





'''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


lowerCAmelCase :					Optional[Any] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):





     def __init__(			self		: Union[str, Any]     ,				lowercase		: int     ,				lowercase		: int     ,				lowercase		: float     ,				**lowercase		: str						):





        """simple docstring"""




        lowercase_      :int         =			feature_size
        lowercase_      :Any         =			sampling_rate
        lowercase_      :List[Any]         =			padding_value

        lowercase_      :Dict         =			kwargs.pop("padding_side"     ,				"right"						)
        lowercase_      :int         =			kwargs.pop("return_attention_mask"     ,				lowercase						)

        super().__init__(**lowercase						)





     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: Union[
             BatchFeature,
             List[BatchFeature],
             Dict[str, BatchFeature],
             Dict[str, List[BatchFeature]],
             List[Dict[str, BatchFeature]],
         ]     ,				lowercase		: Union[bool, str, PaddingStrategy] = True     ,				lowercase		: Optional[int] = None     ,				lowercase		: bool = False     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				):





        """simple docstring"""




        if isinstance(lowercase     ,				(list, tuple)						) and isinstance(processed_features[0]     ,				(dict, BatchFeature)						):
           lowercase_      :Dict         =			{
               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()						)}'						)

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

        if len(lowercase						) == 0:
           if return_attention_mask:
              lowercase_      :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

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

        if return_tensors is None:
           if is_tf_tensor(lowercase						):
              lowercase_      :Optional[int]         =			"tf"
           elif is_torch_tensor(lowercase						):
              lowercase_      :int         =			"pt"
           elif isinstance(lowercase     ,				(int, float, list, tuple, np.ndarray)						):
              lowercase_      :Any         =			"np"
           else:
              raise ValueError(
                  F'type of {first_element} unknown: {type(lowercase						)}. '
                  "Should be one of a python, numpy, pytorch or tensorflow object."						)

        for key, value in processed_features.items():
           if isinstance(value[0]     ,				(int, float)						):
              lowercase_      :List[Any]         =			to_numpy(lowercase						)
           else:
              lowercase_      :Dict         =			[to_numpy(lowercase						) for v in value]

        # Convert padding_strategy in PaddingStrategy
        lowercase_      :Optional[Any]         =			self._get_padding_strategies(padding=lowercase     ,				max_length=lowercase						)

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

        lowercase_      :Optional[Any]         =			len(lowercase						)
        if not all(len(lowercase						) == batch_size for v in processed_features.values()						):
           raise ValueError("Some items in the output dictionary have a different batch size than others."						)

        lowercase_      :int         =			[]
        for i in range(lowercase						):
           lowercase_      :Tuple         =			{k: v[i] for k, v in processed_features.items()}
           # truncation
           lowercase_      :Any         =			self._truncate(
               lowercase     ,				max_length=lowercase     ,				pad_to_multiple_of=lowercase     ,				truncation=lowercase     ,				)
           truncated_inputs.append(lowercase						)

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

        lowercase_      :Dict         =			{}
        for i in range(lowercase						):
           # padding
           lowercase_      :Union[str, Any]         =			self._pad(
               truncated_inputs[i]     ,				max_length=lowercase     ,				padding_strategy=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_attention_mask=lowercase     ,				)

           for key, value in outputs.items():
              if key not in batch_outputs:
                 lowercase_      :Dict         =			[]
              if value.dtype is np.dtype(np.floataa						):
                 lowercase_      :Any         =			value.astype(np.floataa						)
              batch_outputs[key].append(lowercase						)

        return BatchFeature(lowercase     ,				tensor_type=lowercase						)





     def 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: Union[Dict[str, np.ndarray], BatchFeature]     ,				lowercase		: Optional[int] = None     ,				lowercase		: PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				):





        """simple docstring"""




        lowercase_      :List[str]         =			processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
           lowercase_      :List[str]         =			len(lowercase						)

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

        lowercase_      :str         =			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase						) < max_length

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

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

        return processed_features





     def 							lowercase__				(			self		: int     ,				lowercase		: Union[Dict[str, np.ndarray], BatchFeature]     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[int] = None     ,				lowercase		: 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."						)

        lowercase_      :Optional[int]         =			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):
           lowercase_      :Optional[int]         =			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowercase_      :Union[str, Any]         =			len(lowercase						) > max_length

        if needs_to_be_truncated:
           lowercase_      :str         =			processed_features[self.model_input_names[0]][:max_length]
           if "attention_mask" in processed_features:
              lowercase_      :Optional[int]         =			processed_features["attention_mask"][:max_length]

        return processed_features







     def 							lowercase__				(			self		: Dict     ,				lowercase		: Dict=False     ,				lowercase		: Tuple=None						):





        """simple docstring"""




        if padding is not False:
           if padding is True:
              lowercase_      :str         =			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(lowercase     ,				lowercase						):
              lowercase_      :Dict         =			PaddingStrategy(lowercase						)
           elif isinstance(lowercase     ,				lowercase						):
              lowercase_      :Union[str, Any]         =			padding
        else:
           lowercase_      :Dict         =			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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)

lowerCAmelCase :					str 							={
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "cvt"





     def __init__(			self		: Tuple     ,				lowercase		: str=3     ,				lowercase		: str=[7, 3, 3]     ,				lowercase		: List[str]=[4, 2, 2]     ,				lowercase		: Dict=[2, 1, 1]     ,				lowercase		: int=[64, 192, 384]     ,				lowercase		: Dict=[1, 3, 6]     ,				lowercase		: Dict=[1, 2, 10]     ,				lowercase		: Any=[4.0, 4.0, 4.0]     ,				lowercase		: Tuple=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.1]     ,				lowercase		: Any=[True, True, True]     ,				lowercase		: Any=[False, False, True]     ,				lowercase		: Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]     ,				lowercase		: int=[3, 3, 3]     ,				lowercase		: str=[1, 1, 1]     ,				lowercase		: List[Any]=[2, 2, 2]     ,				lowercase		: Tuple=[1, 1, 1]     ,				lowercase		: Optional[Any]=[1, 1, 1]     ,				lowercase		: str=0.02     ,				lowercase		: str=1e-1_2     ,				**lowercase		: str     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :List[Any]         =			num_channels
        lowercase_      :Union[str, Any]         =			patch_sizes
        lowercase_      :Tuple         =			patch_stride
        lowercase_      :List[Any]         =			patch_padding
        lowercase_      :List[Any]         =			embed_dim
        lowercase_      :Union[str, Any]         =			num_heads
        lowercase_      :Any         =			depth
        lowercase_      :str         =			mlp_ratio
        lowercase_      :List[str]         =			attention_drop_rate
        lowercase_      :List[Any]         =			drop_rate
        lowercase_      :Union[str, Any]         =			drop_path_rate
        lowercase_      :Any         =			qkv_bias
        lowercase_      :Dict         =			cls_token
        lowercase_      :int         =			qkv_projection_method
        lowercase_      :Union[str, Any]         =			kernel_qkv
        lowercase_      :Optional[Any]         =			padding_kv
        lowercase_      :Optional[Any]         =			stride_kv
        lowercase_      :Dict         =			padding_q
        lowercase_      :Any         =			stride_q
        lowercase_      :Dict         =			initializer_range
        lowercase_      :Optional[Any]         =			layer_norm_eps


