



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


_lowerCamelCase						    =  logging.get_logger(__name__)

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							int     ,  UpperCamelCase :							int     ,  UpperCamelCase :							int     ,  UpperCamelCase :							float     ,  **UpperCamelCase :							Dict				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	feature_size
      __SCREAMING_SNAKE_CASE       :    str       =	sampling_rate
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	padding_value

      __SCREAMING_SNAKE_CASE       :    List[str]       =	kwargs.pop("padding_side"     ,  "right"				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	kwargs.pop("return_attention_mask"     ,  UpperCamelCase				)

      super().__init__(**UpperCamelCase				)





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							Union[
             BatchFeature,
             List[BatchFeature],
             Dict[str, BatchFeature],
             Dict[str, List[BatchFeature]],
             List[Dict[str, BatchFeature]],
         ]     ,  UpperCamelCase :							Union[bool, str, PaddingStrategy] = True     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[Union[str, TensorType]] = None     ,  )->BatchFeature:
      # 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(UpperCamelCase     ,  (list, tuple)				) and isinstance(processed_features[0]     ,  (dict, BatchFeature)				):
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{
           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()				)}"""				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	processed_features[self.model_input_names[0]]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(UpperCamelCase				) == 0:
       if return_attention_mask:
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[]
       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

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

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

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

        # Convert padding_strategy in PaddingStrategy
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self._get_padding_strategies(padding=UpperCamelCase     ,  max_length=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    str       =	processed_features[self.model_input_names[0]]

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

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

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

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

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

      return BatchFeature(UpperCamelCase     ,  tensor_type=UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Union[Dict[str, np.ndarray], BatchFeature]     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  )->dict:
      __SCREAMING_SNAKE_CASE       :    int       =	processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
       __SCREAMING_SNAKE_CASE       :    int       =	len(UpperCamelCase				)

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

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCamelCase				) < max_length

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

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

      return processed_features





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Union[Dict[str, np.ndarray], BatchFeature]     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  )->Tuple:
      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."				)

      __SCREAMING_SNAKE_CASE       :    int       =	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):
       __SCREAMING_SNAKE_CASE       :    Tuple       =	((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	len(UpperCamelCase				) > max_length

      if needs_to_be_truncated:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
        __SCREAMING_SNAKE_CASE       :    List[str]       =	processed_features["attention_mask"][:max_length]

      return processed_features






     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Tuple=False     ,  UpperCamelCase :							Tuple=None				)->int:

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











import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_lowerCamelCase						    =  importlib.util.find_spec("""s3fs""") is not None

if _has_safs:
      from .safilesystem import SaFileSystem  # noqa: F401

_lowerCamelCase						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
      if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
            warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
      fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 if "://" in dataset_path:
  __SCREAMING_SNAKE_CASE       :    Any       =	dataset_path.split("://"     )[1]
 return dataset_path






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem     ):



 """simple docstring"""





 if fs is not None and fs.protocol != "file":
  return True
 else:
  return False






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	not is_remote_filesystem(__lowerCamelCase     )
 if is_local:
  # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
  shutil.move(fs._strip_protocol(__lowerCamelCase     )		,	fs._strip_protocol(__lowerCamelCase     )     )
 else:
  fs.mv(__lowerCamelCase		,	__lowerCamelCase		,	recursive=__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 if hasattr(fsspec.asyn		,	"reset_lock"     ):
  # for future fsspec>2022.05.0
  fsspec.asyn.reset_lock()
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	None
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None
  __SCREAMING_SNAKE_CASE       :    Dict       =	threading.Lock()






