code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional, Text
from rich.console import Console
from ruth.constants import TEXT
from ruth.nlu.featurizers.sparse_featurizers.constants import (
CLASS_FEATURIZER_UNIQUE_NAME,
)
from ruth.nlu.featurizers.sparse_featurizers.sparse_featurizer ... | /ruth_python-0.0.8-py3-none-any.whl/ruth/nlu/featurizers/sparse_featurizers/tfidf_vector_featurizer.py | 0.832645 | 0.279872 | tfidf_vector_featurizer.py | pypi |
import os
import random
import uuid
from time import time
from urllib import request
import requests
import torch
import torch.nn.functional as F
import progressbar
import torchaudio
from ruth_tts_transformer.models.classifier import AudioMiniEncoderWithClassifierHead
from ruth_tts_transformer.models.diffusion_decoder... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/parser.py | 0.522202 | 0.203985 | parser.py | pypi |
import torch
import torch.nn as nn
from ruth_tts_transformer.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
class ResBlock(nn.Module):
def __init__(
self,
channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shif... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/models/classifier.py | 0.946014 | 0.277954 | classifier.py | pypi |
import math
import random
from abc import abstractmethod
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from ruth_tts_transformer.models.arch_util import normalization, AttentionBlock
def is_latent(t):
return t.dtype == torch.float
def is_sequence(t):
return ... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/models/diffusion_decoder.py | 0.945883 | 0.615001 | diffusion_decoder.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from ruth_tts_transformer.models.arch_util import AttentionBlock
from ruth_tts_transformer.models.xtransformers import ContinuousTransformerWrapper, Encoder
def exists(val):
return val is not None
def masked_mean(t, mas... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/models/cvvp.py | 0.947015 | 0.367185 | cvvp.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
MAX_WAV_VALUE = 32768.0
class KernelPredictor(torch.nn.Module):
''' Kernel predictor for the location-variable convolutions'''
def __init__(
self,
cond_channels,
conv_in_channels,
conv_out_chann... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/models/vocoder.py | 0.954774 | 0.386185 | vocoder.py | pypi |
from functools import partial
import torch
import torch.nn.functional as F
from einops import rearrange
from rotary_embedding_torch import RotaryEmbedding, broadcat
from torch import nn
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def cast_tupl... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/models/transformer.py | 0.946076 | 0.456591 | transformer.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from ruth_tts_transformer.models.arch_util import CheckpointedXTransformerEncoder
from ruth_tts_transformer.models.transformer import Transformer
from ruth_tts_transformer.models.xtransformers import Encoder
def exists(val):
... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/models/clvp.py | 0.888795 | 0.320143 | clvp.py | pypi |
import os
import functools
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from ruth_tts_transformer.models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
def zero_module(module):
"""
Zero out the parameters of a module and return it.
... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/models/arch_util.py | 0.924099 | 0.537648 | arch_util.py | pypi |
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
from transformers.utils.model_parallel_utils import get_device_map, assert_device... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/models/autoregressive.py | 0.948894 | 0.386069 | autoregressive.py | pypi |
import os
import subprocess
from glob import glob
import librosa
import torch
import torchaudio
import numpy as np
from scipy.io.wavfile import read
from ruth_tts_transformer.utils.stft import STFT
BUILTIN_VOICES_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../voices')
if not os.path.isdir(BUILTI... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/utils/audio.py | 0.652906 | 0.293019 | audio.py | pypi |
import re
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor
from ruth_tts_transformer.utils.audio import load_audio
def max_alignment(s1, s2, skip_character='~', record=None):
"""
A clever function that aligns s1 to s2 a... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/utils/wav2vec_alignment.py | 0.679072 | 0.424412 | wav2vec_alignment.py | pypi |
import os
import re
import inflect
import torch
from tokenizers import Tokenizer
# Regular expression matching whitespace:
from unidecode import unidecode
_whitespace_re = re.compile(r'\s+')
# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IG... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/utils/tokenizer.py | 0.50708 | 0.308503 | tokenizer.py | pypi |
import re
def split_and_recombine_text(text, desired_length=200, max_length=300):
"""Split text it into chunks of a desired length trying to keep sentences intact."""
