

import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)


def 							__lowerCAmelCase     (							)		->				List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      argparse.ArgumentParser()
 parser.add_argument('--model_ckpt'		,				type=_UpperCamelCase		,				default='microsoft/unixcoder-base-nine'						)
 parser.add_argument('--num_epochs'		,				type=_UpperCamelCase		,				default=5						)
 parser.add_argument('--batch_size'		,				type=_UpperCamelCase		,				default=6						)
 parser.add_argument('--gradient_accumulation_steps'		,				type=_UpperCamelCase		,				default=1						)
 parser.add_argument('--freeze'		,				type=_UpperCamelCase		,				default=_UpperCamelCase						)
 parser.add_argument('--learning_rate'		,				type=_UpperCamelCase		,				default=5e-4						)
 parser.add_argument('--seed'		,				type=_UpperCamelCase		,				default=0						)
 parser.add_argument('--lr_scheduler_type'		,				type=_UpperCamelCase		,				default='cosine'						)
 parser.add_argument('--num_warmup_steps'		,				type=_UpperCamelCase		,				default=10						)
 parser.add_argument('--weight_decay'		,				type=_UpperCamelCase		,				default=0.01						)
 parser.add_argument('--output_dir'		,				type=_UpperCamelCase		,				default='./results'						)
 return parser.parse_args()


a_     :    Dict     		=   load("accuracy")


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      eval_pred
 SCREAMING_SNAKE_CASE		    =      np.argmax(_UpperCamelCase		,				axis=1						)
 return metric.compute(predictions=_UpperCamelCase		,				references=_UpperCamelCase						)




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):

    def __init__(      self			:   Any				,     snake_case__			:   Optional[Any]     ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE		    =      trainer




    def UpperCamelCase      (      self			:   int				,     snake_case__			:   int				,     snake_case__			:   List[str]				,     snake_case__			:   Tuple				,     **snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     if control.should_evaluate:
      SCREAMING_SNAKE_CASE		    =      deepcopy(snake_case__     )
      self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,     metric_key_prefix='train'     )
      return control_copy


def 							__lowerCAmelCase     (							)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      get_args()
 set_seed(args.seed						)

 SCREAMING_SNAKE_CASE		    =      load_dataset('codeparrot/codecomplex'		,				split='train'						)
 SCREAMING_SNAKE_CASE		    =      dataset.train_test_split(test_size=0.2						)
 SCREAMING_SNAKE_CASE		    =      train_test['test'].train_test_split(test_size=0.5						)
 SCREAMING_SNAKE_CASE		    =      DatasetDict(
     {
         'train': train_test['train'],
         'test': test_validation['train'],
         'valid': test_validation['test'],
     }						)

 print('Loading tokenizer and model'						)
 SCREAMING_SNAKE_CASE		    =      AutoTokenizer.from_pretrained(args.model_ckpt						)
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token
 SCREAMING_SNAKE_CASE		    =      AutoModelForSequenceClassification.from_pretrained(args.model_ckpt		,				num_labels=7						)
 SCREAMING_SNAKE_CASE		    =      model.config.eos_token_id

 if args.freeze:
  for param in model.roberta.parameters():
   SCREAMING_SNAKE_CASE		    =      False

 SCREAMING_SNAKE_CASE		    =      ClassLabel(num_classes=7		,				names=list(set(train_test_validation['train']['complexity']						)						)						)

 def tokenize(_UpperCamelCase		:	str						):
  SCREAMING_SNAKE_CASE		    =      tokenizer(example['src']		,				truncation=_UpperCamelCase		,				max_length=10_24						)
  SCREAMING_SNAKE_CASE		    =      labels.straint(example['complexity']						)
  return {
      "input_ids": inputs["input_ids"],
      "attention_mask": inputs["attention_mask"],
      "label": label,
  }

