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


a  :      str            =    ["""\nclass""", """\ndef""", """\n#""", """\n@""", """\nprint""", """\nif"""]

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=None       ,						lowerCamelCase__=1 ):
       lowerCAmelCase_:							Optional[int]       =	tokenizer
       lowerCAmelCase_:							int       =	dataset
       lowerCAmelCase_:							List[Any]       =	len(lowerCamelCase__ ) if n_tasks is None else n_tasks
       lowerCAmelCase_:							List[Any]       =	n_copies




    def __iter__(    self ):
       lowerCAmelCase_:							List[str]       =	[]
       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() )
       lowerCAmelCase_:							Union[str, Any]       =	self.tokenizer(lowerCamelCase__       ,						padding=lowerCamelCase__       ,						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 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							List[Any]       =	start_length
       lowerCAmelCase_:							Tuple       =	eof_strings
       lowerCAmelCase_:							str       =	tokenizer




    def __call__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
       lowerCAmelCase_:							int       =	[]
       for decoded_generation in decoded_generations:
          done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
       return all(lowerCamelCase__ )





def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							List[str]       =	re.split("(%s)" % "|".join(lowercase					)							,				lowercase					)
   # last string should be ""
   return "".join(string_list[:-2]					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase=20							,				**lowercase					):
   lowerCAmelCase_:							str       =	defaultdict(lowercase					)  # dict of list of generated tokens
   for step, batch in tqdm(enumerate(lowercase					)					):
      with torch.no_grad():
         lowerCAmelCase_:							Union[str, Any]       =	batch["ids"].shape[-1]
         lowerCAmelCase_:							List[str]       =	accelerator.unwrap_model(lowercase					).generate(
             input_ids=batch["ids"][:, : batch["input_len"]]							,				num_return_sequences=lowercase							,				**lowercase					)
         # each task is generated batch_size times
         lowerCAmelCase_:							List[str]       =	batch["task_id"].repeat(lowercase					)
         lowerCAmelCase_:							List[str]       =	accelerator.pad_across_processes(
             lowercase							,				dim=1							,				pad_index=tokenizer.pad_token_id					)

         lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	accelerator.gather((generated_tokens, generated_tasks)					)
         lowerCAmelCase_:							Any       =	generated_tokens.cpu().numpy()
         lowerCAmelCase_:							Union[str, Any]       =	generated_tasks.cpu().numpy()

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

   lowerCAmelCase_:							Any       =	[[] for _ in range(lowercase					)]
   for task, generated_tokens in gen_token_dict.items():
      for s in generated_tokens:
         lowerCAmelCase_:							str       =	tokenizer.decode(lowercase							,				skip_special_tokens=lowercase							,				clean_up_tokenization_spaces=lowercase					)
         code_gens[task].append(remove_last_block(lowercase					)					)
   return code_gens
def 		snake_case__	(		):
   # Setup configuration
   lowerCAmelCase_:							Optional[Any]       =	HfArgumentParser(lowercase					)
   lowerCAmelCase_:							List[Any]       =	parser.parse_args()

   transformers.logging.set_verbosity_error()
   # enables code execution in code_eval metric
   lowerCAmelCase_:							Dict       =	args.HF_ALLOW_CODE_EVAL
   # make sure tokenizer plays nice with multiprocessing
   lowerCAmelCase_:							List[Any]       =	"false"

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

   # Use dataset load to feed to accelerate
   lowerCAmelCase_:							List[str]       =	Accelerator()
   set_seed(args.seed							,				device_specific=lowercase					)

   # Load model and tokenizer
   lowerCAmelCase_:							List[Any]       =	AutoTokenizer.from_pretrained(args.model_ckpt					)
   lowerCAmelCase_:							Any       =	tokenizer.eos_token
   lowerCAmelCase_:							int       =	AutoModelForCausalLM.from_pretrained(args.model_ckpt					)

   # Generation settings
   lowerCAmelCase_:							Tuple       =	{
       "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							,				lowercase							,				lowercase					)]					),
   }

   # Load evaluation dataset and metric
   lowerCAmelCase_:							int       =	load_dataset("openai_humaneval"					)
   lowerCAmelCase_:							Optional[Any]       =	load_metric("code_eval"					)

   lowerCAmelCase_:							int       =	args.num_tasks if args.num_tasks is not None else len(human_eval["test"]					)
   lowerCAmelCase_:							str       =	args.n_samples // args.batch_size

   lowerCAmelCase_:							Union[str, Any]       =	TokenizedDataset(lowercase							,				human_eval["test"]							,				n_copies=lowercase							,				n_tasks=lowercase					)
   # do not confuse args.batch_size, which is actually the num_return_sequences
   lowerCAmelCase_:							List[Any]       =	DataLoader(lowercase							,				batch_size=1					)

   # Run a quick test to see if code evaluation is enabled
   try:
      lowerCAmelCase_:							Dict       =	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

   lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	accelerator.prepare(lowercase							,				lowercase					)

   lowerCAmelCase_:							int       =	complete_code(
       lowercase							,				lowercase							,				lowercase							,				lowercase							,				n_tasks=lowercase							,				batch_size=args.batch_size							,				**lowercase							,				)

   if accelerator.is_main_process:
      lowerCAmelCase_:							str       =	[]

      for task in tqdm(range(lowercase					)					):
         lowerCAmelCase_:							int       =	human_eval["test"][task]["test"]
         lowerCAmelCase_:							Union[str, Any]       =	F'''check({human_eval["test"][task]["entry_point"]})'''
         references.append("\n" + test_func + "\n" + entry_point					)

      # Evaluate completions with "code_eval" metric
      lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	code_eval_metric.compute(
          references=lowercase							,				predictions=lowercase							,				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(lowercase							,				lowercase					)


# 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()
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


a  :      List[Any]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {
    """artists_file""": """artists.json""",
    """lyrics_file""": """lyrics.json""",
    """genres_file""": """genres.json""",
}

a  :      str            =    {
    """artists_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
    },
    """genres_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
    },
    """lyrics_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
    },
}

a  :      Any            =    {
    """jukebox""": 5_1_2,
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[int]          =    VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE:				List[Any]          =    PRETRAINED_LYRIC_TOKENS_SIZES
    SCREAMING_SNAKE_CASE:				Dict          =    ['input_ids', 'attention_mask']




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=["v3", "v2", "v2"]       ,						lowerCamelCase__=512       ,						lowerCamelCase__=5       ,						lowerCamelCase__="<|endoftext|>"       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							List[str]       =	AddedToken(lowerCamelCase__       ,						lstrip=lowerCamelCase__       ,						rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__       ,						lowerCamelCase__ ) else unk_token
       super().__init__(
           unk_token=lowerCamelCase__       ,						n_genres=lowerCamelCase__       ,						version=lowerCamelCase__       ,						max_n_lyric_tokens=lowerCamelCase__       ,						**lowerCamelCase__       ,						)
       lowerCAmelCase_:							Any       =	version
       lowerCAmelCase_:							str       =	max_n_lyric_tokens
       lowerCAmelCase_:							Optional[Any]       =	n_genres

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							List[Any]       =	json.load(lowerCamelCase__ )

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							Tuple       =	json.load(lowerCamelCase__ )

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							Tuple       =	json.load(lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	R"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
       # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
       if len(self.lyrics_encoder ) == 79:
          lowerCAmelCase_:							List[Any]       =	oov.replace(R"\-'"       ,						R"\-+'" )

       lowerCAmelCase_:							List[Any]       =	regex.compile(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	{v: k for k, v in self.artists_encoder.items()}
       lowerCAmelCase_:							List[str]       =	{v: k for k, v in self.genres_encoder.items()}
       lowerCAmelCase_:							int       =	{v: k for k, v in self.lyrics_encoder.items()}




    @property
    def      _a    (    self ):
       return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )




    def      _a    (    self ):
       return dict(self.artists_encoder       ,						self.genres_encoder       ,						self.lyrics_encoder )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	[self.artists_encoder.get(lowerCamelCase__       ,						0 ) for artist in list_artists]
       for genres in range(len(lowerCamelCase__ ) ):
          lowerCAmelCase_:							Tuple       =	[self.genres_encoder.get(lowerCamelCase__       ,						0 ) for genre in list_genres[genres]]
          lowerCAmelCase_:							Any       =	list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))

       lowerCAmelCase_:							Optional[Any]       =	[[self.lyrics_encoder.get(lowerCamelCase__       ,						0 ) for character in list_lyrics[0]], [], []]
       return artists_id, list_genres, lyric_ids




    def      _a    (    self       ,						lowerCamelCase__ ):
       return list(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.prepare_for_tokenization(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	self._tokenize(lowerCamelCase__ )
       return artist, genre, lyrics




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = False ):
       for idx in range(len(self.version ) ):
          if self.version[idx] == "v3":
             lowerCAmelCase_:							List[Any]       =	artists[idx].lower()
             lowerCAmelCase_:							Tuple       =	[genres[idx].lower()]
          else:
             lowerCAmelCase_:							Optional[Any]       =	self._normalize(artists[idx] ) + ".v2"
             lowerCAmelCase_:							int       =	[
                 self._normalize(lowerCamelCase__ ) + ".v2" for genre in genres[idx].split("_" )
             ]  # split is for the full dictionary with combined genres

       if self.version[0] == "v2":
          lowerCAmelCase_:							List[Any]       =	regex.compile(R"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+" )
          lowerCAmelCase_:							Optional[int]       =	"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
          lowerCAmelCase_:							Union[str, Any]       =	{vocab[index]: index + 1 for index in range(len(lowerCamelCase__ ) )}
          lowerCAmelCase_:							List[Any]       =	0
          lowerCAmelCase_:							str       =	len(lowerCamelCase__ ) + 1
          lowerCAmelCase_:							Union[str, Any]       =	self.vocab
          lowerCAmelCase_:							List[str]       =	{v: k for k, v in self.vocab.items()}
          lowerCAmelCase_:							Optional[Any]       =	""
       else:
          lowerCAmelCase_:							Optional[int]       =	regex.compile(R"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+" )

       lowerCAmelCase_:							int       =	self._run_strip_accents(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	lyrics.replace("\\"       ,						"\n" )
       lowerCAmelCase_:							List[str]       =	self.out_of_vocab.sub(""       ,						lowerCamelCase__ ), [], []
       return artists, genres, lyrics




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	unicodedata.normalize("NFD"       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	[]
       for char in text:
          lowerCAmelCase_:							Optional[Any]       =	unicodedata.category(lowerCamelCase__ )
          if cat == "Mn":
             continue
          output.append(lowerCamelCase__ )
       return "".join(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	(
           [chr(lowerCamelCase__ ) for i in range(ord("a" )       ,						ord("z" ) + 1 )]
           + [chr(lowerCamelCase__ ) for i in range(ord("A" )       ,						ord("Z" ) + 1 )]
           + [chr(lowerCamelCase__ ) for i in range(ord("0" )       ,						ord("9" ) + 1 )]
           + ["."]
       )
       lowerCAmelCase_:							List[str]       =	frozenset(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	re.compile(R"_+" )
       lowerCAmelCase_:							List[Any]       =	"".join([c if c in accepted else "_" for c in text.lower()] )
       lowerCAmelCase_:							Union[str, Any]       =	pattern.sub("_"       ,						lowerCamelCase__ ).strip("_" )
       return text




    def      _a    (    self       ,						lowerCamelCase__ ):
       return " ".join(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = False ):
       # Convert to TensorType
       if not isinstance(lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							str       =	TensorType(lowerCamelCase__ )

       # Get a function reference for the correct framework
       if tensor_type == TensorType.TENSORFLOW:
          if not is_tf_available():
             raise ImportError(
                 "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed." )
          import tensorflow as tf

          lowerCAmelCase_:							Optional[int]       =	tf.constant
          lowerCAmelCase_:							str       =	tf.is_tensor
       elif tensor_type == TensorType.PYTORCH:
          if not is_torch_available():
             raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed." )
          import torch

          lowerCAmelCase_:							Optional[Any]       =	torch.tensor
          lowerCAmelCase_:							Optional[Any]       =	torch.is_tensor
       elif tensor_type == TensorType.JAX:
          if not is_flax_available():
             raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed." )
          import jax.numpy as jnp  # noqa: F811

          lowerCAmelCase_:							Optional[Any]       =	jnp.array
          lowerCAmelCase_:							List[str]       =	_is_jax
       else:
          lowerCAmelCase_:							Optional[int]       =	np.asarray
          lowerCAmelCase_:							int       =	_is_numpy

       # Do the tensor conversion in batch

       try:
          if prepend_batch_axis:
             lowerCAmelCase_:							Any       =	[inputs]

          if not is_tensor(lowerCamelCase__ ):
             lowerCAmelCase_:							Optional[int]       =	as_tensor(lowerCamelCase__ )
       except:  # noqa E722
          raise ValueError(
              "Unable to create tensor, you should probably activate truncation and/or padding "
              "with 'padding=True' 'truncation=True' to have batched tensors with the same length." )

       return inputs




    def __call__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=""       ,						lowerCamelCase__="pt" ):
       lowerCAmelCase_:							Any       =	[0, 0, 0]
       lowerCAmelCase_:							Dict       =	[artist] * len(self.version )
       lowerCAmelCase_:							int       =	[genres] * len(self.version )

       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.tokenize(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self._convert_token_to_id(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							List[Any]       =	[-INFINITY] * len(full_tokens[-1] )
       lowerCAmelCase_:							Optional[Any]       =	[
           self.convert_to_tensors(
               [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]       ,						tensor_type=lowerCamelCase__ )
           for i in range(len(self.version ) )
       ]
       return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks} )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       if not os.path.isdir(lowerCamelCase__ ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
          return

       lowerCAmelCase_:							Dict       =	os.path.join(
           lowerCamelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"] )
       with open(lowerCamelCase__       ,						"w"       ,						encoding="utf-8" ) as f:
          f.write(json.dumps(self.artists_encoder       ,						ensure_ascii=lowerCamelCase__ ) )

       lowerCAmelCase_:							Optional[Any]       =	os.path.join(
           lowerCamelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"] )
       with open(lowerCamelCase__       ,						"w"       ,						encoding="utf-8" ) as f:
          f.write(json.dumps(self.genres_encoder       ,						ensure_ascii=lowerCamelCase__ ) )

       lowerCAmelCase_:							Dict       =	os.path.join(
           lowerCamelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"] )
       with open(lowerCamelCase__       ,						"w"       ,						encoding="utf-8" ) as f:
          f.write(json.dumps(self.lyrics_encoder       ,						ensure_ascii=lowerCamelCase__ ) )

       return (artists_file, genres_file, lyrics_file)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	self.artists_decoder.get(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	[self.genres_decoder.get(lowerCamelCase__ ) for genre in genres_index]
       lowerCAmelCase_:							Optional[int]       =	[self.lyrics_decoder.get(lowerCamelCase__ ) for character in lyric_index]
       return artist, genres, lyrics