
"""simple docstring"""





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


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

_a								=						"""\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper \"Evaluating Large Language Models Trained on Code\"
(https://arxiv.org/abs/2107.03374).
"""


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


_a								=						"""
################################################################################
                                  !!!WARNING!!!
################################################################################
The \"code_eval\" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper \"Evaluating Large
Language Models Trained on Code\" (https://arxiv.org/abs/2107.03374).

Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this
with:

>>> import os
>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"

################################################################################\
"""

_a								=						"""The MIT License

Copyright (c) OpenAI (https://openai.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the \"Software\"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE."""






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



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							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							,						__a							,						__a							,						__a=[1, 10, 1_00]							,						__a=4							,						__a=3.0)			->			int:






							'''simple docstring'''

							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=__a) as executor:
												_UpperCamelCase									=    []
												_UpperCamelCase									=    Counter()
												_UpperCamelCase									=    0
												_UpperCamelCase									=    defaultdict(__a)

												for task_id, (candidates, test_case) in enumerate(zip(__a							,						__a)):
																	for candidate in candidates:
																						_UpperCamelCase									=    candidate + '''\n''' + test_case
																						_UpperCamelCase									=    (test_program, timeout, task_id, completion_id[task_id])
																						_UpperCamelCase									=    executor.submit(__a							,						*__a)
																						futures.append(__a)
																						completion_id[task_id] += 1
																						n_samples += 1

												for future in as_completed(__a):
																	_UpperCamelCase									=    future.result()
																	results[result["task_id"]].append((result['''completion_id'''], result))

							_UpperCamelCase					,    _UpperCamelCase									=    [], []
							for result in results.values():
												result.sort()
												_UpperCamelCase									=    [r[1]['''passed'''] for r in result]
												total.append(len(__a))
												correct.append(sum(__a))
							_UpperCamelCase									=    np.array(__a)
							_UpperCamelCase									=    np.array(__a)

							_UpperCamelCase									=    k
							_UpperCamelCase									=    {F'''pass@{k}''': estimate_pass_at_k(__a							,						__a							,						__a).mean() for k in ks if (total >= k).all()}

							return pass_at_k, results





def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					def estimator(__snake_case,	__snake_case,	__snake_case      ) -> 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(__snake_case,	__snake_case      ):
										_UpperCamelCase									=    itertools.repeat(__snake_case,	len(__snake_case      )      )
					else:
										assert len(__snake_case      ) == len(__snake_case      )
										_UpperCamelCase									=    iter(__snake_case      )

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








"""simple docstring"""





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
				from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=64							,						__a=2							,						__a=3							,						__a=True							,						__a=True							,						__a=32							,						__a=5							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=10							,						__a=0.02							,						__a=[1, 16, 4, 4]							,						__a=None							,						)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    patch_size
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							_UpperCamelCase									=    (self.image_size // 32) ** 2
							_UpperCamelCase									=    num_patches + 1



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.type_sequence_label_size)

							_UpperCamelCase									=    self.get_config()

							return config, pixel_values, labels



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [4, 8, 16, 32],
							    '''num_groups''': 2,
							}

							return ViTHybridConfig(
							    image_size=self.image_size							,						patch_size=self.patch_size							,						num_channels=self.num_channels							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						is_decoder=__a							,						initializer_range=self.initializer_range							,						backbone_featmap_shape=self.backbone_featmap_shape							,						backbone_config=__a							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.type_sequence_label_size
							_UpperCamelCase									=    ViTHybridForImageClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size))




		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    config_and_inputs
							_UpperCamelCase									=    {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		lowercase__         = (
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViT does not use inputs_embeds''')
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)
												self.assertIsInstance(model.get_input_embeddings()							,						(nn.Module))
												_UpperCamelCase									=    model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__a							,						nn.Linear))



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)
												_UpperCamelCase									=    inspect.signature(model.forward)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_UpperCamelCase									=    [*signature.parameters.keys()]

												_UpperCamelCase									=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,						__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    _config_zero_init(__a)
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(config=__a)
												# Skip the check for the backbone
												for name, module in model.named_modules():
																	if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																						_UpperCamelCase									=    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																						break

												for name, param in model.named_parameters():
																	if param.requires_grad:
																						if name in backbone_params:
																											continue
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()							,						[0.0, 1.0]							,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,						)




		@slow
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    ViTHybridModel.from_pretrained(__a)
												self.assertIsNotNone(__a)





def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image






@require_torch
@require_vision
class        _UpperCAmelCase(       unittest.TestCase      ):



		@cached_property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
							    if is_vision_available()
							    else None
							)



		@slow
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
							    __a)

							_UpperCamelCase									=    self.default_image_processor
							_UpperCamelCase									=    prepare_img()
							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''').to(__a)

							# forward pass
							with torch.no_grad():
												_UpperCamelCase									=    model(**__a)

							# verify the logits
							_UpperCamelCase									=    torch.Size((1, 10_00))
							self.assertEqual(outputs.logits.shape							,						__a)

							_UpperCamelCase									=    torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]							,						__a							,						atol=1e-4))




		@slow
		@require_accelerate
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''							,						device_map='''auto''')

							_UpperCamelCase									=    prepare_img()

							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''')
							_UpperCamelCase									=    model(**__a)
							_UpperCamelCase									=    outputs.logits
							# model predicts one of the 1000 ImageNet classes
							_UpperCamelCase									=    logits.argmax(-1).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]							,						'''tabby, tabby cat''')






