,							help="Location on where to store experiment tracking logs` and relevent project information"					,							)
			__UpperCAmelCase						:			Union[str, Any]												=       parser.parse_args()
			__UpperCAmelCase						:			Optional[Any]												=       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(UpperCAmelCase_					,							UpperCAmelCase_				)


if __name__ == "__main__":
					main()






'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.xglm.modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
      )





@require_tf
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   Dict					=     XGLMConfig
    lowerCamelCase    :   List[str]					=     {}
    lowerCamelCase    :   Union[str, Any]					=     'gelu'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=14     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=99     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	)							->	Any:
         __lowerCamelCase  :      int														=						parent
         __lowerCamelCase  :      Optional[int]														=						batch_size
         __lowerCamelCase  :      Optional[Any]														=						seq_length
         __lowerCamelCase  :      Optional[int]														=						is_training
         __lowerCamelCase  :      str														=						use_input_mask
         __lowerCamelCase  :      Dict														=						use_labels
         __lowerCamelCase  :      Union[str, Any]														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						d_model
         __lowerCamelCase  :      List[Any]														=						num_hidden_layers
         __lowerCamelCase  :      List[Any]														=						num_attention_heads
         __lowerCamelCase  :      Optional[Any]														=						ffn_dim
         __lowerCamelCase  :      List[Any]														=						activation_function
         __lowerCamelCase  :      List[Any]														=						activation_dropout
         __lowerCamelCase  :      List[Any]														=						attention_dropout
         __lowerCamelCase  :      Union[str, Any]														=						max_position_embeddings
         __lowerCamelCase  :      Tuple														=						initializer_range
         __lowerCamelCase  :      int														=						None
         __lowerCamelCase  :      int														=						0
         __lowerCamelCase  :      Tuple														=						2
         __lowerCamelCase  :      Tuple														=						1







    def  lowercase_							(				self      )							->	Any:
         return XGLMConfig.from_pretrained('facebook/xglm-564M'      )







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      Optional[Any]														=						tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )     ,	clip_value_min=0     ,	clip_value_max=3      )

         __lowerCamelCase  :      Optional[int]														=						None
         if self.use_input_mask:
              __lowerCamelCase  :      Any														=						random_attention_mask([self.batch_size, self.seq_length]      )

         __lowerCamelCase  :      str														=						self.get_config()

         __lowerCamelCase  :      List[Any]														=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]     ,	2      )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )







    def  lowercase_							(				self      )							->	Optional[int]:
         return XGLMConfig(
             vocab_size=self.vocab_size     ,	d_model=self.hidden_size     ,	num_layers=self.num_hidden_layers     ,	attention_heads=self.num_attention_heads     ,	ffn_dim=self.ffn_dim     ,	activation_function=self.activation_function     ,	activation_dropout=self.activation_dropout     ,	attention_dropout=self.attention_dropout     ,	max_position_embeddings=self.max_position_embeddings     ,	initializer_range=self.initializer_range     ,	use_cache=SCREAMING_SNAKE_CASE_     ,	bos_token_id=self.bos_token_id     ,	eos_token_id=self.eos_token_id     ,	pad_token_id=self.pad_token_id     ,	return_dict=SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[Any]														=						self.prepare_config_and_inputs()

         (
             (
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			
         )  :      str														=						config_and_inputs

         __lowerCamelCase  :      Union[str, Any]														=						{
             'input_ids': input_ids,
             'head_mask': head_mask,
         }

         return config, inputs_dict







@require_tf
class   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[Any]					=     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    lowerCamelCase    :   List[Any]					=     (TFXGLMForCausalLM,) if is_tf_available() else ()
    lowerCamelCase    :   Any					=     (
        {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    lowerCamelCase    :   List[Any]					=     False
    lowerCamelCase    :   Dict					=     False
    lowerCamelCase    :   Union[str, Any]					=     False







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      str														=						TFXGLMModelTester(self      )
         __lowerCamelCase  :      List[Any]														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	n_embd=37      )







    def  lowercase_							(				self      )							->	Dict:
         self.config_tester.run_common_tests()







    @slow
    def  lowercase_							(				self      )							->	Optional[int]:
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase  :      Optional[Any]														=						TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )







    @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'      )
    def  lowercase_							(				self      )							->	Any:
         super().test_resize_token_embeddings()







@require_tf
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    @slow
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=True      )							->	List[str]:
         __lowerCamelCase  :      Optional[Any]														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      int														=						tf.convert_to_tensor([[2, 2_68, 98_65]]     ,	dtype=tf.intaa      )  # The dog
         # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
         # fmt: off
         __lowerCamelCase  :      Optional[int]														=						[2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
         # fmt: on
         __lowerCamelCase  :      Any														=						model.generate(SCREAMING_SNAKE_CASE_     ,	do_sample=SCREAMING_SNAKE_CASE_     ,	num_beams=1      )
         if verify_outputs:
              self.assertListEqual(output_ids[0].numpy().tolist()     ,	SCREAMING_SNAKE_CASE_      )







    @slow
    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Any														=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      Tuple														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )

         tf.random.set_seed(0      )
         __lowerCamelCase  :      List[Any]														=						tokenizer('Today is a nice day and'     ,	return_tensors='tf'      )
         __lowerCamelCase  :      int														=						tokenized.input_ids
         # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
         with tf.device(':/CPU:0'      ):
              __lowerCamelCase  :      Tuple														=						model.generate(SCREAMING_SNAKE_CASE_     ,	do_sample=SCREAMING_SNAKE_CASE_     ,	seed=[7, 0]      )
         __lowerCamelCase  :      Optional[Any]														=						tokenizer.decode(output_ids[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Union[str, Any]														=						(
             'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
         )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    @slow
    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Tuple														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      Any														=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )

         __lowerCamelCase  :      Any														=						'left'

         # use different length sentences to test batching
         __lowerCamelCase  :      Any														=						[
             'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
             'left-padding, such as in batched generation. The output for the sequence below should be the same '
             'regardless of whether left padding is applied or not. When',
             'Hello, my dog is a little',
         ]

         __lowerCamelCase  :      Any														=						tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors='tf'     ,	padding=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[int]														=						inputs['input_ids']

         __lowerCamelCase  :      str														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	attention_mask=inputs['attention_mask']     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Optional[int]														=						tokenizer(sentences[0]     ,	return_tensors='tf'      ).input_ids
         __lowerCamelCase  :      int														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Optional[Any]														=						tokenizer(sentences[1]     ,	return_tensors='tf'      ).input_ids
         __lowerCamelCase  :      Optional[Any]														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.decode(output_non_padded[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.decode(output_padded[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[int]														=						[
             'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
             'left-padding, such as in batched generation. The output for the sequence below should be the same '
             'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
             'a single',
             'Hello, my dog is a little bit of a shy one, but he is very friendly',
         ]
         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	[non_padded_sentence, padded_sentence]      )






