code
stringlengths 87
55.2k
| code_codestyle
int64 0
349
| style_context
stringlengths 135
49.1k
| style_context_codestyle
int64 0
349
| label
int64 0
1
|
|---|---|---|---|---|
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
UpperCamelCase = hex_num[0] == """-"""
if is_negative:
UpperCamelCase = hex_num[1:]
try:
UpperCamelCase = int(__UpperCamelCase , 16 )
except ValueError:
raise ValueError("""Invalid value was passed to the function""" )
UpperCamelCase = """"""
while int_num > 0:
UpperCamelCase = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(("""-""" + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 | 1 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ = namedtuple('covid_data', 'cases deaths recovered')
def lowercase__ ( __UpperCamelCase = "https://www.worldometers.info/coronavirus/" )-> covid_data:
UpperCamelCase = """//div[@class = \"maincounter-number\"]/span/text()"""
return covid_data(*html.fromstring(requests.get(__UpperCamelCase ).content ).xpath(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE__ = 'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'
print(fmt.format(*covid_stats()))
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def lowercase__ ( __UpperCamelCase )-> datetime:
UpperCamelCase = year % 19
UpperCamelCase = year % 4
UpperCamelCase = year % 7
UpperCamelCase = math.floor(year / 100 )
UpperCamelCase = math.floor((13 + 8 * leap_day_inhibits) / 25 )
UpperCamelCase = leap_day_inhibits / 4
UpperCamelCase = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
UpperCamelCase = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
UpperCamelCase = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
UpperCamelCase = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(__UpperCamelCase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(__UpperCamelCase , 4 , 18 )
else:
return datetime(__UpperCamelCase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
SCREAMING_SNAKE_CASE__ = 'will be' if year > datetime.now().year else 'was'
print(f'Easter in {year} {tense} {gauss_easter(year)}')
| 321 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 1 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
SCREAMING_SNAKE_CASE__ = 'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append('importlib_metadata')
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=None )-> Union[str, Any]:
require_version(deps[pkg] , __UpperCamelCase )
| 321 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = min(__UpperCamelCase ) # min() finds the minimum value
UpperCamelCase = max(__UpperCamelCase ) # max() finds the maximum value
UpperCamelCase = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCamelCase = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCamelCase = 0
for count in range(__UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
UpperCamelCase = count + min_val
i += 1
def lowercase__ ( )-> Any:
UpperCamelCase = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__UpperCamelCase )
print("""Sorted order is:""" , """ """.join(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
print("""Loading config file...""" )
def flatten_yaml_as_dict(__UpperCamelCase , __UpperCamelCase="" , __UpperCamelCase="." ):
UpperCamelCase = []
for k, v in d.items():
UpperCamelCase = parent_key + sep + k if parent_key else k
if isinstance(__UpperCamelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(__UpperCamelCase , __UpperCamelCase , sep=__UpperCamelCase ).items() )
else:
items.append((new_key, v) )
return dict(__UpperCamelCase )
UpperCamelCase = argparse.Namespace()
with open(__UpperCamelCase , """r""" ) as yaml_file:
try:
UpperCamelCase = yaml.load(__UpperCamelCase , Loader=yaml.FullLoader )
UpperCamelCase = flatten_yaml_as_dict(__UpperCamelCase )
for k, v in flat_cfg.items():
setattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
except yaml.YAMLError as exc:
logger.error("""Error while loading config file: {}. Error message: {}""".format(__UpperCamelCase , str(__UpperCamelCase ) ) )
return config
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> str:
UpperCamelCase = MobileViTVaConfig()
UpperCamelCase = False
# dataset
if task_name.startswith("""imagenet1k_""" ):
UpperCamelCase = 1000
if int(task_name.strip().split("""_""" )[-1] ) == 384:
UpperCamelCase = 384
else:
UpperCamelCase = 256
UpperCamelCase = """imagenet-1k-id2label.json"""
elif task_name.startswith("""imagenet21k_to_1k_""" ):
UpperCamelCase = 21000
if int(task_name.strip().split("""_""" )[-1] ) == 384:
UpperCamelCase = 384
else:
UpperCamelCase = 256
UpperCamelCase = """imagenet-22k-id2label.json"""
elif task_name.startswith("""ade20k_""" ):
UpperCamelCase = 151
UpperCamelCase = 512
UpperCamelCase = """ade20k-id2label.json"""
UpperCamelCase = True
elif task_name.startswith("""voc_""" ):
UpperCamelCase = 21
UpperCamelCase = 512
UpperCamelCase = """pascal-voc-id2label.json"""
UpperCamelCase = True
# orig_config
UpperCamelCase = load_orig_config_file(__UpperCamelCase )
assert getattr(__UpperCamelCase , """model.classification.name""" , -1 ) == "mobilevit_v2", "Invalid model"
UpperCamelCase = getattr(__UpperCamelCase , """model.classification.mitv2.width_multiplier""" , 1.0 )
assert (
getattr(__UpperCamelCase , """model.classification.mitv2.attn_norm_layer""" , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCamelCase = getattr(__UpperCamelCase , """model.classification.activation.name""" , """swish""" )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCamelCase = getattr(__UpperCamelCase , """model.segmentation.output_stride""" , 16 )
if "_deeplabv3" in task_name:
UpperCamelCase = getattr(__UpperCamelCase , """model.segmentation.deeplabv3.aspp_rates""" , [12, 24, 36] )
UpperCamelCase = getattr(__UpperCamelCase , """model.segmentation.deeplabv3.aspp_out_channels""" , 512 )
UpperCamelCase = getattr(__UpperCamelCase , """model.segmentation.deeplabv3.aspp_dropout""" , 0.1 )
# id2label
UpperCamelCase = """huggingface/label-files"""
UpperCamelCase = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
UpperCamelCase = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
UpperCamelCase = idalabel
UpperCamelCase = {v: k for k, v in idalabel.items()}
return config
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Dict:
UpperCamelCase = dct.pop(__UpperCamelCase )
UpperCamelCase = val
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=False )-> Optional[int]:
if base_model:
UpperCamelCase = """"""
else:
UpperCamelCase = """mobilevitv2."""
UpperCamelCase = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCamelCase = k[8:]
else:
UpperCamelCase = k
if ".block." in k:
UpperCamelCase = k_new.replace(""".block.""" , """.""" )
if ".conv." in k:
UpperCamelCase = k_new.replace(""".conv.""" , """.convolution.""" )
if ".norm." in k:
UpperCamelCase = k_new.replace(""".norm.""" , """.normalization.""" )
if "conv_1." in k:
UpperCamelCase = k_new.replace("""conv_1.""" , F"{model_prefix}conv_stem." )
for i in [1, 2]:
if F"layer_{i}." in k:
UpperCamelCase = k_new.replace(F"layer_{i}." , F"{model_prefix}encoder.layer.{i-1}.layer." )
if ".exp_1x1." in k:
UpperCamelCase = k_new.replace(""".exp_1x1.""" , """.expand_1x1.""" )
if ".red_1x1." in k:
UpperCamelCase = k_new.replace(""".red_1x1.""" , """.reduce_1x1.""" )
for i in [3, 4, 5]:
if F"layer_{i}.0." in k:
UpperCamelCase = k_new.replace(F"layer_{i}.0." , F"{model_prefix}encoder.layer.{i-1}.downsampling_layer." )
if F"layer_{i}.1.local_rep.0." in k:
UpperCamelCase = k_new.replace(F"layer_{i}.1.local_rep.0." , F"{model_prefix}encoder.layer.{i-1}.conv_kxk." )
if F"layer_{i}.1.local_rep.1." in k:
UpperCamelCase = k_new.replace(F"layer_{i}.1.local_rep.1." , F"{model_prefix}encoder.layer.{i-1}.conv_1x1." )
for i in [3, 4, 5]:
if i == 3:
UpperCamelCase = [0, 1]
elif i == 4:
UpperCamelCase = [0, 1, 2, 3]
elif i == 5:
UpperCamelCase = [0, 1, 2]
for j in j_in:
if F"layer_{i}.1.global_rep.{j}." in k:
UpperCamelCase = k_new.replace(
F"layer_{i}.1.global_rep.{j}." , F"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}." )
if F"layer_{i}.1.global_rep.{j+1}." in k:
UpperCamelCase = k_new.replace(
F"layer_{i}.1.global_rep.{j+1}." , F"{model_prefix}encoder.layer.{i-1}.layernorm." )
if F"layer_{i}.1.conv_proj." in k:
UpperCamelCase = k_new.replace(F"layer_{i}.1.conv_proj." , F"{model_prefix}encoder.layer.{i-1}.conv_projection." )
if "pre_norm_attn.0." in k:
UpperCamelCase = k_new.replace("""pre_norm_attn.0.""" , """layernorm_before.""" )
if "pre_norm_attn.1." in k:
UpperCamelCase = k_new.replace("""pre_norm_attn.1.""" , """attention.""" )
if "pre_norm_ffn.0." in k:
UpperCamelCase = k_new.replace("""pre_norm_ffn.0.""" , """layernorm_after.""" )
if "pre_norm_ffn.1." in k:
UpperCamelCase = k_new.replace("""pre_norm_ffn.1.""" , """ffn.conv1.""" )
if "pre_norm_ffn.3." in k:
UpperCamelCase = k_new.replace("""pre_norm_ffn.3.""" , """ffn.conv2.""" )
if "classifier.1." in k:
UpperCamelCase = k_new.replace("""classifier.1.""" , """classifier.""" )
if "seg_head." in k:
UpperCamelCase = k_new.replace("""seg_head.""" , """segmentation_head.""" )
if ".aspp_layer." in k:
UpperCamelCase = k_new.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in k:
UpperCamelCase = k_new.replace(""".aspp_pool.""" , """.""" )
rename_keys.append((k, k_new) )
return rename_keys
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = []
for k in state_dict.keys():
if k.startswith("""seg_head.aux_head.""" ):
keys_to_ignore.append(__UpperCamelCase )
for k in keys_to_ignore:
state_dict.pop(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( )-> Tuple:
UpperCamelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCamelCase = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[Any]:
UpperCamelCase = get_mobilevitva_config(__UpperCamelCase , __UpperCamelCase )
# load original state_dict
UpperCamelCase = torch.load(__UpperCamelCase , map_location="""cpu""" )
# load huggingface model
if task_name.startswith("""ade20k_""" ) or task_name.startswith("""voc_""" ):
UpperCamelCase = MobileViTVaForSemanticSegmentation(__UpperCamelCase ).eval()
UpperCamelCase = False
else:
UpperCamelCase = MobileViTVaForImageClassification(__UpperCamelCase ).eval()
UpperCamelCase = False
# remove and rename some keys of load the original model
UpperCamelCase = checkpoint
remove_unused_keys(__UpperCamelCase )
UpperCamelCase = create_rename_keys(__UpperCamelCase , base_model=__UpperCamelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# load modified state_dict
model.load_state_dict(__UpperCamelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCamelCase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCamelCase = image_processor(images=prepare_img() , return_tensors="""pt""" )
UpperCamelCase = model(**__UpperCamelCase )
# verify classification model
if task_name.startswith("""imagenet""" ):
UpperCamelCase = outputs.logits
UpperCamelCase = logits.argmax(-1 ).item()
print("""Predicted class:""" , model.config.idalabel[predicted_class_idx] )
if task_name.startswith("""imagenet1k_256""" ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCamelCase = torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01] )
assert torch.allclose(logits[0, :3] , __UpperCamelCase , atol=1E-4 )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(F"Saving model {task_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCamelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--task',
default='imagenet1k_256',
type=str,
help=(
'Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '
'\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n '
),
choices=[
'imagenet1k_256',
'imagenet1k_384',
'imagenet21k_to_1k_256',
'imagenet21k_to_1k_384',
'ade20k_deeplabv3',
'voc_deeplabv3',
],
)
parser.add_argument(
'--orig_checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
)
parser.add_argument('--orig_config_path', required=True, type=str, help='Path to the original config file.')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 321 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class a_ ( unittest.TestCase ):
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = 0
UpperCamelCase = [0]
UpperCamelCase = [0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(k.knapsack(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , 0 )
UpperCamelCase = [60]
UpperCamelCase = [10]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(k.knapsack(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , 0 )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = 3
UpperCamelCase = [1, 2, 3]
UpperCamelCase = [3, 2, 1]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(k.knapsack(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , 5 )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = 50
UpperCamelCase = [60, 100, 120]
UpperCamelCase = [10, 20, 30]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(k.knapsack(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , 220 )
if __name__ == "__main__":
unittest.main()
| 321 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 1000 )-> int:
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCamelCase = n - a - b
if c * c == (a * a + b * b):
UpperCamelCase = a * b * c
if candidate >= product:
UpperCamelCase = candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False )-> Optional[int]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
if not is_sharded:
UpperCamelCase = os.path.abspath(__UpperCamelCase )
logger.info(F"Loading PyTorch weights from {pt_path}" )
UpperCamelCase = torch.load(__UpperCamelCase , map_location="""cpu""" )
logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters." )
UpperCamelCase = convert_pytorch_state_dict_to_flax(__UpperCamelCase , __UpperCamelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
UpperCamelCase = convert_pytorch_sharded_state_dict_to_flax(__UpperCamelCase , __UpperCamelCase )
return flax_state_dict
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(__UpperCamelCase ) -> bool:
return len(set(__UpperCamelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
UpperCamelCase = pt_tuple_key[:-1] + ("""scale""",)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__UpperCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
UpperCamelCase = pt_tuple_key[:-1] + ("""mean""",)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__UpperCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
