


'''simple docstring'''
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__SCREAMING_SNAKE_CASE				:							Dict		       =		logging.get_logger(__name__)






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					Dict      	="mt5"
						_UpperCAmelCase		:					int      	=["past_key_values"]






						def __init__(     self       :			Optional[int]   ,     lowerCAmelCase       :			List[Any]=25_01_12   ,     lowerCAmelCase       :			str=5_12   ,     lowerCAmelCase       :			Optional[Any]=64   ,     lowerCAmelCase       :			Optional[Any]=10_24   ,     lowerCAmelCase       :			Any=8   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			List[Any]=6   ,     lowerCAmelCase       :			Union[str, Any]=32   ,     lowerCAmelCase       :			Any=1_28   ,     lowerCAmelCase       :			Optional[Any]=0.1   ,     lowerCAmelCase       :			Dict=1e-6   ,     lowerCAmelCase       :			Any=1.0   ,     lowerCAmelCase       :			Optional[int]="gated-gelu"   ,     lowerCAmelCase       :			Dict=True   ,     lowerCAmelCase       :			Dict=True   ,     lowerCAmelCase       :			List[Any]="T5Tokenizer"   ,     lowerCAmelCase       :			Dict=False   ,     lowerCAmelCase       :			int=0   ,     lowerCAmelCase       :			Optional[int]=1   ,     lowerCAmelCase       :			Any=0   ,     **lowerCAmelCase       :			Tuple   ,     ):
													super().__init__(
													    is_encoder_decoder=lowerCAmelCase   ,     tokenizer_class=lowerCAmelCase   ,     tie_word_embeddings=lowerCAmelCase   ,     pad_token_id=lowerCAmelCase   ,     eos_token_id=lowerCAmelCase   ,     decoder_start_token_id=lowerCAmelCase   ,     **lowerCAmelCase   ,     )
													A_ 						=		vocab_size
													A_ 						=		d_model
													A_ 						=		d_kv
													A_ 						=		d_ff
													A_ 						=		num_layers
													A_ 						=		(
													    num_decoder_layers if num_decoder_layers is not None else self.num_layers
													)  # default = symmetry
													A_ 						=		num_heads
													A_ 						=		relative_attention_num_buckets
													A_ 						=		relative_attention_max_distance
													A_ 						=		dropout_rate
													A_ 						=		layer_norm_epsilon
													A_ 						=		initializer_factor
													A_ 						=		feed_forward_proj
													A_ 						=		use_cache

													A_ 						=		self.feed_forward_proj.split("-"  )
													A_ 						=		act_info[-1]
													A_ 						=		act_info[0] == "gated"

													if len(lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase  ) > 2:
																				raise ValueError(
																				    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
																				    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
																				    "'gated-gelu' or 'relu'"  )

													# for backwards compatibility
													if feed_forward_proj == "gated-gelu":
																				A_ 						=		"gelu_new"






						@property
						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													return self.d_model






						@property
						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													return self.num_heads






						@property
						def 							_UpperCAmelCase					(     self       :			Dict  ):
													return self.num_layers






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 							_UpperCAmelCase					(     self       :			List[str]  ):
													A_ 						=		{
													    "input_ids": {0: "batch", 1: "encoder_sequence"},
													    "attention_mask": {0: "batch", 1: "encoder_sequence"},
													}
													if self.use_past:
																				A_ 						=		"past_encoder_sequence + sequence"
																				A_ 						=		{0: "batch"}
																				A_ 						=		{0: "batch", 1: "past_decoder_sequence + sequence"}
													else:
																				A_ 						=		{0: "batch", 1: "decoder_sequence"}
																				A_ 						=		{0: "batch", 1: "decoder_sequence"}

													if self.use_past:
																				self.fill_with_past_key_values_(lowerCAmelCase   ,     direction="inputs"  )

													return common_inputs






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 							_UpperCAmelCase					(     self       :			str  ):
													return 13






						@property
						def 							_UpperCAmelCase					(     self       :			str  ):
													return 5e-4









'''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


__SCREAMING_SNAKE_CASE				:							int		       =		get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[str]  ):
													A_ 						=		tempfile.mkdtemp()

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

													A_ 						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )
													tokenizer.save_pretrained(self.tmpdirname  )

													A_ 						=		{
													    "feature_size": 24,
													    "num_mel_bins": 24,
													    "padding_value": 0.0,
													    "sampling_rate": 1_60_00,
													    "return_attention_mask": False,
													    "do_normalize": True,
													}
													save_json(lowerCAmelCase   ,     save_dir / FEATURE_EXTRACTOR_NAME  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     **lowerCAmelCase       :			str  ):
													return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     **lowerCAmelCase       :			Optional[Any]  ):
													return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Any  ):
													shutil.rmtree(self.tmpdirname  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													A_ 						=		self.get_tokenizer()
													A_ 						=		self.get_feature_extractor()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													processor.save_pretrained(self.tmpdirname  )
													A_ 						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname  )

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

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






						def 							_UpperCAmelCase					(     self       :			Dict  ):
													A_ 						=		SpeechaTextProcessor(
													    tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()  )
													processor.save_pretrained(self.tmpdirname  )

													A_ 						=		self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"  )
													A_ 						=		self.get_feature_extractor(do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													A_ 						=		SpeechaTextProcessor.from_pretrained(
													    self.tmpdirname   ,     bos_token="(BOS)"   ,     eos_token="(EOS)"   ,     do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

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

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






						def 							_UpperCAmelCase					(     self       :			str  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		floats_list((3, 10_00)  )

													A_ 						=		feature_extractor(lowerCAmelCase   ,     return_tensors="np"  )
													A_ 						=		processor(lowerCAmelCase   ,     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 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		"This is a test string"

													A_ 						=		processor(text=lowerCAmelCase  )

													A_ 						=		tokenizer(lowerCAmelCase  )

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






						def 							_UpperCAmelCase					(     self       :			int  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													A_ 						=		processor.batch_decode(lowerCAmelCase  )
													A_ 						=		tokenizer.batch_decode(lowerCAmelCase  )

													self.assertListEqual(lowerCAmelCase   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

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





