




"""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
#
########################################################################


_A													=      1_6
_A													=      3_2



def lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase = 16				)						->    Union[str, Any]:
      lowerCAmelCase__      :			Union[str, Any]   =	AutoTokenizer.from_pretrained("""bert-base-cased"""				)
      lowerCAmelCase__      :			Optional[Any]   =	load_dataset("""glue"""    ,		"""mrpc"""				)

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

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

      # Instantiate dataloaders.
      lowerCAmelCase__      :			List[Any]   =	DataLoader(
          tokenized_datasets["""train"""]    ,		shuffle=UpperCAmelCase_    ,		collate_fn=UpperCAmelCase_    ,		batch_size=UpperCAmelCase_				)
      lowerCAmelCase__      :			Union[str, Any]   =	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 lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:





      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,		UpperCAmelCase_				) == "1":
            lowerCAmelCase__      :			str   =	2
      # Initialize accelerator
      lowerCAmelCase__      :			Any   =	Accelerator(cpu=args.cpu    ,		mixed_precision=args.mixed_precision				)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase__      :			str   =	config['lr']
      lowerCAmelCase__      :			Tuple   =	int(config["""num_epochs"""]				)
      lowerCAmelCase__      :			Any   =	int(config["""seed"""]				)
      lowerCAmelCase__      :			Union[str, Any]   =	int(config["""batch_size"""]				)

      lowerCAmelCase__      :			int   =	evaluate.load("""glue"""    ,		"""mrpc"""				)

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

      set_seed(UpperCAmelCase_				)
      lowerCAmelCase__      :			List[Any]   =	get_dataloaders(UpperCAmelCase_    ,		UpperCAmelCase_				)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCAmelCase__      :			List[Any]   =	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).
      lowerCAmelCase__      :			str   =	model.to(accelerator.device				)

      # Instantiate optimizer
      lowerCAmelCase__      :			Optional[int]   =	AdamW(params=model.parameters()    ,		lr=UpperCAmelCase_				)

      # Instantiate scheduler
      lowerCAmelCase__      :			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.
      lowerCAmelCase__      :			str   =	accelerator.prepare(
          UpperCAmelCase_    ,		UpperCAmelCase_    ,		UpperCAmelCase_    ,		UpperCAmelCase_    ,		UpperCAmelCase_				)

      # Now we train the model
      for epoch in range(UpperCAmelCase_				):
            model.train()
            for step, batch in enumerate(UpperCAmelCase_				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  lowerCAmelCase__      :			Union[str, Any]   =	model(**UpperCAmelCase_				)
                  lowerCAmelCase__      :			Optional[Any]   =	outputs.loss
                  lowerCAmelCase__      :			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()
            lowerCAmelCase__      :			Optional[Any]   =	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				)
                  with torch.no_grad():
                        lowerCAmelCase__      :			Union[str, Any]   =	model(**UpperCAmelCase_				)
                  lowerCAmelCase__      :			List[Any]   =	outputs.logits.argmax(dim=-1				)
                  lowerCAmelCase__      :			int   =	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(UpperCAmelCase_				) - 1:
                              # Last batch needs to be truncated on distributed systems as it contains additional samples
                              lowerCAmelCase__      :			Tuple   =	predictions[: len(eval_dataloader.dataset				) - samples_seen]
                              lowerCAmelCase__      :			Optional[Any]   =	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=UpperCAmelCase_    ,		references=UpperCAmelCase_    ,		)

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



def lowercase_				(			)						->    Tuple:
      lowerCAmelCase__      :			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."""				)
      lowerCAmelCase__      :			Dict   =	parser.parse_args()
      lowerCAmelCase__      :			Optional[Any]   =	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(UpperCAmelCase_    ,		UpperCAmelCase_				)


if __name__ == "__main__":
  main()











"""simple docstring"""


from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=1E-12				)						->    Dict:
      lowerCAmelCase__      :			Optional[int]   =	jnp.divide(emb_a.T    ,		jnp.clip(jnp.linalg.norm(__UpperCAmelCase    ,		axis=1				)    ,		a_min=__UpperCAmelCase				)				).T
      lowerCAmelCase__      :			Any   =	jnp.divide(emb_a.T    ,		jnp.clip(jnp.linalg.norm(__UpperCAmelCase    ,		axis=1				)    ,		a_min=__UpperCAmelCase				)				).T
      return jnp.matmul(__UpperCAmelCase    ,		norm_emb_a.T				)


class _lowerCamelCase    (					nn.Module						):
   _lowerCamelCase    :CLIPConfig
   _lowerCamelCase    :jnp.dtype     				=     jnp.floataa






