


"""simple docstring"""


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


A     :   int			    =			{
    "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": 1_2_8,
    "add_cross_attention": True,
    "tie_encoder_decoder": True,
    "max_length": 5_0,
    "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": 1_0,
    "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": 1_0,
    "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   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							@classmethod
							def 			snake_case				(     cls	):
										__lowerCAmelCase				    =			TOKEN
										HfFolder.save_token(__a	)

							@classmethod
							def 			snake_case				(     cls	):
										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 			snake_case				(     self	):
										__lowerCAmelCase				    =			BertConfig(
										    vocab_size=99    ,   hidden_size=32    ,   num_hidden_layers=5    ,   num_attention_heads=4    ,   intermediate_size=37	)
										config.push_to_hub("test-config"    ,   use_auth_token=self._token	)

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

        # 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(__a    ,   repo_id="test-config"    ,   push_to_hub=__a    ,   use_auth_token=self._token	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			BertConfig(
										    vocab_size=99    ,   hidden_size=32    ,   num_hidden_layers=5    ,   num_attention_heads=4    ,   intermediate_size=37	)
										config.push_to_hub("valid_org/test-config-org"    ,   use_auth_token=self._token	)

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

        # 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(
													    __a    ,   repo_id="valid_org/test-config-org"    ,   push_to_hub=__a    ,   use_auth_token=self._token	)

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



							def 			snake_case				(     self	):
										CustomConfig.register_for_auto_class()
										__lowerCAmelCase				    =			CustomConfig(attribute=42	)

										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"}	)

										__lowerCAmelCase				    =			AutoConfig.from_pretrained(f"{USER}/test-dynamic-config"    ,   trust_remote_code=__a	)
										# 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    ,   42	)
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			GPTaConfig()

										# attempt to modify each of int/float/bool/str config records and verify they were updated
										__lowerCAmelCase				    =			c.n_embd + 1  # int
										__lowerCAmelCase				    =			c.resid_pdrop + 1.0  # float
										__lowerCAmelCase				    =			not c.scale_attn_weights  # bool
										__lowerCAmelCase				    =			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(__a    ,   c.n_embd    ,   "mismatch for key: n_embd"	)
										self.assertEqual(__a    ,   c.resid_pdrop    ,   "mismatch for key: resid_pdrop"	)
										self.assertEqual(__a    ,   c.scale_attn_weights    ,   "mismatch for key: scale_attn_weights"	)
										self.assertEqual(__a    ,   c.summary_type    ,   "mismatch for key: summary_type"	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			PretrainedConfig()
										__lowerCAmelCase				    =			[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(
										    __a    ,   ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]	)
										__lowerCAmelCase				    =			[key for key, value in config_common_kwargs.items() if value == getattr(__a    ,   __a	)]
										if len(__a	) > 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(__a	)}."	)

							def 			snake_case				(     self	):
										with self.assertRaises(__a	):
													# config is in subfolder, the following should not work without specifying the subfolder
													__lowerCAmelCase				    =			BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"	)

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

										self.assertIsNotNone(__a	)

							def 			snake_case				(     self	):
										# A mock response for an HTTP head request to emulate server down
										__lowerCAmelCase				    =			mock.Mock()
										__lowerCAmelCase				    =			5_00
										__lowerCAmelCase				    =			{}
										__lowerCAmelCase				    =			HTTPError
										__lowerCAmelCase				    =			{}

										# Download this model to make sure it's in the cache.
										__lowerCAmelCase				    =			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=__a	) as mock_head:
													__lowerCAmelCase				    =			BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"	)
													# This check we did call the fake head request
													mock_head.assert_called()

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			AutoConfig.from_pretrained("bert-base-cased"	)
										__lowerCAmelCase				    =			["config.4.0.0.json"]

										with tempfile.TemporaryDirectory() as tmp_dir:
													configuration.save_pretrained(__a	)
													__lowerCAmelCase				    =			2
													json.dump(configuration.to_dict()    ,   open(os.path.join(__a    ,   "config.4.0.0.json"	)    ,   "w"	)	)

													# This should pick the new configuration file as the version of Transformers is > 4.0.0
													__lowerCAmelCase				    =			AutoConfig.from_pretrained(__a	)
													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
													__lowerCAmelCase				    =			["config.42.0.0.json"]
													__lowerCAmelCase				    =			7_68
													configuration.save_pretrained(__a	)
													shutil.move(os.path.join(__a    ,   "config.4.0.0.json"	)    ,   os.path.join(__a    ,   "config.42.0.0.json"	)	)
													__lowerCAmelCase				    =			AutoConfig.from_pretrained(__a	)
													self.assertEqual(new_configuration.hidden_size    ,   7_68	)



							def 			snake_case				(     self	):
										# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
										__lowerCAmelCase				    =			"hf-internal-testing/test-two-configs"

										import transformers as new_transformers

										__lowerCAmelCase				    =			"v4.0.0"
										__lowerCAmelCase    ,	__lowerCAmelCase				    =			new_transformers.models.auto.AutoConfig.from_pretrained(
										    __a    ,   return_unused_kwargs=__a	)
										self.assertEqual(new_configuration.hidden_size    ,   2	)
										# This checks `_configuration_file` ia not kept in the kwargs by mistake.
										self.assertDictEqual(__a    ,   {}	)

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

										__lowerCAmelCase				    =			"v3.0.0"
										__lowerCAmelCase				    =			old_transformers.models.auto.AutoConfig.from_pretrained(__a	)
										self.assertEqual(old_configuration.hidden_size    ,   7_68	)








import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__lowerCAmelCase	: Optional[Any]  				='\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


__lowerCAmelCase	: Any  				='\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


__lowerCAmelCase	: Optional[Any]  				=r'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION       )
class        _lowercase      (			datasets.Metric       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Optional[Any]	)		->			List[str]:
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
										        {
										            '''predictions''': datasets.Value('''string'''	),
										            '''references''': datasets.Value('''string'''	),
										        }	)			,			homepage='''https://github.com/hendrycks/math'''			,			codebase_urls=['''https://github.com/hendrycks/math''']			,			)



				def 		__magic_name__(       self			:Any			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Any	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		0.0
										for i, j in zip(lowerCAmelCase__			,			lowerCAmelCase__	):
																n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase__			,			lowerCAmelCase__	) else 0.0
										__SCREAMING_SNAKE_CASE						:     str					=		n_correct / len(lowerCAmelCase__	)
										return {
										    "accuracy": accuracy,
										}
