

"""simple docstring"""


import sacrebleu as scb
from packaging import version

import datasets


__A		     =    "\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__A		     =    "\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

__A		     =    "\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"





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

				def 							lowercase_  (			self							:       Tuple)->     List[str]:



						'''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="https://github.com/mjpost/sacreBLEU"  ,						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"]  ,						reference_urls=[
						        "https://github.com/mjpost/sacreBLEU",
						        "https://en.wikipedia.org/wiki/BLEU",
						        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
						    ]  ,						)




				def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Any="exp"  ,						UpperCamelCase__							:       Optional[Any]=None  ,						UpperCamelCase__							:       List[str]=False  ,						UpperCamelCase__							:       Optional[Any]=False  ,						UpperCamelCase__							:       Tuple=None  ,						UpperCamelCase__							:       Any=False  ,						)->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    len(references[0])
						if any(len(UpperCamelCase__) != references_per_prediction for refs in references):
								raise ValueError("Sacrebleu requires the same number of references for each prediction")
						__lowerCAmelCase: Optional[Any]					   =    [[refs[i] for refs in references] for i in range(UpperCamelCase__)]
						__lowerCAmelCase: List[str]					   =    scb.corpus_bleu(
						    UpperCamelCase__  ,						UpperCamelCase__  ,						smooth_method=UpperCamelCase__  ,						smooth_value=UpperCamelCase__  ,						force=UpperCamelCase__  ,						lowercase=UpperCamelCase__  ,						use_effective_order=UpperCamelCase__  ,						**({"tokenize": tokenize} if tokenize else {})  ,						)
						__lowerCAmelCase: Tuple					   =    {
						    "score": output.score,
						    "counts": output.counts,
						    "totals": output.totals,
						    "precisions": output.precisions,
						    "bp": output.bp,
						    "sys_len": output.sys_len,
						    "ref_len": output.ref_len,
						}
						return output_dict





"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers.models.openai.modeling_tf_openai import (
			    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFOpenAIGPTDoubleHeadsModel,
			    TFOpenAIGPTForSequenceClassification,
			    TFOpenAIGPTLMHeadModel,
			    TFOpenAIGPTModel,
			)





class 					snake_case			:

				def __init__(			self							:       str  ,						UpperCamelCase__							:       Optional[int]  ,						)->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    parent
						__lowerCAmelCase: Union[str, Any]					   =    1_3
						__lowerCAmelCase: Union[str, Any]					   =    7
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: Any					   =    True
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: str					   =    True
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: Tuple					   =    9_9
						__lowerCAmelCase: Dict					   =    3_2
						__lowerCAmelCase: Optional[Any]					   =    2
						__lowerCAmelCase: Union[str, Any]					   =    4
						__lowerCAmelCase: Union[str, Any]					   =    3_7
						__lowerCAmelCase: List[str]					   =    "gelu"
						__lowerCAmelCase: List[str]					   =    0.1
						__lowerCAmelCase: List[str]					   =    0.1
						__lowerCAmelCase: Union[str, Any]					   =    5_1_2
						__lowerCAmelCase: int					   =    1_6
						__lowerCAmelCase: int					   =    2
						__lowerCAmelCase: str					   =    0.02
						__lowerCAmelCase: Union[str, Any]					   =    3
						__lowerCAmelCase: str					   =    4
						__lowerCAmelCase: List[Any]					   =    None
						__lowerCAmelCase: int					   =    self.vocab_size - 1

				def 							lowercase_  (			self							:       Union[str, Any])->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size)

						__lowerCAmelCase: str					   =    None
						if self.use_input_mask:
								__lowerCAmelCase: List[Any]					   =    random_attention_mask([self.batch_size, self.seq_length])

						__lowerCAmelCase: int					   =    None
						if self.use_token_type_ids:
								__lowerCAmelCase: Any					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.type_vocab_size)

						__lowerCAmelCase: Dict					   =    None
						if self.use_mc_token_ids:
								__lowerCAmelCase: Union[str, Any]					   =    ids_tensor([self.batch_size, self.num_choices]  ,						self.seq_length)

						__lowerCAmelCase: int					   =    None
						__lowerCAmelCase: Optional[Any]					   =    None
						__lowerCAmelCase: int					   =    None
						if self.use_labels:
								__lowerCAmelCase: Tuple					   =    ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)
								__lowerCAmelCase: str					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels)
								__lowerCAmelCase: int					   =    ids_tensor([self.batch_size]  ,						self.num_choices)

						__lowerCAmelCase: int					   =    OpenAIGPTConfig(
						    vocab_size=self.vocab_size  ,						n_embd=self.hidden_size  ,						n_layer=self.num_hidden_layers  ,						n_head=self.num_attention_heads  ,						n_positions=self.max_position_embeddings  ,						pad_token_id=self.pad_token_id  ,						)

