




import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


UpperCAmelCase_		:					int          =  logging.get_logger(__name__)




class 		__A   (      UpperCamelCase__      ):

						def __init__(		self :Tuple     ,			*__snake_case :str     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										warnings.warn(
										    """The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
										    """ Please use MobileViTImageProcessor instead."""     ,			__snake_case     ,			)
										super().__init__(*__snake_case     ,			**__snake_case			)










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_		:					Dict          =  logging.get_logger(__name__)




class 		__A   (      UpperCamelCase__      ):

						def __init__(		self :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :float     ,			**__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=feature_size
										__magic_name__						:						Union[str, Any]										=sampling_rate
										__magic_name__						:						List[Any]										=padding_value

										__magic_name__						:						List[str]										=kwargs.pop("""padding_side"""     ,			"""right"""			)
										__magic_name__						:						Tuple										=kwargs.pop("""return_attention_mask"""     ,			__snake_case			)

										super().__init__(**__snake_case			)

						def      A__    (		self :Any     ,			__snake_case :Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]     ,			__snake_case :Union[bool, str, PaddingStrategy] = True     ,			__snake_case :Optional[int] = None     ,			__snake_case :bool = False     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			__snake_case :Optional[Union[str, TensorType]] = None     ,			):
										'''simple docstring'''

										if isinstance(__snake_case     ,			(list, tuple)			) and isinstance(processed_features[0]     ,			(dict, BatchFeature)			):
														__magic_name__						:						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()			)}"			)

										__magic_name__						:						int										=processed_features[self.model_input_names[0]]
										__magic_name__						:						Union[str, Any]										=(
										    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
										)

										if len(__snake_case			) == 0:
														if return_attention_mask:
																		__magic_name__						:						List[str]										=[]
														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

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

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

        # Convert padding_strategy in PaddingStrategy
										__magic_name__						:						Dict										=self._get_padding_strategies(padding=__snake_case     ,			max_length=__snake_case			)

										__magic_name__						:						Optional[Any]										=processed_features[self.model_input_names[0]]

										__magic_name__						:						Dict										=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."""			)

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

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

										return BatchFeature(__snake_case     ,			tensor_type=__snake_case			)

						def      A__    (		self :Any     ,			__snake_case :Union[Dict[str, np.ndarray], BatchFeature]     ,			__snake_case :Optional[int] = None     ,			__snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			):
										'''simple docstring'''

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

										if padding_strategy == PaddingStrategy.LONGEST:
														__magic_name__						:						Any										=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):
														__magic_name__						:						Dict										=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__						:						List[Any]										=padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case			) < max_length

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

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

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

										__magic_name__						:						Any										=len(__snake_case			) > max_length

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

										return processed_features


						def      A__    (		self :List[Any]     ,			__snake_case :str=False     ,			__snake_case :Optional[int]=None			):
										'''simple docstring'''

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