UpperCamelCase = pt_tuple_key[:-1] + ("""var""",)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__UpperCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
UpperCamelCase = pt_tuple_key[:-1] + ("""embedding""",)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__UpperCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCamelCase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__UpperCamelCase ):
UpperCamelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCamelCase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__UpperCamelCase ):
UpperCamelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCamelCase = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCamelCase = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
UpperCamelCase = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
UpperCamelCase = pt_tuple_key[-2] + """_g"""
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
UpperCamelCase = pt_tuple_key[-2] + """_v"""
if name is not None:
UpperCamelCase = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> List[str]:
# convert pytorch tensor to numpy
UpperCamelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
UpperCamelCase = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
UpperCamelCase = flax_model.params["""params"""]
else:
UpperCamelCase = flax_model.params
UpperCamelCase = flatten_dict(__UpperCamelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
UpperCamelCase = flatten_dict(flax_model.params["""batch_stats"""] )
random_flax_state_dict.update(__UpperCamelCase )
UpperCamelCase = {}
UpperCamelCase = (model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
UpperCamelCase = (model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCamelCase = tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
UpperCamelCase = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCamelCase = pt_tuple_key[1:]
# Correctly rename weight parameters
UpperCamelCase ,UpperCamelCase = rename_key_and_reshape_tensor(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# add model prefix if necessary
UpperCamelCase = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
UpperCamelCase = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
UpperCamelCase = jnp.asarray(__UpperCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__UpperCamelCase , __UpperCamelCase )
continue
# also add unexpected weight so that warning is thrown
UpperCamelCase = jnp.asarray(__UpperCamelCase )
else:
# also add unexpected weight so that warning is thrown
UpperCamelCase = jnp.asarray(__UpperCamelCase )
return unflatten_dict(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
import torch
# Load the index
UpperCamelCase = {}
for shard_file in shard_filenames:
# load using msgpack utils
UpperCamelCase = torch.load(__UpperCamelCase )
UpperCamelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
UpperCamelCase = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
UpperCamelCase = flax_model.params["""params"""]
UpperCamelCase = flatten_dict(__UpperCamelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""] ) )
else:
UpperCamelCase = flax_model.params
UpperCamelCase = flatten_dict(__UpperCamelCase )
UpperCamelCase = (model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
UpperCamelCase = (model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCamelCase = tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
UpperCamelCase = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCamelCase = pt_tuple_key[1:]
# Correctly rename weight parameters
UpperCamelCase ,UpperCamelCase = rename_key_and_reshape_tensor(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# add model prefix if necessary
UpperCamelCase = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
UpperCamelCase = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
UpperCamelCase = jnp.asarray(__UpperCamelCase )
continue
if "var" in flax_key[-1]:
UpperCamelCase = jnp.asarray(__UpperCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__UpperCamelCase , __UpperCamelCase )
continue
# also add unexpected weight so that warning is thrown
UpperCamelCase = jnp.asarray(__UpperCamelCase )
else:
# also add unexpected weight so that warning is thrown
UpperCamelCase = jnp.asarray(__UpperCamelCase )
return unflatten_dict(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
UpperCamelCase = os.path.abspath(__UpperCamelCase )
logger.info(F"Loading Flax weights from {flax_checkpoint_path}" )
# import correct flax class
UpperCamelCase = getattr(__UpperCamelCase , """Flax""" + model.__class__.__name__ )
# load flax weight dict
with open(__UpperCamelCase , """rb""" ) as state_f:
try:
UpperCamelCase = from_bytes(__UpperCamelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. " )
return load_flax_weights_in_pytorch_model(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
UpperCamelCase = flatten_dict(jax.tree_util.tree_map(lambda __UpperCamelCase : x.dtype == jnp.bfloataa , __UpperCamelCase ) ).values()
if any(__UpperCamelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
UpperCamelCase = jax.tree_util.tree_map(
lambda __UpperCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __UpperCamelCase )
UpperCamelCase = flatten_dict(__UpperCamelCase )
UpperCamelCase = pt_model.state_dict()
UpperCamelCase = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
UpperCamelCase = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
UpperCamelCase = []
UpperCamelCase = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
UpperCamelCase = flax_key_tuple[0] == pt_model.base_model_prefix
UpperCamelCase = """.""".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCamelCase = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
UpperCamelCase = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__UpperCamelCase ) not in pt_model_dict:
# conv layer
UpperCamelCase = flax_key_tuple[:-1] + ("""weight""",)
UpperCamelCase = jnp.transpose(__UpperCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase ) not in pt_model_dict:
# linear layer
UpperCamelCase = flax_key_tuple[:-1] + ("""weight""",)
UpperCamelCase = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
UpperCamelCase = flax_key_tuple[:-1] + ("""weight""",)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
UpperCamelCase = flax_key_tuple[:-1] + ("""running_mean""",)
elif "var" in flax_key_tuple[-1]:
UpperCamelCase = flax_key_tuple[:-1] + ("""running_var""",)
if "batch_stats" in flax_state:
UpperCamelCase = """.""".join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
UpperCamelCase = """.""".join(__UpperCamelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
UpperCamelCase = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
UpperCamelCase = key.split(""".""" )
UpperCamelCase = None
if key_components[-3::2] == ["parametrizations", "original0"]:
UpperCamelCase = key_components[-2] + """_g"""
elif key_components[-3::2] == ["parametrizations", "original1"]:
UpperCamelCase = key_components[-2] + """_v"""
if name is not None:
UpperCamelCase = key_components[:-3] + [name]
UpperCamelCase = """.""".join(__UpperCamelCase )
UpperCamelCase = key
if flax_key in special_pt_names:
UpperCamelCase = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." )
else:
# add weight to pytorch dict
UpperCamelCase = np.asarray(__UpperCamelCase ) if not isinstance(__UpperCamelCase , np.ndarray ) else flax_tensor
UpperCamelCase = torch.from_numpy(__UpperCamelCase )
# remove from missing keys
missing_keys.remove(__UpperCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__UpperCamelCase )
pt_model.load_state_dict(__UpperCamelCase )
# re-transform missing_keys to list
UpperCamelCase = list(__UpperCamelCase )
if len(__UpperCamelCase ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
else:
logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n" )
if len(__UpperCamelCase ) > 0:
logger.warning(
F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
""" use it for predictions and inference.""" )
else:
logger.warning(
F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
"""If your task is similar to the task the model of the checkpoint was trained on, """
F"you can already use {pt_model.__class__.__name__} for predictions without further training." )
return pt_model
| 321 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
UpperCamelCase = grid[0]
for row_n in range(1 , len(__UpperCamelCase ) ):
UpperCamelCase = grid[row_n]
UpperCamelCase = fill_row(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = grid[row_n]
return grid[-1][-1]
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list:
current_row[0] += row_above[0]
for cell_n in range(1 , len(__UpperCamelCase ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE__ = _symbol_database.Default()
SCREAMING_SNAKE_CASE__ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
SCREAMING_SNAKE_CASE__ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
SCREAMING_SNAKE_CASE__ = 4_5
SCREAMING_SNAKE_CASE__ = 1_5_8_1
SCREAMING_SNAKE_CASE__ = 1_5_1_7
SCREAMING_SNAKE_CASE__ = 1_5_7_0
SCREAMING_SNAKE_CASE__ = 1_5_8_4
SCREAMING_SNAKE_CASE__ = 1_7_9_3
SCREAMING_SNAKE_CASE__ = 1_7_9_5
SCREAMING_SNAKE_CASE__ = 1_9_1_6
SCREAMING_SNAKE_CASE__ = 1_8_6_4
SCREAMING_SNAKE_CASE__ = 1_9_0_5
SCREAMING_SNAKE_CASE__ = 1_9_1_9
SCREAMING_SNAKE_CASE__ = 2_4_2_9
SCREAMING_SNAKE_CASE__ = 2_2_0_8
SCREAMING_SNAKE_CASE__ = 2_4_1_8
SCREAMING_SNAKE_CASE__ = 2_3_2_3
SCREAMING_SNAKE_CASE__ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase = False )-> str:
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCamelCase = F"Expected string as input, found {type(__UpperCamelCase )}"
raise ValueError(__UpperCamelCase )
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCamelCase = F"Expected boolean as use_pascal parameter, found {type(__UpperCamelCase )}"
raise ValueError(__UpperCamelCase )
UpperCamelCase = input_str.split("""_""" )
UpperCamelCase = 0 if use_pascal else 1
UpperCamelCase = words[start_index:]
UpperCamelCase = [word[0].upper() + word[1:] for word in words_to_capitalize]
UpperCamelCase = """""" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}
class a_ ( lowerCamelCase ):
lowercase = """xlnet"""
lowercase = ["""mems"""]
lowercase = {
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _SCREAMING_SNAKE_CASE=32000 , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=24 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=4096 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="bi" , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1e-12 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=-1 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="last" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="tanh" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=2 , **_SCREAMING_SNAKE_CASE , ) -> Tuple:
"""simple docstring"""
UpperCamelCase = vocab_size
UpperCamelCase = d_model
UpperCamelCase = n_layer
UpperCamelCase = n_head
if d_model % n_head != 0:
raise ValueError(F"'d_model % n_head' ({d_model % n_head}) should be equal to 0" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" )
UpperCamelCase = d_model // n_head
UpperCamelCase = ff_activation
UpperCamelCase = d_inner
UpperCamelCase = untie_r
UpperCamelCase = attn_type
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = dropout
UpperCamelCase = mem_len
UpperCamelCase = reuse_len
UpperCamelCase = bi_data
UpperCamelCase = clamp_len
UpperCamelCase = same_length
UpperCamelCase = summary_type
UpperCamelCase = summary_use_proj
UpperCamelCase = summary_activation
UpperCamelCase = summary_last_dropout
UpperCamelCase = start_n_top
UpperCamelCase = end_n_top
UpperCamelCase = bos_token_id
UpperCamelCase = pad_token_id
UpperCamelCase = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"""The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"""
""" instead.""" , _SCREAMING_SNAKE_CASE , )
UpperCamelCase = kwargs["""use_cache"""]
UpperCamelCase = use_mems_eval
UpperCamelCase = use_mems_train
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
raise NotImplementedError(
F"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 321 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE__ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __UpperCamelCase )-> str:
if "://" in dataset_path:
UpperCamelCase = dataset_path.split("""://""" )[1]
return dataset_path
def lowercase__ ( __UpperCamelCase )-> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = not is_remote_filesystem(__UpperCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__UpperCamelCase ) , fs._strip_protocol(__UpperCamelCase ) )
else:
fs.mv(__UpperCamelCase , __UpperCamelCase , recursive=__UpperCamelCase )
def lowercase__ ( )-> None:
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = threading.Lock()
| 321 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class a_ ( unittest.TestCase ):
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = BlipImageProcessor()
UpperCamelCase = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
UpperCamelCase = BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
UpperCamelCase = InstructBlipProcessor(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE ).tokenizer
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE ).image_processor
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE ).qformer_tokenizer
def A__ ( self ) -> int:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCamelCase = [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCamelCase = self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE , padding_value=1.0 )
UpperCamelCase = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor.qformer_tokenizer , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_qformer_tokenizer()
UpperCamelCase = InstructBlipProcessor(
tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE , qformer_tokenizer=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = image_processor(_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
UpperCamelCase = processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_qformer_tokenizer()
UpperCamelCase = InstructBlipProcessor(
tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE , qformer_tokenizer=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """lower newer"""
