


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


__UpperCamelCase     		=							logging.get_logger(__name__)







class 							lowerCamelCase__						(				_SCREAMING_SNAKE_CASE   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							feature_size
						UpperCamelCase__       =							sampling_rate
						UpperCamelCase__       =							padding_value

						UpperCamelCase__       =							kwargs.pop("padding_side"   ,    "right"  )
						UpperCamelCase__       =							kwargs.pop("return_attention_mask"   ,    snake_case  )

						super().__init__(**snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = True   ,    snake_case = None   ,    snake_case = False   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    ):

						'''simple docstring'''


						if isinstance(snake_case   ,    (list, tuple)  ) and isinstance(processed_features[0]   ,    (dict, BatchFeature)  ):
									UpperCamelCase__       =							{
									    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()  )}'''  )

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

						if len(snake_case  ) == 0:
									if return_attention_mask:
												UpperCamelCase__       =							[]
									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

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

						if return_tensors is None:
									if is_tf_tensor(snake_case  ):
												UpperCamelCase__       =							"""tf"""
									elif is_torch_tensor(snake_case  ):
												UpperCamelCase__       =							"""pt"""
									elif isinstance(snake_case   ,    (int, float, list, tuple, np.ndarray)  ):
												UpperCamelCase__       =							"""np"""
									else:
												raise ValueError(
												    F'''type of {first_element} unknown: {type(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)  ):
												UpperCamelCase__       =							to_numpy(snake_case  )
									else:
												UpperCamelCase__       =							[to_numpy(snake_case  ) for v in value]

									# Convert padding_strategy in PaddingStrategy
						UpperCamelCase__       =							self._get_padding_strategies(padding=snake_case   ,    max_length=snake_case  )

						UpperCamelCase__       =							processed_features[self.model_input_names[0]]

						UpperCamelCase__       =							len(snake_case  )
						if not all(len(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."  )

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

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

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

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

						return BatchFeature(snake_case   ,    tensor_type=snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = PaddingStrategy.DO_NOT_PAD   ,    snake_case = None   ,    snake_case = None   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
									UpperCamelCase__       =							len(snake_case  )

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

						UpperCamelCase__       =							padding_strategy != PaddingStrategy.DO_NOT_PAD and len(snake_case  ) < max_length

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

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

						return processed_features




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    ):

						'''simple docstring'''


						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."  )

						UpperCamelCase__       =							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):
									UpperCamelCase__       =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						UpperCamelCase__       =							len(snake_case  ) > max_length

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

						return processed_features







			def 					snake_case__					(				self   ,    snake_case=False   ,    snake_case=None  ):

						'''simple docstring'''


						if padding is not False:
									if padding is True:
												UpperCamelCase__       =							PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
									elif not isinstance(snake_case   ,    snake_case  ):
												UpperCamelCase__       =							PaddingStrategy(snake_case  )
									elif isinstance(snake_case   ,    snake_case  ):
												UpperCamelCase__       =							padding
						else:
									UpperCamelCase__       =							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






'''simple docstring'''






from typing import Union

import fire
import torch
from tqdm import tqdm







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  str = "cpu"				,       A__   :  Union[str, None] = None   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  torch.load(A__				,       map_location=A__   )
  for k, v in tqdm(state_dict.items()   ):
    if not isinstance(A__				,       torch.Tensor   ):
      raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""   )
    lowerCAmelCase_     : List[str]     =  v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_     : List[str]     =  src_path
  torch.save(A__				,       A__   )


if __name__ == "__main__":
  fire.Fire(convert)



