




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  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     feature_size
        lowerCAmelCase__				:  Tuple		       =     sampling_rate
        lowerCAmelCase__				:  Optional[Any]		       =     padding_value

        lowerCAmelCase__				:  Dict		       =     kwargs.pop('''padding_side'''      ,				'''right'''		)
        lowerCAmelCase__				:  List[str]		       =     kwargs.pop('''return_attention_mask'''      ,				__UpperCAmelCase		)

        super().__init__(**__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = True      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = False      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = 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(__UpperCAmelCase      ,				(list, tuple)		) and isinstance(processed_features[0]      ,				(dict, BatchFeature)		):
          lowerCAmelCase__				:  str		       =     {
              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()		)}"""		)

        lowerCAmelCase__				:  Dict		       =     processed_features[self.model_input_names[0]]
        lowerCAmelCase__				:  Optional[int]		       =     (
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(__UpperCAmelCase		) == 0:
          if return_attention_mask:
            lowerCAmelCase__				:  int		       =     []
          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

        lowerCAmelCase__				:  Optional[Any]		       =     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.
          lowerCAmelCase__				:  List[Any]		       =     0
          while len(required_input[index]		) == 0:
            index += 1
          if index < len(__UpperCAmelCase		):
            lowerCAmelCase__				:  List[str]		       =     required_input[index][0]

        if return_tensors is None:
          if is_tf_tensor(__UpperCAmelCase		):
            lowerCAmelCase__				:  int		       =     '''tf'''
          elif is_torch_tensor(__UpperCAmelCase		):
            lowerCAmelCase__				:  Any		       =     '''pt'''
          elif isinstance(__UpperCAmelCase      ,				(int, float, list, tuple, np.ndarray)		):
            lowerCAmelCase__				:  List[str]		       =     '''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)		):
            lowerCAmelCase__				:  Dict		       =     to_numpy(__UpperCAmelCase		)
          else:
            lowerCAmelCase__				:  int		       =     [to_numpy(__UpperCAmelCase		) for v in value]

        # Convert padding_strategy in PaddingStrategy
        lowerCAmelCase__				:  int		       =     self._get_padding_strategies(padding=__UpperCAmelCase      ,				max_length=__UpperCAmelCase		)

        lowerCAmelCase__				:  List[Any]		       =     processed_features[self.model_input_names[0]]

        lowerCAmelCase__				:  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.'''		)

        lowerCAmelCase__				:  int		       =     []
        for i in range(__UpperCAmelCase		):
          lowerCAmelCase__				:  Any		       =     {k: v[i] for k, v in processed_features.items()}
          # truncation
          lowerCAmelCase__				:  int		       =     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
          lowerCAmelCase__				:  Union[str, Any]		       =     max(len(input_slice[self.model_input_names[0]]		) for input_slice in truncated_inputs		)
          lowerCAmelCase__				:  Any		       =     PaddingStrategy.MAX_LENGTH

        lowerCAmelCase__				:  Any		       =     {}
        for i in range(__UpperCAmelCase		):
          # padding
          lowerCAmelCase__				:  int		       =     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:
              lowerCAmelCase__				:  Union[str, Any]		       =     []
            if value.dtype is np.dtype(np.floataa		):
              lowerCAmelCase__				:  List[str]		       =     value.astype(np.floataa		)
            batch_outputs[key].append(__UpperCAmelCase		)

        return BatchFeature(__UpperCAmelCase      ,				tensor_type=__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = PaddingStrategy.DO_NOT_PAD      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				):
        lowerCAmelCase__				:  Dict		       =     processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
          lowerCAmelCase__				:  Dict		       =     len(__UpperCAmelCase		)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
          lowerCAmelCase__				:  Optional[Any]		       =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowerCAmelCase__				:  Tuple		       =     padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCAmelCase		) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
          lowerCAmelCase__				:  Union[str, Any]		       =     np.ones(len(__UpperCAmelCase		)      ,				dtype=np.intaa		)

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

        return processed_features

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = 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.'''		)

        lowerCAmelCase__				:  Union[str, 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):
          lowerCAmelCase__				:  List[str]		       =     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowerCAmelCase__				:  List[Any]		       =     len(__UpperCAmelCase		) > max_length

        if needs_to_be_truncated:
          lowerCAmelCase__				:  int		       =     processed_features[self.model_input_names[0]][:max_length]
          if "attention_mask" in processed_features:
            lowerCAmelCase__				:  List[Any]		       =     processed_features['''attention_mask'''][:max_length]

        return processed_features







      def 	__magic_name__(   self      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=None		):

        # Get padding strategy
        if padding is not False:
          if padding is True:
            lowerCAmelCase__				:  Union[str, Any]		       =     PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
          elif not isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
            lowerCAmelCase__				:  Union[str, Any]		       =     PaddingStrategy(__UpperCAmelCase		)
          elif isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
            lowerCAmelCase__				:  Union[str, Any]		       =     padding
        else:
          lowerCAmelCase__				:  List[Any]		       =     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 os
from datetime import datetime as dt

from github import Github


lowerCAmelCase_					    = [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def      __lowerCAmelCase			(					)     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     Github(os.environ['''GITHUB_TOKEN''']      )
  lowerCAmelCase__				:  str		       =     g.get_repo('''huggingface/diffusers'''      )
  lowerCAmelCase__				:  List[str]		       =     repo.get_issues(state='''open'''      )

  for issue in open_issues:
    lowerCAmelCase__				:  Optional[Any]		       =     sorted(issue.get_comments()	,      key=lambda UpperCamelCase      : i.created_at	,      reverse=UpperCamelCase      )
    lowerCAmelCase__				:  int		       =     comments[0] if len(UpperCamelCase      ) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''      )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''      )
      issue.remove_from_labels('''stale'''      )
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''      )
      issue.add_to_labels('''stale'''      )


if __name__ == "__main__":
     main()