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer(_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE )
UpperCamelCase = qformer_tokenizer(_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["""qformer_""" + key] )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_qformer_tokenizer()
UpperCamelCase = InstructBlipProcessor(
tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE , qformer_tokenizer=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """lower newer"""
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE , images=_SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
# test if it raises when no input is passed
with pytest.raises(_SCREAMING_SNAKE_CASE ):
processor()
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_qformer_tokenizer()
UpperCamelCase = InstructBlipProcessor(
tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE , qformer_tokenizer=_SCREAMING_SNAKE_CASE )
UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase = processor.batch_decode(_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_qformer_tokenizer()
UpperCamelCase = InstructBlipProcessor(
tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE , qformer_tokenizer=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """lower newer"""
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE , images=_SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 321 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = 'docs/source/en/_toctree.yml'
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = defaultdict(__UpperCamelCase )
UpperCamelCase = []
UpperCamelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__UpperCamelCase )
UpperCamelCase = new_doc_list
UpperCamelCase = [key for key, value in counts.items() if value > 1]
UpperCamelCase = []
for duplicate_key in duplicates:
UpperCamelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCamelCase = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowercase__ ( __UpperCamelCase=False )-> List[str]:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCamelCase = api_doc[scheduler_idx]["""sections"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
UpperCamelCase = False
if new_scheduler_doc != scheduler_doc:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_scheduler_doc
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase__ ( __UpperCamelCase=False )-> Tuple:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCamelCase = False
UpperCamelCase = api_doc[pipeline_idx]["""sections"""]
UpperCamelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCamelCase = pipeline_doc["""section"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCamelCase = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_pipeline_docs
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 321 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = 'docs/source/en/_toctree.yml'
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = defaultdict(__UpperCamelCase )
UpperCamelCase = []
UpperCamelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__UpperCamelCase )
UpperCamelCase = new_doc_list
UpperCamelCase = [key for key, value in counts.items() if value > 1]
UpperCamelCase = []
for duplicate_key in duplicates:
UpperCamelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCamelCase = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowercase__ ( __UpperCamelCase=False )-> List[str]:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCamelCase = api_doc[scheduler_idx]["""sections"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
UpperCamelCase = False
if new_scheduler_doc != scheduler_doc:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_scheduler_doc
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase__ ( __UpperCamelCase=False )-> Tuple:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCamelCase = False
UpperCamelCase = api_doc[pipeline_idx]["""sections"""]
UpperCamelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCamelCase = pipeline_doc["""section"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCamelCase = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_pipeline_docs
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = b, a + b
return sum(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 321 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCamelCase = 1.5
UpperCamelCase = int(factor * num_class_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 )
os.makedirs(F"{class_data_dir}/images" , exist_ok=__UpperCamelCase )
if len(list(Path(F"{class_data_dir}/images" ).iterdir() ) ) >= num_class_images:
return
while True:
UpperCamelCase = client.query(text=__UpperCamelCase )
if len(__UpperCamelCase ) >= factor * num_class_images or num_images > 1E4:
break
else:
UpperCamelCase = int(factor * num_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 , )
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = tqdm(desc="""downloading real regularization images""" , total=__UpperCamelCase )
with open(F"{class_data_dir}/caption.txt" , """w""" ) as fa, open(F"{class_data_dir}/urls.txt" , """w""" ) as fa, open(
F"{class_data_dir}/images.txt" , """w""" ) as fa:
while total < num_class_images:
UpperCamelCase = class_images[count]
count += 1
try:
UpperCamelCase = requests.get(images["""url"""] )
if img.status_code == 200:
UpperCamelCase = Image.open(BytesIO(img.content ) )
with open(F"{class_data_dir}/images/{total}.jpg" , """wb""" ) as f:
f.write(img.content )
fa.write(images["""caption"""] + """\n""" )
fa.write(images["""url"""] + """\n""" )
fa.write(F"{class_data_dir}/images/{total}.jpg" + """\n""" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def lowercase__ ( )-> str:
UpperCamelCase = argparse.ArgumentParser("""""" , add_help=__UpperCamelCase )
parser.add_argument("""--class_prompt""" , help="""text prompt to retrieve images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--class_data_dir""" , help="""path to save images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--num_class_images""" , help="""number of images to download""" , default=200 , type=__UpperCamelCase )
return parser.parse_args()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 321 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class a_ :
lowercase = 42 # [batch_size x 3]
lowercase = 42 # [batch_size x 3]
lowercase = 42 # [batch_size x 3]
lowercase = 42 # [batch_size x 3]
lowercase = 42
lowercase = 42
lowercase = 42
lowercase = 42
lowercase = 42
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def A__ ( self ) -> torch.Tensor:
"""simple docstring"""
UpperCamelCase = torch.arange(self.height * self.width )
UpperCamelCase = torch.stack(
[
pixel_indices % self.width,
torch.div(_SCREAMING_SNAKE_CASE , self.width , rounding_mode="""trunc""" ),
] , axis=1 , )
return coords
@property
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase ,*UpperCamelCase = self.shape
UpperCamelCase = int(np.prod(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = self.get_image_coords()
UpperCamelCase = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
UpperCamelCase = self.get_camera_rays(_SCREAMING_SNAKE_CASE )
UpperCamelCase = rays.view(_SCREAMING_SNAKE_CASE , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> torch.Tensor:
"""simple docstring"""
UpperCamelCase ,*UpperCamelCase ,UpperCamelCase = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
UpperCamelCase = coords.view(_SCREAMING_SNAKE_CASE , -1 , 2 )
UpperCamelCase = self.resolution()
UpperCamelCase = self.fov()
UpperCamelCase = (flat.float() / (res - 1)) * 2 - 1
UpperCamelCase = fracs * torch.tan(fov / 2 )
UpperCamelCase = fracs.view(_SCREAMING_SNAKE_CASE , -1 , 2 )
UpperCamelCase = (
self.z.view(_SCREAMING_SNAKE_CASE , 1 , 3 )
+ self.x.view(_SCREAMING_SNAKE_CASE , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(_SCREAMING_SNAKE_CASE , 1 , 3 ) * fracs[:, :, 1:]
)
UpperCamelCase = directions / directions.norm(dim=-1 , keepdim=_SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.stack(
[
torch.broadcast_to(self.origin.view(_SCREAMING_SNAKE_CASE , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , 2 , 3 )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> "DifferentiableProjectiveCamera":
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , x_fov=self.x_fov , y_fov=self.y_fov , )
def lowercase__ ( __UpperCamelCase )-> DifferentiableProjectiveCamera:
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
UpperCamelCase = np.array([np.sin(__UpperCamelCase ), np.cos(__UpperCamelCase ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
UpperCamelCase = -z * 4
UpperCamelCase = np.array([np.cos(__UpperCamelCase ), -np.sin(__UpperCamelCase ), 0.0] )
UpperCamelCase = np.cross(__UpperCamelCase , __UpperCamelCase )
origins.append(__UpperCamelCase )
xs.append(__UpperCamelCase )
ys.append(__UpperCamelCase )
zs.append(__UpperCamelCase )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(__UpperCamelCase , axis=0 ) ).float() , x=torch.from_numpy(np.stack(__UpperCamelCase , axis=0 ) ).float() , y=torch.from_numpy(np.stack(__UpperCamelCase , axis=0 ) ).float() , z=torch.from_numpy(np.stack(__UpperCamelCase , axis=0 ) ).float() , width=__UpperCamelCase , height=__UpperCamelCase , x_fov=0.7 , y_fov=0.7 , shape=(1, len(__UpperCamelCase )) , )
| 321 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class a_ :
lowercase = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The column name of the images in the files."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the training data."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the validation data."""} )
lowercase = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = {}
if self.train_dir is not None:
UpperCamelCase = self.train_dir
if self.validation_dir is not None:
UpperCamelCase = self.validation_dir
UpperCamelCase = data_files if data_files else None
@dataclass
class a_ :
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowercase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class a_ ( lowerCamelCase ):
lowercase = field(
default=1E-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowercase__ ( )-> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , __UpperCamelCase , __UpperCamelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCamelCase = training_args.get_process_log_level()
logger.setLevel(__UpperCamelCase )
transformers.utils.logging.set_verbosity(__UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
UpperCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCamelCase = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __UpperCamelCase ) and data_args.train_val_split > 0.0:
UpperCamelCase = ds["""train"""].train_test_split(data_args.train_val_split )
UpperCamelCase = split["""train"""]
UpperCamelCase = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.config_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
UpperCamelCase = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase = ViTMAEForPreTraining(__UpperCamelCase )
if training_args.do_train:
UpperCamelCase = ds["""train"""].column_names
else:
UpperCamelCase = ds["""validation"""].column_names
if data_args.image_column_name is not None:
UpperCamelCase = data_args.image_column_name
elif "image" in column_names:
UpperCamelCase = """image"""
elif "img" in column_names:
UpperCamelCase = """img"""
else:
UpperCamelCase = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
UpperCamelCase = image_processor.size["""shortest_edge"""]
else:
UpperCamelCase = (image_processor.size["""height"""], image_processor.size["""width"""])
UpperCamelCase = Compose(
[
Lambda(lambda __UpperCamelCase : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(__UpperCamelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(__UpperCamelCase ):
UpperCamelCase = [transforms(__UpperCamelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
UpperCamelCase = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
UpperCamelCase = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__UpperCamelCase )
# Compute absolute learning rate
UpperCamelCase = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
UpperCamelCase = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
UpperCamelCase = Trainer(
model=__UpperCamelCase , args=__UpperCamelCase , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=__UpperCamelCase , data_collator=__UpperCamelCase , )
# Training
if training_args.do_train:
UpperCamelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCamelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCamelCase = last_checkpoint
UpperCamelCase = trainer.train(resume_from_checkpoint=__UpperCamelCase )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCamelCase = trainer.evaluate()
trainer.log_metrics("""eval""" , __UpperCamelCase )
trainer.save_metrics("""eval""" , __UpperCamelCase )
# Write model card and (optionally) push to hub
UpperCamelCase = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__UpperCamelCase )
else:
trainer.create_model_card(**__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> List[str]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> tuple[int, int]:
try:
UpperCamelCase = float(__UpperCamelCase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
UpperCamelCase = decimal - int(__UpperCamelCase )
if fractional_part == 0:
return int(__UpperCamelCase ), 1
else:
UpperCamelCase = len(str(__UpperCamelCase ).split(""".""" )[1] )
UpperCamelCase = int(decimal * (10**number_of_frac_digits) )
UpperCamelCase = 10**number_of_frac_digits
UpperCamelCase ,UpperCamelCase = denominator, numerator
while True:
UpperCamelCase = dividend % divisor
if remainder == 0:
break
UpperCamelCase ,UpperCamelCase = divisor, remainder
UpperCamelCase ,UpperCamelCase = numerator / divisor, denominator / divisor
return int(__UpperCamelCase ), int(__UpperCamelCase )
if __name__ == "__main__":
print(f'{decimal_to_fraction(2) = }')
print(f'{decimal_to_fraction(89.0) = }')
print(f'{decimal_to_fraction("67") = }')
print(f'{decimal_to_fraction("45.0") = }')
print(f'{decimal_to_fraction(1.5) = }')
print(f'{decimal_to_fraction("6.25") = }')
print(f'{decimal_to_fraction("78td") = }')
| 321 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE__ = 2_5_6
class a_ ( lowerCamelCase ):
lowercase = ["""melgan"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase = math.log(1e-5 ) # Matches MelGAN training.
