'''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 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_     		= 16
lowercase_     		= 32



def 							UpperCamelCase__			(			a__			,      a__ = 1_6      ):


						'''simple docstring'''






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

						def tokenize_function(a__      ):
												# max_length=None => use the model max length (it's actually the default)
												_lowerCAmelCase										=tokenizer(examples['sentence1']			,      examples['sentence2']			,      truncation=a__			,      max_length=a__      )
												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(
												    a__			,      batched=a__			,      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(a__      ):
												# 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(
												    a__			,      padding='longest'			,      max_length=a__			,      pad_to_multiple_of=a__			,      return_tensors='pt'			,      )

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

						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 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						if os.environ.get('TESTING_MOCKED_DATALOADERS'			,      a__      ) == "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(a__      )
						_lowerCAmelCase						,				_lowerCAmelCase										=get_dataloaders(a__			,      a__      )
						# 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=a__      )

						# 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=a__      )

						# Instantiate scheduler
						_lowerCAmelCase										=get_linear_schedule_with_warmup(
						    optimizer=a__			,      num_warmup_steps=1_0_0			,      num_training_steps=(len(a__      ) * 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(
						    a__			,      a__			,      a__			,      a__			,      a__      )

						# Now we train the model
						for epoch in range(a__      ):
												model.train()
												for step, batch in enumerate(a__      ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device      )
																		_lowerCAmelCase										=model(**a__      )
																		_lowerCAmelCase										=outputs.loss
																		_lowerCAmelCase										=loss / gradient_accumulation_steps
																		accelerator.backward(a__      )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												_lowerCAmelCase										=0
												for step, batch in enumerate(a__      ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device      )
																		with torch.no_grad():
																								_lowerCAmelCase										=model(**a__      )
																		_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(a__      ) - 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=a__			,      references=a__			,      )

												_lowerCAmelCase										=metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''			,      a__      )



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=argparse.ArgumentParser(description='Simple example of training script.'      )
						parser.add_argument(
						    '--mixed_precision'			,      type=a__			,      default=a__			,      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(a__			,      a__      )


if __name__ == "__main__":
							main()






'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase):







		"""simple docstring"""






		@register_to_config
		def __init__(   self		,						__A = 128		,						__A = 256		,						__A = 2_000.0		,						__A = 768		,						__A = 12		,						__A = 12		,						__A = 64		,						__A = 2048		,						__A = 0.1		,						) ->     str:
								super().__init__()

								_lowerCAmelCase										=nn.Sequential(
								    nn.Linear(__A		,						d_model * 4		,						bias=__A				)		,						nn.SiLU()		,						nn.Linear(d_model * 4		,						d_model * 4		,						bias=__A				)		,						nn.SiLU()		,						)

								_lowerCAmelCase										=nn.Embedding(__A		,						__A				)
								_lowerCAmelCase										=False

								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)

								_lowerCAmelCase										=nn.Dropout(p=__A				)

								_lowerCAmelCase										=nn.ModuleList()
								for lyr_num in range(__A				):
														# FiLM conditional T5 decoder
														_lowerCAmelCase										=DecoderLayer(d_model=__A		,						d_kv=__A		,						num_heads=__A		,						d_ff=__A		,						dropout_rate=__A				)
														self.decoders.append(__A				)

								_lowerCAmelCase										=TaLayerNorm(__A				)

								_lowerCAmelCase										=nn.Dropout(p=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Any:
								_lowerCAmelCase										=torch.mul(query_input.unsqueeze(-1				)		,						key_input.unsqueeze(-2				)				)
								return mask.unsqueeze(-3				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								_lowerCAmelCase										=get_timestep_embedding(
								    decoder_noise_time * self.config.max_decoder_noise_time		,						embedding_dim=self.config.d_model		,						max_period=self.config.max_decoder_noise_time		,						).to(dtype=self.dtype				)

								_lowerCAmelCase										=self.conditioning_emb(__A				).unsqueeze(1				)

								assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

								_lowerCAmelCase										=decoder_input_tokens.shape[1]

								# If we want to use relative positions for audio context, we can just offset
								# this sequence by the length of encodings_and_masks.
								_lowerCAmelCase										=torch.broadcast_to(
								    torch.arange(__A		,						device=decoder_input_tokens.device				)		,						(batch, seq_length)		,						)

								_lowerCAmelCase										=self.position_encoding(__A				)

								_lowerCAmelCase										=self.continuous_inputs_projection(__A				)
								inputs += position_encodings
								_lowerCAmelCase										=self.dropout(__A				)

								# decoder: No padding present.
								_lowerCAmelCase										=torch.ones(
								    decoder_input_tokens.shape[:2]		,						device=decoder_input_tokens.device		,						dtype=inputs.dtype				)

