


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''wavlm'''





     def __init__(self		,     UpperCAmelCase=3_2		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-5		,     UpperCAmelCase="group"		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,     UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2)		,     UpperCAmelCase=False		,     UpperCAmelCase=1_2_8		,     UpperCAmelCase=1_6		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=8_0_0		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=0.05		,     UpperCAmelCase=1_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.0		,     UpperCAmelCase=1_0		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_0		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=0.1		,     UpperCAmelCase="mean"		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)		,     UpperCAmelCase=(5, 3, 3, 1, 1)		,     UpperCAmelCase=(1, 2, 3, 1, 1)		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=8_0		,     UpperCAmelCase=0		,     UpperCAmelCase=1		,     UpperCAmelCase=2		,     UpperCAmelCase=False		,     UpperCAmelCase=3		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Optional[Any]:
      super().__init__(**UpperCAmelCase		,     pad_token_id=UpperCAmelCase		,     bos_token_id=UpperCAmelCase		,     eos_token_id=UpperCAmelCase       )
      _lowercase							      =hidden_size
      _lowercase							      =feat_extract_norm
      _lowercase							      =feat_extract_activation
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =conv_bias
      _lowercase							      =num_buckets
      _lowercase							      =max_bucket_distance
      _lowercase							      =num_conv_pos_embeddings
      _lowercase							      =num_conv_pos_embedding_groups
      _lowercase							      =len(self.conv_dim       )
      _lowercase							      =num_hidden_layers
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =num_attention_heads
      _lowercase							      =hidden_dropout
      _lowercase							      =attention_dropout
      _lowercase							      =activation_dropout
      _lowercase							      =feat_proj_dropout
      _lowercase							      =final_dropout
      _lowercase							      =layerdrop
      _lowercase							      =layer_norm_eps
      _lowercase							      =initializer_range
      _lowercase							      =num_ctc_classes
      _lowercase							      =vocab_size
      _lowercase							      =do_stable_layer_norm
      _lowercase							      =use_weighted_layer_sum
      _lowercase							      =classifier_proj_size

      if (
          (len(self.conv_stride       ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
          or (len(self.conv_dim       ) != self.num_feat_extract_layers)
      ):
       raise ValueError(
           '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
           ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
           f" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"
           f" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."       )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _lowercase							      =apply_spec_augment
      _lowercase							      =mask_time_prob
      _lowercase							      =mask_time_length
      _lowercase							      =mask_time_min_masks
      _lowercase							      =mask_feature_prob
      _lowercase							      =mask_feature_length

      # parameters for pretraining with codevector quantized representations
      _lowercase							      =num_codevectors_per_group
      _lowercase							      =num_codevector_groups
      _lowercase							      =contrastive_logits_temperature
      _lowercase							      =num_negatives
      _lowercase							      =codevector_dim
      _lowercase							      =proj_codevector_dim
      _lowercase							      =diversity_loss_weight

      # ctc loss
      _lowercase							      =ctc_loss_reduction
      _lowercase							      =ctc_zero_infinity

      # adapter
      _lowercase							      =add_adapter
      _lowercase							      =adapter_kernel_size
      _lowercase							      =adapter_stride
      _lowercase							      =num_adapter_layers
      _lowercase							      =output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      _lowercase							      =classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =xvector_output_dim





     @property
     def      __A					(self       )     ->   int:
      return functools.reduce(operator.mul		,     self.conv_stride		,     1       )





import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


UpperCAmelCase__   	=							['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=1       )     ->   Dict:
      _lowercase							      =tokenizer
      _lowercase							      =dataset
      _lowercase							      =len(UpperCAmelCase       ) if n_tasks is None else n_tasks
      _lowercase							      =n_copies





     def __iter__(self       )     ->   Optional[Any]:
      _lowercase							      =[]
      for task in range(self.n_tasks       ):
       # without strip, the model generate commented codes ...
       prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip()       )
      _lowercase							      =self.tokenizer(UpperCAmelCase		,     padding=UpperCAmelCase		,     return_tensors='''pt'''       )
      for task in range(self.n_tasks       ):
       for _ in range(self.n_copies       ):
        yield {
            "ids": outputs.input_ids[task],
            "task_id": task,
            "input_len": outputs.attention_mask[task].sum(),
        }







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =start_length
      _lowercase							      =eof_strings
      _lowercase							      =tokenizer





