
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed

import numpy as np

import datasets

from .execute import check_correctness


__lowerCAmelCase					:			Optional[int]			      =		"\\n@misc{chen2021evaluating,\n      title={Evaluating Large Language Models Trained on Code},\n      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n      year={2021},\n      eprint={2107.03374},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n"

__lowerCAmelCase					:			List[str]			      =		"\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\n(https://arxiv.org/abs/2107.03374).\n"


__lowerCAmelCase					:			Optional[Any]			      =		"\nCalculates how good are predictions given some references, using certain scores\nArgs:\n    predictions: list of candidates to evaluate. Each candidates should be a list\n        of strings with several code candidates to solve the problem.\n    references: a list with a test for each prediction. Each test should evaluate the\n        correctness of a code candidate.\n    k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n    num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n    timeout:\nReturns:\n    pass_at_k: dict with pass rates for each k\n    results: dict with granular results of each unittest\nExamples:\n    >>> code_eval = datasets.load_metric(\"code_eval\")\n    >>> test_cases = [\"assert add(2,3)==5\"]\n    >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\n    >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n    >>> print(pass_at_k)\n    {'pass@1': 0.5, 'pass@2': 1.0}\n"


__lowerCAmelCase					:			Dict			      =		"\n################################################################################\n                                  !!!WARNING!!!\n################################################################################\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\n\n################################################################################\\n"

__lowerCAmelCase					:			Union[str, Any]			      =		"The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE."


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION      )
class 						__lowerCAmelCase	(	datasets.Metric      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Dict				):
          return datasets.MetricInfo(
              # This is the description that will appear on the metrics page.
              description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
                  {
                      '''predictions''': datasets.Sequence(datasets.Value('''string'''				)				),
                      '''references''': datasets.Value('''string'''				),
                  }				)				,       homepage='''https://github.com/openai/human-eval'''				,       codebase_urls=['''https://github.com/openai/human-eval''']				,       reference_urls=['''https://github.com/openai/human-eval''']				,       license=_LICENSE				,       )




      def 		snake_case_			(      self		:       str				,       _snake_case		:       Dict				,       _snake_case		:       List[Any]				,       _snake_case		:       int=[1, 10, 100]				,       _snake_case		:       int=4				,       _snake_case		:       Optional[Any]=3.0				):

          if os.getenv('''HF_ALLOW_CODE_EVAL'''				,       0				) != "1":
              raise ValueError(_WARNING				)

          if os.name == "nt":
              raise NotImplementedError('''This metric is currently not supported on Windows.'''				)

          with ThreadPoolExecutor(max_workers=_snake_case				) as executor:
              __lowercase		:			Dict	    =       []
              __lowercase		:			List[Any]	    =       Counter()
              __lowercase		:			Tuple	    =       0
              __lowercase		:			Optional[Any]	    =       defaultdict(_snake_case				)

              for task_id, (candidates, test_case) in enumerate(zip(_snake_case				,       _snake_case				)				):
                  for candidate in candidates:
                      __lowercase		:			Any	    =       candidate + '''\n''' + test_case
                      __lowercase		:			List[Any]	    =       (test_program, timeout, task_id, completion_id[task_id])
                      __lowercase		:			Optional[int]	    =       executor.submit(_snake_case				,       *_snake_case				)
                      futures.append(_snake_case				)
                      completion_id[task_id] += 1
                      n_samples += 1

              for future in as_completed(_snake_case				):
                  __lowercase		:			str	    =       future.result()
                  results[result["task_id"]].append((result['''completion_id'''], result)				)

          __lowercase    ,      __lowercase		:			List[str]	    =       [], []
          for result in results.values():
              result.sort()
              __lowercase		:			Optional[int]	    =       [r[1]['''passed'''] for r in result]
              total.append(len(_snake_case				)				)
              correct.append(sum(_snake_case				)				)
          __lowercase		:			int	    =       np.array(_snake_case				)
          __lowercase		:			Union[str, Any]	    =       np.array(_snake_case				)

