

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[Any]            =						logging.get_logger(__name__)



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					Tuple       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					float       ,						**lowerCAmelCase__  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	feature_size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	sampling_rate
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	padding_value

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	kwargs.pop("padding_side"       ,						"right")
									SCREAMING_SNAKE_CASE_:			str  		=	kwargs.pop("return_attention_mask"       ,						lowerCAmelCase__)

									super().__init__(**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Union[
			        BatchFeature,
			        List[BatchFeature],
			        Dict[str, BatchFeature],
			        Dict[str, List[BatchFeature]],
			        List[Dict[str, BatchFeature]],
			    ]       ,						lowerCAmelCase__  :					Union[bool, str, PaddingStrategy] = True       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					bool = False       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					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(lowerCAmelCase__       ,						(list, tuple)) and isinstance(processed_features[0]       ,						(dict, BatchFeature)):
															SCREAMING_SNAKE_CASE_:			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())}")

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	processed_features[self.model_input_names[0]]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	(
									    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
									)

									if len(lowerCAmelCase__) == 0:
															if return_attention_mask:
																					SCREAMING_SNAKE_CASE_:			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

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

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

									for key, value in processed_features.items():
															if isinstance(value[0]       ,						(int, float)):
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	to_numpy(lowerCAmelCase__)
															else:
																					SCREAMING_SNAKE_CASE_:			int  		=	[to_numpy(lowerCAmelCase__) for v in value]

        # Convert padding_strategy in PaddingStrategy
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self._get_padding_strategies(padding=lowerCAmelCase__       ,						max_length=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_:			str  		=	len(lowerCAmelCase__)
									if not all(len(lowerCAmelCase__) == batch_size for v in processed_features.values()):
															raise ValueError("Some items in the output dictionary have a different batch size than others.")

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

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

									SCREAMING_SNAKE_CASE_:			Any  		=	{}
									for i in range(lowerCAmelCase__):
															# padding
															SCREAMING_SNAKE_CASE_:			Tuple  		=	self._pad(
															    truncated_inputs[i]       ,						max_length=lowerCAmelCase__       ,						padding_strategy=lowerCAmelCase__       ,						pad_to_multiple_of=lowerCAmelCase__       ,						return_attention_mask=lowerCAmelCase__       ,						)

															for key, value in outputs.items():
																					if key not in batch_outputs:
																											SCREAMING_SNAKE_CASE_:			Any  		=	[]
																					if value.dtype is np.dtype(np.floataa):
																											SCREAMING_SNAKE_CASE_:			List[str]  		=	value.astype(np.floataa)
																					batch_outputs[key].append(lowerCAmelCase__)

									return BatchFeature(lowerCAmelCase__       ,						tensor_type=lowerCAmelCase__)



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

									if padding_strategy == PaddingStrategy.LONGEST:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	len(lowerCAmelCase__)

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

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

									if return_attention_mask and "attention_mask" not in processed_features:
															SCREAMING_SNAKE_CASE_:			Tuple  		=	np.ones(len(lowerCAmelCase__)       ,						dtype=np.intaa)

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

									return processed_features



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

									SCREAMING_SNAKE_CASE_:			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):
															SCREAMING_SNAKE_CASE_:			Any  		=	((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

									SCREAMING_SNAKE_CASE_:			Dict  		=	len(lowerCAmelCase__) > max_length

									if needs_to_be_truncated:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	processed_features[self.model_input_names[0]][:max_length]
															if "attention_mask" in processed_features:
																					SCREAMING_SNAKE_CASE_:			Any  		=	processed_features["attention_mask"][:max_length]

									return processed_features




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[Any]=False       ,						lowerCAmelCase__  :					Dict=None):

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









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


lowerCAmelCase							:  Optional[int]            =						logging.get_logger(__name__)

lowerCAmelCase							:  int            =						{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					'''fnet'''



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[str]=3_2000       ,						lowerCAmelCase__  :					Union[str, Any]=768       ,						lowerCAmelCase__  :					Optional[Any]=12       ,						lowerCAmelCase__  :					Tuple=3072       ,						lowerCAmelCase__  :					Any="gelu_new"       ,						lowerCAmelCase__  :					Union[str, Any]=0.1       ,						lowerCAmelCase__  :					Optional[Any]=512       ,						lowerCAmelCase__  :					int=4       ,						lowerCAmelCase__  :					List[Any]=0.02       ,						lowerCAmelCase__  :					Union[str, Any]=1E-12       ,						lowerCAmelCase__  :					Optional[int]=False       ,						lowerCAmelCase__  :					Any=512       ,						lowerCAmelCase__  :					Optional[int]=3       ,						lowerCAmelCase__  :					int=1       ,						lowerCAmelCase__  :					List[Any]=2       ,						**lowerCAmelCase__  :					Optional[int]       ,						):
									super().__init__(pad_token_id=lowerCAmelCase__       ,						bos_token_id=lowerCAmelCase__       ,						eos_token_id=lowerCAmelCase__       ,						**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			Any  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	type_vocab_size
									SCREAMING_SNAKE_CASE_:			int  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	use_tpu_fourier_optimizations
									SCREAMING_SNAKE_CASE_:			int  		=	tpu_short_seq_length






