





import argparse

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)
#
# 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  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = 16		)    ->       Optional[Any]:
  lowercase__           =  AutoTokenizer.from_pretrained('bert-base-cased'		)
  lowercase__           =  load_dataset('glue'		,						'mrpc'		)

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

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

    return tokenizer.pad(
        _SCREAMING_SNAKE_CASE		,						padding='longest'		,						max_length=_SCREAMING_SNAKE_CASE		,						pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,						return_tensors='pt'		,						)

  # Instantiate dataloaders.
  lowercase__           =  DataLoader(
      tokenized_datasets['train']		,						shuffle=_SCREAMING_SNAKE_CASE		,						collate_fn=_SCREAMING_SNAKE_CASE		,						batch_size=_SCREAMING_SNAKE_CASE		,						drop_last=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  DataLoader(
      tokenized_datasets['validation']		,						shuffle=_SCREAMING_SNAKE_CASE		,						collate_fn=_SCREAMING_SNAKE_CASE		,						batch_size=_SCREAMING_SNAKE_CASE		,						drop_last=(accelerator.mixed_precision == 'fp8')		,						)

  return train_dataloader, eval_dataloader




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  lowercase__           =  Accelerator(cpu=args.cpu		,						mixed_precision=args.mixed_precision		)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase__           =  config['lr']
  lowercase__           =  int(config['num_epochs']		)
  lowercase__           =  int(config['seed']		)
  lowercase__           =  int(config['batch_size']		)

  lowercase__           =  evaluate.load('glue'		,						'mrpc'		)

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

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

  # 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).
  lowercase__           =  model.to(accelerator.device		)
  # Instantiate optimizer
  lowercase__           =  AdamW(params=model.parameters()		,						lr=_SCREAMING_SNAKE_CASE		)

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

  lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__           =  accelerator.prepare(
      _SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

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

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

    lowercase__           =  metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"""epoch {epoch}:"""		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  ()    ->       Tuple:
  lowercase__           =  argparse.ArgumentParser(description='Simple example of training script.'		)
  parser.add_argument(
      '--mixed_precision'		,						type=_SCREAMING_SNAKE_CASE		,						default=_SCREAMING_SNAKE_CASE		,						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.'		)
  lowercase__           =  parser.parse_args()
  lowercase__           =  {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      main()









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       (UpperCAmelCase      ,     UpperCAmelCase				):


   @register_to_config
   def __init__(   self      :    str      ,					a      :    int = 128      ,					a      :    int = 256      ,					a      :    float = 2000.0      ,					a      :    int = 768      ,					a      :    int = 12      ,					a      :    int = 12      ,					a      :    int = 64      ,					a      :    int = 2_048      ,					a      :    float = 0.1      ,					)->       Any:





     """simple docstring"""







     super().__init__()

     lowercase__           =  nn.Sequential(
         nn.Linear(a      ,					d_model * 4      ,					bias=a	)      ,					nn.SiLU()      ,					nn.Linear(d_model * 4      ,					d_model * 4      ,					bias=a	)      ,					nn.SiLU()      ,					)

     lowercase__           =  nn.Embedding(a      ,					a	)
     lowercase__           =  False

     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)

     lowercase__           =  nn.Dropout(p=a	)

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

     lowercase__           =  TaLayerNorm(a	)

     lowercase__           =  nn.Dropout(p=a	)
     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    int      ,					a      :    List[Any]	)->       str:





     """simple docstring"""







     lowercase__           =  torch.mul(query_input.unsqueeze(-1	)      ,					key_input.unsqueeze(-2	)	)
     return mask.unsqueeze(-3	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    Dict      ,					a      :    int      ,					a      :    int	)->       int:





     """simple docstring"""







     lowercase__      ,   lowercase__      ,   lowercase__           =  decoder_input_tokens.shape
     assert decoder_noise_time.shape == (batch,)

     # decoder_noise_time is in [0, 1), so rescale to expected timing range.
     lowercase__           =  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	)

     lowercase__           =  self.conditioning_emb(a	).unsqueeze(1	)

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

     lowercase__           =  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.
     lowercase__           =  torch.broadcast_to(
         torch.arange(a      ,					device=decoder_input_tokens.device	)      ,					(batch, seq_length)      ,					)

     lowercase__           =  self.position_encoding(a	)

     lowercase__           =  self.continuous_inputs_projection(a	)
     inputs += position_encodings
     lowercase__           =  self.dropout(a	)

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

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

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

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

     lowercase__           =  self.decoder_norm(a	)
     lowercase__           =  self.post_dropout(a	)

     lowercase__           =  self.spec_out(a	)
     return spec_out






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Dict      ,					a      :    List[Any]      ,					a      :    str      ,					a      :    Union[str, Any]      ,					a      :    Optional[int]      ,					a      :    Optional[int]      ,					a      :    Tuple=1E-6	)->       Union[str, Any]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    str      ,					a      :    Union[str, Any]=None      ,					a      :    Dict=None      ,					a      :    Union[str, Any]=None      ,					a      :    List[Any]=None      ,					a      :    List[str]=None      ,					)->       List[Any]:





     """simple docstring"""







     lowercase__           =  self.layer[0](
         a      ,					conditioning_emb=a      ,					attention_mask=a      ,					)

     if encoder_hidden_states is not None:
       lowercase__           =  torch.where(encoder_attention_mask > 0      ,					0      ,					-1E1_0	).to(
           encoder_hidden_states.dtype	)

       lowercase__           =  self.layer[1](
           a      ,					key_value_states=a      ,					attention_mask=a      ,					)

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

     return (hidden_states,)






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    List[str]      ,					a      :    List[Any]      ,					a      :    Tuple      ,					a      :    Union[str, Any]      ,					a      :    int	)->       List[str]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  TaLayerNorm(a	)
     lowercase__           =  TaFiLMLayer(in_features=d_model * 4      ,					out_features=a	)
     lowercase__           =  Attention(query_dim=a      ,					heads=a      ,					dim_head=a      ,					out_bias=a      ,					scale_qk=a	)
     lowercase__           =  nn.Dropout(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    List[Any]      ,					a      :    Union[str, Any]=None      ,					a      :    Tuple=None      ,					)->       int:





     """simple docstring"""







     lowercase__           =  self.layer_norm(a	)

     if conditioning_emb is not None:
       lowercase__           =  self.FiLMLayer(a      ,					a	)

     # Self-attention block
     lowercase__           =  self.attention(a	)

     lowercase__           =  hidden_states + self.dropout(a	)

     return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Optional[int]      ,					a      :    Optional[int]      ,					a      :    int      ,					a      :    Optional[int]      ,					a      :    Dict      ,					a      :    Any	)->       Optional[int]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  Attention(query_dim=a      ,					heads=a      ,					dim_head=a      ,					out_bias=a      ,					scale_qk=a	)
     lowercase__           =  TaLayerNorm(a      ,					eps=a	)
     lowercase__           =  nn.Dropout(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    List[Any]      ,					a      :    str=None      ,					a      :    Dict=None      ,					)->       int:





     """simple docstring"""







     lowercase__           =  self.layer_norm(a	)
     lowercase__           =  self.attention(
         a      ,					encoder_hidden_states=a      ,					attention_mask=attention_mask.squeeze(1	)      ,					)
     lowercase__           =  hidden_states + self.dropout(a	)
     return layer_output






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Dict      ,					a      :    str      ,					a      :    int      ,					a      :    List[Any]      ,					a      :    List[str]	)->       Dict:





     """simple docstring"""







     super().__init__()
     lowercase__           =  TaDenseGatedActDense(d_model=a      ,					d_ff=a      ,					dropout_rate=a	)
     lowercase__           =  TaFiLMLayer(in_features=d_model * 4      ,					out_features=a	)
     lowercase__           =  TaLayerNorm(a      ,					eps=a	)
     lowercase__           =  nn.Dropout(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    List[Any]      ,					a      :    List[Any]=None	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  self.layer_norm(a	)
     if conditioning_emb is not None:
       lowercase__           =  self.film(a      ,					a	)

     lowercase__           =  self.DenseReluDense(a	)
     lowercase__           =  hidden_states + self.dropout(a	)
     return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Optional[Any]      ,					a      :    Tuple      ,					a      :    Tuple      ,					a      :    Dict	)->       Union[str, Any]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)
     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)
     lowercase__           =  nn.Linear(a      ,					a      ,					bias=a	)
     lowercase__           =  nn.Dropout(a	)
     lowercase__           =  NewGELUActivation()


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str	)->       str:





     """simple docstring"""







     lowercase__           =  self.act(self.wi_a(a	)	)
     lowercase__           =  self.wi_a(a	)
     lowercase__           =  hidden_gelu * hidden_linear
     lowercase__           =  self.dropout(a	)

     lowercase__           =  self.wo(a	)
     return hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    Dict      ,					a      :    Dict      ,					a      :    Dict=1E-6	)->       Optional[int]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  nn.Parameter(torch.ones(a	)	)
     lowercase__           =  eps


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







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

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

     return self.weight * hidden_states






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    torch.Tensor	)->       torch.Tensor:





     """simple docstring"""







     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.044715 * torch.pow(a      ,					3.0	))	))






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    int      ,					a      :    Optional[Any]      ,					a      :    Any	)->       Optional[Any]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  nn.Linear(a      ,					out_features * 2      ,					bias=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    Tuple      ,					a      :    Dict	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  self.scale_bias(a	)
     lowercase__      ,   lowercase__           =  torch.chunk(a      ,					2      ,					-1	)
     lowercase__           =  x * (1 + scale) + shift
     return x