          __lowercase		:			Union[str, Any]	    =       k
          __lowercase		:			List[Any]	    =       {F'pass@{k}': estimate_pass_at_k(_snake_case				,       _snake_case				,       _snake_case				).mean() for k in ks if (total >= k).all()}

          return pass_at_k, results






def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						int:

    def estimator(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) -> float:
        if n - c < k:
            return 1.0
        return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1	,					n + 1	)	)

    if isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
        __lowercase		:			Any	    =       itertools.repeat(__lowerCAmelCase	,					len(__lowerCAmelCase	)	)
    else:
        assert len(__lowerCAmelCase	) == len(__lowerCAmelCase	)
        __lowercase		:			Any	    =       iter(__lowerCAmelCase	)

    return np.array([estimator(int(__lowerCAmelCase	)	,					int(__lowerCAmelCase	)	,					__lowerCAmelCase	) for n, c in zip(__lowerCAmelCase	,					__lowerCAmelCase	)]	)




import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__lowerCAmelCase					:			Tuple			      =		logging.get_logger(__name__)


__lowerCAmelCase					:			int			      =		{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
__lowerCAmelCase					:			List[Any]			      =		{
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

__lowerCAmelCase					:			List[Any]			      =		{
    "allenai/led-base-16384": 16_384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     UpperCAmelCase_  (						) ->						List[Any]:
    __lowercase		:			Union[str, Any]	    =       (
        list(range(ord('''!'''	)	,					ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	)	,					ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	)	,					ord('''ÿ'''	) + 1	)	)
    )
    __lowercase		:			Any	    =       bs[:]
    __lowercase		:			Union[str, Any]	    =       0
    for b in range(2**8	):
        if b not in bs:
            bs.append(__lowerCAmelCase	)
            cs.append(2**8 + n	)
            n += 1
    __lowercase		:			List[str]	    =       [chr(__lowerCAmelCase	) for n in cs]
    return dict(zip(__lowerCAmelCase	,					__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						List[str]:
    __lowercase		:			Optional[int]	    =       set()
    __lowercase		:			Dict	    =       word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)	)
        __lowercase		:			List[Any]	    =       char
    return pairs





class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			str        =    VOCAB_FILES_NAMES
      A__	:			Any        =    PRETRAINED_VOCAB_FILES_MAP
      A__	:			int        =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__	:			Union[str, Any]        =    ['''input_ids''', '''attention_mask''']




      def __init__(      self		:       Dict				,       _snake_case		:       List[Any]				,       _snake_case		:       Dict				,       _snake_case		:       int="replace"				,       _snake_case		:       Optional[int]="<s>"				,       _snake_case		:       List[Any]="</s>"				,       _snake_case		:       str="</s>"				,       _snake_case		:       Optional[int]="<s>"				,       _snake_case		:       List[Any]="<unk>"				,       _snake_case		:       Optional[Any]="<pad>"				,       _snake_case		:       List[Any]="<mask>"				,       _snake_case		:       Optional[Any]=False				,       **_snake_case		:       Dict				,       ):
          __lowercase		:			str	    =       AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case				) if isinstance(_snake_case				,       _snake_case				) else bos_token
          __lowercase		:			List[Any]	    =       AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case				) if isinstance(_snake_case				,       _snake_case				) else eos_token
          __lowercase		:			Union[str, Any]	    =       AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case				) if isinstance(_snake_case				,       _snake_case				) else sep_token
          __lowercase		:			List[str]	    =       AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case				) if isinstance(_snake_case				,       _snake_case				) else cls_token
          __lowercase		:			Any	    =       AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case				) if isinstance(_snake_case				,       _snake_case				) else unk_token
          __lowercase		:			Optional[Any]	    =       AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case				) if isinstance(_snake_case				,       _snake_case				) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          __lowercase		:			Any	    =       AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case				) if isinstance(_snake_case				,       _snake_case				) else mask_token