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	FlaxCLIPVisionModule(self.config.vision_config   )
         lowerCAmelCase__      :			Dict   =	nn.Dense(self.config.projection_dim   ,     use_bias=UpperCamelCase   ,     dtype=self.dtype   )

         lowerCAmelCase__      :			Optional[int]   =	self.param("""concept_embeds"""   ,     jax.nn.initializers.ones   ,     (17, self.config.projection_dim)   )
         lowerCAmelCase__      :			Optional[int]   =	self.param(
             """special_care_embeds"""   ,     jax.nn.initializers.ones   ,     (3, self.config.projection_dim)   )

         lowerCAmelCase__      :			List[Any]   =	self.param("""concept_embeds_weights"""   ,     jax.nn.initializers.ones   ,     (17,)   )
         lowerCAmelCase__      :			List[Any]   =	self.param("""special_care_embeds_weights"""   ,     jax.nn.initializers.ones   ,     (3,)   )






   def __call__(		self       :		str   ,     UpperCamelCase       :		List[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.vision_model(UpperCamelCase   )[1]
         lowerCAmelCase__      :			List[Any]   =	self.visual_projection(UpperCamelCase   )

         lowerCAmelCase__      :			str   =	jax_cosine_distance(UpperCamelCase   ,     self.special_care_embeds   )
         lowerCAmelCase__      :			Optional[int]   =	jax_cosine_distance(UpperCamelCase   ,     self.concept_embeds   )

         # increase this value to create a stronger `nfsw` filter
         # at the cost of increasing the possibility of filtering benign image inputs
         lowerCAmelCase__      :			Any   =	0.0

         lowerCAmelCase__      :			List[Any]   =	special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
         lowerCAmelCase__      :			Any   =	jnp.round(UpperCamelCase   ,     3   )
         lowerCAmelCase__      :			List[str]   =	jnp.any(special_scores > 0   ,     axis=1   ,     keepdims=UpperCamelCase   )
         # Use a lower threshold if an image has any special care concept
         lowerCAmelCase__      :			Tuple   =	is_special_care * 0.01

         lowerCAmelCase__      :			List[Any]   =	cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
         lowerCAmelCase__      :			Dict   =	jnp.round(UpperCamelCase   ,     3   )
         lowerCAmelCase__      :			List[str]   =	jnp.any(concept_scores > 0   ,     axis=1   )

         return has_nsfw_concepts






class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[Any]     				=     CLIPConfig
   _lowerCamelCase    :Dict     				=     "clip_input"
   _lowerCamelCase    :int     				=     FlaxStableDiffusionSafetyCheckerModule






   def __init__(		self       :		int   ,     UpperCamelCase       :		CLIPConfig   ,     UpperCamelCase       :		Optional[Tuple] = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		jnp.dtype = jnp.floataa   ,     UpperCamelCase       :		bool = True   ,     **UpperCamelCase       :		int   ,     )			->						str:

         """simple docstring"""
         if input_shape is None:
               lowerCAmelCase__      :			str   =	(1, 2_24, 2_24, 3)
         lowerCAmelCase__      :			Union[str, Any]   =	self.module_class(config=UpperCamelCase   ,     dtype=UpperCamelCase   ,     **UpperCamelCase   )
         super().__init__(UpperCamelCase   ,     UpperCamelCase   ,     input_shape=UpperCamelCase   ,     seed=UpperCamelCase   ,     dtype=UpperCamelCase   ,     _do_init=_do_init   )






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		jax.random.KeyArray   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		FrozenDict = None   )			->						FrozenDict:

         """simple docstring"""
         # init input tensor
         lowerCAmelCase__      :			Union[str, Any]   =	jax.random.normal(UpperCamelCase   ,     UpperCamelCase   )

         lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	jax.random.split(UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	{"""params""": params_rng, """dropout""": dropout_rng}

         lowerCAmelCase__      :			str   =	self.module.init(UpperCamelCase   ,     UpperCamelCase   )["""params"""]

         return random_params






   def __call__(		self       :		Dict   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		dict = None   ,     )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	jnp.transpose(UpperCamelCase   ,     (0, 2, 3, 1)   )

         return self.module.apply(
             {"""params""": params or self.params}   ,     jnp.array(UpperCamelCase   ,     dtype=jnp.floataa   )   ,     rngs={}   ,     )