						__lowerCAmelCase: Union[str, Any]					   =    ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,						2)

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						    token_type_ids,
						    mc_token_ids,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						)

				def 							lowercase_  (			self							:       Optional[int]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Dict  ,						*UpperCamelCase__							:       Dict)->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    TFOpenAIGPTModel(config=UpperCamelCase__)
						__lowerCAmelCase: Tuple					   =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						__lowerCAmelCase: List[Any]					   =    model(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    [input_ids, input_mask]
						__lowerCAmelCase: Tuple					   =    model(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    model(UpperCamelCase__)

						self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size))

				def 							lowercase_  (			self							:       Dict  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       int  ,						*UpperCamelCase__							:       Optional[Any])->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    TFOpenAIGPTLMHeadModel(config=UpperCamelCase__)
						__lowerCAmelCase: int					   =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						__lowerCAmelCase: Any					   =    model(UpperCamelCase__)
						self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size))

				def 							lowercase_  (			self							:       Tuple  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Tuple  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Optional[Any]  ,						*UpperCamelCase__							:       Optional[Any])->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Union[str, Any]					   =    TFOpenAIGPTDoubleHeadsModel(config=UpperCamelCase__)

						__lowerCAmelCase: List[str]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))
						__lowerCAmelCase: Union[str, Any]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))
						__lowerCAmelCase: Optional[Any]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))

						__lowerCAmelCase: str					   =    {
						    "input_ids": multiple_choice_inputs_ids,
						    "mc_token_ids": mc_token_ids,
						    "attention_mask": multiple_choice_input_mask,
						    "token_type_ids": multiple_choice_token_type_ids,
						}
						__lowerCAmelCase: Optional[int]					   =    model(UpperCamelCase__)
						self.parent.assertEqual(
						    result.logits.shape  ,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size))
						self.parent.assertEqual(result.mc_logits.shape  ,						(self.batch_size, self.num_choices))

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Optional[Any]  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Optional[Any]  ,						*UpperCamelCase__							:       List[str])->     Any:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    self.num_labels
						__lowerCAmelCase: Dict					   =    ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)
						__lowerCAmelCase: List[str]					   =    {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						    "labels": sequence_labels,
						}
						__lowerCAmelCase: str					   =    TFOpenAIGPTForSequenceClassification(UpperCamelCase__)
						__lowerCAmelCase: Optional[int]					   =    model(UpperCamelCase__)
						self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels))




				def 							lowercase_  (			self							:       Any)->     List[str]:



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    self.prepare_config_and_inputs()

						(
						    (
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				
						): List[Any]					   =    config_and_inputs

						__lowerCAmelCase: List[Any]					   =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
						return config, inputs_dict





@require_tf
class 					snake_case			(       __snake_case,							__snake_case,							unittest.TestCase						):
				SCREAMING_SNAKE_CASE_     :   Dict        =						(
				    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_     :   List[str]        =						(
				    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
				SCREAMING_SNAKE_CASE_     :   int        =						(
				    {
				        """feature-extraction""": TFOpenAIGPTModel,
				        """text-classification""": TFOpenAIGPTForSequenceClassification,
				        """text-generation""": TFOpenAIGPTLMHeadModel,
				        """zero-shot""": TFOpenAIGPTForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_     :   List[Any]        =						False
				SCREAMING_SNAKE_CASE_     :   Tuple        =						False

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Optional[Any])->     Union[str, Any]:



						'''simple docstring'''


						if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
								# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
								# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
								# tiny config could not be created.
								return True

						return False

				def 							lowercase_  (			self							:       str)->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    TFOpenAIGPTModelTester(self)
						__lowerCAmelCase: List[Any]					   =    ConfigTester(self  ,						config_class=UpperCamelCase__  ,						n_embd=3_7)

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



						'''simple docstring'''


						self.config_tester.run_common_tests()

				def 							lowercase_  (			self							:       Tuple)->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_model(*UpperCamelCase__)

				def 							lowercase_  (			self							:       Union[str, Any])->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_lm_head(*UpperCamelCase__)

				def 							lowercase_  (			self							:       Optional[Any])->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_double_head(*UpperCamelCase__)

				def 							lowercase_  (			self							:       List[str])->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCamelCase__)




				@slow
				def 							lowercase_  (			self							:       str)->     Dict:



						'''simple docstring'''


						for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowerCAmelCase: str					   =    TFOpenAIGPTModel.from_pretrained(UpperCamelCase__)
								self.assertIsNotNone(UpperCamelCase__)





@require_tf
class 					snake_case			(       unittest.TestCase						):

				@slow
				def 							lowercase_  (			self							:       Union[str, Any])->     List[str]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
						__lowerCAmelCase: List[str]					   =    tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]  ,						dtype=tf.intaa)  # the president is
						__lowerCAmelCase: Union[str, Any]					   =    [
						    4_8_1,
						    4_7_3_5,
						    5_4_4,
						    2_4_6,
						    9_6_3,
						    8_7_0,
						    7_6_2,
						    2_3_9,
						    2_4_4,
						    4_0_4_7_7,
						    2_4_4,
						    2_4_9,
						    7_1_9,
						    8_8_1,
						    4_8_7,
						    5_4_4,
						    2_4_0,
						    2_4_4,
						    6_0_3,
						    4_8_1,
						]  # the president is a very good man. " \n " i\'m sure he is, " said the

						__lowerCAmelCase: str					   =    model.generate(UpperCamelCase__  ,						do_sample=UpperCamelCase__)
						self.assertListEqual(output_ids[0].numpy().tolist()  ,						UpperCamelCase__)