UpperCamelCase = 4.0 # Largest value for most examples
UpperCamelCase = 128
self.register_modules(
notes_encoder=_SCREAMING_SNAKE_CASE , continuous_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , melgan=_SCREAMING_SNAKE_CASE , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = output_range
if clip:
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , self.min_value , self.max_value )
# Scale to [0, 1].
UpperCamelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = input_range
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = input_tokens > 0
UpperCamelCase ,UpperCamelCase = self.notes_encoder(
encoder_input_tokens=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.continuous_encoder(
encoder_inputs=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = noise_time
if not torch.is_tensor(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(_SCREAMING_SNAKE_CASE ) and len(timesteps.shape ) == 0:
UpperCamelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
UpperCamelCase = self.decoder(
encodings_and_masks=_SCREAMING_SNAKE_CASE , decoder_input_tokens=_SCREAMING_SNAKE_CASE , decoder_noise_time=_SCREAMING_SNAKE_CASE )
return logits
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "numpy" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(_SCREAMING_SNAKE_CASE )}." )
UpperCamelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
UpperCamelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
UpperCamelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE ):
if i == 0:
UpperCamelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase = ones
UpperCamelCase = self.scale_features(
_SCREAMING_SNAKE_CASE , output_range=[-1.0, 1.0] , clip=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=_SCREAMING_SNAKE_CASE , continuous_mask=_SCREAMING_SNAKE_CASE , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase = self.decode(
encodings_and_masks=_SCREAMING_SNAKE_CASE , input_tokens=_SCREAMING_SNAKE_CASE , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
UpperCamelCase = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = self.scale_to_features(_SCREAMING_SNAKE_CASE , input_range=[-1.0, 1.0] )
UpperCamelCase = mel[:1]
UpperCamelCase = mel.cpu().float().numpy()
UpperCamelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
logger.info("""Generated segment""" , _SCREAMING_SNAKE_CASE )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
UpperCamelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase )-> bool:
UpperCamelCase = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase = 1 / 12345 )-> int:
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 3
while True:
UpperCamelCase = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__UpperCamelCase ):
UpperCamelCase = int(__UpperCamelCase )
total_partitions += 1
if check_partition_perfect(__UpperCamelCase ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__UpperCamelCase )
integer += 1
if __name__ == "__main__":
print(f'{solution() = }')
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = b, a + b
return sum(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Dict:
UpperCamelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
UpperCamelCase = key.replace("""heads.cmd.mim_head.cls.predictions""" , """mmm_image_head""" )
UpperCamelCase = key.replace("""heads.cmd.mlm_head.cls.predictions""" , """mmm_text_head""" )
UpperCamelCase = key.replace("""heads.cmd.itm_head.cls""" , """itm_head""" )
UpperCamelCase = key.replace("""heads.cmd.itm_head.pooler""" , """itm_head.pooler""" )
UpperCamelCase = key.replace("""heads.cmd.clip_head.logit_scale""" , """flava.logit_scale""" )
UpperCamelCase = key.replace("""heads.fairseq_mlm.cls.predictions""" , """mlm_head""" )
UpperCamelCase = key.replace("""heads.imagenet.mim_head.cls.predictions""" , """mim_head""" )
UpperCamelCase = key.replace("""mm_text_projection""" , """flava.text_to_mm_projection""" )
UpperCamelCase = key.replace("""mm_image_projection""" , """flava.image_to_mm_projection""" )
UpperCamelCase = key.replace("""image_encoder.module""" , """flava.image_model""" )
UpperCamelCase = key.replace("""text_encoder.module""" , """flava.text_model""" )
UpperCamelCase = key.replace("""mm_encoder.module.encoder.cls_token""" , """flava.multimodal_model.cls_token""" )
UpperCamelCase = key.replace("""mm_encoder.module""" , """flava.multimodal_model""" )
UpperCamelCase = key.replace("""text_projection""" , """flava.text_projection""" )
UpperCamelCase = key.replace("""image_projection""" , """flava.image_projection""" )
UpperCamelCase = value.float()
for key, value in codebook_state_dict.items():
UpperCamelCase = value
return upgrade
@torch.no_grad()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None )-> Any:
if config_path is not None:
UpperCamelCase = FlavaConfig.from_pretrained(__UpperCamelCase )
else:
UpperCamelCase = FlavaConfig()
UpperCamelCase = FlavaForPreTraining(__UpperCamelCase ).eval()
UpperCamelCase = convert_dalle_checkpoint(__UpperCamelCase , __UpperCamelCase , save_checkpoint=__UpperCamelCase )
if os.path.exists(__UpperCamelCase ):
UpperCamelCase = torch.load(__UpperCamelCase , map_location="""cpu""" )
else:
UpperCamelCase = torch.hub.load_state_dict_from_url(__UpperCamelCase , map_location="""cpu""" )
UpperCamelCase = upgrade_state_dict(__UpperCamelCase , __UpperCamelCase )
hf_model.load_state_dict(__UpperCamelCase )
UpperCamelCase = hf_model.state_dict()
UpperCamelCase = count_parameters(__UpperCamelCase )
UpperCamelCase = count_parameters(__UpperCamelCase ) + count_parameters(__UpperCamelCase )
assert torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-3 )
hf_model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
# Base Case
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] , __UpperCamelCase , __UpperCamelCase ):
# Color current vertex
UpperCamelCase = i
# Validate coloring
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , index + 1 ):
return True
# Backtrack
UpperCamelCase = -1
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[int]:
UpperCamelCase = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , 0 ):
return colored_vertices
return []
| 321 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> None:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
UpperCamelCase ,UpperCamelCase = array[indexa], array[indexa]
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> None:
if length > 1:
UpperCamelCase = int(length / 2 )
for i in range(__UpperCamelCase , low + middle ):
comp_and_swap(__UpperCamelCase , __UpperCamelCase , i + middle , __UpperCamelCase )
bitonic_merge(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
bitonic_merge(__UpperCamelCase , low + middle , __UpperCamelCase , __UpperCamelCase )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> None:
if length > 1:
UpperCamelCase = int(length / 2 )
bitonic_sort(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , 1 )
bitonic_sort(__UpperCamelCase , low + middle , __UpperCamelCase , 0 )
bitonic_merge(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE__ = [int(item.strip()) for item in user_input.split(',')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('\nSorted array in ascending order is: ', end='')
print(*unsorted, sep=', ')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('Sorted array in descending order is: ', end='')
print(*unsorted, sep=', ')
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __UpperCamelCase ):
UpperCamelCase = 1
UpperCamelCase = 0
for i in range(__UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=1 )-> Dict:
if n_shave_prefix_segments >= 0:
return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
else:
return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=0 )-> Optional[int]:
UpperCamelCase = []
for old_item in old_list:
UpperCamelCase = old_item.replace("""in_layers.0""" , """norm1""" )
UpperCamelCase = new_item.replace("""in_layers.2""" , """conv1""" )
UpperCamelCase = new_item.replace("""out_layers.0""" , """norm2""" )
UpperCamelCase = new_item.replace("""out_layers.3""" , """conv2""" )
UpperCamelCase = new_item.replace("""emb_layers.1""" , """time_emb_proj""" )
UpperCamelCase = new_item.replace("""skip_connection""" , """conv_shortcut""" )
UpperCamelCase = shave_segments(__UpperCamelCase , n_shave_prefix_segments=__UpperCamelCase )
mapping.append({"""old""": old_item, """new""": new_item} )
return mapping
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=0 )-> Optional[int]:
UpperCamelCase = []
for old_item in old_list:
UpperCamelCase = old_item
UpperCamelCase = new_item.replace("""norm.weight""" , """group_norm.weight""" )
UpperCamelCase = new_item.replace("""norm.bias""" , """group_norm.bias""" )
UpperCamelCase = new_item.replace("""proj_out.weight""" , """proj_attn.weight""" )
UpperCamelCase = new_item.replace("""proj_out.bias""" , """proj_attn.bias""" )
UpperCamelCase = shave_segments(__UpperCamelCase , n_shave_prefix_segments=__UpperCamelCase )
mapping.append({"""old""": old_item, """new""": new_item} )
return mapping
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None )-> int:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
UpperCamelCase = old_checkpoint[path]
UpperCamelCase = old_tensor.shape[0] // 3
UpperCamelCase = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
UpperCamelCase = old_tensor.shape[0] // config["""num_head_channels"""] // 3
UpperCamelCase = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = old_tensor.split(channels // num_heads , dim=1 )
UpperCamelCase = query.reshape(__UpperCamelCase )
UpperCamelCase = key.reshape(__UpperCamelCase )
UpperCamelCase = value.reshape(__UpperCamelCase )
for path in paths:
UpperCamelCase = path["""new"""]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
UpperCamelCase = new_path.replace("""middle_block.0""" , """mid_block.resnets.0""" )
UpperCamelCase = new_path.replace("""middle_block.1""" , """mid_block.attentions.0""" )
UpperCamelCase = new_path.replace("""middle_block.2""" , """mid_block.resnets.1""" )
if additional_replacements is not None:
for replacement in additional_replacements:
UpperCamelCase = new_path.replace(replacement["""old"""] , replacement["""new"""] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
UpperCamelCase = old_checkpoint[path["""old"""]][:, :, 0]
else:
UpperCamelCase = old_checkpoint[path["""old"""]]
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> str:
UpperCamelCase = {}
UpperCamelCase = checkpoint["""time_embed.0.weight"""]
UpperCamelCase = checkpoint["""time_embed.0.bias"""]
UpperCamelCase = checkpoint["""time_embed.2.weight"""]
UpperCamelCase = checkpoint["""time_embed.2.bias"""]
UpperCamelCase = checkpoint["""input_blocks.0.0.weight"""]
UpperCamelCase = checkpoint["""input_blocks.0.0.bias"""]
UpperCamelCase = checkpoint["""out.0.weight"""]
UpperCamelCase = checkpoint["""out.0.bias"""]
UpperCamelCase = checkpoint["""out.2.weight"""]
UpperCamelCase = checkpoint["""out.2.bias"""]
# Retrieves the keys for the input blocks only
UpperCamelCase = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """input_blocks""" in layer} )
UpperCamelCase = {
layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
for layer_id in range(__UpperCamelCase )
}
# Retrieves the keys for the middle blocks only
UpperCamelCase = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """middle_block""" in layer} )
UpperCamelCase = {
layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
for layer_id in range(__UpperCamelCase )
}
# Retrieves the keys for the output blocks only
UpperCamelCase = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """output_blocks""" in layer} )
UpperCamelCase = {
layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
for layer_id in range(__UpperCamelCase )
}
for i in range(1 , __UpperCamelCase ):
UpperCamelCase = (i - 1) // (config["""num_res_blocks"""] + 1)
UpperCamelCase = (i - 1) % (config["""num_res_blocks"""] + 1)
UpperCamelCase = [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
UpperCamelCase = [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]
if F"input_blocks.{i}.0.op.weight" in checkpoint:
UpperCamelCase = checkpoint[
F"input_blocks.{i}.0.op.weight"
]
UpperCamelCase = checkpoint[
F"input_blocks.{i}.0.op.bias"
]
continue
UpperCamelCase = renew_resnet_paths(__UpperCamelCase )
UpperCamelCase = {"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
UpperCamelCase = {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path, resnet_op] , config=__UpperCamelCase )
if len(__UpperCamelCase ):
UpperCamelCase = renew_attention_paths(__UpperCamelCase )
UpperCamelCase = {
"""old""": F"input_blocks.{i}.1",
"""new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
}
UpperCamelCase = {
F"input_blocks.{i}.1.qkv.bias": {
"""key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
"""query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
"""value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
},
F"input_blocks.{i}.1.qkv.weight": {
"""key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
"""query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
"""value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=__UpperCamelCase , config=__UpperCamelCase , )
UpperCamelCase = middle_blocks[0]
UpperCamelCase = middle_blocks[1]
UpperCamelCase = middle_blocks[2]
UpperCamelCase = renew_resnet_paths(__UpperCamelCase )
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , config=__UpperCamelCase )
UpperCamelCase = renew_resnet_paths(__UpperCamelCase )
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , config=__UpperCamelCase )
UpperCamelCase = renew_attention_paths(__UpperCamelCase )
UpperCamelCase = {
"""middle_block.1.qkv.bias""": {
"""key""": """mid_block.attentions.0.key.bias""",
"""query""": """mid_block.attentions.0.query.bias""",
"""value""": """mid_block.attentions.0.value.bias""",
},
"""middle_block.1.qkv.weight""": {
"""key""": """mid_block.attentions.0.key.weight""",
"""query""": """mid_block.attentions.0.query.weight""",
"""value""": """mid_block.attentions.0.value.weight""",
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , attention_paths_to_split=__UpperCamelCase , config=__UpperCamelCase )
for i in range(__UpperCamelCase ):
UpperCamelCase = i // (config["""num_res_blocks"""] + 1)
UpperCamelCase = i % (config["""num_res_blocks"""] + 1)
UpperCamelCase = [shave_segments(__UpperCamelCase , 2 ) for name in output_blocks[i]]
UpperCamelCase = {}
for layer in output_block_layers:
UpperCamelCase ,UpperCamelCase = layer.split(""".""" )[0], shave_segments(__UpperCamelCase , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(__UpperCamelCase )
else:
UpperCamelCase = [layer_name]
if len(__UpperCamelCase ) > 1:
UpperCamelCase = [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
UpperCamelCase = [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]
UpperCamelCase = renew_resnet_paths(__UpperCamelCase )
UpperCamelCase = renew_resnet_paths(__UpperCamelCase )
UpperCamelCase = {"""old""": F"output_blocks.{i}.0", """new""": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
if ["conv.weight", "conv.bias"] in output_block_list.values():
UpperCamelCase = list(output_block_list.values() ).index(["""conv.weight""", """conv.bias"""] )
UpperCamelCase = checkpoint[
F"output_blocks.{i}.{index}.conv.weight"
]
UpperCamelCase = checkpoint[
F"output_blocks.{i}.{index}.conv.bias"
]
