
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


A							     = logging.get_logger(__name__)

A							     = {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			'plbart'
  _UpperCamelCase							=			['past_key_values']
  _UpperCamelCase							=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(self	:			List[str]						,			lowerCAmelCase_	:			str=5_00_05						,			lowerCAmelCase_	:			Any=10_24						,			lowerCAmelCase_	:			Dict=6						,			lowerCAmelCase_	:			int=30_72						,			lowerCAmelCase_	:			Union[str, Any]=12						,			lowerCAmelCase_	:			Union[str, Any]=6						,			lowerCAmelCase_	:			Any=30_72						,			lowerCAmelCase_	:			List[Any]=12						,			lowerCAmelCase_	:			str=0.0						,			lowerCAmelCase_	:			Any=0.0						,			lowerCAmelCase_	:			Optional[int]=True						,			lowerCAmelCase_	:			Optional[Any]=True						,			lowerCAmelCase_	:			Optional[int]="gelu"						,			lowerCAmelCase_	:			Tuple=7_68						,			lowerCAmelCase_	:			str=0.1						,			lowerCAmelCase_	:			Union[str, Any]=0.1						,			lowerCAmelCase_	:			str=0.0						,			lowerCAmelCase_	:			int=0.02						,			lowerCAmelCase_	:			List[Any]=0.0						,			lowerCAmelCase_	:			Optional[Any]=True						,			lowerCAmelCase_	:			List[str]=1						,			lowerCAmelCase_	:			Union[str, Any]=0						,			lowerCAmelCase_	:			List[str]=2						,			lowerCAmelCase_	:			Union[str, Any]=2						,			**lowerCAmelCase_	:			List[str]						,			) ->							Dict:
        snake_case_    							=				vocab_size
        snake_case_    							=				max_position_embeddings
        snake_case_    							=				d_model
        snake_case_    							=				encoder_ffn_dim
        snake_case_    							=				encoder_layers
        snake_case_    							=				encoder_attention_heads
        snake_case_    							=				decoder_ffn_dim
        snake_case_    							=				decoder_layers
        snake_case_    							=				decoder_attention_heads
        snake_case_    							=				dropout
        snake_case_    							=				attention_dropout
        snake_case_    							=				activation_dropout
        snake_case_    							=				activation_function
        snake_case_    							=				init_std
        snake_case_    							=				encoder_layerdrop
        snake_case_    							=				decoder_layerdrop
        snake_case_    							=				classifier_dropout
        snake_case_    							=				use_cache
        snake_case_    							=				encoder_layers
        snake_case_    							=				scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase_						,			bos_token_id=lowerCAmelCase_						,			eos_token_id=lowerCAmelCase_						,			is_encoder_decoder=lowerCAmelCase_						,			forced_eos_token_id=lowerCAmelCase_						,			**lowerCAmelCase_						,			)



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  @property
  def 		_lowerCamelCase						(self	:			Any     ) ->							Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "sequence"}),
                ("attention_mask", {0: "batch", 1: "sequence"}),
            ]     )


  @property
  def 		_lowerCamelCase						(self	:			Dict     ) ->							Mapping[str, Mapping[int, str]]:
        if self.use_past:
              return OrderedDict(
                  [
                      ("last_hidden_state", {0: "batch", 1: "sequence"}),
                      ("past_keys", {0: "batch", 2: "sequence"}),
                      ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                  ]     )
        else:
              return OrderedDict(
                  [
                      ("last_hidden_state", {0: "batch", 1: "sequence"}),
                      ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                  ]     )



'''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							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			List[str]     ) ->							List[str]:
        snake_case_    							=				tempfile.mkdtemp()

        snake_case_    							=				["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
        snake_case_    							=				dict(zip(lowerCAmelCase_						,			range(len(lowerCAmelCase_     )     )     )     )
        snake_case_    							=				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"]     )

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

        snake_case_    							=				{
            "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 		_lowerCamelCase						(self	:			Dict						,			**lowerCAmelCase_	:			Any     ) ->							Union[str, Any]:
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			List[str]     ) ->							Any:
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			int     ) ->							str:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Optional[Any]:
        snake_case_    							=				self.get_tokenizer()
        snake_case_    							=				self.get_feature_extractor()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )
        snake_case_    							=				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 		_lowerCamelCase						(self	:			str     ) ->							int:
        snake_case_    							=				SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()						,			feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

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

        snake_case_    							=				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 		_lowerCamelCase						(self	:			Tuple     ) ->							Optional[Any]:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        snake_case_    							=				floats_list((3, 10_00)     )

        snake_case_    							=				feature_extractor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				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 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        snake_case_    							=				"This is a test string"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				tokenizer(lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

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

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			int     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				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"						,			)

