

"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list

class 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Any						=						"""<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """  )
        UpperCAmelCase__ :       Optional[Any]						=						dict(zip(_lowerCamelCase       ,					range(len(_lowerCamelCase  )  )  )  )

        UpperCAmelCase__ :       Optional[Any]						=						{
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        UpperCAmelCase__ :       Union[str, Any]						=						{
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

        UpperCAmelCase__ :       Optional[Any]						=						tempfile.mkdtemp()
        UpperCAmelCase__ :       Optional[int]						=						os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["""vocab_file"""]  )
        UpperCAmelCase__ :       Optional[Any]						=						os.path.join(self.tmpdirname       ,					_lowerCamelCase  )
        with open(self.vocab_file       ,					"""w"""       ,					encoding="""utf-8"""  ) as fp:
              fp.write(json.dumps(_lowerCamelCase  ) + """\n"""  )

        with open(self.feature_extraction_file       ,					"""w"""       ,					encoding="""utf-8"""  ) as fp:
              fp.write(json.dumps(_lowerCamelCase  ) + """\n"""  )



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[Any]						=						self.add_kwargs_tokens_map.copy()
        kwargs.update(_lowerCamelCase  )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  )



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        shutil.rmtree(self.tmpdirname  )



  def    _a			(self  ):


        """simple docstring"""

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

        UpperCAmelCase__ :       List[Any]						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )

        processor.save_pretrained(self.tmpdirname  )
        UpperCAmelCase__ :       Tuple						=						WavaVecaProcessor.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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,					feature_extractor=self.get_feature_extractor()  )
        processor.save_pretrained(self.tmpdirname  )

        UpperCAmelCase__ :       Any						=						self.get_tokenizer(bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""  )
        UpperCAmelCase__ :       Any						=						self.get_feature_extractor(do_normalize=_lowerCamelCase       ,					padding_value=1.0  )

        UpperCAmelCase__ :       Union[str, Any]						=						WavaVecaProcessor.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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						self.get_feature_extractor()
        UpperCAmelCase__ :       Tuple						=						self.get_tokenizer()

        UpperCAmelCase__ :       int						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )

        UpperCAmelCase__ :       int						=						floats_list((3, 1000)  )

        UpperCAmelCase__ :       Optional[int]						=						feature_extractor(_lowerCamelCase       ,					return_tensors="""np"""  )
        UpperCAmelCase__ :       List[str]						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Any						=						self.get_feature_extractor()
        UpperCAmelCase__ :       Dict						=						self.get_tokenizer()

        UpperCAmelCase__ :       Dict						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )

        UpperCAmelCase__ :       Any						=						"""This is a test string"""

        UpperCAmelCase__ :       Optional[Any]						=						processor(text=_lowerCamelCase  )

        UpperCAmelCase__ :       int						=						tokenizer(_lowerCamelCase  )

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



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Union[str, Any]						=						self.get_feature_extractor()
        UpperCAmelCase__ :       int						=						self.get_tokenizer()

        UpperCAmelCase__ :       str						=						WavaVecaProcessor(tokenizer=_lowerCamelCase       ,					feature_extractor=_lowerCamelCase  )

        UpperCAmelCase__ :       Any						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCAmelCase__ :       Dict						=						processor.batch_decode(_lowerCamelCase  )
        UpperCAmelCase__ :       List[Any]						=						tokenizer.batch_decode(_lowerCamelCase  )

        self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						self.get_feature_extractor()
        UpperCAmelCase__ :       str						=						self.get_tokenizer()

        UpperCAmelCase__ :       Any						=						WavaVecaProcessor(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"""       ,					)




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A						=     {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""GPT2TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPT2DoubleHeadsModel""",
        """GPT2ForQuestionAnswering""",
        """GPT2ForSequenceClassification""",
        """GPT2ForTokenClassification""",
        """GPT2LMHeadModel""",
        """GPT2Model""",
        """GPT2PreTrainedModel""",
        """load_tf_weights_in_gpt2""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFGPT2DoubleHeadsModel""",
        """TFGPT2ForSequenceClassification""",
        """TFGPT2LMHeadModel""",
        """TFGPT2MainLayer""",
        """TFGPT2Model""",
        """TFGPT2PreTrainedModel""",
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""TFGPT2Tokenizer"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
    from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
    from .tokenization_gpta import GPTaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_fast import GPTaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_tf import TFGPTaTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
    import sys

    _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