# Clear attentions as they have been attributed above.
if len(__UpperCamelCase ) == 2:
UpperCamelCase = []
if len(__UpperCamelCase ):
UpperCamelCase = renew_attention_paths(__UpperCamelCase )
UpperCamelCase = {
"""old""": F"output_blocks.{i}.1",
"""new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
}
UpperCamelCase = {
F"output_blocks.{i}.1.qkv.bias": {
"""key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
"""query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
"""value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
},
F"output_blocks.{i}.1.qkv.weight": {
"""key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
"""query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
"""value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions ) else None , config=__UpperCamelCase , )
else:
UpperCamelCase = renew_resnet_paths(__UpperCamelCase , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
UpperCamelCase = """.""".join(["""output_blocks""", str(__UpperCamelCase ), path["""old"""]] )
UpperCamelCase = """.""".join(["""up_blocks""", str(__UpperCamelCase ), """resnets""", str(__UpperCamelCase ), path["""new"""]] )
UpperCamelCase = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
SCREAMING_SNAKE_CASE__ = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
SCREAMING_SNAKE_CASE__ = json.loads(f.read())
SCREAMING_SNAKE_CASE__ = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
SCREAMING_SNAKE_CASE__ = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
SCREAMING_SNAKE_CASE__ = DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
SCREAMING_SNAKE_CASE__ = VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))
SCREAMING_SNAKE_CASE__ = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 321 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ = TypeVar('KEY')
SCREAMING_SNAKE_CASE__ = TypeVar('VAL')
@dataclass(frozen=lowerCamelCase , slots=lowerCamelCase )
class a_ ( Generic[KEY, VAL] ):
lowercase = 42
lowercase = 42
class a_ ( _Item ):
def __init__( self ) -> None:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __bool__( self ) -> bool:
"""simple docstring"""
return False
SCREAMING_SNAKE_CASE__ = _DeletedItem()
class a_ ( MutableMapping[KEY, VAL] ):
def __init__( self , _SCREAMING_SNAKE_CASE = 8 , _SCREAMING_SNAKE_CASE = 0.7_5 ) -> None:
"""simple docstring"""
UpperCamelCase = initial_block_size
UpperCamelCase = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
UpperCamelCase = capacity_factor
UpperCamelCase = 0
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return hash(_SCREAMING_SNAKE_CASE ) % len(self._buckets )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return (ind + 1) % len(self._buckets )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
UpperCamelCase = self._buckets[ind]
if not stored:
UpperCamelCase = _Item(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self._len += 1
return True
elif stored.key == key:
UpperCamelCase = _Item(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return True
else:
return False
def A__ ( self ) -> bool:
"""simple docstring"""
UpperCamelCase = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> bool:
"""simple docstring"""
if len(self._buckets ) <= self._initial_block_size:
return False
UpperCamelCase = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = self._buckets
UpperCamelCase = [None] * new_size
UpperCamelCase = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def A__ ( self ) -> None:
"""simple docstring"""
self._resize(len(self._buckets ) * 2 )
def A__ ( self ) -> None:
"""simple docstring"""
self._resize(len(self._buckets ) // 2 )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Iterator[int]:
"""simple docstring"""
UpperCamelCase = self._get_bucket_index(_SCREAMING_SNAKE_CASE )
for _ in range(len(self._buckets ) ):
yield ind
UpperCamelCase = self._get_next_ind(_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ):
if self._try_set(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
break
def __setitem__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if self._is_full():
self._size_up()
self._add_item(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __delitem__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = self._buckets[ind]
if item is None:
raise KeyError(_SCREAMING_SNAKE_CASE )
if item is _deleted:
continue
if item.key == key:
UpperCamelCase = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , _SCREAMING_SNAKE_CASE ) -> VAL:
"""simple docstring"""
for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_SCREAMING_SNAKE_CASE )
def __len__( self ) -> int:
"""simple docstring"""
return self._len
def __iter__( self ) -> Iterator[KEY]:
"""simple docstring"""
yield from (item.key for item in self._buckets if item)
def __repr__( self ) -> str:
"""simple docstring"""
UpperCamelCase = """ ,""".join(
F"{item.key}: {item.val}" for item in self._buckets if item )
return F"HashMap({val_string})"
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 | 1 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
SCREAMING_SNAKE_CASE__ = {
'b0': {
'hidden_dim': 1_2_8_0,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 2_2_4,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1_2_8_0,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 2_4_0,
'dropout_rate': 0.2,
'dw_padding': [1_6],
},
'b2': {
'hidden_dim': 1_4_0_8,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 2_6_0,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 1_6],
},
'b3': {
'hidden_dim': 1_5_3_6,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 3_0_0,
'dropout_rate': 0.3,
'dw_padding': [5, 1_8],
},
'b4': {
'hidden_dim': 1_7_9_2,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 3_8_0,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2_0_4_8,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 4_5_6,
'dropout_rate': 0.4,
'dw_padding': [1_3, 2_7],
},
'b6': {
'hidden_dim': 2_3_0_4,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 5_2_8,
'dropout_rate': 0.5,
'dw_padding': [3_1],
},
'b7': {
'hidden_dim': 2_5_6_0,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 6_0_0,
'dropout_rate': 0.5,
'dw_padding': [1_8],
},
}
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = EfficientNetConfig()
UpperCamelCase = CONFIG_MAP[model_name]["""hidden_dim"""]
UpperCamelCase = CONFIG_MAP[model_name]["""width_coef"""]
UpperCamelCase = CONFIG_MAP[model_name]["""depth_coef"""]
UpperCamelCase = CONFIG_MAP[model_name]["""image_size"""]
UpperCamelCase = CONFIG_MAP[model_name]["""dropout_rate"""]
UpperCamelCase = CONFIG_MAP[model_name]["""dw_padding"""]
UpperCamelCase = """huggingface/label-files"""
UpperCamelCase = """imagenet-1k-id2label.json"""
UpperCamelCase = 1000
UpperCamelCase = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
UpperCamelCase = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
UpperCamelCase = idalabel
UpperCamelCase = {v: k for k, v in idalabel.items()}
return config
def lowercase__ ( )-> Optional[int]:
UpperCamelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCamelCase = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
def lowercase__ ( __UpperCamelCase )-> List[Any]:
UpperCamelCase = CONFIG_MAP[model_name]["""image_size"""]
UpperCamelCase = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] , do_center_crop=__UpperCamelCase , )
return preprocessor
def lowercase__ ( __UpperCamelCase )-> List[str]:
UpperCamelCase = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
UpperCamelCase = sorted(set(__UpperCamelCase ) )
UpperCamelCase = len(__UpperCamelCase )
UpperCamelCase = {b: str(__UpperCamelCase ) for b, i in zip(__UpperCamelCase , range(__UpperCamelCase ) )}
UpperCamelCase = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
UpperCamelCase = block_name_mapping[b]
rename_keys.append((F"block{b}_expand_conv/kernel:0", F"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((F"block{b}_expand_bn/gamma:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((F"block{b}_expand_bn/beta:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(F"block{b}_expand_bn/moving_mean:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(F"block{b}_expand_bn/moving_variance:0", F"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(F"block{b}_dwconv/depthwise_kernel:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((F"block{b}_bn/gamma:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((F"block{b}_bn/beta:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(F"block{b}_bn/moving_mean:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(F"block{b}_bn/moving_variance:0", F"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((F"block{b}_se_reduce/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((F"block{b}_se_reduce/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((F"block{b}_se_expand/kernel:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((F"block{b}_se_expand/bias:0", F"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(F"block{b}_project_conv/kernel:0", F"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((F"block{b}_project_bn/gamma:0", F"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((F"block{b}_project_bn/beta:0", F"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(F"block{b}_project_bn/moving_mean:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(F"block{b}_project_bn/moving_variance:0", F"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
UpperCamelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
UpperCamelCase = """efficientnet.""" + item[1]
UpperCamelCase = """classifier.weight"""
UpperCamelCase = """classifier.bias"""
return key_mapping
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
for key, value in tf_params.items():
if "normalization" in key:
continue
UpperCamelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
UpperCamelCase = torch.from_numpy(__UpperCamelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
UpperCamelCase = torch.from_numpy(__UpperCamelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
UpperCamelCase = torch.from_numpy(np.transpose(__UpperCamelCase ) )
else:
UpperCamelCase = torch.from_numpy(__UpperCamelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(__UpperCamelCase )
@torch.no_grad()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = model_classes[model_name](
include_top=__UpperCamelCase , weights="""imagenet""" , input_tensor=__UpperCamelCase , input_shape=__UpperCamelCase , pooling=__UpperCamelCase , classes=1000 , classifier_activation="""softmax""" , )
UpperCamelCase = original_model.trainable_variables
UpperCamelCase = original_model.non_trainable_variables
UpperCamelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
UpperCamelCase = param.numpy()
UpperCamelCase = list(tf_params.keys() )
# Load HuggingFace model
UpperCamelCase = get_efficientnet_config(__UpperCamelCase )
UpperCamelCase = EfficientNetForImageClassification(__UpperCamelCase ).eval()
UpperCamelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
UpperCamelCase = rename_keys(__UpperCamelCase )
replace_params(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Initialize preprocessor and preprocess input image
UpperCamelCase = convert_image_processor(__UpperCamelCase )
UpperCamelCase = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
UpperCamelCase = hf_model(**__UpperCamelCase )
UpperCamelCase = outputs.logits.detach().numpy()
# Original model inference
UpperCamelCase = False
UpperCamelCase = CONFIG_MAP[model_name]["""image_size"""]
UpperCamelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
UpperCamelCase = image.img_to_array(__UpperCamelCase )
UpperCamelCase = np.expand_dims(__UpperCamelCase , axis=0 )
UpperCamelCase = original_model.predict(__UpperCamelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(__UpperCamelCase ):
os.mkdir(__UpperCamelCase )
# Save converted model and image processor
hf_model.save_pretrained(__UpperCamelCase )
preprocessor.save_pretrained(__UpperCamelCase )
if push_to_hub:
# Push model and image processor to hub
print(F"Pushing converted {model_name} to the hub..." )
UpperCamelCase = F"efficientnet-{model_name}"
preprocessor.push_to_hub(__UpperCamelCase )
hf_model.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 321 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 321 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class a_ :
def __init__( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = False
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
if not self.initialized:
UpperCamelCase = RagRetriever(
_SCREAMING_SNAKE_CASE , question_encoder_tokenizer=_SCREAMING_SNAKE_CASE , generator_tokenizer=_SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE , init_retrieval=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = True
def A__ ( self ) -> List[Any]:
"""simple docstring"""
self.retriever.index.init_index()
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return doc_ids, retrieved_doc_embeds
class a_ ( lowerCamelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
"""simple docstring"""
if index is not None and index.is_initialized() and len(_SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(
"""When using Ray for distributed fine-tuning, """
"""you'll need to provide the paths instead, """
"""as the dataset and the index are loaded """
"""separately. More info in examples/rag/use_own_knowledge_dataset.py """ )
super().__init__(
_SCREAMING_SNAKE_CASE , question_encoder_tokenizer=_SCREAMING_SNAKE_CASE , generator_tokenizer=_SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE , init_retrieval=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for worker in self.retrieval_workers
] )
def A__ ( self ) -> str:
"""simple docstring"""
logger.info("""initializing retrieval""" )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
UpperCamelCase = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
UpperCamelCase ,UpperCamelCase = ray.get(random_worker.retrieve.remote(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
else:
UpperCamelCase ,UpperCamelCase = self._main_retrieve(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_SCREAMING_SNAKE_CASE )
@classmethod
def A__ ( cls , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
return super(_SCREAMING_SNAKE_CASE , cls ).get_tokenizers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@classmethod
def A__ ( cls , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = kwargs.pop("""config""" , _SCREAMING_SNAKE_CASE ) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE , config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = rag_tokenizer.question_encoder
UpperCamelCase = rag_tokenizer.generator
if indexed_dataset is not None:
UpperCamelCase = """custom"""
UpperCamelCase = CustomHFIndex(config.retrieval_vector_size , _SCREAMING_SNAKE_CASE )
else:
UpperCamelCase = cls._build_index(_SCREAMING_SNAKE_CASE )
return cls(
_SCREAMING_SNAKE_CASE , question_encoder_tokenizer=_SCREAMING_SNAKE_CASE , generator_tokenizer=_SCREAMING_SNAKE_CASE , retrieval_workers=_SCREAMING_SNAKE_CASE , index=_SCREAMING_SNAKE_CASE , )
| 321 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 | 1 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
try:
with open(__UpperCamelCase , """rb""" ) as flax_state_f:
UpperCamelCase = from_bytes(__UpperCamelCase , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(__UpperCamelCase ) as f:
if f.read().startswith("""version""" ):
raise OSError(
"""You seem to have cloned a repository without having git-lfs installed. Please"""
""" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
""" folder you cloned.""" )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. " )
return load_flax_weights_in_pytorch_model(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Tuple:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
UpperCamelCase = flatten_dict(jax.tree_util.tree_map(lambda __UpperCamelCase : x.dtype == jnp.bfloataa , __UpperCamelCase ) ).values()
if any(__UpperCamelCase ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
UpperCamelCase = jax.tree_util.tree_map(
lambda __UpperCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __UpperCamelCase )
UpperCamelCase = """"""
UpperCamelCase = flatten_dict(__UpperCamelCase , sep=""".""" )
UpperCamelCase = pt_model.state_dict()
# keep track of unexpected & missing keys
UpperCamelCase = []
UpperCamelCase = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
UpperCamelCase = flax_key_tuple.split(""".""" )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
UpperCamelCase = flax_key_tuple_array[:-1] + ["""weight"""]
UpperCamelCase = jnp.transpose(__UpperCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
UpperCamelCase = flax_key_tuple_array[:-1] + ["""weight"""]
UpperCamelCase = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
UpperCamelCase = flax_key_tuple_array[:-1] + ["""weight"""]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(__UpperCamelCase ):
UpperCamelCase = (
flax_key_tuple_string.replace("""_0""" , """.0""" )
.replace("""_1""" , """.1""" )
.replace("""_2""" , """.2""" )
.replace("""_3""" , """.3""" )
.replace("""_4""" , """.4""" )
.replace("""_5""" , """.5""" )
.replace("""_6""" , """.6""" )
.replace("""_7""" , """.7""" )
.replace("""_8""" , """.8""" )
.replace("""_9""" , """.9""" )
)
UpperCamelCase = """.""".join(__UpperCamelCase )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." )
else:
# add weight to pytorch dict
UpperCamelCase = np.asarray(__UpperCamelCase ) if not isinstance(__UpperCamelCase , np.ndarray ) else flax_tensor
UpperCamelCase = torch.from_numpy(__UpperCamelCase )
# remove from missing keys
missing_keys.remove(__UpperCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__UpperCamelCase )
pt_model.load_state_dict(__UpperCamelCase )
# re-transform missing_keys to list
UpperCamelCase = list(__UpperCamelCase )
if len(__UpperCamelCase ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
if len(__UpperCamelCase ) > 0:
logger.warning(
F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
""" use it for predictions and inference.""" )
