





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


UpperCamelCase__      ='\\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'

UpperCamelCase__      ='\\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'


UpperCamelCase__      ='\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'


UpperCamelCase__      ='\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'

UpperCamelCase__      ='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     UpperCamelCase_			(      self							)			->     int:
            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     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase=[1, 1_0, 1_0_0]    ,						__lowerCamelCase=4    ,						__lowerCamelCase=3.0							)			->     Dict:

            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=__lowerCamelCase							) as executor:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							[]
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							Counter()
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							0
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							defaultdict(__lowerCamelCase							)

                 for task_id, (candidates, test_case) in enumerate(zip(__lowerCamelCase    ,						__lowerCamelCase							)							):
                      for candidate in candidates:
                           _SCREAMING_SNAKE_CASE       :	Tuple						    =							candidate + "\n" + test_case
                           _SCREAMING_SNAKE_CASE       :	List[str]						    =							(test_program, timeout, task_id, completion_id[task_id])
                           _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							executor.submit(__lowerCamelCase    ,						*__lowerCamelCase							)
                           futures.append(__lowerCamelCase							)
                           completion_id[task_id] += 1
                           n_samples += 1

                 for future in as_completed(__lowerCamelCase							):
                      _SCREAMING_SNAKE_CASE       :	int						    =							future.result()
                      results[result["task_id"]].append((result["completion_id"], result)							)

            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Dict						    =							[], []
            for result in results.values():
                 result.sort()
                 _SCREAMING_SNAKE_CASE       :	Any						    =							[r[1]["passed"] for r in result]
                 total.append(len(__lowerCamelCase							)							)
                 correct.append(sum(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							np.array(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							np.array(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	int						    =							k
            _SCREAMING_SNAKE_CASE       :	int						    =							{F"""pass@{k}""": estimate_pass_at_k(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							).mean() for k in ks if (total >= k).all()}

            return pass_at_k, results






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):

     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	):
          _SCREAMING_SNAKE_CASE       :	str						    =							itertools.repeat(__lowerCamelCase,       len(__lowerCamelCase	)	)
     else:
          assert len(__lowerCamelCase	) == len(__lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	int						    =							iter(__lowerCamelCase	)

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






import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402


UpperCamelCase__      ={
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 128,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 50,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 10,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 10,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}





@is_staging_test
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       @classmethod
       def     UpperCamelCase_			(      cls							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							TOKEN
            HfFolder.save_token(__lowerCamelCase							)




       @classmethod
       def     UpperCamelCase_			(      cls							)			->     List[str]:
            try:
                 delete_repo(token=cls._token    ,						repo_id="test-config"							)
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,						repo_id="valid_org/test-config-org"							)
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,						repo_id="test-dynamic-config"							)
            except HTTPError:
                 pass




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							BertConfig(
                vocab_size=9_9    ,						hidden_size=3_2    ,						num_hidden_layers=5    ,						num_attention_heads=4    ,						intermediate_size=3_7							)
            config.push_to_hub("test-config"    ,						use_auth_token=self._token							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BertConfig.from_pretrained(F"""{USER}/test-config"""							)
            for k, v in config.to_dict().items():
                 if k != "transformers_version":
                      self.assertEqual(__lowerCamelCase    ,						getattr(__lowerCamelCase    ,						__lowerCamelCase							)							)

        # Reset repo
            delete_repo(token=self._token    ,						repo_id="test-config"							)

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(__lowerCamelCase    ,						repo_id="test-config"    ,						push_to_hub=__lowerCamelCase    ,						use_auth_token=self._token							)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							BertConfig.from_pretrained(F"""{USER}/test-config"""							)
            for k, v in config.to_dict().items():
                 if k != "transformers_version":
                      self.assertEqual(__lowerCamelCase    ,						getattr(__lowerCamelCase    ,						__lowerCamelCase							)							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	int						    =							BertConfig(
                vocab_size=9_9    ,						hidden_size=3_2    ,						num_hidden_layers=5    ,						num_attention_heads=4    ,						intermediate_size=3_7							)
            config.push_to_hub("valid_org/test-config-org"    ,						use_auth_token=self._token							)

            _SCREAMING_SNAKE_CASE       :	str						    =							BertConfig.from_pretrained("valid_org/test-config-org"							)
            for k, v in config.to_dict().items():
                 if k != "transformers_version":
                      self.assertEqual(__lowerCamelCase    ,						getattr(__lowerCamelCase    ,						__lowerCamelCase							)							)

        # Reset repo
            delete_repo(token=self._token    ,						repo_id="valid_org/test-config-org"							)

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(
                     __lowerCamelCase    ,						repo_id="valid_org/test-config-org"    ,						push_to_hub=__lowerCamelCase    ,						use_auth_token=self._token							)

            _SCREAMING_SNAKE_CASE       :	int						    =							BertConfig.from_pretrained("valid_org/test-config-org"							)
            for k, v in config.to_dict().items():
                 if k != "transformers_version":
                      self.assertEqual(__lowerCamelCase    ,						getattr(__lowerCamelCase    ,						__lowerCamelCase							)							)







       def     UpperCamelCase_			(      self							)			->     int:
            CustomConfig.register_for_auto_class()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							CustomConfig(attribute=4_2							)

            config.push_to_hub("test-dynamic-config"    ,						use_auth_token=self._token							)

