from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


SCREAMING_SNAKE_CASE :	int							     =		logging.get_logger(__name__)
class    A_ (       a_ ):

  def __init__(		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  feature_size
   __a       =  sampling_rate
   __a       =  padding_value

   __a       =  kwargs.pop("padding_side"     ,							"right"       )
   __a       =  kwargs.pop("return_attention_mask"     ,							__SCREAMING_SNAKE_CASE       )

   super().__init__(**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]     ,							__SCREAMING_SNAKE_CASE :      Union[bool, str, PaddingStrategy] = True     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, TensorType]] = None     ,							):
   # If we have a list of dicts, let's convert it in a dict of lists
   # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
   if isinstance(__SCREAMING_SNAKE_CASE     ,							(list, tuple)       ) and isinstance(processed_features[0]     ,							(dict, BatchFeature)       ):
    __a       =  {
        key: [example[key] for example in processed_features] for key in processed_features[0].keys()
    }

   # The model's main input name, usually `input_values`, has be passed for padding
   if self.model_input_names[0] not in processed_features:
    raise ValueError(
        "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
        f""" to this method that includes {self.model_input_names[0]}, but you provided"""
        f""" {list(processed_features.keys()       )}"""       )

   __a       =  processed_features[self.model_input_names[0]]
   __a       =  (
       return_attention_mask if return_attention_mask is not None else self.return_attention_mask
   )

   if len(__SCREAMING_SNAKE_CASE       ) == 0:
    if return_attention_mask:
     __a       =  []
    return processed_features

   # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
   # and rebuild them afterwards if no return_tensors is specified
   # Note that we lose the specific device the tensor may be on for PyTorch

   __a       =  required_input[0]
   if isinstance(__SCREAMING_SNAKE_CASE     ,							(list, tuple)       ):
    # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
    __a       =  0
    while len(required_input[index]       ) == 0:
     index += 1
    if index < len(__SCREAMING_SNAKE_CASE       ):
     __a       =  required_input[index][0]

   if return_tensors is None:
    if is_tf_tensor(__SCREAMING_SNAKE_CASE       ):
     __a       =  "tf"
    elif is_torch_tensor(__SCREAMING_SNAKE_CASE       ):
     __a       =  "pt"
    elif isinstance(__SCREAMING_SNAKE_CASE     ,							(int, float, list, tuple, np.ndarray)       ):
     __a       =  "np"
    else:
     raise ValueError(
         f"""type of {first_element} unknown: {type(__SCREAMING_SNAKE_CASE       )}. """
         "Should be one of a python, numpy, pytorch or tensorflow object."       )

   for key, value in processed_features.items():
    if isinstance(value[0]     ,							(int, float)       ):
     __a       =  to_numpy(__SCREAMING_SNAKE_CASE       )
    else:
     __a       =  [to_numpy(__SCREAMING_SNAKE_CASE       ) for v in value]

        # Convert padding_strategy in PaddingStrategy
   __a       =  self._get_padding_strategies(padding=__SCREAMING_SNAKE_CASE     ,							max_length=__SCREAMING_SNAKE_CASE       )

   __a       =  processed_features[self.model_input_names[0]]

   __a       =  len(__SCREAMING_SNAKE_CASE       )
   if not all(len(__SCREAMING_SNAKE_CASE       ) == batch_size for v in processed_features.values()       ):
    raise ValueError("Some items in the output dictionary have a different batch size than others."       )

   __a       =  []
   for i in range(__SCREAMING_SNAKE_CASE       ):
    __a       =  {k: v[i] for k, v in processed_features.items()}
    # truncation
    __a       =  self._truncate(
        __SCREAMING_SNAKE_CASE     ,							max_length=__SCREAMING_SNAKE_CASE     ,							pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,							truncation=__SCREAMING_SNAKE_CASE     ,							)
    truncated_inputs.append(__SCREAMING_SNAKE_CASE       )

   if padding_strategy == PaddingStrategy.LONGEST:
    # make sure that `max_length` cannot be longer than the longest truncated length
    __a       =  max(len(input_slice[self.model_input_names[0]]       ) for input_slice in truncated_inputs       )
    __a       =  PaddingStrategy.MAX_LENGTH