return pt_model
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'nielsr/canine-s': 2_0_4_8,
}
# Unicode defines 1,114,112 total “codepoints”
SCREAMING_SNAKE_CASE__ = 1_1_1_4_1_1_2
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0Xe000
SCREAMING_SNAKE_CASE__ = 0Xe001
SCREAMING_SNAKE_CASE__ = 0Xe002
SCREAMING_SNAKE_CASE__ = 0Xe003
SCREAMING_SNAKE_CASE__ = 0Xe004
# Maps special codepoints to human-readable names.
SCREAMING_SNAKE_CASE__ = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
SCREAMING_SNAKE_CASE__ = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class a_ ( lowerCamelCase ):
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE=chr(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=2048 , **_SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else bos_token
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else eos_token
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else sep_token
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else cls_token
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE , model_max_length=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# Creates a mapping for looking up the IDs of special symbols.
UpperCamelCase = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
UpperCamelCase = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
UpperCamelCase = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
UpperCamelCase = UNICODE_VOCAB_SIZE
UpperCamelCase = len(self._special_codepoints )
@property
def A__ ( self ) -> int:
"""simple docstring"""
return self._unicode_vocab_size
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return list(_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
try:
return ord(_SCREAMING_SNAKE_CASE )
except TypeError:
raise ValueError(F"invalid token: '{token}'" )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(_SCREAMING_SNAKE_CASE )
except TypeError:
raise ValueError(F"invalid id: {index}" )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return "".join(_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
UpperCamelCase = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
UpperCamelCase = [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
if token_ids_a is not None:
result += ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return result
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
UpperCamelCase = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Optional[int]:
"""simple docstring"""
return ()
| 321 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
SCREAMING_SNAKE_CASE__ = re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
SCREAMING_SNAKE_CASE__ = re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
SCREAMING_SNAKE_CASE__ = re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
SCREAMING_SNAKE_CASE__ = re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
SCREAMING_SNAKE_CASE__ = re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
SCREAMING_SNAKE_CASE__ = re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only: ["foo", "bar"],
SCREAMING_SNAKE_CASE__ = re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
SCREAMING_SNAKE_CASE__ = re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
SCREAMING_SNAKE_CASE__ = re.compile(R'^\s*try:')
# Catches a line with else:
SCREAMING_SNAKE_CASE__ = re.compile(R'^\s*else:')
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
if _re_test_backend.search(__UpperCamelCase ) is None:
return None
UpperCamelCase = [b[0] for b in _re_backend.findall(__UpperCamelCase )]
backends.sort()
return "_and_".join(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> Dict:
with open(__UpperCamelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCamelCase = f.readlines()
UpperCamelCase = 0
while line_index < len(__UpperCamelCase ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__UpperCamelCase ):
return None
# First grab the objects without a specific backend in _import_structure
UpperCamelCase = []
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
UpperCamelCase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__UpperCamelCase ):
UpperCamelCase = _re_one_line_import_struct.search(__UpperCamelCase ).groups()[0]
UpperCamelCase = re.findall("""\[([^\]]+)\]""" , __UpperCamelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
UpperCamelCase = _re_import_struct_key_value.search(__UpperCamelCase )
if single_line_import_search is not None:
UpperCamelCase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(__UpperCamelCase ) > 0]
objects.extend(__UpperCamelCase )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
UpperCamelCase = {"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
UpperCamelCase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCamelCase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCamelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
UpperCamelCase = lines[line_index]
if _re_import_struct_add_one.search(__UpperCamelCase ) is not None:
objects.append(_re_import_struct_add_one.search(__UpperCamelCase ).groups()[0] )
elif _re_import_struct_add_many.search(__UpperCamelCase ) is not None:
UpperCamelCase = _re_import_struct_add_many.search(__UpperCamelCase ).groups()[0].split(""", """ )
UpperCamelCase = [obj[1:-1] for obj in imports if len(__UpperCamelCase ) > 0]
objects.extend(__UpperCamelCase )
elif _re_between_brackets.search(__UpperCamelCase ) is not None:
UpperCamelCase = _re_between_brackets.search(__UpperCamelCase ).groups()[0].split(""", """ )
UpperCamelCase = [obj[1:-1] for obj in imports if len(__UpperCamelCase ) > 0]
objects.extend(__UpperCamelCase )
elif _re_quote_object.search(__UpperCamelCase ) is not None:
objects.append(_re_quote_object.search(__UpperCamelCase ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
UpperCamelCase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
UpperCamelCase = []
while (
line_index < len(__UpperCamelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
UpperCamelCase = lines[line_index]
UpperCamelCase = _re_import.search(__UpperCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
UpperCamelCase = {"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(__UpperCamelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
UpperCamelCase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCamelCase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCamelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
UpperCamelCase = lines[line_index]
UpperCamelCase = _re_import.search(__UpperCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
UpperCamelCase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Dict:
def find_duplicates(__UpperCamelCase ):
return [k for k, v in collections.Counter(__UpperCamelCase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
UpperCamelCase = []
for key in import_dict_objects.keys():
UpperCamelCase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}" )
UpperCamelCase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
UpperCamelCase = """base imports""" if key == """none""" else F"{key} backend"
errors.append(F"Differences for {name}:" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F" {a} in TYPE_HINT but not in _import_structure." )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F" {a} in _import_structure but not in TYPE_HINT." )
return errors
def lowercase__ ( )-> Optional[Any]:
UpperCamelCase = []
for root, _, files in os.walk(__UpperCamelCase ):
if "__init__.py" in files:
UpperCamelCase = os.path.join(__UpperCamelCase , """__init__.py""" )
UpperCamelCase = parse_init(__UpperCamelCase )
if objects is not None:
UpperCamelCase = analyze_results(*__UpperCamelCase )
if len(__UpperCamelCase ) > 0:
UpperCamelCase = F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append("""\n""".join(__UpperCamelCase ) )
if len(__UpperCamelCase ) > 0:
raise ValueError("""\n\n""".join(__UpperCamelCase ) )
def lowercase__ ( )-> Optional[Any]:
UpperCamelCase = []
for path, directories, files in os.walk(__UpperCamelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(__UpperCamelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__UpperCamelCase ) / folder).glob("""*.py""" ) ) ) == 0:
continue
UpperCamelCase = str((Path(__UpperCamelCase ) / folder).relative_to(__UpperCamelCase ) )
UpperCamelCase = short_path.replace(os.path.sep , """.""" )
submodules.append(__UpperCamelCase )
for fname in files:
if fname == "__init__.py":
continue
UpperCamelCase = str((Path(__UpperCamelCase ) / fname).relative_to(__UpperCamelCase ) )
UpperCamelCase = short_path.replace(""".py""" , """""" ).replace(os.path.sep , """.""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(__UpperCamelCase )
return submodules
SCREAMING_SNAKE_CASE__ = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
]
def lowercase__ ( )-> List[Any]:
# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase = importlib.util.spec_from_file_location(
"""transformers""" , os.path.join(__UpperCamelCase , """__init__.py""" ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
UpperCamelCase = spec.loader.load_module()
UpperCamelCase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(__UpperCamelCase ) > 0:
UpperCamelCase = """\n""".join(F"- {module}" for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registered in the main init of Transformers:\n"""
F"{list_of_modules}\n"
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 321 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = min(__UpperCamelCase ) # min() finds the minimum value
UpperCamelCase = max(__UpperCamelCase ) # max() finds the maximum value
UpperCamelCase = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCamelCase = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCamelCase = 0
for count in range(__UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
UpperCamelCase = count + min_val
i += 1
def lowercase__ ( )-> Any:
UpperCamelCase = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__UpperCamelCase )
print("""Sorted order is:""" , """ """.join(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=5 )-> List[Any]:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("""<mask>""" ) == 1
UpperCamelCase = torch.tensor(tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase ) ).unsqueeze(0 ) # Batch size 1
UpperCamelCase = model(__UpperCamelCase )[0] # The last hidden-state is the first element of the output tuple
UpperCamelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
UpperCamelCase = logits[0, masked_index, :]
UpperCamelCase = logits.softmax(dim=0 )
UpperCamelCase ,UpperCamelCase = prob.topk(k=__UpperCamelCase , dim=0 )
UpperCamelCase = """ """.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(__UpperCamelCase ) )] )
UpperCamelCase = tokenizer.mask_token
UpperCamelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """ ) ):
UpperCamelCase = predicted_token_bpe.replace("""\u2581""" , """ """ )
if " {0}".format(__UpperCamelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(""" {0}""".format(__UpperCamelCase ) , __UpperCamelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(__UpperCamelCase , __UpperCamelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
SCREAMING_SNAKE_CASE__ = CamembertTokenizer.from_pretrained('camembert-base')
SCREAMING_SNAKE_CASE__ = CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()
SCREAMING_SNAKE_CASE__ = 'Le camembert est <mask> :)'
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 321 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
SCREAMING_SNAKE_CASE__ = getLogger(__name__)
SCREAMING_SNAKE_CASE__ = 'cuda' if torch.cuda.is_available() else 'cpu'
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 8 , __UpperCamelCase = DEFAULT_DEVICE , __UpperCamelCase=False , __UpperCamelCase="summarization" , __UpperCamelCase=None , **__UpperCamelCase , )-> Dict:
UpperCamelCase = Path(__UpperCamelCase ).open("""w""" , encoding="""utf-8""" )
UpperCamelCase = str(__UpperCamelCase )
UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase ).to(__UpperCamelCase )
if fpaa:
UpperCamelCase = model.half()
UpperCamelCase = AutoTokenizer.from_pretrained(__UpperCamelCase )
logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type.
