





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						{
    """configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
    """feature_extraction_wav2vec2""": ["""Wav2Vec2FeatureExtractor"""],
    """processing_wav2vec2""": ["""Wav2Vec2Processor"""],
    """tokenization_wav2vec2""": ["""Wav2Vec2CTCTokenizer""", """Wav2Vec2Tokenizer"""],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE__       :			Any			      =						[
        """WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """Wav2Vec2ForAudioFrameClassification""",
        """Wav2Vec2ForCTC""",
        """Wav2Vec2ForMaskedLM""",
        """Wav2Vec2ForPreTraining""",
        """Wav2Vec2ForSequenceClassification""",
        """Wav2Vec2ForXVector""",
        """Wav2Vec2Model""",
        """Wav2Vec2PreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE__       :			List[Any]			      =						[
        """TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFWav2Vec2ForCTC""",
        """TFWav2Vec2Model""",
        """TFWav2Vec2PreTrainedModel""",
        """TFWav2Vec2ForSequenceClassification""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						[
        """FlaxWav2Vec2ForCTC""",
        """FlaxWav2Vec2ForPreTraining""",
        """FlaxWav2Vec2Model""",
        """FlaxWav2Vec2PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
    from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
    from .processing_wavaveca import WavaVecaProcessor
    from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_wavaveca import (
            WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaForAudioFrameClassification,
            WavaVecaForCTC,
            WavaVecaForMaskedLM,
            WavaVecaForPreTraining,
            WavaVecaForSequenceClassification,
            WavaVecaForXVector,
            WavaVecaModel,
            WavaVecaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_wavaveca import (
            TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFWavaVecaForCTC,
            TFWavaVecaForSequenceClassification,
            TFWavaVecaModel,
            TFWavaVecaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_wavaveca import (
            FlaxWavaVecaForCTC,
            FlaxWavaVecaForPreTraining,
            FlaxWavaVecaModel,
            FlaxWavaVecaPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE__       :			Any			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def       _A  (      lowerCamelCase   ):

   # A local function to see if a dot lands in the circle.
   def is_in_circle(lowerCamelCase							, lowerCamelCase   ) -> bool:
      a__	:						Any      		=  sqrt((x**2) + (y**2)   )
      # Our circle has a radius of 1, so a distance
      # greater than 1 would land outside the circle.
      return distance_from_centre <= 1

   # The proportion of guesses that landed in the circle
   a__	:						Union[str, Any]      		=  mean(
       int(is_in_circle(uniform(-1.0							, 1.0   )							, uniform(-1.0							, 1.0   )   )   )
       for _ in range(lowerCamelCase   )   )
   # The ratio of the area for circle to square is pi/4.
   a__	:						Any      		=  proportion * 4
   print(F"""The estimated value of pi is {pi_estimate}"""   )
   print(F"""The numpy value of pi is {pi}"""   )
   print(F"""The total error is {abs(pi - pi_estimate   )}"""   )






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase = 0.0							, lowerCamelCase = 1.0							, ):
   return mean(
       function_to_integrate(uniform(lowerCamelCase							, lowerCamelCase   )   ) for _ in range(lowerCamelCase   )   ) * (max_value - min_value)






def       _A  (      lowerCamelCase							, lowerCamelCase = 0.0							, lowerCamelCase = 1.0   ):

   def identity_function(lowerCamelCase   ) -> float:
      return x

   a__	:						int      		=  area_under_curve_estimator(
       lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   )
   a__	:						int      		=  (max_value * max_value - min_value * min_value) / 2

   print("******************"   )
   print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}"""   )
   print(F"""Estimated value is {estimated_value}"""   )
   print(F"""Expected value is {expected_value}"""   )
   print(F"""Total error is {abs(estimated_value - expected_value   )}"""   )
   print("******************"   )






def       _A  (      lowerCamelCase   ):

   def function_to_integrate(lowerCamelCase   ) -> float:
      return sqrt(4.0 - x * x   )

   a__	:						int      		=  area_under_curve_estimator(
       lowerCamelCase							, lowerCamelCase							, 0.0							, 2.0   )

   print("******************"   )
   print("Estimating pi using area_under_curve_estimator"   )
   print(F"""Estimated value is {estimated_value}"""   )
   print(F"""Expected value is {pi}"""   )
   print(F"""Total error is {abs(estimated_value - pi   )}"""   )
   print("******************"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