# normalize text, remove redundant whitespace and convert non-ascii quotes to ascii
text = re.sub(r'\n\n+', '\n', text)
text = re.sub(r... | /ruth_text_to_speech-0.0.39-py3-none-any.whl/ruth_tts_transformer/utils/text.py | 0.419767 | 0.425725 | text.py | pypi |
import os
import random
import uuid
from urllib import request
import torch
import torch.nn.functional as F
import progressbar
import torchaudio
from ruth_tts_transformer.ruth_tts.models.classifier import AudioMiniEncoderWithClassifierHead
from ruth_tts_transformer.ruth_tts.models.cvvp import CVVP
from ruth_tts_trans... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/api.py | 0.506591 | 0.218357 | api.py | pypi |
import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from ruth_tts_transformer.ruth_tts.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
class ResBlock(nn.Module):
def __init__(
self,
channels,
dropout,
out_channe... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/models/classifier.py | 0.953008 | 0.292523 | classifier.py | pypi |
import math
import random
from abc import abstractmethod
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from ruth_tts_transformer.ruth_tts.models.arch_util import normalization, AttentionBlock
def is_latent(t):
return t.dtype == torch.float
def is_sequence(t):
... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/models/diffusion_decoder.py | 0.93441 | 0.634628 | diffusion_decoder.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from torch.utils.checkpoint import checkpoint
from ruth_tts_transformer.ruth_tts.models.arch_util import AttentionBlock
from ruth_tts_transformer.ruth_tts.models.xtransformers import ContinuousTransformerWrapper, Encoder
def ... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/models/cvvp.py | 0.938166 | 0.377311 | cvvp.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
MAX_WAV_VALUE = 32768.0
class KernelPredictor(torch.nn.Module):
''' Kernel predictor for the location-variable convolutions'''
def __init__(
self,
cond_channels,
conv_in_channels,
conv_out_chann... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/models/vocoder.py | 0.954774 | 0.386185 | vocoder.py | pypi |
from functools import partial
import torch
import torch.nn.functional as F
from einops import rearrange
from rotary_embedding_torch import RotaryEmbedding, broadcat
from torch import nn
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def cast_tupl... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/models/transformer.py | 0.946076 | 0.456591 | transformer.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from ruth_tts_transformer.ruth_tts.models.arch_util import CheckpointedXTransformerEncoder
from ruth_tts_transformer.ruth_tts.models.transformer import Transformer
from ruth_tts_transformer.ruth_tts.models.xtransformers import ... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/models/clvp.py | 0.888263 | 0.32146 | clvp.py | pypi |
import functools
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from ruth_tts_transformer.ruth_tts.models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
def zero_module(module):
"""
Zero out the parameters of a module and return it.
... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/models/arch_util.py | 0.956497 | 0.597461 | arch_util.py | pypi |
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
from transformers.utils.model_parallel_utils import get_device_map, assert_device... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/models/autoregressive.py | 0.937667 | 0.354796 | autoregressive.py | pypi |
import os
from glob import glob
import librosa
import torch
import torchaudio
import numpy as np
from scipy.io.wavfile import read
from ruth_tts_transformer.ruth_tts.utils.stft import STFT
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
if data.dtype == np.int32:
norm_fix = 2... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/utils/audio.py | 0.774669 | 0.403009 | audio.py | pypi |
import re
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor
from ruth_tts_transformer.ruth_tts.utils.audio import load_audio
def max_alignment(s1, s2, skip_character='~', record={}):
"""
A clever function that aligns s1 ... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/utils/wav2vec_alignment.py | 0.680135 | 0.430506 | wav2vec_alignment.py | pypi |
import json
import re
import inflect
import requests
import torch
from tokenizers import Tokenizer
# Regular expression matching whitespace:
from unidecode import unidecode
_whitespace_re = re.compile(r'\s+')
# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = [(re.compile('\\b%s\\... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/utils/tokenizer.py | 0.543106 | 0.305956 | tokenizer.py | pypi |
import re
def split_and_recombine_text(text, desired_length=200, max_length=300):
"""Split text it into chunks of a desired length trying to keep sentences intact."""
# normalize text, remove redundant whitespace and convert non-ascii quotes to ascii
text = re.sub(r'\n\n+', '\n', text)
text = re.sub(r... | /ruth-tts-converter-python-0.0.2.tar.gz/ruth-tts-converter-python-0.0.2/src/ruth_tts_transformer/ruth_tts/utils/text.py | 0.455199 | 0.376337 | text.py | pypi |
import os
import random
import uuid
from urllib import request
import torch
import torch.nn.functional as F
import progressbar
import torchaudio
from ruth_tts_transformer.ruth_tts.models.classifier import AudioMiniEncoderWithClassifierHead
from ruth_tts_transformer.ruth_tts.models.cvvp import CVVP
from ruth_tts_trans... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/api.py | 0.506591 | 0.218357 | api.py | pypi |
import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from ruth_tts_transformer.ruth_tts.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
class ResBlock(nn.Module):
def __init__(
self,
channels,
dropout,
out_channe... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/models/classifier.py | 0.953008 | 0.292523 | classifier.py | pypi |
import math
import random
from abc import abstractmethod
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast
from ruth_tts_transformer.ruth_tts.models.arch_util import normalization, AttentionBlock
def is_latent(t):
return t.dtype == torch.float
def is_sequence(t):
... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/models/diffusion_decoder.py | 0.93441 | 0.634628 | diffusion_decoder.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from torch.utils.checkpoint import checkpoint
from ruth_tts_transformer.ruth_tts.models.arch_util import AttentionBlock
from ruth_tts_transformer.ruth_tts.models.xtransformers import ContinuousTransformerWrapper, Encoder
def ... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/models/cvvp.py | 0.938166 | 0.377311 | cvvp.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
MAX_WAV_VALUE = 32768.0
class KernelPredictor(torch.nn.Module):
''' Kernel predictor for the location-variable convolutions'''
def __init__(
self,
cond_channels,
conv_in_channels,
conv_out_chann... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/models/vocoder.py | 0.954774 | 0.386185 | vocoder.py | pypi |
from functools import partial
import torch
import torch.nn.functional as F
from einops import rearrange
from rotary_embedding_torch import RotaryEmbedding, broadcat
from torch import nn
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def cast_tupl... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/models/transformer.py | 0.946076 | 0.456591 | transformer.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from ruth_tts_transformer.ruth_tts.models.arch_util import CheckpointedXTransformerEncoder
from ruth_tts_transformer.ruth_tts.models.transformer import Transformer
from ruth_tts_transformer.ruth_tts.models.xtransformers import ... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/models/clvp.py | 0.888263 | 0.32146 | clvp.py | pypi |
import functools
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from ruth_tts_transformer.ruth_tts.models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
def zero_module(module):
"""
Zero out the parameters of a module and return it.
... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/models/arch_util.py | 0.956497 | 0.597461 | arch_util.py | pypi |
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
from transformers.utils.model_parallel_utils import get_device_map, assert_device... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/models/autoregressive.py | 0.937667 | 0.354796 | autoregressive.py | pypi |
import os
from glob import glob
import librosa
import torch
import torchaudio
import numpy as np
from scipy.io.wavfile import read
from ruth_tts_transformer.ruth_tts.utils.stft import STFT
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
if data.dtype == np.int32:
norm_fix = 2... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/utils/audio.py | 0.774669 | 0.403009 | audio.py | pypi |
import re
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor
from ruth_tts_transformer.ruth_tts.utils.audio import load_audio
def max_alignment(s1, s2, skip_character='~', record={}):
"""
A clever function that aligns s1 ... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/utils/wav2vec_alignment.py | 0.680135 | 0.430506 | wav2vec_alignment.py | pypi |
import json
import re
import inflect
import requests
import torch
from tokenizers import Tokenizer
# Regular expression matching whitespace:
from unidecode import unidecode
_whitespace_re = re.compile(r'\s+')
# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = [(re.compile('\\b%s\\... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/utils/tokenizer.py | 0.543106 | 0.305956 | tokenizer.py | pypi |
import re
def split_and_recombine_text(text, desired_length=200, max_length=300):
"""Split text it into chunks of a desired length trying to keep sentences intact."""
# normalize text, remove redundant whitespace and convert non-ascii quotes to ascii
text = re.sub(r'\n\n+', '\n', text)
text = re.sub(r... | /ruth-tts-converter-0.0.2.tar.gz/ruth-tts-converter-0.0.2/src/ruth_tts_transformer/ruth_tts/utils/text.py | 0.455199 | 0.376337 | text.py | pypi |
from .enums import Url
from .exceptions import ServerException
from typing import Union
from requests import Session
# More about API: http://api.rutracker.org/v1/docs/
class ApiProvider(object):
"""This class provides access to some official methods of the Rutracker API"""
def __init__(self, session: Sessi... | /rutracker-api-0.22.92.tar.gz/rutracker-api-0.22.92/rutracker_api/api_provider.py | 0.83772 | 0.191177 | api_provider.py | pypi |
from .utils import format_size, generate_magnet
from datetime import datetime
from .enums import Url
class Torrent(object):
"""Stores data about the torrent"""
def __init__(
self,
author=None,
category=None,
downloads=None,
host=None,
leeches=None,
regi... | /rutracker-api-0.22.92.tar.gz/rutracker-api-0.22.92/rutracker_api/torrent.py | 0.732018 | 0.166574 | torrent.py | pypi |
from __future__ import division
import math
import logging
import struct
log = logging.getLogger(__name__)
class Df5Decoder(object):
"""
Decodes data from RuuviTag with Data Format 5
Protocol specification:
https://github.com/ruuvi/ruuvi-sensor-protocols
"""
def _get_temperature(self, data)... | /ruuvi_decoders-0.2.0.tar.gz/ruuvi_decoders-0.2.0/ruuvi_decoders/df5_decoder.py | 0.830009 | 0.541712 | df5_decoder.py | pypi |
from __future__ import division
import base64
import logging
log = logging.getLogger(__name__)
class UrlDecoder(object):
"""
Decodes data from RuuviTag url
Protocol specification:
https://github.com/ruuvi/ruuvi-sensor-protocols
Decoder operations are ported from:
https://github.com/ruuvi/se... | /ruuvi_decoders-0.2.0.tar.gz/ruuvi_decoders-0.2.0/ruuvi_decoders/url_decoder.py | 0.841858 | 0.322313 | url_decoder.py | pypi |
from typing import Dict, Tuple
from aiohttp.client import ClientSession
import aiohttp
from result import Ok, Err, Result
from ruuvi_decoders import get_decoder
from ruuvi_gateway_client.types import SensorData, SensorPayload, ParsedDatas, Payload
from ruuvi_gateway_client.parser import parse_session_cookie, parse_pas... | /ruuvi_gateway_client-0.1.0-py3-none-any.whl/ruuvi_gateway_client/gateway.py | 0.579638 | 0.217545 | gateway.py | pypi |