UpperCamelCase = time.time()
# update config with task specific params
use_task_specific_params(__UpperCamelCase , __UpperCamelCase )
if prefix is None:
UpperCamelCase = prefix or getattr(model.config , """prefix""" , """""" ) or """"""
for examples_chunk in tqdm(list(chunks(__UpperCamelCase , __UpperCamelCase ) ) ):
UpperCamelCase = [prefix + text for text in examples_chunk]
UpperCamelCase = tokenizer(__UpperCamelCase , return_tensors="""pt""" , truncation=__UpperCamelCase , padding="""longest""" ).to(__UpperCamelCase )
UpperCamelCase = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **__UpperCamelCase , )
UpperCamelCase = tokenizer.batch_decode(__UpperCamelCase , skip_special_tokens=__UpperCamelCase , clean_up_tokenization_spaces=__UpperCamelCase )
for hypothesis in dec:
fout.write(hypothesis + """\n""" )
fout.flush()
fout.close()
UpperCamelCase = int(time.time() - start_time ) # seconds
UpperCamelCase = len(__UpperCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def lowercase__ ( )-> str:
return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S""" )
def lowercase__ ( __UpperCamelCase=True )-> str:
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument("""model_name""" , type=__UpperCamelCase , help="""like facebook/bart-large-cnn,t5-base, etc.""" )
parser.add_argument("""input_path""" , type=__UpperCamelCase , help="""like cnn_dm/test.source""" )
parser.add_argument("""save_path""" , type=__UpperCamelCase , help="""where to save summaries""" )
parser.add_argument("""--reference_path""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""like cnn_dm/test.target""" )
parser.add_argument("""--score_path""" , type=__UpperCamelCase , required=__UpperCamelCase , default="""metrics.json""" , help="""where to save metrics""" )
parser.add_argument("""--device""" , type=__UpperCamelCase , required=__UpperCamelCase , default=__UpperCamelCase , help="""cuda, cuda:1, cpu etc.""" )
parser.add_argument(
"""--prefix""" , type=__UpperCamelCase , required=__UpperCamelCase , default=__UpperCamelCase , help="""will be added to the begininng of src examples""" )
parser.add_argument("""--task""" , type=__UpperCamelCase , default="""summarization""" , help="""used for task_specific_params + metrics""" )
parser.add_argument("""--bs""" , type=__UpperCamelCase , default=8 , required=__UpperCamelCase , help="""batch size""" )
parser.add_argument(
"""--n_obs""" , type=__UpperCamelCase , default=-1 , required=__UpperCamelCase , help="""How many observations. Defaults to all.""" )
parser.add_argument("""--fp16""" , action="""store_true""" )
parser.add_argument("""--dump-args""" , action="""store_true""" , help="""print the custom hparams with the results""" )
parser.add_argument(
"""--info""" , nargs="""?""" , type=__UpperCamelCase , const=datetime_now() , help=(
"""use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."""
""" lang=en-ru. If no value is passed, the current datetime string will be used."""
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
UpperCamelCase ,UpperCamelCase = parser.parse_known_args()
UpperCamelCase = parse_numeric_n_bool_cl_kwargs(__UpperCamelCase )
if parsed_args and verbose:
print(F"parsed the following generate kwargs: {parsed_args}" )
UpperCamelCase = [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
UpperCamelCase = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=__UpperCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c." )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError("""Can't mix --fp16 and --device cpu""" )
UpperCamelCase = generate_summaries_or_translations(
__UpperCamelCase , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **__UpperCamelCase , )
if args.reference_path is None:
return {}
# Compute scores
UpperCamelCase = calculate_bleu if """translation""" in args.task else calculate_rouge
UpperCamelCase = [x.rstrip() for x in open(args.save_path ).readlines()]
UpperCamelCase = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(__UpperCamelCase )]
UpperCamelCase = score_fn(__UpperCamelCase , __UpperCamelCase )
scores.update(__UpperCamelCase )
if args.dump_args:
scores.update(__UpperCamelCase )
if args.info:
UpperCamelCase = args.info
if verbose:
print(__UpperCamelCase )
if args.score_path is not None:
json.dump(__UpperCamelCase , open(args.score_path , """w""" ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 321 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 1 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 1000 )-> int:
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCamelCase = n - a - b
if c * c == (a * a + b * b):
UpperCamelCase = a * b * c
if candidate >= product:
UpperCamelCase = candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class a_ :
lowercase = 42
lowercase = 42
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
UpperCamelCase = size
def __getitem__( self , _SCREAMING_SNAKE_CASE ) -> Iterator[Edge]:
"""simple docstring"""
return iter(self._graph[vertex] )
@property
def A__ ( self ) -> Tuple:
"""simple docstring"""
return self._size
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | None:
"""simple docstring"""
UpperCamelCase = deque([start_vertex] )
UpperCamelCase = [None] * self.size
UpperCamelCase = 0
while queue:
UpperCamelCase = queue.popleft()
UpperCamelCase = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
UpperCamelCase = current_distance + edge.weight
UpperCamelCase = distances[edge.destination_vertex]
if (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and new_distance >= dest_vertex_distance
):
continue
UpperCamelCase = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {
'configuration_efficientnet': [
'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientNetConfig',
'EfficientNetOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['EfficientNetImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientNetForImageClassification',
'EfficientNetModel',
'EfficientNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 321 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE__ = _symbol_database.Default()
SCREAMING_SNAKE_CASE__ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
SCREAMING_SNAKE_CASE__ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
SCREAMING_SNAKE_CASE__ = 4_5
SCREAMING_SNAKE_CASE__ = 1_5_8_1
SCREAMING_SNAKE_CASE__ = 1_5_1_7
SCREAMING_SNAKE_CASE__ = 1_5_7_0
SCREAMING_SNAKE_CASE__ = 1_5_8_4
SCREAMING_SNAKE_CASE__ = 1_7_9_3
SCREAMING_SNAKE_CASE__ = 1_7_9_5
SCREAMING_SNAKE_CASE__ = 1_9_1_6
SCREAMING_SNAKE_CASE__ = 1_8_6_4
SCREAMING_SNAKE_CASE__ = 1_9_0_5
SCREAMING_SNAKE_CASE__ = 1_9_1_9
SCREAMING_SNAKE_CASE__ = 2_4_2_9
SCREAMING_SNAKE_CASE__ = 2_2_0_8
SCREAMING_SNAKE_CASE__ = 2_4_1_8
SCREAMING_SNAKE_CASE__ = 2_3_2_3
SCREAMING_SNAKE_CASE__ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 321 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class a_ ( lowerCamelCase , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class a_ ( unittest.TestCase ):
@property
def A__ ( self ) -> Dict:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = ort.SessionOptions()
UpperCamelCase = False
return options
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
UpperCamelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """A red cat sitting on a park bench"""
UpperCamelCase = np.random.RandomState(0 )
UpperCamelCase = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=10 , generator=_SCREAMING_SNAKE_CASE , output_type="""np""" , )
UpperCamelCase = output.images
UpperCamelCase = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCamelCase = np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
UpperCamelCase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , subfolder="""scheduler""" , revision="""onnx""" )
UpperCamelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , scheduler=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """A red cat sitting on a park bench"""
UpperCamelCase = np.random.RandomState(0 )
UpperCamelCase = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=20 , generator=_SCREAMING_SNAKE_CASE , output_type="""np""" , )
UpperCamelCase = output.images
UpperCamelCase = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
UpperCamelCase = np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 321 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE__ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __UpperCamelCase )-> str:
if "://" in dataset_path:
UpperCamelCase = dataset_path.split("""://""" )[1]
return dataset_path
def lowercase__ ( __UpperCamelCase )-> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = not is_remote_filesystem(__UpperCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__UpperCamelCase ) , fs._strip_protocol(__UpperCamelCase ) )
else:
fs.mv(__UpperCamelCase , __UpperCamelCase , recursive=__UpperCamelCase )
def lowercase__ ( )-> None:
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = threading.Lock()
| 321 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
SCREAMING_SNAKE_CASE__ = [
'small',
'small-base',
'medium',
'medium-base',
'intermediate',
'intermediate-base',
'large',
'large-base',
'xlarge',
'xlarge-base',
]
SCREAMING_SNAKE_CASE__ = {
'vocab_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt',
'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt',
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt',
'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt',
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json',
'funnel-transformer/small-base': (
'https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'
),
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json',
'funnel-transformer/large-base': (
'https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'
),
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'
),
},
}
SCREAMING_SNAKE_CASE__ = {f'funnel-transformer/{name}': 5_1_2 for name in _model_names}
SCREAMING_SNAKE_CASE__ = {f'funnel-transformer/{name}': {'do_lower_case': True} for name in _model_names}
class a_ ( lowerCamelCase ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_INIT_CONFIGURATION
lowercase = FunnelTokenizer
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = 2
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<sep>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<cls>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="##" , **_SCREAMING_SNAKE_CASE , ) -> Tuple:
"""simple docstring"""
super().__init__(
_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , do_lower_case=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , clean_text=_SCREAMING_SNAKE_CASE , tokenize_chinese_chars=_SCREAMING_SNAKE_CASE , strip_accents=_SCREAMING_SNAKE_CASE , wordpieces_prefix=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , _SCREAMING_SNAKE_CASE ) != do_lower_case
or normalizer_state.get("""strip_accents""" , _SCREAMING_SNAKE_CASE ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , _SCREAMING_SNAKE_CASE ) != tokenize_chinese_chars
):
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , normalizer_state.pop("""type""" ) )
UpperCamelCase = do_lower_case
UpperCamelCase = strip_accents
UpperCamelCase = tokenize_chinese_chars
UpperCamelCase = normalizer_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = do_lower_case
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Dict:
"""simple docstring"""
UpperCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
UpperCamelCase = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE )
return tuple(_SCREAMING_SNAKE_CASE )
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 321 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> str | Literal[False]:
UpperCamelCase = list(__UpperCamelCase )
UpperCamelCase = list(__UpperCamelCase )
UpperCamelCase = 0
for i in range(len(__UpperCamelCase ) ):
if lista[i] != lista[i]:
count += 1
UpperCamelCase = """_"""
if count > 1:
return False
else:
return "".join(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> list[str]:
UpperCamelCase = []
while True:
UpperCamelCase = ["""$"""] * len(__UpperCamelCase )
UpperCamelCase = []
for i in range(len(__UpperCamelCase ) ):
for j in range(i + 1 , len(__UpperCamelCase ) ):
UpperCamelCase = compare_string(binary[i] , binary[j] )
if k is False:
UpperCamelCase = """*"""
UpperCamelCase = """*"""
temp.append("""X""" )
for i in range(len(__UpperCamelCase ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(__UpperCamelCase ) == 0:
return pi
UpperCamelCase = list(set(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[str]:
UpperCamelCase = []
for minterm in minterms:
UpperCamelCase = """"""
for _ in range(__UpperCamelCase ):
UpperCamelCase = str(minterm % 2 ) + string
minterm //= 2
temp.append(__UpperCamelCase )
return temp
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
UpperCamelCase = list(__UpperCamelCase )
UpperCamelCase = list(__UpperCamelCase )
UpperCamelCase = 0
for i in range(len(__UpperCamelCase ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[str]:
UpperCamelCase = []
UpperCamelCase = [0] * len(__UpperCamelCase )
for i in range(len(chart[0] ) ):
UpperCamelCase = 0
UpperCamelCase = -1
for j in range(len(__UpperCamelCase ) ):
if chart[j][i] == 1:
count += 1
UpperCamelCase = j
if count == 1:
UpperCamelCase = 1
for i in range(len(__UpperCamelCase ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(__UpperCamelCase ) ):
UpperCamelCase = 0
temp.append(prime_implicants[i] )
while True:
UpperCamelCase = 0
UpperCamelCase = -1
UpperCamelCase = 0
for i in range(len(__UpperCamelCase ) ):
