





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


lowercase_		:				Optional[Any]				=						{
    '''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 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						@classmethod
						def 		__UpperCAmelCase       (    cls			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   TOKEN
										HfFolder.save_token(lowerCamelCase_					)







						@classmethod
						def 		__UpperCAmelCase       (    cls			:		Optional[Any]					):





										'''simple docstring'''







										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			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Any  						=   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					)

										_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_					)					)

        # 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					)

										_snake_case :			List[str]  						=   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[Any]					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   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					)

										_snake_case :			Optional[Any]  						=   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					)

										_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			:		str					):





										'''simple docstring'''







										CustomConfig.register_for_auto_class()
										_snake_case :			Union[str, Any]  						=   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'}					)

										_snake_case :			int  						=   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 ,				42					)



class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   GPTaConfig()

										# attempt to modify each of int/float/bool/str config records and verify they were updated
										_snake_case :			Dict  						=   c.n_embd + 1  # int
										_snake_case :			Union[str, Any]  						=   c.resid_pdrop + 1.0  # float
										_snake_case :			int  						=   not c.scale_attn_weights  # bool
										_snake_case :			Dict  						=   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			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   PretrainedConfig()
										_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']					)
										_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			:		Optional[Any]					):





										'''simple docstring'''







										with self.assertRaises(lowerCamelCase_					):
														# config is in subfolder, the following should not work without specifying the subfolder
														_snake_case :			Optional[int]  						=   BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'					)

										_snake_case :			Tuple  						=   BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' ,				subfolder='bert'					)

										self.assertIsNotNone(lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			str  						=   mock.Mock()
										_snake_case :			Optional[int]  						=   5_00
										_snake_case :			Any  						=   {}
										_snake_case :			List[str]  						=   HTTPError
										_snake_case :			Any  						=   {}

										# Download this model to make sure it's in the cache.
										_snake_case :			Union[str, 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:
														_snake_case :			List[str]  						=   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			:		Any					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   BertConfig.from_pretrained(
										    'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'					)







						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   AutoConfig.from_pretrained('bert-base-cased'					)
										_snake_case :			Union[str, Any]  						=   ['config.4.0.0.json']

										with tempfile.TemporaryDirectory() as tmp_dir:
														configuration.save_pretrained(lowerCamelCase_					)
														_snake_case :			str  						=   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
														_snake_case :			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
														_snake_case :			List[str]  						=   ['config.42.0.0.json']
														_snake_case :			Dict  						=   7_68
														configuration.save_pretrained(lowerCamelCase_					)
														shutil.move(os.path.join(lowerCamelCase_ ,				'config.4.0.0.json'					) ,				os.path.join(lowerCamelCase_ ,				'config.42.0.0.json'					)					)
														_snake_case :			Optional[int]  						=   AutoConfig.from_pretrained(lowerCamelCase_					)
														self.assertEqual(new_configuration.hidden_size ,				7_68					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   'hf-internal-testing/test-two-configs'

										import transformers as new_transformers

										_snake_case :			Union[str, Any]  						=   'v4.0.0'
										_snake_case :			List[Any]  						=   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

										_snake_case :			Tuple  						=   'v3.0.0'
										_snake_case :			Any  						=   old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase_					)
										self.assertEqual(old_configuration.hidden_size ,				7_68					)













import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowercase_		:				List[str]				=						'''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowercase_		:				Optional[int]				=						'''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

lowercase_		:				Any				=						'''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''


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



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										if version.parse(scb.__version__					) < version.parse('1.4.12'					):
														raise ImportWarning(
														    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
														    'You can install it with `pip install "sacrebleu>=1.4.12"`.'					)
										return datasets.MetricInfo(
										    description=_DESCRIPTION ,				citation=_CITATION ,				homepage='http://www.cs.umd.edu/~snover/tercom/' ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
										        {
										            'predictions': datasets.Value('string' ,				id='sequence'					),
										            'references': datasets.Sequence(datasets.Value('string' ,				id='sequence'					) ,				id='references'					),
										        }					) ,				codebase_urls=['https://github.com/mjpost/sacreBLEU#ter'] ,				reference_urls=[
										        'https://github.com/jhclark/tercom',
										    ] ,				)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										_snake_case :			str  						=   len(references[0]					)
										if any(len(lowerCamelCase_					) != references_per_prediction for refs in references					):
														raise ValueError('Sacrebleu requires the same number of references for each prediction'					)
										_snake_case :			int  						=   [[refs[i] for refs in references] for i in range(lowerCamelCase_					)]

										_snake_case :			Optional[int]  						=   TER(
										    normalized=lowerCamelCase_ ,				no_punct=lowerCamelCase_ ,				asian_support=lowerCamelCase_ ,				case_sensitive=lowerCamelCase_ ,				)
										_snake_case :			Optional[Any]  						=   sb_ter.corpus_score(lowerCamelCase_ ,				lowerCamelCase_					)

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