import argparse
import logging
import json
from concurrent.futures import Future
import asyncio
from aiohttp import ClientSession, TCPConnector
from typing import Callable, Dict, List, Union
async def handle_queue(
args: argparse.Namespace,
queue,
future: Future,
verify_ssl=True,
api_key: Union[s... | /ruuvi_lapio-0.3.1.tar.gz/ruuvi_lapio-0.3.1/ruuvi_lapio/main.py | 0.528533 | 0.155848 | main.py | pypi |
import time
import random
from typing import Callable, List, Union
from ruuvitag_sensor.ruuvi import MacAndSensorData, RunFlag
class MockSensor:
def __init__(self):
# Generate random mac address
self.mac = "".join(random.choice("0123456789ABCDEF") for _ in range(12)).lower()
self.battery ... | /ruuvi_lapio-0.3.1.tar.gz/ruuvi_lapio-0.3.1/ruuvi_lapio/mock_sensor.py | 0.796372 | 0.276633 | mock_sensor.py | pypi |
from __future__ import annotations
import math
import struct
class DataFormat5Decoder:
def __init__(self, raw_data: bytes) -> None:
if len(raw_data) < 24:
raise ValueError("Data must be at least 24 bytes long for data format 5")
self.data: tuple[int, ...] = struct.unpack(">BhHHhhhHBH6... | /ruuvitag_ble-0.1.2.tar.gz/ruuvitag_ble-0.1.2/src/ruuvitag_ble/df5_decoder.py | 0.90673 | 0.451629 | df5_decoder.py | pypi |
import logging
import time
from multiprocessing import Manager
from multiprocessing.managers import ListProxy
from typing import AsyncGenerator, Callable, Dict, Generator, List, Optional
from warnings import warn
from ruuvitag_sensor.adapters import get_ble_adapter, is_async_adapter
from ruuvitag_sensor.data_formats i... | /ruuvitag_sensor-2.1.0-py3-none-any.whl/ruuvitag_sensor/ruuvi.py | 0.832747 | 0.343617 | ruuvi.py | pypi |
import time
from concurrent.futures import ProcessPoolExecutor
from datetime import datetime
from multiprocessing import Manager
from multiprocessing.managers import DictProxy
from queue import Queue
from threading import Thread
from typing import List
from reactivex import Subject
from ruuvitag_sensor.ruuvi import R... | /ruuvitag_sensor-2.1.0-py3-none-any.whl/ruuvitag_sensor/ruuvi_rx.py | 0.7011 | 0.158826 | ruuvi_rx.py | pypi |
import os
import logging
from ruv_dl.date_utils import parse_date
from ruv_dl.constants import DATE_FORMAT
logger = logging.getLogger(__name__)
class Episode:
def __init__(self, data):
self.data = data or {}
self.id = self.data.get('id', None)
@property
def number(self):
return ... | /ruv-dl-0.5.4.tar.gz/ruv-dl-0.5.4/ruv_dl/data.py | 0.467818 | 0.182098 | data.py | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | /rv_distributions-1.0.tar.gz/rv_distributions-1.0/rv_distributions/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
from decimal import Decimal
from .rv_api import RVapi
from .objetos.recarga import Recarga
class Transacao5(RVapi):
def execute(
self,
compra: int,
produto: str,
ddd: str,
fone: str,
codigo_assinante: str = None,
valor: Decimal ... | /rv-schubert-sdk-1.1.0.tar.gz/rv-schubert-sdk-1.1.0/rv_schubert_sdk/resources/transacao_5.py | 0.489992 | 0.249973 | transacao_5.py | pypi |
class ErroRV(Exception):
def __init__(self, message):
Exception.__init__(self, message)
class FoneIncompletoInvalido(ErroRV):
def __init__(self, *args):
ErroRV.__init__(self, "Fone Incompleto / Invalido [Codigo 1]")
class LimiteCreditoInsuficiente(ErroRV):
def __init__(self, *args):
... | /rv-schubert-sdk-1.1.0.tar.gz/rv-schubert-sdk-1.1.0/rv_schubert_sdk/resources/exceptions.py | 0.512937 | 0.210837 | exceptions.py | pypi |
# Robotics, Vision & Control: 3rd edition in Python (2023)
[](https://github.com/petercorke/robotics-toolbox-python)
[:
# accelerometer
g0 = unitvec( [0, 0... | /rvc3python-0.9.0.tar.gz/rvc3python-0.9.0/RVC3/examples/imu_data.py | 0.889361 | 0.66296 | imu_data.py | pypi |
import numpy as np
from math import pi, sqrt, inf
quadrotor = {}
quadrotor["nrotors"] = 4 # 4 rotors
quadrotor["g"] = 9.81 # g Gravity
quadrotor["rho"] = 1.184 # rho Density of air
quadrotor["muv"] = 1.5e-5 # muv Viscosity of air
# Airframe
quadrotor["M"] = 4 # M Mass
Ixx = 0.082
Iyy = 0.082
Izz = 0.... | /rvc3python-0.9.0.tar.gz/rvc3python-0.9.0/RVC3/models/quad_model.py | 0.568895 | 0.408985 | quad_model.py | pypi |