								# Translate encoding masks to encoder-decoder masks.
								_lowerCAmelCase										=[(x, self.encoder_decoder_mask(__A		,						__A				)) for x, y in encodings_and_masks]

								# cross attend style: concat encodings
								_lowerCAmelCase										=torch.cat([x[0] for x in encodings_and_encdec_masks]		,						dim=1				)
								_lowerCAmelCase										=torch.cat([x[1] for x in encodings_and_encdec_masks]		,						dim=-1				)

								for lyr in self.decoders:
														_lowerCAmelCase										=lyr(
														    __A		,						conditioning_emb=__A		,						encoder_hidden_states=__A		,						encoder_attention_mask=__A		,						)[0]

								_lowerCAmelCase										=self.decoder_norm(__A				)
								_lowerCAmelCase										=self.post_dropout(__A				)

								_lowerCAmelCase										=self.spec_out(__A				)
								return spec_out

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A=1E-6				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.ModuleList()

								# cond self attention: layer 0
								self.layer.append(
								    TaLayerSelfAttentionCond(d_model=__A		,						d_kv=__A		,						num_heads=__A		,						dropout_rate=__A				)				)

								# cross attention: layer 1
								self.layer.append(
								    TaLayerCrossAttention(
								        d_model=__A		,						d_kv=__A		,						num_heads=__A		,						dropout_rate=__A		,						layer_norm_epsilon=__A		,						)				)

								# Film Cond MLP + dropout: last layer
								self.layer.append(
								    TaLayerFFCond(d_model=__A		,						d_ff=__A		,						dropout_rate=__A		,						layer_norm_epsilon=__A				)				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						) ->     Any:
								_lowerCAmelCase										=self.layer[0](
								    __A		,						conditioning_emb=__A		,						attention_mask=__A		,						)

								if encoder_hidden_states is not None:
														_lowerCAmelCase										=torch.where(encoder_attention_mask > 0		,						0		,						-1E10				).to(
														    encoder_hidden_states.dtype				)

														_lowerCAmelCase										=self.layer[1](
														    __A		,						key_value_states=__A		,						attention_mask=__A		,						)

								# Apply Film Conditional Feed Forward layer
								_lowerCAmelCase										=self.layer[-1](__A		,						__A				)

								return (hidden_states,)

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaLayerNorm(__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     List[Any]:
								# pre_self_attention_layer_norm
								_lowerCAmelCase										=self.layer_norm(__A				)

								if conditioning_emb is not None:
														_lowerCAmelCase										=self.FiLMLayer(__A		,						__A				)

								# Self-attention block
								_lowerCAmelCase										=self.attention(__A				)

								_lowerCAmelCase										=hidden_states + self.dropout(__A				)

								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								super().__init__()
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     Tuple:
								_lowerCAmelCase										=self.layer_norm(__A				)
								_lowerCAmelCase										=self.attention(
								    __A		,						encoder_hidden_states=__A		,						attention_mask=attention_mask.squeeze(1				)		,						)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return layer_output

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaDenseGatedActDense(d_model=__A		,						d_ff=__A		,						dropout_rate=__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     List[Any]:
								_lowerCAmelCase										=self.layer_norm(__A				)
								if conditioning_emb is not None:
														_lowerCAmelCase										=self.film(__A		,						__A				)

								_lowerCAmelCase										=self.DenseReluDense(__A				)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)
								_lowerCAmelCase										=NewGELUActivation()






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=self.act(self.wi_a(__A				)				)
								_lowerCAmelCase										=self.wi_a(__A				)
								_lowerCAmelCase										=hidden_gelu * hidden_linear
								_lowerCAmelCase										=self.dropout(__A				)

								_lowerCAmelCase										=self.wo(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=1E-6				) ->     int:
								super().__init__()
								_lowerCAmelCase										=nn.Parameter(torch.ones(__A				)				)
								_lowerCAmelCase										=eps






		def 		UpperCamelCase__       (   self		,						__A				) ->     Dict:
								# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
								# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
								# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
								# half-precision inputs is done in fp32

								_lowerCAmelCase										=hidden_states.to(torch.floataa				).pow(2				).mean(-1		,						keepdim=__A				)
								_lowerCAmelCase										=hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

								# convert into half-precision if necessary
								if self.weight.dtype in [torch.floataa, torch.bfloataa]:
														_lowerCAmelCase										=hidden_states.to(self.weight.dtype				)

								return self.weight * hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A				) ->     torch.Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.044_715 * torch.pow(__A		,						3.0				))				))

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						out_features * 2		,						bias=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase										=self.scale_bias(__A				)
								_lowerCAmelCase						,				_lowerCAmelCase										=torch.chunk(__A		,						2		,						-1				)
								_lowerCAmelCase										=x * (1 + scale) + shift
								return x





