


from __future__ import annotations

from collections.abc import Iterable







class     lowerCamelCase__		:






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    float(SCREAMING_SNAKE_CASE__       ), float(SCREAMING_SNAKE_CASE__       )




			def __eq__(							self ,  snake_case       ):

						'''simple docstring'''


						return self.x == other.x and self.y == other.y




			def __ne__(							self ,  snake_case       ):

						'''simple docstring'''


						return not self == other




			def __gt__(							self ,  snake_case       ):

						'''simple docstring'''


						if self.x > other.x:
									return True
						elif self.x == other.x:
									return self.y > other.y
						return False




			def __lt__(							self ,  snake_case       ):

						'''simple docstring'''


						return not self > other




			def __ge__(							self ,  snake_case       ):

						'''simple docstring'''


						if self.x > other.x:
									return True
						elif self.x == other.x:
									return self.y >= other.y
						return False




			def __le__(							self ,  snake_case       ):

						'''simple docstring'''


						if self.x < other.x:
									return True
						elif self.x == other.x:
									return self.y <= other.y
						return False




			def __repr__(							self       ):

						'''simple docstring'''


						return F'''({self.x}, {self.y})'''







			def __hash__(							self       ):

						'''simple docstring'''


						return hash(self.x       )



def     UpperCamelCase_(			_A				:Optional[Any]			,       )->						List[Any]:
			UpperCamelCase__   =    []
			if list_of_tuples:
						for p in list_of_tuples:
									if isinstance(_A			,       _A					):
												points.append(_A					)
									else:
												try:
															points.append(Point(p[0]			,       p[1]					)					)
												except (IndexError, TypeError):
															print(
															    F'''Ignoring deformed point {p}. All points'''
															    " must have at least 2 coordinates."					)
			return points
def     UpperCamelCase_(			_A				:Tuple					)->						List[str]:

			if not hasattr(_A			,       "__iter__"					):
						UpperCamelCase__   =    F'''Expecting an iterable object but got an non-iterable type {points}'''
						raise ValueError(_A					)

			if not points:
						UpperCamelCase__   =    F'''Expecting a list of points but got {points}'''
						raise ValueError(_A					)

			return _construct_points(_A					)
def     UpperCamelCase_(			_A				:List[str]			,       _A				:List[Any]			,       _A				:Dict					)->						Optional[Any]:
			UpperCamelCase__   =    (a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
			return det
def     UpperCamelCase_(			_A				:Tuple					)->						Any:
			UpperCamelCase__   =    sorted(_validate_input(_A					)					)
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    set()

			for i in range(n - 1					):
						for j in range(i + 1			,       _A					):
									UpperCamelCase__   =    False
									UpperCamelCase__   =    True
									for k in range(_A					):
												if k != i and k != j:
															UpperCamelCase__   =    _det(points[i]			,       points[j]			,       points[k]					)

															if det_k > 0:
																		UpperCamelCase__   =    True
															elif det_k < 0:
																		UpperCamelCase__   =    True
															else:
																		# point[i], point[j], point[k] all lie on a straight line
																		# if point[k] is to the left of point[i] or it's to the
																		# right of point[j], then point[i], point[j] cannot be
																		# part of the convex hull of A
																		if points[k] < points[i] or points[k] > points[j]:
																					UpperCamelCase__   =    False
																					break

												if points_left_of_ij and points_right_of_ij:
															UpperCamelCase__   =    False
															break

									if ij_part_of_convex_hull:
												convex_set.update([points[i], points[j]]					)

			return sorted(_A					)
def     UpperCamelCase_(			_A				:str					)->						List[str]:
			UpperCamelCase__   =    sorted(_validate_input(_A					)					)
			UpperCamelCase__   =    len(_A					)

			# divide all the points into an upper hull and a lower hull
			# the left most point and the right most point are definitely
			# members of the convex hull by definition.
			# use these two anchors to divide all the points into two hulls,
			# an upper hull and a lower hull.