          super().__init__(
              errors=_snake_case				,       bos_token=_snake_case				,       eos_token=_snake_case				,       unk_token=_snake_case				,       sep_token=_snake_case				,       cls_token=_snake_case				,       pad_token=_snake_case				,       mask_token=_snake_case				,       add_prefix_space=_snake_case				,       **_snake_case				,       )

          with open(_snake_case				,       encoding='''utf-8'''				) as vocab_handle:
              __lowercase		:			List[str]	    =       json.load(_snake_case				)
          __lowercase		:			Optional[Any]	    =       {v: k for k, v in self.encoder.items()}
          __lowercase		:			Dict	    =       errors  # how to handle errors in decoding
          __lowercase		:			str	    =       bytes_to_unicode()
          __lowercase		:			Dict	    =       {v: k for k, v in self.byte_encoder.items()}
          with open(_snake_case				,       encoding='''utf-8'''				) as merges_handle:
              __lowercase		:			Any	    =       merges_handle.read().split('''\n'''				)[1:-1]
          __lowercase		:			Tuple	    =       [tuple(merge.split()				) for merge in bpe_merges]
          __lowercase		:			Dict	    =       dict(zip(_snake_case				,       range(len(_snake_case				)				)				)				)
          __lowercase		:			Dict	    =       {}
          __lowercase		:			str	    =       add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          __lowercase		:			str	    =       re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''				)




      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 		snake_case_			(      self		:       Dict				):
          return len(self.encoder				)




      def 		snake_case_			(      self		:       List[str]				):
          return dict(self.encoder				,       **self.added_tokens_encoder				)




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       str				):
          if token in self.cache:
              return self.cache[token]
          __lowercase		:			Optional[Any]	    =       tuple(_snake_case				)
          __lowercase		:			str	    =       get_pairs(_snake_case				)

          if not pairs:
              return token

          while True:
              __lowercase		:			Dict	    =       min(_snake_case				,       key=lambda _snake_case				: self.bpe_ranks.get(_snake_case				,       float('''inf'''				)				)				)
              if bigram not in self.bpe_ranks:
                  break
              __lowercase    ,      __lowercase		:			Tuple	    =       bigram
              __lowercase		:			Dict	    =       []
              __lowercase		:			int	    =       0
              while i < len(_snake_case				):
                  try:
                      __lowercase		:			List[Any]	    =       word.index(_snake_case				,       _snake_case				)
                  except ValueError:
                      new_word.extend(word[i:]				)
                      break
                  else:
                      new_word.extend(word[i:j]				)
                      __lowercase		:			Optional[int]	    =       j

                  if word[i] == first and i < len(_snake_case				) - 1 and word[i + 1] == second:
                      new_word.append(first + second				)
                      i += 2
                  else:
                      new_word.append(word[i]				)
                      i += 1
              __lowercase		:			str	    =       tuple(_snake_case				)
              __lowercase		:			Tuple	    =       new_word
              if len(_snake_case				) == 1:
                  break
              else:
                  __lowercase		:			Any	    =       get_pairs(_snake_case				)
          __lowercase		:			Union[str, Any]	    =       ''' '''.join(_snake_case				)
          __lowercase		:			Union[str, Any]	    =       word
          return word




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       List[Any]				):
          __lowercase		:			Optional[int]	    =       []
          for token in re.findall(self.pat				,       _snake_case				):
              __lowercase		:			Dict	    =       ''''''.join(
                  self.byte_encoder[b] for b in token.encode('''utf-8'''				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_snake_case				).split(''' '''				)				)
          return bpe_tokens




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       List[Any]				):
          return self.encoder.get(_snake_case				,       self.encoder.get(self.unk_token				)				)




