'''simple docstring'''






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_	   =			logging.get_logger(__name__)




class    a (       __a  ):



  """simple docstring"""







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

    '''simple docstring'''







    __UpperCAmelCase:					int		= feature_size
    __UpperCAmelCase:					Tuple		= sampling_rate
    __UpperCAmelCase:					List[Any]		= padding_value

    __UpperCAmelCase:					int		= kwargs.pop("""padding_side"""   ,    """right"""					)
    __UpperCAmelCase:					Any		= kwargs.pop("""return_attention_mask"""   ,    A__					)

    super().__init__(**A__					)







  def    lowercase_  (							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(A__   ,    (list, tuple)					) and isinstance(processed_features[0]   ,    (dict, BatchFeature)					):
      __UpperCAmelCase:					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()					)}'''					)

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

    if len(A__					) == 0:
      if return_attention_mask:
        __UpperCAmelCase:					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

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

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

        # Convert padding_strategy in PaddingStrategy
    __UpperCAmelCase:					List[str]		= self._get_padding_strategies(padding=A__   ,    max_length=A__					)

    __UpperCAmelCase:					Tuple		= processed_features[self.model_input_names[0]]

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

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

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

    __UpperCAmelCase:					List[str]		= {}
    for i in range(A__					):
      # padding
      __UpperCAmelCase:					Any		= self._pad(
          truncated_inputs[i]   ,    max_length=A__   ,    padding_strategy=A__   ,    pad_to_multiple_of=A__   ,    return_attention_mask=A__   ,    )

      for key, value in outputs.items():
        if key not in batch_outputs:
          __UpperCAmelCase:					Optional[Any]		= []
        if value.dtype is np.dtype(np.floataa					):
          __UpperCAmelCase:					str		= value.astype(np.floataa					)
        batch_outputs[key].append(A__					)

    return BatchFeature(A__   ,    tensor_type=A__					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = PaddingStrategy.DO_NOT_PAD   ,    snake_case_ = None   ,    snake_case_ = None   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= processed_features[self.model_input_names[0]]

    if padding_strategy == PaddingStrategy.LONGEST:
      __UpperCAmelCase:					Optional[Any]		= len(A__					)

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

    __UpperCAmelCase:					List[str]		= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__					) < max_length

    if return_attention_mask and "attention_mask" not in processed_features:
      __UpperCAmelCase:					Tuple		= np.ones(len(A__					)   ,    dtype=np.intaa					)

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

    return processed_features







  def    lowercase_  (							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:					List[Any]		= 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:					List[str]		= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

    __UpperCAmelCase:					List[str]		= len(A__					) > max_length

    if needs_to_be_truncated:
      __UpperCAmelCase:					Any		= processed_features[self.model_input_names[0]][:max_length]
      if "attention_mask" in processed_features:
        __UpperCAmelCase:					Union[str, Any]		= processed_features["""attention_mask"""][:max_length]

    return processed_features







  def    lowercase_  (							self   ,    snake_case_=False   ,    snake_case_=None					):

    '''simple docstring'''







    if padding is not False:
      if padding is True:
        __UpperCAmelCase:					Tuple		= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
      elif not isinstance(A__   ,    A__					):
        __UpperCAmelCase:					Optional[Any]		= PaddingStrategy(A__					)
      elif isinstance(A__   ,    A__					):
        __UpperCAmelCase:					Any		= padding
    else:
      __UpperCAmelCase:					int		= 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'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE_	   =			['small', 'medium', 'large']

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    parser.add_argument('--dialogpt_path', default='.', type=str)
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        SCREAMING_SNAKE_CASE_	   =			os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
        SCREAMING_SNAKE_CASE_	   =			F"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )