
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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


lowercase   =  1_6
lowercase   =  3_2



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Accelerator			,				UpperCAmelCase__				:      int = 1_6       )  ->    List[str]:
							lowerCamelCase_							  =					AutoTokenizer.from_pretrained("""bert-base-cased"""       )
							lowerCamelCase_							  =					load_dataset("""glue"""			,				"""mrpc"""       )

							def tokenize_function(UpperCAmelCase__				:      Any       ):
														# 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(UpperCAmelCase__				:      Dict       ):
														# 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

		lowercase   =  mocked_dataloaders  # noqa: F811



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    List[str]:
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,				lowerCamelCase_       ) == "1":
														lowerCamelCase_							  =					2
							# Initialize accelerator
							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"""]       )

							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

							set_seed(lowerCamelCase_       )
							lowerCamelCase_  ,   lowerCamelCase_							  =					get_dataloaders(lowerCamelCase_			,				lowerCamelCase_       )
							# 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_       )

							# Now we train the model
							for epoch in range(lowerCamelCase_       ):
														model.train()
														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
																					lowerCamelCase_							  =					loss / gradient_accumulation_steps
																					accelerator.backward(lowerCamelCase_       )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														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       )
																					with torch.no_grad():
																												lowerCamelCase_							  =					model(**lowerCamelCase_       )
																					lowerCamelCase_							  =					outputs.logits.argmax(dim=-1       )
																					lowerCamelCase_  ,   lowerCamelCase_							  =					accelerator.gather((predictions, batch["""labels"""])       )
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(lowerCamelCase_       ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			lowerCamelCase_							  =					predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																																			lowerCamelCase_							  =					references[: len(eval_dataloader.dataset       ) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # 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_       )



def      __lowerCAmelCase						(							)  ->    str:
							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."""       )
							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()







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase        =  logging.get_logger(__name__)

lowercase        =  {
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''wav2vec2'''

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=3_2   ,					__UpperCamelCase					:     List[str]=7_6_8   ,					__UpperCamelCase					:     Tuple=1_2   ,					__UpperCamelCase					:     str=1_2   ,					__UpperCamelCase					:     Any=3_0_7_2   ,					__UpperCamelCase					:     Tuple="gelu"   ,					__UpperCamelCase					:     Tuple=0.1   ,					__UpperCamelCase					:     Any=0.1   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     List[Any]=0.0   ,					__UpperCamelCase					:     str=0.0   ,					__UpperCamelCase					:     str=0.1   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     Dict=0.02   ,					__UpperCamelCase					:     int=1E-5   ,					__UpperCamelCase					:     Dict="group"   ,					__UpperCamelCase					:     Tuple="gelu"   ,					__UpperCamelCase					:     Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)   ,					__UpperCamelCase					:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,					__UpperCamelCase					:     int=(1_0, 3, 3, 3, 3, 2, 2)   ,					__UpperCamelCase					:     List[Any]=False   ,					__UpperCamelCase					:     Tuple=1_2_8   ,					__UpperCamelCase					:     Any=1_6   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Dict=0.05   ,					__UpperCamelCase					:     Union[str, Any]=1_0   ,					__UpperCamelCase					:     int=2   ,					__UpperCamelCase					:     List[Any]=0.0   ,					__UpperCamelCase					:     List[Any]=1_0   ,					__UpperCamelCase					:     List[str]=0   ,					__UpperCamelCase					:     Optional[Any]=3_2_0   ,					__UpperCamelCase					:     Optional[int]=2   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     int=1_0_0   ,					__UpperCamelCase					:     int=2_5_6   ,					__UpperCamelCase					:     str=2_5_6   ,					__UpperCamelCase					:     Dict=0.1   ,					__UpperCamelCase					:     Union[str, Any]="sum"   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     Dict=2_5_6   ,					__UpperCamelCase					:     List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)   ,					__UpperCamelCase					:     int=(5, 3, 3, 1, 1)   ,					__UpperCamelCase					:     Optional[Any]=(1, 2, 3, 1, 1)   ,					__UpperCamelCase					:     Dict=5_1_2   ,					__UpperCamelCase					:     Any=0   ,					__UpperCamelCase					:     Union[str, Any]=1   ,					__UpperCamelCase					:     List[str]=2   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=2   ,					__UpperCamelCase					:     Optional[Any]=3   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     Tuple=None   ,					**__UpperCamelCase					:     Tuple   ,					):
												super().__init__(**__UpperCamelCase   ,					pad_token_id=__UpperCamelCase   ,					bos_token_id=__UpperCamelCase   ,					eos_token_id=__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					feat_extract_norm
												lowerCamelCase_							  =					feat_extract_activation
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					conv_bias
												lowerCamelCase_							  =					num_conv_pos_embeddings
												lowerCamelCase_							  =					num_conv_pos_embedding_groups
												lowerCamelCase_							  =					len(self.conv_dim   )
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					intermediate_size
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					hidden_dropout
												lowerCamelCase_							  =					attention_dropout
												lowerCamelCase_							  =					activation_dropout
												lowerCamelCase_							  =					feat_proj_dropout
												lowerCamelCase_							  =					final_dropout
												lowerCamelCase_							  =					layerdrop
												lowerCamelCase_							  =					layer_norm_eps
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					do_stable_layer_norm
												lowerCamelCase_							  =					use_weighted_layer_sum

												if (
												    (len(self.conv_stride   ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim   ) != self.num_feat_extract_layers)
												):
																			raise ValueError(
																			    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																			    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																			    F''' {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,'''
																			    F''' `len(config.conv_kernel) = {len(self.conv_kernel   )}`.'''   )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												lowerCamelCase_							  =					apply_spec_augment
												lowerCamelCase_							  =					mask_time_prob
												lowerCamelCase_							  =					mask_time_length
												lowerCamelCase_							  =					mask_time_min_masks
												lowerCamelCase_							  =					mask_feature_prob
												lowerCamelCase_							  =					mask_feature_length
												lowerCamelCase_							  =					mask_feature_min_masks

												# parameters for pretraining with codevector quantized representations
												lowerCamelCase_							  =					num_codevectors_per_group
												lowerCamelCase_							  =					num_codevector_groups
												lowerCamelCase_							  =					contrastive_logits_temperature
												lowerCamelCase_							  =					feat_quantizer_dropout
												lowerCamelCase_							  =					num_negatives
												lowerCamelCase_							  =					codevector_dim
												lowerCamelCase_							  =					proj_codevector_dim
												lowerCamelCase_							  =					diversity_loss_weight

												# ctc loss
												lowerCamelCase_							  =					ctc_loss_reduction
												lowerCamelCase_							  =					ctc_zero_infinity

												# adapter
												lowerCamelCase_							  =					add_adapter
												lowerCamelCase_							  =					adapter_kernel_size
												lowerCamelCase_							  =					adapter_stride
												lowerCamelCase_							  =					num_adapter_layers
												lowerCamelCase_							  =					output_hidden_size or hidden_size
												lowerCamelCase_							  =					adapter_attn_dim

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												lowerCamelCase_							  =					classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					xvector_output_dim



					@property
					def    lowercase__      (	self					:     Dict   ):
												return functools.reduce(operator.mul   ,					self.conv_stride   ,					1   )