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       List[Any]				):
          return self.decoder.get(_snake_case				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Dict				):
          __lowercase		:			List[Any]	    =       ''''''.join(_snake_case				)
          __lowercase		:			Any	    =       bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''				,       errors=self.errors				)
          return text




      def 		snake_case_			(      self		:       Optional[Any]				,       _snake_case		:       str				,       _snake_case		:       Optional[str] = None				):
          if not os.path.isdir(_snake_case				):
              logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
              return
          __lowercase		:			Union[str, Any]	    =       os.path.join(
              _snake_case				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
          __lowercase		:			str	    =       os.path.join(
              _snake_case				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

          with open(_snake_case				,       '''w'''				,       encoding='''utf-8'''				) as f:
              f.write(json.dumps(self.encoder				,       indent=2				,       sort_keys=_snake_case				,       ensure_ascii=_snake_case				) + '''\n'''				)

          __lowercase		:			List[str]	    =       0
          with open(_snake_case				,       '''w'''				,       encoding='''utf-8'''				) as writer:
              writer.write('''#version: 0.2\n'''				)
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,       key=lambda _snake_case				: kv[1]				):
                  if index != token_index:
                      logger.warning(
                          F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                          ''' Please check that the tokenizer is not corrupted!'''				)
                      __lowercase		:			List[str]	    =       token_index
                  writer.write(''' '''.join(_snake_case				) + '''\n'''				)
                  index += 1

          return vocab_file, merge_file




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				):
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __lowercase		:			int	    =       [self.cls_token_id]
          __lowercase		:			int	    =       [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				,       _snake_case		:       bool = False				):
          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_snake_case				,       token_ids_a=_snake_case				,       already_has_special_tokens=_snake_case				)

          if token_ids_a is None:
              return [1] + ([0] * len(_snake_case				)) + [1]
          return [1] + ([0] * len(_snake_case				)) + [1, 1] + ([0] * len(_snake_case				)) + [1]




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       List[int]				,       _snake_case		:       Optional[List[int]] = None				):
          __lowercase		:			Dict	    =       [self.sep_token_id]
          __lowercase		:			List[str]	    =       [self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep				) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]




      def 		snake_case_			(      self		:       List[str]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Optional[int]=False				,       **_snake_case		:       List[Any]				):
          __lowercase		:			int	    =       kwargs.pop('''add_prefix_space'''				,       self.add_prefix_space				)
          if (is_split_into_words or add_prefix_space) and (len(_snake_case				) > 0 and not text[0].isspace()):
              __lowercase		:			Optional[Any]	    =       ''' ''' + text
          return (text, kwargs)




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       Union[Dict[str, EncodedInput], BatchEncoding]				,       _snake_case		:       Optional[int] = None				,       _snake_case		:       PaddingStrategy = PaddingStrategy.DO_NOT_PAD				,       _snake_case		:       Optional[int] = None				,       _snake_case		:       Optional[bool] = None				,       ):
          __lowercase		:			Optional[Any]	    =       super()._pad(
              encoded_inputs=_snake_case				,       max_length=_snake_case				,       padding_strategy=_snake_case				,       pad_to_multiple_of=_snake_case				,       return_attention_mask=_snake_case				,       )

          # Load from model defaults
          if return_attention_mask is None:
              __lowercase		:			List[str]	    =       '''attention_mask''' in self.model_input_names

          if return_attention_mask and "global_attention_mask" in encoded_inputs:
              __lowercase		:			Dict	    =       encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              __lowercase		:			Tuple	    =       len(encoded_inputs['''global_attention_mask''']				) != len(_snake_case				)

              if needs_to_be_padded:
                  __lowercase		:			List[Any]	    =       len(_snake_case				) - len(encoded_inputs['''global_attention_mask''']				)

                  if self.padding_side == "right":
                      # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                      __lowercase		:			Union[str, Any]	    =       (
                          encoded_inputs['''global_attention_mask'''] + [-1] * difference
                      )
                  elif self.padding_side == "left":
                      __lowercase		:			Union[str, Any]	    =       [-1] * difference + encoded_inputs[
                          '''global_attention_mask'''
                      ]
                  else:
                      raise ValueError('''Invalid padding strategy:''' + str(self.padding_side				)				)

          return encoded_inputs