     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Dict:
      _lowercase							      =self.tokenizer.batch_decode(input_ids[:, self.start_length :]       )
      _lowercase							      =[]
      for decoded_generation in decoded_generations:
       done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings       )       )
      return all(UpperCAmelCase       )



def 				UpperCAmelCase_					(							__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =re.split('''(%s)''' % '''|'''.join(__snake_case						)     ,						__snake_case						)
 # last string should be ""
 return "".join(string_list[:-2]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=20     ,						**__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =defaultdict(__snake_case						)  # dict of list of generated tokens
 for step, batch in tqdm(enumerate(__snake_case						)						):
  with torch.no_grad():
   _lowercase							      =batch['''ids'''].shape[-1]
   _lowercase							      =accelerator.unwrap_model(__snake_case						).generate(
       input_ids=batch['''ids'''][:, : batch['''input_len''']]     ,						num_return_sequences=__snake_case     ,						**__snake_case						)
   # each task is generated batch_size times
   _lowercase							      =batch['''task_id'''].repeat(__snake_case						)
   _lowercase							      =accelerator.pad_across_processes(
       __snake_case     ,						dim=1     ,						pad_index=tokenizer.pad_token_id						)

   _lowercase      ,    _lowercase							      =accelerator.gather((generated_tokens, generated_tasks)						)
   _lowercase							      =generated_tokens.cpu().numpy()
   _lowercase							      =generated_tasks.cpu().numpy()

   for task, generated_tokens in zip(__snake_case     ,						__snake_case						):
    gen_token_dict[task].append(__snake_case						)

 _lowercase							      =[[] for _ in range(__snake_case						)]
 for task, generated_tokens in gen_token_dict.items():
  for s in generated_tokens:
   _lowercase							      =tokenizer.decode(__snake_case     ,						skip_special_tokens=__snake_case     ,						clean_up_tokenization_spaces=__snake_case						)
   code_gens[task].append(remove_last_block(__snake_case						)						)
 return code_gens
def 				UpperCAmelCase_					(							)     ->			str:







 """simple docstring"""


 _lowercase							      =HfArgumentParser(__snake_case						)
 _lowercase							      =parser.parse_args()

 transformers.logging.set_verbosity_error()
 # enables code execution in code_eval metric
 _lowercase							      =args.HF_ALLOW_CODE_EVAL
 # make sure tokenizer plays nice with multiprocessing
 _lowercase							      ='''false'''

 if args.num_workers is None:
  _lowercase							      =multiprocessing.cpu_count()

 # Use dataset load to feed to accelerate
 _lowercase							      =Accelerator()
 set_seed(args.seed     ,						device_specific=__snake_case						)

 # Load model and tokenizer
 _lowercase							      =AutoTokenizer.from_pretrained(args.model_ckpt						)
 _lowercase							      =tokenizer.eos_token
 _lowercase							      =AutoModelForCausalLM.from_pretrained(args.model_ckpt						)

 # Generation settings
 _lowercase							      ={
     '''do_sample''': args.do_sample,
     '''temperature''': args.temperature,
     '''max_new_tokens''': args.max_new_tokens,
     '''top_p''': args.top_p,
     '''top_k''': args.top_k,
     '''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0     ,						__snake_case     ,						__snake_case						)]						),
 }

 # Load evaluation dataset and metric
 _lowercase							      =load_dataset('''openai_humaneval'''						)
 _lowercase							      =load_metric('''code_eval'''						)

 _lowercase							      =args.num_tasks if args.num_tasks is not None else len(human_eval['''test''']						)
 _lowercase							      =args.n_samples // args.batch_size

 _lowercase							      =TokenizedDataset(__snake_case     ,						human_eval['''test''']     ,						n_copies=__snake_case     ,						n_tasks=__snake_case						)
 # do not confuse args.batch_size, which is actually the num_return_sequences
 _lowercase							      =DataLoader(__snake_case     ,						batch_size=1						)

 # Run a quick test to see if code evaluation is enabled
 try:
  _lowercase							      =code_eval_metric.compute(references=['''''']     ,						predictions=[['''''']]						)
 except ValueError as exception:
  print(
      '''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
      ''' flag to enable code evaluation.'''						)
  raise exception

 _lowercase      ,    _lowercase							      =accelerator.prepare(__snake_case     ,						__snake_case						)

 _lowercase							      =complete_code(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						n_tasks=__snake_case     ,						batch_size=args.batch_size     ,						**__snake_case     ,						)

 if accelerator.is_main_process:
  _lowercase							      =[]

  for task in tqdm(range(__snake_case						)						):
   _lowercase							      =human_eval['''test'''][task]['''test''']
   _lowercase							      =F"check({human_eval['test'][task]['entry_point']})"
   references.append('''\n''' + test_func + '''\n''' + entry_point						)

  # Evaluate completions with "code_eval" metric
  _lowercase      ,    _lowercase							      =code_eval_metric.compute(
      references=__snake_case     ,						predictions=__snake_case     ,						num_workers=args.num_workers						)
  print(F"Results: {pass_at_k}"						)

  # Save results to json file
  with open(args.output_file     ,						'''w'''						) as fp:
   json.dump(__snake_case     ,						__snake_case						)


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
  main()

