




"""simple docstring"""

import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__A							=     get_tests_dir("fixtures/test_sentencepiece.model")
@require_torch
@require_torchaudio
@require_sentencepiece
class      lowerCamelCase__      (					unittest.TestCase     ):


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			tempfile.mkdtemp()

					snake_case						:  Optional[int]        =			["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
					snake_case						:  Any        =			dict(zip(SCREAMING_SNAKE_CASE			,	range(len(SCREAMING_SNAKE_CASE							)							)							)							)
					snake_case						:  str        =			Path(self.tmpdirname							)
					save_json(SCREAMING_SNAKE_CASE			,	save_dir / VOCAB_FILES_NAMES["vocab_file"]							)
					if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
							copyfile(SCREAMING_SNAKE_CASE			,	save_dir / VOCAB_FILES_NAMES["spm_file"]							)

					snake_case						:  int        =			SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
					tokenizer.save_pretrained(self.tmpdirname							)

					snake_case						:  Tuple        =			{
					    "feature_size": 24,
					    "num_mel_bins": 24,
					    "padding_value": 0.0,
					    "sampling_rate": 16_000,
					    "return_attention_mask": False,
					    "do_normalize": True,
					}
					save_json(SCREAMING_SNAKE_CASE			,	save_dir / FEATURE_EXTRACTOR_NAME							)


			def 							lowerCamelCase_			(       self			,	**SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return SpeechaTextTokenizer.from_pretrained(self.tmpdirname			,	**SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self			,	**SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname			,	**SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					shutil.rmtree(self.tmpdirname							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			self.get_tokenizer()
					snake_case						:  Optional[Any]        =			self.get_feature_extractor()

					snake_case						:  List[str]        =			SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE			,	feature_extractor=SCREAMING_SNAKE_CASE							)

					processor.save_pretrained(self.tmpdirname							)
					snake_case						:  List[Any]        =			SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

					self.assertEqual(processor.tokenizer.get_vocab()			,	tokenizer.get_vocab()							)
					self.assertIsInstance(processor.tokenizer			,	SCREAMING_SNAKE_CASE							)

					self.assertEqual(processor.feature_extractor.to_json_string()			,	feature_extractor.to_json_string()							)
					self.assertIsInstance(processor.feature_extractor			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			SpeechaTextProcessor(
					    tokenizer=self.get_tokenizer()			,	feature_extractor=self.get_feature_extractor()							)
					processor.save_pretrained(self.tmpdirname							)

					snake_case						:  List[str]        =			self.get_tokenizer(bos_token="(BOS)"			,	eos_token="(EOS)"							)
					snake_case						:  int        =			self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE			,	padding_value=1.0							)

					snake_case						:  str        =			SpeechaTextProcessor.from_pretrained(
					    self.tmpdirname			,	bos_token="(BOS)"			,	eos_token="(EOS)"			,	do_normalize=SCREAMING_SNAKE_CASE			,	padding_value=1.0							)

					self.assertEqual(processor.tokenizer.get_vocab()			,	tokenizer_add_kwargs.get_vocab()							)
					self.assertIsInstance(processor.tokenizer			,	SCREAMING_SNAKE_CASE							)

					self.assertEqual(processor.feature_extractor.to_json_string()			,	feature_extractor_add_kwargs.to_json_string()							)
					self.assertIsInstance(processor.feature_extractor			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			self.get_feature_extractor()
					snake_case						:  List[str]        =			self.get_tokenizer()

					snake_case						:  Dict        =			SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE			,	feature_extractor=SCREAMING_SNAKE_CASE							)

					snake_case						:  Tuple        =			floats_list((3, 1_000)							)

					snake_case						:  Dict        =			feature_extractor(SCREAMING_SNAKE_CASE			,	return_tensors="np"							)
					snake_case						:  Optional[Any]        =			processor(SCREAMING_SNAKE_CASE			,	return_tensors="np"							)