			# all points to the left (above) the line joining the extreme points belong to the
			# upper hull
			# all points to the right (below) the line joining the extreme points below to the
			# lower hull
			# ignore all points on the line joining the extreme points since they cannot be
			# part of the convex hull

			UpperCamelCase__   =    points[0]
			UpperCamelCase__   =    points[n - 1]

			UpperCamelCase__   =    {left_most_point, right_most_point}
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []

			for i in range(1			,       n - 1					):
						UpperCamelCase__   =    _det(_A			,       _A			,       points[i]					)

						if det > 0:
									upper_hull.append(points[i]					)
						elif det < 0:
									lower_hull.append(points[i]					)

			_construct_hull(_A			,       _A			,       _A			,       _A					)
			_construct_hull(_A			,       _A			,       _A			,       _A					)

			return sorted(_A					)
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:List[Any]			,       _A				:Tuple			,       _A				:Tuple					)->						Union[str, Any]:
			if points:
						UpperCamelCase__   =    None
						UpperCamelCase__   =    float("-inf"					)
						UpperCamelCase__   =    []

						for p in points:
									UpperCamelCase__   =    _det(_A			,       _A			,       _A					)

									if det > 0:
												candidate_points.append(_A					)

												if det > extreme_point_distance:
															UpperCamelCase__   =    det
															UpperCamelCase__   =    p

						if extreme_point:
									_construct_hull(_A			,       _A			,       _A			,       _A					)
									convex_set.add(_A					)
									_construct_hull(_A			,       _A			,       _A			,       _A					)
def     UpperCamelCase_(			_A				:Optional[int]					)->						str:
			UpperCamelCase__   =    sorted(_validate_input(_A					)					)
			UpperCamelCase__   =    len(_A					)

			UpperCamelCase__   =    points[:2]
			for i in range(2			,       _A					):
						UpperCamelCase__   =    _det(convex_hull[1]			,       convex_hull[0]			,       points[i]					)
						if det > 0:
									convex_hull.insert(0			,       points[i]					)
									break
						elif det < 0:
									convex_hull.append(points[i]					)
									break
						else:
									UpperCamelCase__   =    points[i]
			i += 1

			for j in range(_A			,       _A					):
						if (
						    _det(convex_hull[0]			,       convex_hull[-1]			,       points[j]					) > 0
						    and _det(convex_hull[-1]			,       convex_hull[0]			,       points[1]					) < 0
						):
									# The point lies within the convex hull
									continue

						convex_hull.insert(0			,       points[j]					)
						convex_hull.append(points[j]					)
						while _det(convex_hull[0]			,       convex_hull[1]			,       convex_hull[2]					) >= 0:
									del convex_hull[1]
						while _det(convex_hull[-1]			,       convex_hull[-2]			,       convex_hull[-3]					) <= 0:
									del convex_hull[-2]

						# `convex_hull` is contains the convex hull in circular order
			return sorted(convex_hull[1:] if len(_A					) > 3 else convex_hull					)
def     UpperCamelCase_(			)->						Optional[Any]:
			UpperCamelCase__   =    [
			    (0, 3),
			    (2, 2),
			    (1, 1),
			    (2, 1),
			    (3, 0),
			    (0, 0),
			    (3, 3),
			    (2, -1),
			    (2, -4),
			    (1, -3),
			]
			# the convex set of points is
			# [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
			UpperCamelCase__   =    convex_hull_bf(_A					)

			UpperCamelCase__   =    convex_hull_recursive(_A					)
			assert results_bf == results_recursive

			UpperCamelCase__   =    convex_hull_melkman(_A					)
			assert results_bf == results_melkman

			print(_A					)


if __name__ == "__main__":
						main()








import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__UpperCamelCase      						=    logging.get_logger(__name__)

__UpperCamelCase      						=    {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'mask_emb': 'masked_spec_embed',
}
def     UpperCamelCase_(			_A				:int			,       _A				:Dict			,       _A				:int			,       _A				:List[str]			,       _A				:Optional[int]					)->						Any:
			for attribute in key.split("."					):
						UpperCamelCase__   =    getattr(_A			,       _A					)

			if weight_type is not None:
						UpperCamelCase__   =    getattr(_A			,       _A					).shape
			else:
						UpperCamelCase__   =    hf_pointer.shape

			assert hf_shape == value.shape, (
			    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
			    F''' {value.shape} for {full_name}'''
			)

			if weight_type == "weight":
						UpperCamelCase__   =    value
			elif weight_type == "weight_g":
						UpperCamelCase__   =    value
			elif weight_type == "weight_v":
						UpperCamelCase__   =    value
			elif weight_type == "bias":
						UpperCamelCase__   =    value
			else:
						UpperCamelCase__   =    value

			logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''					)
def     UpperCamelCase_(			_A				:Optional[Any]			,       _A				:Dict					)->						List[Any]:
			UpperCamelCase__   =    []
			UpperCamelCase__   =    fairseq_model.state_dict()

