




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
# and perform gradient accumulation
#
# 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
#
########################################################################


lowerCamelCase    :		str			   =							16
lowerCamelCase    :		str			   =							32

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Accelerator     ,							lowercase     :	int = 16					):


      '''simple docstring'''



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

      def tokenize_function(lowercase     :	Optional[Any]					):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase_							=   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():
            lowerCamelCase_							=   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
      lowerCamelCase_							=   tokenized_datasets.rename_column('label'     ,							'labels'					)

      def collate_fn(lowercase     :	Optional[int]					):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowerCamelCase_							=   1_28 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_							=   16
            elif accelerator.mixed_precision != "no":
                  lowerCamelCase_							=   8
            else:
                  lowerCamelCase_							=   None

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

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

      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

 lowerCamelCase    :		Any			   =							mocked_dataloaders  # noqa: F811

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,							lowercase					) == "1":
            lowerCamelCase_							=   2
      # New Code #
      lowerCamelCase_							=   int(args.gradient_accumulation_steps					)
      # Initialize accelerator
      lowerCamelCase_							=   Accelerator(
          cpu=args.cpu     ,							mixed_precision=args.mixed_precision     ,							gradient_accumulation_steps=lowercase					)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'					)
      # 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'					)

      set_seed(lowercase					)
      lowerCamelCase_					,    lowerCamelCase_							=   get_dataloaders(lowercase     ,							lowercase					)
      # 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=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).
      lowerCamelCase_							=   model.to(accelerator.device					)

      # Instantiate optimizer
      lowerCamelCase_							=   AdamW(params=model.parameters()     ,							lr=lowercase					)

      # Instantiate scheduler
      lowerCamelCase_							=   get_linear_schedule_with_warmup(
          optimizer=lowercase     ,							num_warmup_steps=1_00     ,							num_training_steps=(len(lowercase					) * num_epochs)     ,							)

      # 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(
          lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							lowercase					)

      # Now we train the model
      for epoch in range(lowercase					):
            model.train()
            for step, batch in enumerate(lowercase					):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device					)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(lowercase					):
                        lowerCamelCase_							=   model(**lowercase					)
                        lowerCamelCase_							=   output.loss
                        accelerator.backward(lowercase					)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            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():
                        lowerCamelCase_							=   model(**lowercase					)
                  lowerCamelCase_							=   outputs.logits.argmax(dim=-1					)
                  lowerCamelCase_					,    lowerCamelCase_							=   accelerator.gather_for_metrics((predictions, batch['labels'])					)
                  metric.add_batch(
                      predictions=lowercase     ,							references=lowercase     ,							)

            lowerCamelCase_							=   metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:"""     ,							lowercase					)

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   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.'     ,							)
      # New Code #
      parser.add_argument(
          '--gradient_accumulation_steps'     ,							type=lowercase     ,							default=1     ,							help='The number of minibatches to be ran before gradients are accumulated.'     ,							)
      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': 42, 'batch_size': 16}
      training_function(lowercase     ,							lowercase					)


if __name__ == "__main__":
 main()









from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						42







class   A(						UpperCamelCase							,				UpperCamelCase       ):



 '''simple docstring'''


 @register_to_config
 def __init__(    self :  Tuple						,							A_ :  int = 32						,							A_ :  int = 64						,							A_ :  int = 20						,							A_ :  int = 768						,							A_ :  Optional[Any]=77						,							A_ :  Optional[int]=4						,							A_ :  float = 0.0						,							A_ :  str = "silu"						,							A_ :  Optional[str] = None						,							A_ :  Optional[str] = None						,							A_ :  Optional[str] = "linear"						,							A_ :  Optional[str] = "prd"						,							A_ :  Optional[int] = None						,							A_ :  Optional[int] = None						,							A_ :  Optional[int] = None						,							)       ->     List[Any]:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   attention_head_dim
       lowerCamelCase_							=   num_attention_heads * attention_head_dim
       lowerCamelCase_							=   additional_embeddings

       lowerCamelCase_							=   time_embed_dim or inner_dim
       lowerCamelCase_							=   embedding_proj_dim or embedding_dim
       lowerCamelCase_							=   clip_embed_dim or embedding_dim

