

"""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_       :						Optional[Any]					=    get_tests_dir('fixtures/test_sentencepiece.model')



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



    """simple docstring"""



    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         A__				      =	tempfile.mkdtemp()

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

    def 					UpperCamelCase						(  self:			Tuple     ,    **UpperCamelCase:			List[Any]							):






         """simple docstring"""


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

    def 					UpperCamelCase						(  self:			Union[str, Any]     ,    **UpperCamelCase:			str							):






         """simple docstring"""


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

    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         shutil.rmtree(self.tmpdirname							)

    def 					UpperCamelCase						(  self:			Optional[int]							):






         """simple docstring"""


         A__				      =	self.get_tokenizer()
         A__				      =	self.get_feature_extractor()

         A__				      =	SpeechaTextProcessor(tokenizer=UpperCamelCase     ,    feature_extractor=UpperCamelCase							)

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

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

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

    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         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=UpperCamelCase     ,    padding_value=1.0							)

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

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

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

    def 					UpperCamelCase						(  self:			Optional[Any]							):






         """simple docstring"""


         A__				      =	self.get_feature_extractor()
         A__				      =	self.get_tokenizer()

         A__				      =	SpeechaTextProcessor(tokenizer=UpperCamelCase     ,    feature_extractor=UpperCamelCase							)

         A__				      =	floats_list((3, 10_00)							)

         A__				      =	feature_extractor(UpperCamelCase     ,    return_tensors="""np"""							)
         A__				      =	processor(UpperCamelCase     ,    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[Any]							):






         """simple docstring"""


         A__				      =	self.get_feature_extractor()
         A__				      =	self.get_tokenizer()

         A__				      =	SpeechaTextProcessor(tokenizer=UpperCamelCase     ,    feature_extractor=UpperCamelCase							)

         A__				      =	"""This is a test string"""

         A__				      =	processor(text=UpperCamelCase							)

         A__				      =	tokenizer(UpperCamelCase							)

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

    def 					UpperCamelCase						(  self:			Optional[int]							):






         """simple docstring"""


         A__				      =	self.get_feature_extractor()
         A__				      =	self.get_tokenizer()

         A__				      =	SpeechaTextProcessor(tokenizer=UpperCamelCase     ,    feature_extractor=UpperCamelCase							)

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

         A__				      =	processor.batch_decode(UpperCamelCase							)
         A__				      =	tokenizer.batch_decode(UpperCamelCase							)

         self.assertListEqual(UpperCamelCase     ,    UpperCamelCase							)





    def 					UpperCamelCase						(  self:			Dict							):






         """simple docstring"""


         A__				      =	self.get_feature_extractor()
         A__				      =	self.get_tokenizer()

         A__				      =	SpeechaTextProcessor(tokenizer=UpperCamelCase     ,    feature_extractor=UpperCamelCase							)

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









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       "swiftformer"

    def __init__(  self:			Tuple     ,    UpperCamelCase:			int=3     ,    UpperCamelCase:			Union[str, Any]=[3, 3, 6, 4]     ,    UpperCamelCase:			List[str]=[48, 56, 1_12, 2_20]     ,    UpperCamelCase:			List[Any]=4     ,    UpperCamelCase:			List[Any]=[True, True, True, True]     ,    UpperCamelCase:			str="gelu"     ,    UpperCamelCase:			Tuple=3     ,    UpperCamelCase:			Dict=2     ,    UpperCamelCase:			int=1     ,    UpperCamelCase:			List[Any]=0.0     ,    UpperCamelCase:			Any=True     ,    UpperCamelCase:			List[Any]=1e-5     ,    UpperCamelCase:			List[Any]=1e-5     ,    **UpperCamelCase:			Any     ,    ):






         """simple docstring"""


         super().__init__(**UpperCamelCase							)
         A__				      =	num_channels
         A__				      =	depths
         A__				      =	embed_dims
         A__				      =	mlp_ratio
         A__				      =	downsamples
         A__				      =	hidden_act
         A__				      =	down_patch_size
         A__				      =	down_stride
         A__				      =	down_pad
         A__				      =	drop_path_rate
         A__				      =	use_layer_scale
         A__				      =	layer_scale_init_value
         A__				      =	batch_norm_eps



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       version.parse("1.11"					)

    @property
    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]							)





    @property
    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         return 1e-4






