






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_UpperCAmelCase			:		Tuple   				=    logging.get_logger(__name__)
class  lowerCAmelCase_       (				__UpperCAmelCase					):
       UpperCamelCase_		:List[Any]		      =    ["input_values", "padding_mask"]




       def __init__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   int = 1 ,							SCREAMING_SNAKE_CASE_     :   int = 24_000 ,							SCREAMING_SNAKE_CASE_     :   float = 0.0 ,							SCREAMING_SNAKE_CASE_     :   float = None ,							SCREAMING_SNAKE_CASE_     :   float = None ,							**SCREAMING_SNAKE_CASE_     :   str ,							):
           super().__init__(feature_size=_lowerCamelCase ,							sampling_rate=_lowerCamelCase ,							padding_value=_lowerCamelCase ,							**_lowerCamelCase	)
           lowerCAmelCase__      			=						chunk_length_s
           lowerCAmelCase__      			=						overlap




       @property
       def 						__snake_case					(   self     :   List[str]	):
           if self.chunk_length_s is None:
               return None
           else:
               return int(self.chunk_length_s * self.sampling_rate	)




       @property
       def 						__snake_case					(   self     :   str	):
           if self.chunk_length_s is None or self.overlap is None:
               return None
           else:
               return max(1 ,							int((1.0 - self.overlap) * self.chunk_length	)	)




       def __call__(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,							SCREAMING_SNAKE_CASE_     :   Optional[Union[bool, str, PaddingStrategy]] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[bool] = False ,							SCREAMING_SNAKE_CASE_     :   Optional[int] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[Union[str, TensorType]] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[int] = None ,							):
           if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                       f' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                       f' {self.sampling_rate} and not {sampling_rate}.'	)
           else:
               logger.warning(
                   '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                   '''Failing to do so can result in silent errors that might be hard to debug.'''	)

           if padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''	)
           elif padding is None:
               # by default let's pad the inputs
               lowerCAmelCase__      			=						True

           lowerCAmelCase__      			=						bool(
               isinstance(_lowerCamelCase ,							(list, tuple)	) and (isinstance(raw_audio[0] ,							(np.ndarray, tuple, list)	))	)

           if is_batched:
               lowerCAmelCase__      			=						[np.asarray(_lowerCamelCase ,							dtype=np.floataa	).T for audio in raw_audio]
           elif not is_batched and not isinstance(_lowerCamelCase ,							np.ndarray	):
               lowerCAmelCase__      			=						np.asarray(_lowerCamelCase ,							dtype=np.floataa	)
           elif isinstance(_lowerCamelCase ,							np.ndarray	) and raw_audio.dtype is np.dtype(np.floataa	):
               lowerCAmelCase__      			=						raw_audio.astype(np.floataa	)

           # always return batch
           if not is_batched:
               lowerCAmelCase__      			=						[np.asarray(_lowerCamelCase	).T]

           # verify inputs are valid
           for idx, example in enumerate(_lowerCamelCase	):
               if example.ndim > 2:
                   raise ValueError(f'Expected input shape (channels, length) but got shape {example.shape}'	)
               if self.feature_size == 1 and example.ndim != 1:
                   raise ValueError(f'Expected mono audio but example has {example.shape[-1]} channels'	)
               if self.feature_size == 2 and example.shape[-1] != 2:
                   raise ValueError(f'Expected stereo audio but example has {example.shape[-1]} channels'	)

           lowerCAmelCase__      			=						None
           lowerCAmelCase__      			=						BatchFeature({'''input_values''': raw_audio}	)
           if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                   lowerCAmelCase__      			=						min(array.shape[0] for array in raw_audio	)
                   lowerCAmelCase__      			=						int(np.floor(max_length / self.chunk_stride	)	)
                   lowerCAmelCase__      			=						(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                   lowerCAmelCase__      			=						max(array.shape[0] for array in raw_audio	)
                   lowerCAmelCase__      			=						int(np.ceil(max_length / self.chunk_stride	)	)
                   lowerCAmelCase__      			=						(nb_step - 1) * self.chunk_stride + self.chunk_length
                   lowerCAmelCase__      			=						'''max_length'''
               else:
                   lowerCAmelCase__      			=						input_values

        # normal padding on batch
           if padded_inputs is None:
               lowerCAmelCase__      			=						self.pad(
                   _lowerCamelCase ,							max_length=_lowerCamelCase ,							truncation=_lowerCamelCase ,							padding=_lowerCamelCase ,							return_attention_mask=_lowerCamelCase ,							)
               if padding:
                   lowerCAmelCase__      			=						padded_inputs.pop('''attention_mask'''	)

           lowerCAmelCase__      			=						[]
           for example in padded_inputs.pop('''input_values'''	):
               if self.feature_size == 1:
                   lowerCAmelCase__      			=						example[..., None]
               input_values.append(example.T	)

           lowerCAmelCase__      			=						input_values
           if return_tensors is not None:
               lowerCAmelCase__      			=						padded_inputs.convert_to_tensors(_lowerCamelCase	)

           return padded_inputs













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		List[str]   				=    {
    "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
    "tokenization_electra": ["ElectraTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    ["ElectraTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Optional[Any]   				=    [
			    "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ElectraForCausalLM",
			    "ElectraForMaskedLM",
			    "ElectraForMultipleChoice",
			    "ElectraForPreTraining",
			    "ElectraForQuestionAnswering",
			    "ElectraForSequenceClassification",
			    "ElectraForTokenClassification",
			    "ElectraModel",
			    "ElectraPreTrainedModel",
			    "load_tf_weights_in_electra",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Union[str, Any]   				=    [
			    "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFElectraForMaskedLM",
			    "TFElectraForMultipleChoice",
			    "TFElectraForPreTraining",
			    "TFElectraForQuestionAnswering",
			    "TFElectraForSequenceClassification",
			    "TFElectraForTokenClassification",
			    "TFElectraModel",
			    "TFElectraPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Optional[int]   				=    [
			    "FlaxElectraForCausalLM",
			    "FlaxElectraForMaskedLM",
			    "FlaxElectraForMultipleChoice",
			    "FlaxElectraForPreTraining",
			    "FlaxElectraForQuestionAnswering",
			    "FlaxElectraForSequenceClassification",
			    "FlaxElectraForTokenClassification",
			    "FlaxElectraModel",
			    "FlaxElectraPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
			from .tokenization_electra import ElectraTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_electra_fast import ElectraTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_electra import (
						    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ElectraForCausalLM,
						    ElectraForMaskedLM,
						    ElectraForMultipleChoice,
						    ElectraForPreTraining,
						    ElectraForQuestionAnswering,
						    ElectraForSequenceClassification,
						    ElectraForTokenClassification,
						    ElectraModel,
						    ElectraPreTrainedModel,
						    load_tf_weights_in_electra,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_electra import (
						    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFElectraForMaskedLM,
						    TFElectraForMultipleChoice,
						    TFElectraForPreTraining,
						    TFElectraForQuestionAnswering,
						    TFElectraForSequenceClassification,
						    TFElectraForTokenClassification,
						    TFElectraModel,
						    TFElectraPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						    FlaxElectraPreTrainedModel,
						)

else:
			import sys

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