 SCREAMING_SNAKE_CASE		    =      train_test_validation.map(
     _UpperCamelCase		,				batched=_UpperCamelCase		,				remove_columns=train_test_validation['train'].column_names		,				)
 SCREAMING_SNAKE_CASE		    =      DataCollatorWithPadding(tokenizer=_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      TrainingArguments(
     output_dir=args.output_dir		,				learning_rate=args.learning_rate		,				lr_scheduler_type=args.lr_scheduler_type		,				evaluation_strategy='epoch'		,				save_strategy='epoch'		,				logging_strategy='epoch'		,				per_device_train_batch_size=args.batch_size		,				per_device_eval_batch_size=args.batch_size		,				num_train_epochs=args.num_epochs		,				gradient_accumulation_steps=args.gradient_accumulation_steps		,				weight_decay=0.01		,				metric_for_best_model='accuracy'		,				run_name='complexity-java'		,				report_to='wandb'		,				)

 SCREAMING_SNAKE_CASE		    =      Trainer(
     model=_UpperCamelCase		,				args=_UpperCamelCase		,				train_dataset=tokenized_datasets['train']		,				eval_dataset=tokenized_datasets['valid']		,				tokenizer=_UpperCamelCase		,				data_collator=_UpperCamelCase		,				compute_metrics=_UpperCamelCase		,				)

 print('Training...'						)
 trainer.add_callback(CustomCallback(_UpperCamelCase						)						)
 trainer.train()


if __name__ == "__main__":
      main()




import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp
      from flax.jax_utils import replicate
      from flax.training.common_utils import shard

      from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline




@require_flax
class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdirname:
      # pipeline has Flax weights
      SCREAMING_SNAKE_CASE		    =      FlaxDiffusionPipeline.from_pretrained(
          'hf-internal-testing/tiny-stable-diffusion-pipe'				,     safety_checker=snake_case__				,     cache_dir=snake_case__     )

      SCREAMING_SNAKE_CASE		    =      [t[-1] for t in os.walk(os.path.join(snake_case__				,     os.listdir(snake_case__     )[0]				,     'snapshots'     )     )]
      SCREAMING_SNAKE_CASE		    =      [item for sublist in all_root_files for item in sublist]

      # None of the downloaded files should be a PyTorch file even if we have some here:
      # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
      assert not any(f.endswith('.bin'     ) for f in files     )




@slow
@require_flax
class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'hf-internal-testing/tiny-stable-diffusion-pipe'				,     safety_checker=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      4

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
     if jax.device_count() == 8:
      assert np.abs(np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 4.1_514_745     ) < 1E-3
      assert np.abs(np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 49_947.875     ) < 5E-1

     SCREAMING_SNAKE_CASE		    =      pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]     )     )     )
     assert len(snake_case__     ) == num_samples

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='flax'				,     safety_checker=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      5_0

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
      assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 0.05_652_401)     ) < 1E-3
      assert np.abs((np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 2_383_808.2)     ) < 5E-1

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     safety_checker=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      5_0

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
      assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 0.04_003_906)     ) < 1E-3
      assert np.abs((np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 2_373_516.75)     ) < 5E-1

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa     )

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      5_0

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
      assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 0.04_003_906)     ) < 1E-3
      assert np.abs((np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 2_373_516.75)     ) < 5E-1

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      FlaxDDIMScheduler(
         beta_start=0.00_085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     set_alpha_to_one=snake_case__				,     steps_offset=1				,     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     scheduler=snake_case__				,     safety_checker=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      scheduler.create_state()

     SCREAMING_SNAKE_CASE		    =      scheduler_state

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      5_0

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
      assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 0.045_043_945)     ) < 1E-3
      assert np.abs((np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 2_347_693.5)     ) < 5E-1




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      jax.random.split(jax.random.PRNGKey(0     )				,     snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     safety_checker=snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images
     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE		    =      images[2, 0, 2_5_6, 1_0:1_7, 1]

     # With memory efficient attention
     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     safety_checker=snake_case__				,     use_memory_efficient_attention=snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images
     assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE		    =      images[2, 0, 2_5_6, 1_0:1_7, 1]

     # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
     # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
     assert abs(slice_eff - slice     ).max() < 1E-2