       lowerCamelCase_							=   Timesteps(A_						,							A_						,							0  )
       lowerCamelCase_							=   TimestepEmbedding(A_						,							A_						,							out_dim=A_						,							act_fn=A_  )

       lowerCamelCase_							=   nn.Linear(A_						,							A_  )

       if embedding_proj_norm_type is None:
             lowerCamelCase_							=   None
       elif embedding_proj_norm_type == "layer":
             lowerCamelCase_							=   nn.LayerNorm(A_  )
       else:
             raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""  )

       lowerCamelCase_							=   nn.Linear(A_						,							A_  )

       if encoder_hid_proj_type is None:
             lowerCamelCase_							=   None
       elif encoder_hid_proj_type == "linear":
             lowerCamelCase_							=   nn.Linear(A_						,							A_  )
       else:
             raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""  )

       lowerCamelCase_							=   nn.Parameter(torch.zeros(1						,							num_embeddings + additional_embeddings						,							A_  )  )

       if added_emb_type == "prd":
             lowerCamelCase_							=   nn.Parameter(torch.zeros(1						,							1						,							A_  )  )
       elif added_emb_type is None:
             lowerCamelCase_							=   None
       else:
             raise ValueError(
                 f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""  )

       lowerCamelCase_							=   nn.ModuleList(
           [
               BasicTransformerBlock(
                   A_						,							A_						,							A_						,							dropout=A_						,							activation_fn='gelu'						,							attention_bias=A_						,							)
               for d in range(A_  )
           ]  )

       if norm_in_type == "layer":
             lowerCamelCase_							=   nn.LayerNorm(A_  )
       elif norm_in_type is None:
             lowerCamelCase_							=   None
       else:
             raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}."""  )

       lowerCamelCase_							=   nn.LayerNorm(A_  )

       lowerCamelCase_							=   nn.Linear(A_						,							A_  )

       lowerCamelCase_							=   torch.full(
           [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						,							-10000.0  )
       causal_attention_mask.triu_(1  )
       lowerCamelCase_							=   causal_attention_mask[None, ...]
       self.register_buffer('causal_attention_mask'						,							A_						,							persistent=A_  )

       lowerCamelCase_							=   nn.Parameter(torch.zeros(1						,							A_  )  )
       lowerCamelCase_							=   nn.Parameter(torch.zeros(1						,							A_  )  )

 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def     a__			(    self :  str  )       ->     Dict[str, AttentionProcessor]:

       """simple docstring"""






       lowerCamelCase_							=   {}

       def fn_recursive_add_processors(A_ :  str						,							A_ :  torch.nn.Module						,							A_ :  Dict[str, AttentionProcessor]  ):
             if hasattr(A_						,							'set_processor'  ):
                   lowerCamelCase_							=   module.processor

             for sub_name, child in module.named_children():
                   fn_recursive_add_processors(f"""{name}.{sub_name}"""						,							A_						,							A_  )

             return processors

       for name, module in self.named_children():
             fn_recursive_add_processors(A_						,							A_						,							A_  )

       return processors

 def     a__			(    self :  List[Any]						,							A_ :  Union[AttentionProcessor, Dict[str, AttentionProcessor]]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   len(self.attn_processors.keys()  )

       if isinstance(A_						,							A_  ) and len(A_  ) != count:
             raise ValueError(
                 f"""A dict of processors was passed, but the number of processors {len(A_  )} does not match the"""
                 f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""  )

       def fn_recursive_attn_processor(A_ :  str						,							A_ :  torch.nn.Module						,							A_ :  Union[str, Any]  ):
             if hasattr(A_						,							'set_processor'  ):
                   if not isinstance(A_						,							A_  ):
                         module.set_processor(A_  )
                   else:
                         module.set_processor(processor.pop(f"""{name}.processor"""  )  )

             for sub_name, child in module.named_children():
                   fn_recursive_attn_processor(f"""{name}.{sub_name}"""						,							A_						,							A_  )

       for name, module in self.named_children():
             fn_recursive_attn_processor(A_						,							A_						,							A_  )

