

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase	:       Tuple    	=							{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       str    	=							[
          "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LiltForQuestionAnswering",
          "LiltForSequenceClassification",
          "LiltForTokenClassification",
          "LiltModel",
          "LiltPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lilt import (
                LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                LiltForQuestionAnswering,
                LiltForSequenceClassification,
                LiltForTokenClassification,
                LiltModel,
                LiltPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       List[str]    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













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	:       str    	=							logging.get_logger(__name__)







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   Any							,    A			:   int							,    A			:   int							,    A			:   float							,    **A			:   Optional[int]		):
         __snake_case:						List[str]			  =						feature_size
         __snake_case:						Optional[int]			  =						sampling_rate
         __snake_case:						Any			  =						padding_value

         __snake_case:						Dict			  =						kwargs.pop("""padding_side"""							,    """right"""		)
         __snake_case:						Union[str, Any]			  =						kwargs.pop("""return_attention_mask"""							,    A		)

         super().__init__(**A		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Union[
               BatchFeature,
               List[BatchFeature],
               Dict[str, BatchFeature],
               Dict[str, List[BatchFeature]],
               List[Dict[str, BatchFeature]],
           ]							,    A			:   Union[bool, str, PaddingStrategy] = True							,    A			:   Optional[int] = None							,    A			:   bool = False							,    A			:   Optional[int] = None							,    A			:   Optional[bool] = None							,    A			:   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(A							,    (list, tuple)		) and isinstance(processed_features[0]							,    (dict, BatchFeature)		):
           __snake_case:						Optional[int]			  =						{
               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()		)}'''		)

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

         if len(A		) == 0:
           if return_attention_mask:
             __snake_case:						Union[str, 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

         __snake_case:						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.
           __snake_case:						Optional[int]			  =						0
           while len(required_input[index]		) == 0:
             index += 1
           if index < len(A		):
             __snake_case:						Optional[int]			  =						required_input[index][0]

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

        # Convert padding_strategy in PaddingStrategy
         __snake_case:						Union[str, Any]			  =						self._get_padding_strategies(padding=A							,    max_length=A		)

         __snake_case:						Any			  =						processed_features[self.model_input_names[0]]

         __snake_case:						int			  =						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."""		)

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

         __snake_case:						List[Any]			  =						{}
         for i in range(A		):
           # padding
           __snake_case:						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:
               __snake_case:						Optional[Any]			  =						[]
             if value.dtype is np.dtype(np.floataa		):
               __snake_case:						str			  =						value.astype(np.floataa		)
             batch_outputs[key].append(A		)

         return BatchFeature(A							,    tensor_type=A		)




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

         if padding_strategy == PaddingStrategy.LONGEST:
           __snake_case:						List[str]			  =						len(A		)

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

         __snake_case:						Dict			  =						padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A		) < max_length

         if return_attention_mask and "attention_mask" not in processed_features:
           __snake_case:						List[str]			  =						np.ones(len(A		)							,    dtype=np.intaa		)

         if needs_to_be_padded:
           __snake_case:						Any			  =						max_length - len(A		)
           if self.padding_side == "right":
             if return_attention_mask:
               __snake_case:						Optional[int]			  =						np.pad(
                   processed_features["""attention_mask"""]							,    (0, difference)		)
             __snake_case:						Any			  =						((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
             __snake_case:						Union[str, Any]			  =						np.pad(
                 A							,    A							,    """constant"""							,    constant_values=self.padding_value		)
           elif self.padding_side == "left":
             if return_attention_mask:
               __snake_case:						Dict			  =						np.pad(
                   processed_features["""attention_mask"""]							,    (difference, 0)		)
             __snake_case:						Union[str, Any]			  =						((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
             __snake_case:						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 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Union[Dict[str, np.ndarray], BatchFeature]							,    A			:   Optional[int] = None							,    A			:   Optional[int] = None							,    A			:   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."""		)

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

         __snake_case:						Tuple			  =						len(A		) > max_length

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

         return processed_features






       def 							UpperCAmelCase__			(  self			:   int							,    A			:   int=False							,    A			:   int=None		):

         # Get padding strategy
         if padding is not False:
           if padding is True:
             __snake_case:						Optional[int]			  =						PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(A							,    A		):
             __snake_case:						Optional[int]			  =						PaddingStrategy(A		)
           elif isinstance(A							,    A		):
             __snake_case:						Any			  =						padding
         else:
           __snake_case:						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






