

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


_a:    Any 							=			logging.get_logger(__name__)

class 						__UpperCamelCase    (					lowercase						):




	def __init__(		self	:						str		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						float		,	**lowerCAmelCase	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			feature_size
		UpperCAmelCase_       				=			sampling_rate
		UpperCAmelCase_       				=			padding_value

		UpperCAmelCase_       				=			kwargs.pop("padding_side"		,	"right"							)
		UpperCAmelCase_       				=			kwargs.pop("return_attention_mask"		,	lowerCAmelCase							)

		super().__init__(**lowerCAmelCase							)




	def       __A					(		self	:						Any		,	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		,	):


		'''simple docstring'''







		if isinstance(lowerCAmelCase		,	(list, tuple)							) and isinstance(processed_features[0]		,	(dict, BatchFeature)							):
			UpperCAmelCase_       				=			{
			    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()							)}"							)

		UpperCAmelCase_       				=			processed_features[self.model_input_names[0]]
		UpperCAmelCase_       				=			(
		    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
		)

		if len(lowerCAmelCase							) == 0:
			if return_attention_mask:
				UpperCAmelCase_       				=			[]
			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

		UpperCAmelCase_       				=			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.
			UpperCAmelCase_       				=			0
			while len(required_input[index]							) == 0:
				index += 1
			if index < len(lowerCAmelCase							):
				UpperCAmelCase_       				=			required_input[index][0]

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

        # Convert padding_strategy in PaddingStrategy
		UpperCAmelCase_       				=			self._get_padding_strategies(padding=lowerCAmelCase		,	max_length=lowerCAmelCase							)

		UpperCAmelCase_       				=			processed_features[self.model_input_names[0]]

		UpperCAmelCase_       				=			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."							)

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

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

		return BatchFeature(lowerCAmelCase		,	tensor_type=lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]		,	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		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			processed_features[self.model_input_names[0]]

		if padding_strategy == PaddingStrategy.LONGEST:
			UpperCAmelCase_       				=			len(lowerCAmelCase							)

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

		UpperCAmelCase_       				=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase							) < max_length

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

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

		return processed_features




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

		UpperCAmelCase_       				=			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):
			UpperCAmelCase_       				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

		UpperCAmelCase_       				=			len(lowerCAmelCase							) > max_length

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

		return processed_features




	def       __A					(		self	:						str		,	lowerCAmelCase	:						int=False		,	lowerCAmelCase	:						Dict=None							):


		'''simple docstring'''







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


def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			generate_pascal_triangle(A    )
	for row_idx in range(A    ):
		# Print left spaces
		for _ in range(num_rows - row_idx - 1    ):
			print(end=" "    )
		# Print row values
		for col_idx in range(row_idx + 1    ):
			if col_idx != row_idx:
				print(triangle[row_idx][col_idx]       ,     end=" "    )
			else:
				print(triangle[row_idx][col_idx]       ,     end=""    )
		print()



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[]
	for current_row_idx in range(A    ):
		UpperCAmelCase_       				=			populate_current_row(A       ,     A    )
		triangle.append(A    )
	return triangle



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			[-1] * (current_row_idx + 1)
	# first and last elements of current row are equal to 1
	UpperCAmelCase_				,   UpperCAmelCase_       				=			1, 1
	for current_col_idx in range(1       ,     A    ):
		calculate_current_element(
		    A       ,     A       ,     A       ,     A    )
	return current_row



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A       ,     ):
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx - 1]
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx]
	UpperCAmelCase_       				=			above_to_left_elt + above_to_right_elt



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[[1]]

	for row_index in range(1       ,     A    ):
		UpperCAmelCase_       				=			[0] + result[-1] + [0]
		UpperCAmelCase_       				=			row_index + 1
		# Calculate the number of distinct elements in a row
		UpperCAmelCase_       				=			sum(divmod(A       ,     2    )    )
		UpperCAmelCase_       				=			[
		    temp_row[i - 1] + temp_row[i] for i in range(1       ,     distinct_elements + 1    )
		]
		UpperCAmelCase_       				=			row_first_half[: (row_index + 1) // 2]
		row_second_half.reverse()
		UpperCAmelCase_       				=			row_first_half + row_second_half
		result.append(A    )

	return result



def       __lowerCAmelCase      (						):
	from collections.abc import Callable
	from timeit import timeit

	def benchmark_a_function(A       ,     A    ) -> None:
		UpperCAmelCase_       				=			F"{func.__name__}({value})"
		UpperCAmelCase_       				=			timeit(F"__main__.{call}"       ,     setup="import __main__"    )
		# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
		print(F"{call:38} -- {timing:.4f} seconds"    )

	for value in range(15    ):  # (1, 7, 14):
		for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
			benchmark_a_function(A       ,     A    )
		print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()