 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






       self.set_attn_processor(AttnProcessor()  )

 def     a__			(    self :  Dict						,							A_ :  List[Any]						,							A_ :  Union[torch.Tensor, float, int]						,							A_ :  torch.FloatTensor						,							A_ :  Optional[torch.FloatTensor] = None						,							A_ :  Optional[torch.BoolTensor] = None						,							A_ :  bool = True						,							)       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   hidden_states.shape[0]

       lowerCamelCase_							=   timestep
       if not torch.is_tensor(A_  ):
             lowerCamelCase_							=   torch.tensor([timesteps]						,							dtype=torch.long						,							device=hidden_states.device  )
       elif torch.is_tensor(A_  ) and len(timesteps.shape  ) == 0:
             lowerCamelCase_							=   timesteps[None].to(hidden_states.device  )

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       lowerCamelCase_							=   timesteps * torch.ones(A_						,							dtype=timesteps.dtype						,							device=timesteps.device  )

       lowerCamelCase_							=   self.time_proj(A_  )

       # timesteps does not contain any weights and will always return f32 tensors
       # but time_embedding might be fp16, so we need to cast here.
       lowerCamelCase_							=   timesteps_projected.to(dtype=self.dtype  )
       lowerCamelCase_							=   self.time_embedding(A_  )

       if self.embedding_proj_norm is not None:
             lowerCamelCase_							=   self.embedding_proj_norm(A_  )

       lowerCamelCase_							=   self.embedding_proj(A_  )
       if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
             lowerCamelCase_							=   self.encoder_hidden_states_proj(A_  )
       elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
             raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'  )

       lowerCamelCase_							=   self.proj_in(A_  )

       lowerCamelCase_							=   self.positional_embedding.to(hidden_states.dtype  )

       lowerCamelCase_							=   []
       lowerCamelCase_							=   0

       if encoder_hidden_states is not None:
             additional_embeds.append(A_  )
             additional_embeddings_len += encoder_hidden_states.shape[1]

       if len(proj_embeddings.shape  ) == 2:
             lowerCamelCase_							=   proj_embeddings[:, None, :]

       if len(hidden_states.shape  ) == 2:
             lowerCamelCase_							=   hidden_states[:, None, :]

       lowerCamelCase_							=   additional_embeds + [
           proj_embeddings,
           time_embeddings[:, None, :],
           hidden_states,
       ]

       if self.prd_embedding is not None:
             lowerCamelCase_							=   self.prd_embedding.to(hidden_states.dtype  ).expand(A_						,							-1						,							-1  )
             additional_embeds.append(A_  )

       lowerCamelCase_							=   torch.cat(
           A_						,							dim=1						,							)

       # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
       lowerCamelCase_							=   additional_embeddings_len + proj_embeddings.shape[1] + 1
       if positional_embeddings.shape[1] < hidden_states.shape[1]:
             lowerCamelCase_							=   F.pad(
                 A_						,							(
                     0,
                     0,
                     additional_embeddings_len,
                     self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                 )						,							value=0.0						,							)

       lowerCamelCase_							=   hidden_states + positional_embeddings

       if attention_mask is not None:
             lowerCamelCase_							=   (1 - attention_mask.to(hidden_states.dtype  )) * -10000.0
             lowerCamelCase_							=   F.pad(A_						,							(0, self.additional_embeddings)						,							value=0.0  )
             lowerCamelCase_							=   (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype  )
             lowerCamelCase_							=   attention_mask.repeat_interleave(self.config.num_attention_heads						,							dim=0  )

       if self.norm_in is not None:
             lowerCamelCase_							=   self.norm_in(A_  )

       for block in self.transformer_blocks:
             lowerCamelCase_							=   block(A_						,							attention_mask=A_  )

       lowerCamelCase_							=   self.norm_out(A_  )

       if self.prd_embedding is not None:
             lowerCamelCase_							=   hidden_states[:, -1]
       else:
             lowerCamelCase_							=   hidden_states[:, additional_embeddings_len:]

       lowerCamelCase_							=   self.proj_to_clip_embeddings(A_  )

       if not return_dict:
             return (predicted_image_embedding,)

       return PriorTransformerOutput(predicted_image_embedding=A_  )





 def     a__			(    self :  Tuple						,							A_ :  List[Any]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   (prior_latents * self.clip_std) + self.clip_mean
       return prior_latents



