





'''simple docstring'''

from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

__lowerCamelCase : int											=			{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Any 			=		'''efficientformer'''




		def __init__(self      :	List[Any]							,			A__      :	List[int] = [3, 2, 6, 4]							,			A__      :	List[int] = [4_8, 9_6, 2_2_4, 4_4_8]							,			A__      :	List[bool] = [True, True, True, True]							,			A__      :	int = 4_4_8							,			A__      :	int = 3_2							,			A__      :	int = 4							,			A__      :	int = 7							,			A__      :	int = 5							,			A__      :	int = 8							,			A__      :	int = 4							,			A__      :	float = 0.0							,			A__      :	int = 1_6							,			A__      :	int = 3							,			A__      :	int = 3							,			A__      :	int = 3							,			A__      :	int = 2							,			A__      :	int = 1							,			A__      :	float = 0.0							,			A__      :	int = 1							,			A__      :	bool = True							,			A__      :	bool = True							,			A__      :	float = 1e-5							,			A__      :	str = "gelu"							,			A__      :	float = 0.0_2							,			A__      :	float = 1e-12							,			A__      :	int = 2_2_4							,			A__      :	float = 1e-05							,			**A__      :	List[str]							,			)					-> None:
						super().__init__(**A__				)

						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = hidden_sizes
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = patch_size
						lowercase         = num_channels
						lowercase         = depths
						lowercase         = mlp_expansion_ratio
						lowercase         = downsamples
						lowercase         = dim
						lowercase         = key_dim
						lowercase         = attention_ratio
						lowercase         = resolution
						lowercase         = pool_size
						lowercase         = downsample_patch_size
						lowercase         = downsample_stride
						lowercase         = downsample_pad
						lowercase         = drop_path_rate
						lowercase         = num_metaad_blocks
						lowercase         = distillation
						lowercase         = use_layer_scale
						lowercase         = layer_scale_init_value
						lowercase         = image_size
						lowercase         = batch_norm_eps







'''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 : List[str]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Any							,			A__      :	int							,			A__      :	int							,			A__      :	float							,			**A__      :	int				)					-> List[str]:
						lowercase         = feature_size
						lowercase         = sampling_rate
						lowercase         = padding_value

						lowercase         = kwargs.pop("padding_side"							,			"right"				)
						lowercase         = kwargs.pop("return_attention_mask"							,			A__				)

						super().__init__(**A__				)




		def 				UpperCAmelCase__   (self      :	Tuple							,			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							,			)					-> BatchFeature:
						# 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)				):
										lowercase         = {
										    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         = processed_features[self.model_input_names[0]]
						lowercase         = (
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(A__				) == 0:
										if return_attention_mask:
														lowercase         = []
										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         = 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.
										lowercase         = 0
										while len(required_input[index]				) == 0:
														index += 1
										if index < len(A__				):
														lowercase         = required_input[index][0]

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

        # Convert padding_strategy in PaddingStrategy
						lowercase         = self._get_padding_strategies(padding=A__							,			max_length=A__				)

						lowercase         = processed_features[self.model_input_names[0]]

						lowercase         = 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."				)

						lowercase         = []
						for i in range(A__				):
										lowercase         = {k: v[i] for k, v in processed_features.items()}
										# truncation
										lowercase         = 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
										lowercase         = max(len(input_slice[self.model_input_names[0]]				) for input_slice in truncated_inputs				)
										lowercase         = PaddingStrategy.MAX_LENGTH

						lowercase         = {}
						for i in range(A__				):
										# padding
										lowercase         = 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:
																		lowercase         = []
														if value.dtype is np.dtype(np.floataa				):
																		lowercase         = value.astype(np.floataa				)
														batch_outputs[key].append(A__				)

						return BatchFeature(A__							,			tensor_type=A__				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]							,			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							,			)					-> dict:
						lowercase         = processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
										lowercase         = len(A__				)

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

						lowercase         = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__				) < max_length

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

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

						lowercase         = len(A__				) > max_length

						if needs_to_be_truncated:
										lowercase         = processed_features[self.model_input_names[0]][:max_length]
										if "attention_mask" in processed_features:
														lowercase         = processed_features["attention_mask"][:max_length]

						return processed_features





		def 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[Any]=False							,			A__      :	Dict=None				)					-> Optional[int]:

						# Get padding strategy
						if padding is not False:
										if padding is True:
														lowercase         = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
										elif not isinstance(A__							,			A__				):
														lowercase         = PaddingStrategy(A__				)
										elif isinstance(A__							,			A__				):
														lowercase         = padding
						else:
										lowercase         = 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