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,	input_processor[key].sum()			,	delta=1E-2							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			self.get_feature_extractor()
					snake_case						:  int        =			self.get_tokenizer()

					snake_case						:  Any        =			SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE			,	feature_extractor=SCREAMING_SNAKE_CASE							)

					snake_case						:  List[str]        =			"This is a test string"

					snake_case						:  Optional[Any]        =			processor(text=SCREAMING_SNAKE_CASE							)

					snake_case						:  Dict        =			tokenizer(SCREAMING_SNAKE_CASE							)

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]			,	encoded_processor[key]							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			self.get_feature_extractor()
					snake_case						:  List[str]        =			self.get_tokenizer()

					snake_case						:  Optional[Any]        =			SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE			,	feature_extractor=SCREAMING_SNAKE_CASE							)

					snake_case						:  Tuple        =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					snake_case						:  Optional[Any]        =			processor.batch_decode(SCREAMING_SNAKE_CASE							)
					snake_case						:  Union[str, Any]        =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE							)

					self.assertListEqual(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			self.get_feature_extractor()
					snake_case						:  Any        =			self.get_tokenizer()

					snake_case						:  List[str]        =			SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE			,	feature_extractor=SCREAMING_SNAKE_CASE							)

					self.assertListEqual(
					    processor.model_input_names			,	feature_extractor.model_input_names			,	msg="`processor` and `feature_extractor` model input names do not match"			,	)







"""simple docstring"""

import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY
@is_pipeline_test
class      lowerCamelCase__      (					unittest.TestCase     ):
			a__	: List[str]        =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			a__	: List[Any]        =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(model.config			,	SCREAMING_SNAKE_CASE							):
							snake_case     ,			snake_case						:  Optional[Any]        =			list(tokenizer.lang_code_to_id.keys()							)[:2]
							snake_case						:  Tuple        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang=SCREAMING_SNAKE_CASE			,	tgt_lang=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  List[Any]        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					return translator, ["Some string", "Some other text"]


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			translator("Some string"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  Optional[Any]        =			translator(["Some string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  List[Any]        =			translator(["Some string", "other string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}, {"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  Union[str, Any]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  int        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  List[str]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
					                " urine urine urine urine urine urine urine"
					            )
					        }
					    ]			,	)



			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[str]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  Tuple        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
					                " urine urine urine urine urine urine urine"
					            )
					        }
					    ]			,	)







class      lowerCamelCase__      (					unittest.TestCase     ):


			@require_torch
			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation_cn_to_ar"							)

					# but we do for this one
					snake_case						:  List[str]        =			pipeline(task="translation_en_to_de"							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"en"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"de"							)


			@require_torch
			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)
					snake_case						:  Union[str, Any]        =			MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)

					snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					# Missing src_lang, tgt_lang
					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							translator("This is a test"							)

					snake_case						:  Optional[int]        =			translator("This is a test"			,	src_lang="en_XX"			,	tgt_lang="ar_AR"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "هذا إختبار"}]							)

					snake_case						:  Dict        =			translator("This is a test"			,	src_lang="en_XX"			,	tgt_lang="hi_IN"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "यह एक परीक्षण है"}]							)

					# src_lang, tgt_lang can be defined at pipeline call time
					snake_case						:  Tuple        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang="en_XX"			,	tgt_lang="ar_AR"							)
					snake_case						:  int        =			translator("This is a test"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "هذا إختبار"}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			"patrickvonplaten/t5-tiny-random"
					snake_case						:  Tuple        =			pipeline(task="translation_cn_to_ar"			,	model=SCREAMING_SNAKE_CASE							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"cn"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"ar"							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			"patrickvonplaten/t5-tiny-random"
					with pytest.warns(SCREAMING_SNAKE_CASE			,	match=r".*translation_en_to_de.*"							):
							snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE							)
					self.assertEqual(translator.task			,	"translation_en_to_de"							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"en"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"de"							)



			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation"							)