# run with command line -a switch to show animation
import numpy as np
import math
import roboticstoolbox as rtb
import bdsim
# parameters for the path
look_ahead = 5
speed = 1
dt = 0.1
tacc = 1
x0 = [2, 2, 0]
# create the path
path = np.array([[10, 10], [10, 60], [80, 80], [50, 10]])
robot_traj = rtb.mstraj(path[1... | /rvc3python-0.9.0.tar.gz/rvc3python-0.9.0/RVC3/models/drivepursuit.py | 0.454956 | 0.504272 | drivepursuit.py | pypi |
# run with command line -a switch to show animation
from math import pi, sqrt, atan, atan2
import bdsim
sim = bdsim.BDSim(animation=True)
bd = sim.blockdiagram()
# parameters
xg = [5, 5, pi / 2]
Krho = bd.GAIN(1, name="Krho")
Kalpha = bd.GAIN(5, name="Kalpha")
Kbeta = bd.GAIN(-2, name="Kbeta")
xg = [5, 5, pi / 2]
x... | /rvc3python-0.9.0.tar.gz/rvc3python-0.9.0/RVC3/models/driveconfig.py | 0.534855 | 0.474083 | driveconfig.py | pypi |
import numpy as np
import bdsim
def SEA(obstacle_pos=0.8, block=False, graphics=False):
sim = bdsim.BDSim(name="SEA", graphics=graphics)
bd = sim.blockdiagram()
m1 = 0.5
m2 = 1
LQR = np.c_[169.9563, 62.9010, -19.9563, 71.1092].T
print(LQR)
Ks = 5
force_lim = 2
# define the block... | /rvc3python-0.9.0.tar.gz/rvc3python-0.9.0/RVC3/models/SEA.py | 0.563858 | 0.364636 | SEA.py | pypi |
import numpy as np
from scipy import linalg
import bdsim
from roboticstoolbox import models
import spatialmath.base as smb
from spatialmath import SE3
# equation numbers are with reference to:
# A Unified Approach for Motion and Force Control of Robot Manipulators: The
# Operational Space Formulation, Khatib, IEEE J... | /rvc3python-0.9.0.tar.gz/rvc3python-0.9.0/RVC3/models/opspace.py | 0.74512 | 0.487856 | opspace.py | pypi |
import math
import matplotlib.pyplot as plt
from .Generaldistribution import Distribution
class Gaussian(Distribution):
""" Gaussian distribution class for calculating and
visualizing a Gaussian distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing ... | /rvd_distributions-0.1.tar.gz/rvd_distributions-0.1/rvd_distributions/Gaussiandistribution.py | 0.688364 | 0.853058 | Gaussiandistribution.py | pypi |
from typing import List, Tuple
from rvid.networknumbers.client import ExampleIntsClient
from rvid.seq.basic import RIDMaker
from rvid.seq.common import RIDType, epoch_ms2rid, epoch_ms_now
class RIDMakerProxy(RIDMaker):
def __init__(self) -> None:
super().__init__()
self.client = ExampleIntsClient... | /rvid.seq-0.2.0-py3-none-any.whl/rvid/netseq/client.py | 0.664758 | 0.427755 | client.py | pypi |
from logging import getLogger
from typing import Callable, Dict, Sequence
from rvid.networknumbers.request import RequestForNumbers
from rvid.networknumbers.server import NumberRequestTCPHandler
from rvid.seq.common import epoch_ms_now
log = getLogger(__name__)
class TranchKeeper:
"""
TODO: Split out generi... | /rvid.seq-0.2.0-py3-none-any.whl/rvid/netseq/server.py | 0.758958 | 0.167049 | server.py | pypi |
import re
import time
from typing import NewType
_allowed_letters = "A-HJ-NP-Y0-9*#" # No India or Oscar
RID_REGEXP = re.compile(
"^([%s]{3})-([%s]{3})-([%s]{3})$" % ((_allowed_letters,) * 3), re.IGNORECASE
)
BASE35_CHARS = "0123456789ABCDEFGH*JKLMN#PQRSTUVWXY"
RIDType = NewType("RIDType", str) # String like ABC... | /rvid.seq-0.2.0-py3-none-any.whl/rvid/seq/common.py | 0.711431 | 0.20266 | common.py | pypi |
import os
import shutil
from dataclasses import dataclass, field
from logging import getLogger
from pathlib import Path
from typing import List, Optional, Pattern, Union
log = getLogger(__name__)
@dataclass
class ExternalResources:
"""
Represent one or more external file that is referenced from a tex-file.
... | /rvid.tex_runner-0.2.0-py3-none-any.whl/rvid/tex_runner/external_resources.py | 0.84759 | 0.266333 | external_resources.py | pypi |
## `rvlib`
Anyone who has used [`Distributions.jl`](https://github.com/JuliaStats/Distributions.jl) will tell
you how nice the interface is relative to the "exotic" (the most polite word
we can think of) interface to distributions exposed by
[scipy.stats](http://docs.scipy.org/doc/scipy-0.17.1/reference/stats.html).
