

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


lowerCAmelCase__						=	get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_torch
@require_torchaudio
@require_sentencepiece
class    a__  (   unittest.TestCase     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self    )					->  Any:
							'''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": 16000,
							    "return_attention_mask": False,
							    "do_normalize": True,
							}
							save_json(_UpperCamelCase , save_dir / FEATURE_EXTRACTOR_NAME    )


			def        UpperCamelCase						(      self , **lowercase    )					->  Union[str, Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , **lowercase    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							shutil.rmtree(self.tmpdirname    )


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''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    )					->  Tuple:
							'''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    )					->  Tuple:
							'''simple docstring'''






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

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

							A__            =     floats_list((3, 1000)    )

							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    )					->  str:
							'''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    )					->  Tuple:
							'''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    )					->  Optional[int]:
							'''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" , )



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


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							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__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






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


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