			UpperCamelCase__   =    hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCamelCase__   =    False
						if "conv_layers" in name:
									load_conv_layer(
									    _A			,       _A			,       _A			,       _A			,       hf_model.config.feat_extract_norm == "group"			,       )
									UpperCamelCase__   =    True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCamelCase__   =    mapped_key

												if key in name:
															UpperCamelCase__   =    True
															if "*" in mapped_key:
																		UpperCamelCase__   =    name.split(_A					)[0].split("."					)[-2]
																		UpperCamelCase__   =    mapped_key.replace("*"			,       _A					)
															if "weight_g" in name:
																		UpperCamelCase__   =    "weight_g"
															elif "weight_v" in name:
																		UpperCamelCase__   =    "weight_v"
															elif "weight" in name:
																		UpperCamelCase__   =    "weight"
															elif "bias" in name:
																		UpperCamelCase__   =    "bias"
															else:
																		UpperCamelCase__   =    None
															set_recursively(_A			,       _A			,       _A			,       _A			,       _A					)
												continue
						if not is_used:
									unused_weights.append(_A					)

			logger.warning(F'''Unused weights: {unused_weights}'''					)
def     UpperCamelCase_(			_A				:Any			,       _A				:List[Any]			,       _A				:Union[str, Any]			,       _A				:Dict			,       _A				:Union[str, Any]					)->						Optional[Any]:
			UpperCamelCase__   =    full_name.split("conv_layers."					)[-1]
			UpperCamelCase__   =    name.split("."					)
			UpperCamelCase__   =    int(items[0]					)
			UpperCamelCase__   =    int(items[1]					)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    F'''{full_name} has size {value.shape}, but'''
									    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
									)
									UpperCamelCase__   =    value
									logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    F'''{full_name} has size {value.shape}, but'''
									    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
									)
									UpperCamelCase__   =    value
									logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
									    " found."
									)
									UpperCamelCase__   =    value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    F'''{full_name} has size {value.shape}, but'''
									    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
									)
									UpperCamelCase__   =    value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
			else:
						unused_weights.append(_A					)
def     UpperCamelCase_(			_A				:Dict					)->						Union[str, Any]:
			UpperCamelCase__   =    HubertConfig()
			UpperCamelCase__   =    model.config

			UpperCamelCase__   =    fs_config.activation_dropout
			UpperCamelCase__   =    False
			UpperCamelCase__   =    fs_config.attention_dropout
			UpperCamelCase__   =    False
			UpperCamelCase__   =    eval(fs_config.extractor_conv_feature_layers					)
			UpperCamelCase__   =    [x[0] for x in conv_layers]
			UpperCamelCase__   =    [x[1] for x in conv_layers]
			UpperCamelCase__   =    [x[2] for x in conv_layers]
			UpperCamelCase__   =    "gelu"
			UpperCamelCase__   =    "layer" if fs_config.extractor_mode == "layer_norm" else "group"
			UpperCamelCase__   =    False
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    fs_config.activation_fn
			UpperCamelCase__   =    fs_config.dropout
			UpperCamelCase__   =    fs_config.encoder_embed_dim
			UpperCamelCase__   =    0.0_2
			UpperCamelCase__   =    fs_config.encoder_ffn_embed_dim
			UpperCamelCase__   =    1e-5
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    fs_config.encoder_attention_heads
			UpperCamelCase__   =    fs_config.conv_pos_groups
			UpperCamelCase__   =    fs_config.conv_pos
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    fs_config.encoder_layers

			return config
@torch.no_grad()
def     UpperCamelCase_(			_A				:str			,       _A				:Union[str, Any]=None					)->						str:
			UpperCamelCase__   =    distilhubert().model.model

			if config_path is not None:
						UpperCamelCase__   =    HubertConfig.from_pretrained(_A					)
			else:
						UpperCamelCase__   =    convert_config(_A					)
			UpperCamelCase__   =    model.eval()

			UpperCamelCase__   =    WavaVecaFeatureExtractor(
			    feature_size=1			,       sampling_rate=1_60_00			,       padding_value=0			,       do_normalize=_A			,       return_attention_mask=_A			,       )
			UpperCamelCase__   =    HubertModel(_A					)

			recursively_load_weights(_A			,       _A					)

			feature_extractor.save_pretrained(_A					)
			hf_model.save_pretrained(_A					)


if __name__ == "__main__":
						__UpperCamelCase      						=    argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						__UpperCamelCase      						=    parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