`... | /rvlib-0.0.6.tar.gz/rvlib-0.0.6/README.md | 0.476336 | 0.969179 | README.md | pypi |
.. image:: https://img.shields.io/pypi/v/rvmath.svg
:target: https://pypi.python.org/pypi/rvmath
:alt: Latest Version
.. image:: https://img.shields.io/pypi/l/rvmath.svg
:target: https://pypi.python.org/pypi/rvmath
:alt: License
.. image:: https://img.shields.io/pypi/pyversions/rvmath.svg
:target:... | /rvmath-0.1.tar.gz/rvmath-0.1/README.rst | 0.944177 | 0.752013 | README.rst | pypi |
from PIL import Image, ImageDraw, ImageFont
from io import BytesIO
from base64 import b64encode
from requests import get
class ImageToASCII:
def __init__(self, image_path, source='local', font_path=None, font_size=15, charset=list('#Wo- ')):
if source == 'array':
self.image = Image.fromarray(im... | /rvmendillo_image_to_ascii-1.6.4-py3-none-any.whl/rvmendillo_image_to_ascii/__init__.py | 0.551815 | 0.184915 | __init__.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
class Similarity(nn.Module):
def __init__(self, encoder, config):
super(Similarity, self).__init__()
self.config = config
self.encoder = encoder
self.hidden_similarity_size = config.hidden_similarity_size
se... | /rvnn-0.0.1.tar.gz/rvnn-0.0.1/pytree/models/similarity/modeling_similarity.py | 0.924858 | 0.346458 | modeling_similarity.py | pypi |
import nltk
from tqdm.auto import tqdm
import numpy as np
class GloveTokenizer:
def __init__(self, glove_file_path, vocab_size=None):
self.glove_file_path = glove_file_path
vocab, self.embeddings_arr = self._read_embedding_file(glove_file_path, vocab_size)
self.unk_token_id = 1
se... | /rvnn-0.0.1.tar.gz/rvnn-0.0.1/pytree/data/glove_tokenizer.py | 0.773986 | 0.272817 | glove_tokenizer.py | pypi |
import re
def prepare_input_from_constituency_tree(constituency_tree):
cons_tree = ConsTree([])
tree = cons_tree.read_tree(constituency_tree[5:-1])
tree.close_unaries()
tree.left_markovize(dummy_annotation="")
const = cons_tree.linearize_parse_tree(str(tree))
clean_const = re.sub(r'\(([^ ]+) '... | /rvnn-0.0.1.tar.gz/rvnn-0.0.1/pytree/data/constituency_tree.py | 0.630002 | 0.308542 | constituency_tree.py | pypi |
import warnings
import numpy as np
from matplotlib.cm import get_cmap
from matplotlib.colors import LinearSegmentedColormap
def gray_scale_to_color_ramp(gray_scale, colormap, min_colormap_cut=None, max_colormap_cut=None, alpha=False,
output_8bit=True):
"""
Turns normalized gray ... | /rvt_py-2.2.1.tar.gz/rvt_py-2.2.1/rvt/blend_func.py | 0.900004 | 0.532972 | blend_func.py | pypi |
import requests
import pandas as pd
class rw_api_tools:
def __init__(self):
"""constructor for this class"""
self.data = "None yet"
def get_rw_datasets(provider=None):
url = "https://api.resourcewatch.org/v1/dataset?sort=slug,-provider,userId&status=saved&includes=metadata,voc... | /rw_api1-1.0.3-py3-none-any.whl/rw_api_tools/rw_api_tools.py | 0.401219 | 0.21213 | rw_api_tools.py | pypi |
import os
from pathlib import Path
from dependence.function_write import *
from dependence.function_read import *
from dependence.control_folder_exist import *
__all__ = [
"file_rw",
]
def file_rw(file_path, data=None, mode='read', sep=',', file_extension='csv', parent_directory=None, serie=False,
... | /rw_dataframe_data_io-0.1.4.tar.gz/rw_dataframe_data_io-0.1.4/utils/DataIO.py | 0.571049 | 0.276324 | DataIO.py | pypi |
# rw-dynamicworld-cd
A repository holding code and example notebooks for change detection methods and post-classificaiton processing for the Dynamic World Land Cover product. Dynamic World is a joint iniative between the World Resources Institute, Natioanl Geographic Society, Google, and Impact Observatory. The Dynamic... | /rw-dynamicworld-cd-0.0.1.tar.gz/rw-dynamicworld-cd-0.0.1/README.md | 0.89112 | 0.992547 | README.md | pypi |
import os
import ee
import numpy as np
import pandas as pd
import random
import itertools
def pretty_print_confusion_matrix_binary(confusion_list):
"""
Function to print a confusion matrix list
Args:
confusion_list (List): a list of confusion matrix values, can be taken from ee.ConfusionMatrix().g... | /rw-dynamicworld-cd-0.0.1.tar.gz/rw-dynamicworld-cd-0.0.1/wri_change_detection/gee_classifier.py | 0.803637 | 0.595287 | gee_classifier.py | pypi |
import os
import ee
import numpy as np
import pandas as pd
import random
import json
import calendar
import time
#Image bands must be ordered by increasing years
def getYearStackIC(image, band_names, band_indices=[-1,0,1]):
"""
Function takes an image with bands for each time period (e.g. annual) and returns ... | /rw-dynamicworld-cd-0.0.1.tar.gz/rw-dynamicworld-cd-0.0.1/wri_change_detection/preprocessing.py | 0.759047 | 0.545165 | preprocessing.py | pypi |
import pandas
import matplotlib.pyplot as plt
import numpy as np
import argparse
import seaborn as sns
import os
import json
import math
from matplotlib.ticker import LogFormatterSciNotation
parser = argparse.ArgumentParser()
parser.add_argument("csv", type=str, help="Data to plot", nargs="*")
parser.add_argument("--o... | /rw_noise-0.1.2.tar.gz/rw_noise-0.1.2/evaluation/plot_results.py | 0.4206 | 0.395076 | plot_results.py | pypi |
from __future__ import absolute_import
from .generic import *
class ScipyStorable(Storable):
def __init__(self, python_type, key=None, handlers=[]):
Storable.__init__(self, python_type, key, handlers)
self.deprivatize = True
class ScipySpatialStorable(ScipyStorable):
@property
def defaul... | /rwa-python-0.9.3.tar.gz/rwa-python-0.9.3/rwa/scipy.py | 0.41941 | 0.217691 | scipy.py | pypi |
import os
import glob
import logging
from typing import List
import numpy as np
import matplotlib.pyplot as plt
__all__ = ('write_bp_to_disk', 'write_it_to_disk', 'plot_bp')
logger = logging.getLogger(__name__)
def write_bp_to_disk(result_dir: str,
filename: str, bplist: List[float]) -> None:
... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/io.py | 0.807271 | 0.276094 | io.py | pypi |
from typing import Callable, Union
from ..net import Lightpath, Network
from .routing import dijkstra, yen
from .wlassignment import vertex_coloring, first_fit, random_fit
from .ga import GeneticAlgorithm
__all__ = (
'dijkstra_vertex_coloring',
'dijkstra_first_fit',
'yen_vertex_coloring',
'yen_first_f... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/rwa/rwa.py | 0.924135 | 0.635534 | rwa.py | pypi |
import logging
from typing import List, Tuple, Union
from .pop import Population
from .env import evaluate, select, cross, mutate
from ...net import Network
__all__ = (
'GeneticAlgorithm',
)
logger = logging.getLogger(__name__)
class GeneticAlgorithm(object):
"""Genetic algorithm
Chromosomes are encod... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/rwa/ga/ga.py | 0.895271 | 0.593521 | ga.py | pypi |
from __future__ import annotations
import copy
import logging
from typing import List, Set, Union
import numpy as np
from .chromo import Chromosome
__all__ = (
'Population',
)
logger = logging.getLogger(__name__)
class Population(object):
"""Class to store a collection of Chromosome objects
Populati... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/rwa/ga/pop.py | 0.842831 | 0.45048 | pop.py | pypi |
import logging
from itertools import count
from typing import List
import numpy as np
__all__ = (
'Fitness',
'Chromosome'
)
logger = logging.getLogger(__name__)
np.set_printoptions(precision=2)
class Fitness(object):
"""Fitness 'namedtuple'-like object
Easy to handle ready-to-use properties such a... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/rwa/ga/chromo.py | 0.920272 | 0.440168 | chromo.py | pypi |
import logging
import numpy as np
from .utils import gof
from .chromo import Chromosome, Fitness
from .pop import Population
from ...net import Network
__all__ = (
'evaluate',
'select',
'cross',
'mutate',
)
logger = logging.getLogger(__name__)
def evaluate(net: Network, chromosome: Chromosome) -> F... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/rwa/ga/env.py | 0.685739 | 0.566798 | env.py | pypi |
from itertools import count
from typing import Union
import numpy as np
import networkx as nx
# FIXME https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles
from ...net import Network, Lightpath
def vertex_coloring(net: Network, lightpath: Lightpath) -> Union[int, None]:
"""Vertex coloring algor... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/rwa/wlassignment/vcolor.py | 0.541651 | 0.535281 | vcolor.py | pypi |
from typing import Dict, List, Tuple
from collections import OrderedDict
from . import Network
class RedeNacionalPesquisa(Network):
"""Rede (Brasileira) Nacional de Pesquisa (Rede Ipê / RNP)"""
def __init__(self, ch_n):
self._name = 'rnp'
self._fullname = u'Rede Nacional de Pesquisas (Rede I... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/net/rnp.py | 0.791338 | 0.428712 | rnp.py | pypi |
__author__ = 'Cassio Batista'
import logging
from itertools import count
from operator import itemgetter
from typing import Iterable, List, Tuple
import numpy as np
import matplotlib.pyplot as plt
__all__ = (
'Lightpath',
'AdjacencyMatrix',
'WavelengthAvailabilityMatrix',
'TrafficMatrix',
'Networ... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/net/net.py | 0.900825 | 0.617686 | net.py | pypi |
from typing import Dict, List, Tuple
from collections import OrderedDict
from . import Network
class Italian(Network):
"""Italian Network"""
def __init__(self, ch_n):
self._name = 'italian'
self._fullname = u'Italian'
self._s = 0 # FIXME
self._d = 12
super().__init__... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/net/italian.py | 0.49292 | 0.437884 | italian.py | pypi |
from typing import Dict, List, Tuple
from collections import OrderedDict
from . import Network
class AdvancedResearchProjectsAgency(Network):
"""U.S. Advanced Research Projects Agency (ARPANET)"""
def __init__(self, ch_n):
self._name = 'arpa'
self._fullname = u'Advanced Research Projects Age... | /rwa_wdm-0.2.3.tar.gz/rwa_wdm-0.2.3/rwa_wdm/net/arpa.py | 0.496094 | 0.39712 | arpa.py | pypi |
<p align="center">
<img width="350px" src="docs/img/rware.png" align="center" alt="Multi-Robot Warehouse (RWARE)" />
<p align="center">A multi-agent reinforcement learning environment</p>
</p>
[](https://GitHub.com/Naereen/StrapDown.js/graphs/co... | /rware-1.0.3.tar.gz/rware-1.0.3/README.md | 0.755637 | 0.971293 | README.md | pypi |
import math
from typing import Dict, Optional
from rweb_datatable.html import Node
from rweb_datatable.models import Table, TableContext, SortColumn, Dataset, Column, Pagination, PaginationPage
from rweb_datatable.utils import url, make_table_section_id
def get_table_context_from_args(table: Table, args: dict, extra... | /rweb_datatable-0.1.18.tar.gz/rweb_datatable-0.1.18/rweb_datatable/__init__.py | 0.733547 | 0.255646 | __init__.py | pypi |
from dataclasses import dataclass, field
from typing import Union, Callable, Any, Optional, Dict, List
StringOrCallable = Union[None, str, Callable[..., str]]
@dataclass
class Column:
id: str
title: str
is_sortable: bool = field(default=True)
render_header: StringOrCallable = field(default=None)
... | /rweb_datatable-0.1.18.tar.gz/rweb_datatable-0.1.18/rweb_datatable/models.py | 0.861902 | 0.310812 | models.py | pypi |
from copy import copy
from typing import Union, Callable, Optional
from rweb_datatable.html import Node
from rweb_datatable.models import Column, Table, Dataset, TableContext, Pagination
from rweb_datatable.utils import url, make_table_section_id
def render_table_section(
data: Dataset, table: Table, context: Ta... | /rweb_datatable-0.1.18.tar.gz/rweb_datatable-0.1.18/rweb_datatable/renderers/htmx.py | 0.640861 | 0.267381 | htmx.py | pypi |
import abc
import builtins
import datetime
import enum
import typing
import jsii
import jsii.compat
import publication
from ._jsii import *
import aws_cdk.aws_ec2
import aws_cdk.aws_iam
import aws_cdk.aws_lambda
import aws_cdk.aws_logs
import aws_cdk.aws_sqs
import aws_cdk.core
class GolangFunction(aws_cdk.aws_lam... | /rwilinski.aws-lambda-golang-0.1.1.tar.gz/rwilinski.aws-lambda-golang-0.1.1/src/rwilinski/aws-lambda-golang/__init__.py | 0.622459 | 0.18352 | __init__.py | pypi |
# rwkv.cpp
This is a port of [BlinkDL/RWKV-LM](https://github.com/BlinkDL/RWKV-LM) to [ggerganov/ggml](https://github.com/ggerganov/ggml).
Besides the usual **FP32**, it supports **FP16** and **quantized INT4** inference on CPU. This project is **CPU only**.
RWKV is a novel large language model architecture, [with t... | /rwkv_cpp_python-0.0.1.tar.gz/rwkv_cpp_python-0.0.1/README.md | 0.414188 | 0.940134 | README.md | pypi |
import argparse
import os
import pathlib
import time
import sampling
import tokenizers
import rwkv_cpp_model
import rwkv_cpp_shared_library
# ======================================== Script settings ========================================
prompt: str = """# rwkv.cpp
This is a port of [BlinkDL/RWKV-LM](https://git... | /rwkv_cpp_python-0.0.1.tar.gz/rwkv_cpp_python-0.0.1/rwkv/generate_completions.py | 0.558809 | 0.403097 | generate_completions.py | pypi |
import os
import sys
import argparse
import pathlib
import sampling
import tokenizers
import rwkv_cpp_model
import rwkv_cpp_shared_library
# ======================================== Script settings ========================================
# Copied from https://github.com/ggerganov/llama.cpp/blob/6e7801d08d81c931a542... | /rwkv_cpp_python-0.0.1.tar.gz/rwkv_cpp_python-0.0.1/rwkv/chat_with_bot.py | 0.467575 | 0.267193 | chat_with_bot.py | pypi |
import os
import time
import pathlib
import argparse
import tokenizers
import torch
import rwkv_cpp_model
import rwkv_cpp_shared_library
from typing import List
def parse_args():
parser = argparse.ArgumentParser(description='Measure perplexity and per-token latency of an RWKV model on a given text file')
pars... | /rwkv_cpp_python-0.0.1.tar.gz/rwkv_cpp_python-0.0.1/rwkv/measure_pexplexity.py | 0.809878 | 0.321966 | measure_pexplexity.py | pypi |
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