            # This has added the proper auto_map field to the config
            self.assertDictEqual(config.auto_map    ,						{"AutoConfig": "custom_configuration.CustomConfig"}							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							AutoConfig.from_pretrained(F"""{USER}/test-dynamic-config"""    ,						trust_remote_code=__lowerCamelCase							)
            # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
            self.assertEqual(new_config.__class__.__name__    ,						"CustomConfig"							)
            self.assertEqual(new_config.attribute    ,						4_2							)





class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	int						    =							GPTaConfig()

            # attempt to modify each of int/float/bool/str config records and verify they were updated
            _SCREAMING_SNAKE_CASE       :	Any						    =							c.n_embd + 1  # int
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							c.resid_pdrop + 1.0  # float
            _SCREAMING_SNAKE_CASE       :	str						    =							not c.scale_attn_weights  # bool
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							c.summary_type + "foo"  # str
            c.update_from_string(
                F"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"""							)
            self.assertEqual(__lowerCamelCase    ,						c.n_embd    ,						"mismatch for key: n_embd"							)
            self.assertEqual(__lowerCamelCase    ,						c.resid_pdrop    ,						"mismatch for key: resid_pdrop"							)
            self.assertEqual(__lowerCamelCase    ,						c.scale_attn_weights    ,						"mismatch for key: scale_attn_weights"							)
            self.assertEqual(__lowerCamelCase    ,						c.summary_type    ,						"mismatch for key: summary_type"							)




       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	str						    =							PretrainedConfig()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[key for key in base_config.__dict__ if key not in config_common_kwargs]
            # If this part of the test fails, you have arguments to addin config_common_kwargs above.
            self.assertListEqual(
                __lowerCamelCase    ,						["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]							)
            _SCREAMING_SNAKE_CASE       :	int						    =							[key for key, value in config_common_kwargs.items() if value == getattr(__lowerCamelCase    ,						__lowerCamelCase							)]
            if len(__lowerCamelCase							) > 0:
                 raise ValueError(
                     "The following keys are set with the default values in"
                     " `test_configuration_common.config_common_kwargs` pick another value for them:"
                     F""" {', '.join(__lowerCamelCase							)}."""							)




       def     UpperCamelCase_			(      self							)			->     int:
            with self.assertRaises(__lowerCamelCase							):
                 # config is in subfolder, the following should not work without specifying the subfolder
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"							)

            _SCREAMING_SNAKE_CASE       :	int						    =							BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"    ,						subfolder="bert"							)

            self.assertIsNotNone(__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     str:
            # A mock response for an HTTP head request to emulate server down
            _SCREAMING_SNAKE_CASE       :	Any						    =							mock.Mock()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							5_0_0
            _SCREAMING_SNAKE_CASE       :	Any						    =							{}
            _SCREAMING_SNAKE_CASE       :	int						    =							HTTPError
            _SCREAMING_SNAKE_CASE       :	int						    =							{}

            # Download this model to make sure it's in the cache.
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"							)

            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch("requests.Session.request"    ,						return_value=__lowerCamelCase							) as mock_head:
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"							)
                 # This check we did call the fake head request
                 mock_head.assert_called()




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            # This test is for deprecated behavior and can be removed in v5
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BertConfig.from_pretrained(
                "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"							)




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	int						    =							AutoConfig.from_pretrained("bert-base-cased"							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							["config.4.0.0.json"]

            with tempfile.TemporaryDirectory() as tmp_dir:
                 configuration.save_pretrained(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							2
                 json.dump(configuration.to_dict()    ,						open(os.path.join(__lowerCamelCase    ,						"config.4.0.0.json"							)    ,						"w"							)							)

                 # This should pick the new configuration file as the version of Transformers is > 4.0.0
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							AutoConfig.from_pretrained(__lowerCamelCase							)
                 self.assertEqual(new_configuration.hidden_size    ,						2							)

                 # Will need to be adjusted if we reach v42 and this test is still here.
                 # Should pick the old configuration file as the version of Transformers is < 4.42.0
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							["config.42.0.0.json"]
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							7_6_8
                 configuration.save_pretrained(__lowerCamelCase							)
                 shutil.move(os.path.join(__lowerCamelCase    ,						"config.4.0.0.json"							)    ,						os.path.join(__lowerCamelCase    ,						"config.42.0.0.json"							)							)
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							AutoConfig.from_pretrained(__lowerCamelCase							)
                 self.assertEqual(new_configuration.hidden_size    ,						7_6_8							)







       def     UpperCamelCase_			(      self							)			->     Dict:
            # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"hf-internal-testing/test-two-configs"

            import transformers as new_transformers

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"v4.0.0"
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							new_transformers.models.auto.AutoConfig.from_pretrained(
                __lowerCamelCase    ,						return_unused_kwargs=__lowerCamelCase							)
            self.assertEqual(new_configuration.hidden_size    ,						2							)
            # This checks `_configuration_file` ia not kept in the kwargs by mistake.
            self.assertDictEqual(__lowerCamelCase    ,						{}							)

            # Testing an older version by monkey-patching the version in the module it's used.
            import transformers as old_transformers

            _SCREAMING_SNAKE_CASE       :	Dict						    =							"v3.0.0"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							old_transformers.models.auto.AutoConfig.from_pretrained(__lowerCamelCase							)
            self.assertEqual(old_configuration.hidden_size    ,						7_6_8							)