   __a       =  {}
   for i in range(__SCREAMING_SNAKE_CASE       ):
    # padding
    __a       =  self._pad(
        truncated_inputs[i]     ,							max_length=__SCREAMING_SNAKE_CASE     ,							padding_strategy=__SCREAMING_SNAKE_CASE     ,							pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,							return_attention_mask=__SCREAMING_SNAKE_CASE     ,							)

    for key, value in outputs.items():
     if key not in batch_outputs:
      __a       =  []
     if value.dtype is np.dtype(np.floataa       ):
      __a       =  value.astype(np.floataa       )
     batch_outputs[key].append(__SCREAMING_SNAKE_CASE       )

   return BatchFeature(__SCREAMING_SNAKE_CASE     ,							tensor_type=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Union[Dict[str, np.ndarray], BatchFeature]     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							):
   __a       =  processed_features[self.model_input_names[0]]

   if padding_strategy == PaddingStrategy.LONGEST:
    __a       =  len(__SCREAMING_SNAKE_CASE       )

   if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
    __a       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

   __a       =  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__SCREAMING_SNAKE_CASE       ) < max_length

   if return_attention_mask and "attention_mask" not in processed_features:
    __a       =  np.ones(len(__SCREAMING_SNAKE_CASE       )     ,							dtype=np.intaa       )

   if needs_to_be_padded:
    __a       =  max_length - len(__SCREAMING_SNAKE_CASE       )
    if self.padding_side == "right":
     if return_attention_mask:
      __a       =  np.pad(
          processed_features["attention_mask"]     ,							(0, difference)       )
     __a       =  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
     __a       =  np.pad(
         __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							"constant"     ,							constant_values=self.padding_value       )
    elif self.padding_side == "left":
     if return_attention_mask:
      __a       =  np.pad(
          processed_features["attention_mask"]     ,							(difference, 0)       )
     __a       =  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
     __a       =  np.pad(
         __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							"constant"     ,							constant_values=self.padding_value       )
    else:
     raise ValueError("Invalid padding strategy:" + str(self.padding_side       )       )

   return processed_features

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[Dict[str, np.ndarray], BatchFeature]     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							):
   if not truncation:
    return processed_features
   elif truncation and max_length is None:
    raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."       )

   __a       =  processed_features[self.model_input_names[0]]

   # find `max_length` that fits `pad_to_multiple_of`
   if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
    __a       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

   __a       =  len(__SCREAMING_SNAKE_CASE       ) > max_length

   if needs_to_be_truncated:
    __a       =  processed_features[self.model_input_names[0]][:max_length]
    if "attention_mask" in processed_features:
     __a       =  processed_features["attention_mask"][:max_length]

   return processed_features

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[Any]=False     ,							__SCREAMING_SNAKE_CASE :      List[str]=None       ):

   # Get padding strategy
   if padding is not False:
    if padding is True:
     __a       =  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
    elif not isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
     __a       =  PaddingStrategy(__SCREAMING_SNAKE_CASE       )
    elif isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
     __a       =  padding
   else:
    __a       =  PaddingStrategy.DO_NOT_PAD

   # Set max length if needed
   if max_length is None:
    if padding_strategy == PaddingStrategy.MAX_LENGTH:
     raise ValueError(
         f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""       )

        # Test if we have a padding value
   if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
    raise ValueError(
        "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
        " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."       )

   return padding_strategy



from ... import PretrainedConfig


SCREAMING_SNAKE_CASE :	Any							     =		{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  _SCREAMING_SNAKE_CASE    				=			"""nezha"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=2_11_28     ,							__SCREAMING_SNAKE_CASE :      Dict=7_68     ,							__SCREAMING_SNAKE_CASE :      List[Any]=12     ,							__SCREAMING_SNAKE_CASE :      str=12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      Any="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-12     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0     ,							__SCREAMING_SNAKE_CASE :      Any=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  max_relative_position
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  classifier_dropout
   __a       =  use_cache


