






'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm

def 	__UpperCAmelCase       ( __magic_name__="ro"     ,__magic_name__="en"     ,__magic_name__="wmt16"     ,__magic_name__=None						)->    None:



			"""simple docstring"""



			try:
						import datasets
			except (ModuleNotFoundError, ImportError):
						raise ImportError("run pip install datasets"						)
			snake_case_				:					Union[str, Any]   =       F'''{src_lang}-{tgt_lang}'''
			print(F'''Converting {dataset}-{pair}'''						)
			snake_case_				:					str   =       datasets.load_dataset(__magic_name__     ,__magic_name__						)
			if save_dir is None:
						snake_case_				:					List[Any]   =       F'''{dataset}-{pair}'''
			snake_case_				:					Optional[int]   =       Path(__magic_name__						)
			save_dir.mkdir(exist_ok=__magic_name__						)

			for split in ds.keys():
						print(F'''Splitting {split} with {ds[split].num_rows} records'''						)

						# to save to val.source, val.target like summary datasets
						snake_case_				:					int   =       "val" if split == "validation" else split
						snake_case_				:					int   =       save_dir.joinpath(F'''{fn}.source'''						)
						snake_case_				:					Optional[Any]   =       save_dir.joinpath(F'''{fn}.target'''						)
						snake_case_				:					Optional[int]   =       src_path.open("w+"						)
						snake_case_				:					List[Any]   =       tgt_path.open("w+"						)

						# reader is the bottleneck so writing one record at a time doesn't slow things down
						for x in tqdm(ds[split]						):
									snake_case_				:					Optional[int]   =       x["translation"]
									src_fp.write(ex[src_lang] + "\n"						)
									tgt_fp.write(ex[tgt_lang] + "\n"						)

			print(F'''Saved {dataset} dataset to {save_dir}'''						)


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)












'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowerCamelCase   :					List[str]   							=					{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}







@is_pipeline_test
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							a__   		=	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							a__   		=	TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														a__   		=	{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														a__   		=	{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Dict				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       ZeroShotClassificationPipeline(
										    model=lowerCAmelCase__       ,							tokenizer=lowerCAmelCase__       ,							candidate_labels=["polics", "health"]				)
										return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Any				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels="politics"				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										# No kwarg
										snake_case_				:					List[Any]   =       classifier("Who are you voting for in 2020?"       ,							["politics"]				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										snake_case_				:					Dict   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels=["politics"]				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										snake_case_				:					int   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels="politics, public health"				)
										self.assertEqual(
										    lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}				)
										self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]				)				)       ,							1.0				)

										snake_case_				:					Optional[int]   =       classifier("Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}				)
										self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]				)				)       ,							1.0				)

										snake_case_				:					str   =       classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template="This text is about {}"				)
										self.assertEqual(lowerCAmelCase__       ,							{"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				)]}				)

										# https://github.com/huggingface/transformers/issues/13846
										snake_case_				:					Dict   =       classifier(["I am happy"]       ,							["positive", "negative"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							[
										        {"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}
										        for i in range(1				)
										    ]       ,							)
										snake_case_				:					Tuple   =       classifier(["I am happy", "I am sad"]       ,							["positive", "negative"]				)
										self.assertEqual(
										    lowerCAmelCase__       ,							[
										        {"sequence": ANY(lowerCAmelCase__				), "labels": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)], "scores": [ANY(lowerCAmelCase__				), ANY(lowerCAmelCase__				)]}
										        for i in range(2				)
										    ]       ,							)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(""       ,							candidate_labels="politics"				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(lowerCAmelCase__       ,							candidate_labels="politics"				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier("Who are you voting for in 2020?"       ,							candidate_labels=""				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier("Who are you voting for in 2020?"       ,							candidate_labels=lowerCAmelCase__				)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(
													    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template="Not formatting template"       ,							)

										with self.assertRaises(lowerCAmelCase__				):
													classifier(
													    "Who are you voting for in 2020?"       ,							candidate_labels="politics"       ,							hypothesis_template=lowerCAmelCase__       ,							)

										self.run_entailment_id(lowerCAmelCase__				)

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:Pipeline				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					int   =       zero_shot_classifier.model.config
										snake_case_				:					Optional[int]   =       config.labelaid
										snake_case_				:					Tuple   =       zero_shot_classifier.entailment_id

										snake_case_				:					Optional[Any]   =       {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							-1				)

										snake_case_				:					Tuple   =       {"entailment": 0, "neutral": 1, "contradiction": 2}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							0				)

										snake_case_				:					str   =       {"ENTAIL": 0, "NON-ENTAIL": 1}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							0				)

										snake_case_				:					str   =       {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
										self.assertEqual(zero_shot_classifier.entailment_id       ,							2				)

										snake_case_				:					List[str]   =       original_labelaid
										self.assertEqual(lowerCAmelCase__       ,							zero_shot_classifier.entailment_id				)

							@require_torch
							def 							_A	(		self			:Tuple				)				->  Any:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="pt"       ,							)
										# There was a regression in 4.10 for this
										# Adding a test so we don't make the mistake again.
										# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
										zero_shot_classifier(
										    "Who are you voting for in 2020?" * 100       ,							candidate_labels=["politics", "public health", "science"]				)

							@require_torch
							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="pt"       ,							)
										snake_case_				:					int   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["science", "public health", "politics"],
										        "scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
										    }       ,							)

							@require_tf
							def 							_A	(		self			:Union[str, Any]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					List[str]   =       pipeline(
										    "zero-shot-classification"       ,							model="sshleifer/tiny-distilbert-base-cased-distilled-squad"       ,							framework="tf"       ,							)
										snake_case_				:					Optional[int]   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["science", "public health", "politics"],
										        "scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
										    }       ,							)

							@slow
							@require_torch
							def 							_A	(		self			:Union[str, Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					int   =       pipeline("zero-shot-classification"       ,							model="roberta-large-mnli"       ,							framework="pt"				)
										snake_case_				:					str   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["politics", "public health", "science"],
										        "scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
										    }       ,							)
										snake_case_				:					Optional[int]   =       zero_shot_classifier(
										    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
										    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
										    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
										    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
										    " machine translation tasks show these models to be superior in quality while being more parallelizable"
										    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
										    " English-to-German translation task, improving over the existing best results, including ensembles by"
										    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
										    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
										    " fraction of the training costs of the best models from the literature. We show that the Transformer"
										    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
										    " large and limited training data."       ,							candidate_labels=["machine learning", "statistics", "translation", "vision"]       ,							multi_label=lowerCAmelCase__       ,							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": (
										            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
										            " networks in an encoder-decoder configuration. The best performing models also connect the"
										            " encoder and decoder through an attention mechanism. We propose a new simple network"
										            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
										            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
										            " superior in quality while being more parallelizable and requiring significantly less time to"
										            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
										            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
										            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
										            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
										            " costs of the best models from the literature. We show that the Transformer generalizes well to"
										            " other tasks by applying it successfully to English constituency parsing both with large and"
										            " limited training data."
										        ),
										        "labels": ["translation", "machine learning", "vision", "statistics"],
										        "scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
										    }       ,							)






							@slow
							@require_tf
							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					int   =       pipeline("zero-shot-classification"       ,							model="roberta-large-mnli"       ,							framework="tf"				)
										snake_case_				:					Optional[Any]   =       zero_shot_classifier(
										    "Who are you voting for in 2020?"       ,							candidate_labels=["politics", "public health", "science"]				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": "Who are you voting for in 2020?",
										        "labels": ["politics", "public health", "science"],
										        "scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
										    }       ,							)
										snake_case_				:					Tuple   =       zero_shot_classifier(
										    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
										    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
										    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
										    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
										    " machine translation tasks show these models to be superior in quality while being more parallelizable"
										    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
										    " English-to-German translation task, improving over the existing best results, including ensembles by"
										    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
										    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
										    " fraction of the training costs of the best models from the literature. We show that the Transformer"
										    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
										    " large and limited training data."       ,							candidate_labels=["machine learning", "statistics", "translation", "vision"]       ,							multi_label=lowerCAmelCase__       ,							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        "sequence": (
										            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
										            " networks in an encoder-decoder configuration. The best performing models also connect the"
										            " encoder and decoder through an attention mechanism. We propose a new simple network"
										            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
										            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
										            " superior in quality while being more parallelizable and requiring significantly less time to"
										            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
										            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
										            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
										            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
										            " costs of the best models from the literature. We show that the Transformer generalizes well to"
										            " other tasks by applying it successfully to English constituency parsing both with large and"
										            " limited training data."
										        ),
										        "labels": ["translation", "machine learning", "vision", "statistics"],
										        "scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
										    }       ,							)