UpperCamelCase = chart[i].count(1 )
if count_n > max_n:
UpperCamelCase = count_n
UpperCamelCase = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(__UpperCamelCase ) ):
UpperCamelCase = 0
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[list[int]]:
UpperCamelCase = [[0 for x in range(len(__UpperCamelCase ) )] for x in range(len(__UpperCamelCase ) )]
for i in range(len(__UpperCamelCase ) ):
UpperCamelCase = prime_implicants[i].count("""_""" )
for j in range(len(__UpperCamelCase ) ):
if is_for_table(prime_implicants[i] , binary[j] , __UpperCamelCase ):
UpperCamelCase = 1
return chart
def lowercase__ ( )-> None:
UpperCamelCase = int(input("""Enter the no. of variables\n""" ) )
UpperCamelCase = [
float(__UpperCamelCase )
for x in input(
"""Enter the decimal representation of Minterms 'Spaces Separated'\n""" ).split()
]
UpperCamelCase = decimal_to_binary(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = check(__UpperCamelCase )
print("""Prime Implicants are:""" )
print(__UpperCamelCase )
UpperCamelCase = prime_implicant_chart(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = selection(__UpperCamelCase , __UpperCamelCase )
print("""Essential Prime Implicants are:""" )
print(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 321 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = 'docs/source/en/_toctree.yml'
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = defaultdict(__UpperCamelCase )
UpperCamelCase = []
UpperCamelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__UpperCamelCase )
UpperCamelCase = new_doc_list
UpperCamelCase = [key for key, value in counts.items() if value > 1]
UpperCamelCase = []
for duplicate_key in duplicates:
UpperCamelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCamelCase = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowercase__ ( __UpperCamelCase=False )-> List[str]:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCamelCase = api_doc[scheduler_idx]["""sections"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
UpperCamelCase = False
if new_scheduler_doc != scheduler_doc:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_scheduler_doc
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase__ ( __UpperCamelCase=False )-> Tuple:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCamelCase = False
UpperCamelCase = api_doc[pipeline_idx]["""sections"""]
UpperCamelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCamelCase = pipeline_doc["""section"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCamelCase = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_pipeline_docs
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 321 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0.2 , _SCREAMING_SNAKE_CASE=0.2 ) -> Tuple:
"""simple docstring"""
UpperCamelCase = bp_numa
UpperCamelCase = bp_numa
UpperCamelCase = bp_numa
UpperCamelCase = conva_get[:2]
UpperCamelCase = conva_get[2]
UpperCamelCase = size_pa
UpperCamelCase = rate_w
UpperCamelCase = rate_t
UpperCamelCase = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
UpperCamelCase = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
UpperCamelCase = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
UpperCamelCase = -2 * np.random.rand(self.conva[1] ) + 1
UpperCamelCase = -2 * np.random.rand(self.num_bpa ) + 1
UpperCamelCase = -2 * np.random.rand(self.num_bpa ) + 1
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = {
"""num_bp1""": self.num_bpa,
"""num_bp2""": self.num_bpa,
"""num_bp3""": self.num_bpa,
"""conv1""": self.conva,
"""step_conv1""": self.step_conva,
"""size_pooling1""": self.size_poolinga,
"""rate_weight""": self.rate_weight,
"""rate_thre""": self.rate_thre,
"""w_conv1""": self.w_conva,
"""wkj""": self.wkj,
"""vji""": self.vji,
"""thre_conv1""": self.thre_conva,
"""thre_bp2""": self.thre_bpa,
"""thre_bp3""": self.thre_bpa,
}
with open(_SCREAMING_SNAKE_CASE , """wb""" ) as f:
pickle.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(F"Model saved： {save_path}" )
@classmethod
def A__ ( cls , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , """rb""" ) as f:
UpperCamelCase = pickle.load(_SCREAMING_SNAKE_CASE ) # noqa: S301
UpperCamelCase = model_dic.get("""conv1""" )
conv_get.append(model_dic.get("""step_conv1""" ) )
UpperCamelCase = model_dic.get("""size_pooling1""" )
UpperCamelCase = model_dic.get("""num_bp1""" )
UpperCamelCase = model_dic.get("""num_bp2""" )
UpperCamelCase = model_dic.get("""num_bp3""" )
UpperCamelCase = model_dic.get("""rate_weight""" )
UpperCamelCase = model_dic.get("""rate_thre""" )
# create model instance
UpperCamelCase = CNN(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# modify model parameter
UpperCamelCase = model_dic.get("""w_conv1""" )
UpperCamelCase = model_dic.get("""wkj""" )
UpperCamelCase = model_dic.get("""vji""" )
UpperCamelCase = model_dic.get("""thre_conv1""" )
UpperCamelCase = model_dic.get("""thre_bp2""" )
UpperCamelCase = model_dic.get("""thre_bp3""" )
return conv_ins
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return 1 / (1 + np.exp(-1 * x ))
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return round(_SCREAMING_SNAKE_CASE , 3 )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
UpperCamelCase = convs[0]
UpperCamelCase = convs[1]
UpperCamelCase = np.shape(_SCREAMING_SNAKE_CASE )[0]
# get the data slice of original image data, data_focus
UpperCamelCase = []
for i_focus in range(0 , size_data - size_conv + 1 , _SCREAMING_SNAKE_CASE ):
for j_focus in range(0 , size_data - size_conv + 1 , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(_SCREAMING_SNAKE_CASE )
# calculate the feature map of every single kernel, and saved as list of matrix
UpperCamelCase = []
UpperCamelCase = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = []
for i_focus in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = np.asmatrix(_SCREAMING_SNAKE_CASE ).reshape(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
data_featuremap.append(_SCREAMING_SNAKE_CASE )
# expanding the data slice to One dimenssion
UpperCamelCase = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = np.asarray(_SCREAMING_SNAKE_CASE )
return focus_list, data_featuremap
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="average_pool" ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = len(featuremaps[0] )
UpperCamelCase = int(size_map / size_pooling )
UpperCamelCase = []
for i_map in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase = featuremaps[i_map]
UpperCamelCase = []
for i_focus in range(0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for j_focus in range(0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(_SCREAMING_SNAKE_CASE ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = np.asmatrix(_SCREAMING_SNAKE_CASE ).reshape(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
featuremap_pooled.append(_SCREAMING_SNAKE_CASE )
return featuremap_pooled
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
UpperCamelCase = []
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase = np.shape(data[i] )
UpperCamelCase = data[i].reshape(1 , shapes[0] * shapes[1] )
UpperCamelCase = data_listed.getA().tolist()[0]
data_expanded.extend(_SCREAMING_SNAKE_CASE )
UpperCamelCase = np.asarray(_SCREAMING_SNAKE_CASE )
return data_expanded
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = np.asarray(_SCREAMING_SNAKE_CASE )
UpperCamelCase = np.shape(_SCREAMING_SNAKE_CASE )
UpperCamelCase = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = 0
for i_map in range(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = np.ones((size_map, size_map) )
for i in range(0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for j in range(0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = pd_pool[
i_pool
]
UpperCamelCase = i_pool + 1
UpperCamelCase = np.multiply(
_SCREAMING_SNAKE_CASE , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(_SCREAMING_SNAKE_CASE )
return pd_all
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=bool ) -> List[str]:
"""simple docstring"""
print("""----------------------Start Training-------------------------""" )
print((""" - - Shape: Train_Data """, np.shape(_SCREAMING_SNAKE_CASE )) )
print((""" - - Shape: Teach_Data """, np.shape(_SCREAMING_SNAKE_CASE )) )
UpperCamelCase = 0
UpperCamelCase = []
UpperCamelCase = 10000
while rp < n_repeat and mse >= error_accuracy:
UpperCamelCase = 0
print(F"-------------Learning Time {rp}--------------" )
for p in range(len(_SCREAMING_SNAKE_CASE ) ):
# print('------------Learning Image: %d--------------'%p)
UpperCamelCase = np.asmatrix(datas_train[p] )
UpperCamelCase = np.asarray(datas_teach[p] )
UpperCamelCase ,UpperCamelCase = self.convolute(
_SCREAMING_SNAKE_CASE , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
UpperCamelCase = self.pooling(_SCREAMING_SNAKE_CASE , self.size_poolinga )
UpperCamelCase = np.shape(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self._expand(_SCREAMING_SNAKE_CASE )
UpperCamelCase = data_bp_input
UpperCamelCase = np.dot(_SCREAMING_SNAKE_CASE , self.vji.T ) - self.thre_bpa
UpperCamelCase = self.sig(_SCREAMING_SNAKE_CASE )
UpperCamelCase = np.dot(_SCREAMING_SNAKE_CASE , self.wkj.T ) - self.thre_bpa
UpperCamelCase = self.sig(_SCREAMING_SNAKE_CASE )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
UpperCamelCase = np.multiply(
(data_teach - bp_outa) , np.multiply(_SCREAMING_SNAKE_CASE , (1 - bp_outa) ) )
UpperCamelCase = np.multiply(
np.dot(_SCREAMING_SNAKE_CASE , self.wkj ) , np.multiply(_SCREAMING_SNAKE_CASE , (1 - bp_outa) ) )
UpperCamelCase = np.dot(_SCREAMING_SNAKE_CASE , self.vji )
UpperCamelCase = pd_i_all / (self.size_poolinga * self.size_poolinga)
UpperCamelCase = pd_conva_pooled.T.getA().tolist()
UpperCamelCase = self._calculate_gradient_from_pool(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
UpperCamelCase = self._expand_mat(pd_conva_all[k_conv] )
UpperCamelCase = self.rate_weight * np.dot(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
UpperCamelCase = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
UpperCamelCase = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
UpperCamelCase = self.vji + pd_j_all.T * bp_outa * self.rate_weight
UpperCamelCase = self.thre_bpa - pd_k_all * self.rate_thre
UpperCamelCase = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
UpperCamelCase = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
UpperCamelCase = rp + 1
UpperCamelCase = error_count / patterns
all_mse.append(_SCREAMING_SNAKE_CASE )
def draw_error():
UpperCamelCase = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(_SCREAMING_SNAKE_CASE , """+-""" )
plt.plot(_SCREAMING_SNAKE_CASE , """r--""" )
plt.xlabel("""Learning Times""" )
plt.ylabel("""All_mse""" )
plt.grid(_SCREAMING_SNAKE_CASE , alpha=0.5 )
plt.show()
print("""------------------Training Complished---------------------""" )
print((""" - - Training epoch: """, rp, F" - - Mse: {mse:.6f}") )
if draw_e:
draw_error()
return mse
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
UpperCamelCase = []
print("""-------------------Start Testing-------------------------""" )
print((""" - - Shape: Test_Data """, np.shape(_SCREAMING_SNAKE_CASE )) )
for p in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase = np.asmatrix(datas_test[p] )
UpperCamelCase ,UpperCamelCase = self.convolute(
_SCREAMING_SNAKE_CASE , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
UpperCamelCase = self.pooling(_SCREAMING_SNAKE_CASE , self.size_poolinga )
UpperCamelCase = self._expand(_SCREAMING_SNAKE_CASE )
UpperCamelCase = data_bp_input
UpperCamelCase = bp_outa * self.vji.T - self.thre_bpa
UpperCamelCase = self.sig(_SCREAMING_SNAKE_CASE )
UpperCamelCase = bp_outa * self.wkj.T - self.thre_bpa
UpperCamelCase = self.sig(_SCREAMING_SNAKE_CASE )
produce_out.extend(bp_outa.getA().tolist() )
UpperCamelCase = [list(map(self.do_round , _SCREAMING_SNAKE_CASE ) ) for each in produce_out]
return np.asarray(_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = np.asmatrix(_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.convolute(
_SCREAMING_SNAKE_CASE , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
UpperCamelCase = self.pooling(_SCREAMING_SNAKE_CASE , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 321 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCamelCase = 1.5
UpperCamelCase = int(factor * num_class_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 )
os.makedirs(F"{class_data_dir}/images" , exist_ok=__UpperCamelCase )
if len(list(Path(F"{class_data_dir}/images" ).iterdir() ) ) >= num_class_images:
return
while True:
UpperCamelCase = client.query(text=__UpperCamelCase )
if len(__UpperCamelCase ) >= factor * num_class_images or num_images > 1E4:
break
else:
UpperCamelCase = int(factor * num_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 , )
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = tqdm(desc="""downloading real regularization images""" , total=__UpperCamelCase )
with open(F"{class_data_dir}/caption.txt" , """w""" ) as fa, open(F"{class_data_dir}/urls.txt" , """w""" ) as fa, open(
F"{class_data_dir}/images.txt" , """w""" ) as fa:
while total < num_class_images:
UpperCamelCase = class_images[count]
count += 1
try:
UpperCamelCase = requests.get(images["""url"""] )
if img.status_code == 200:
UpperCamelCase = Image.open(BytesIO(img.content ) )
with open(F"{class_data_dir}/images/{total}.jpg" , """wb""" ) as f:
f.write(img.content )
fa.write(images["""caption"""] + """\n""" )
fa.write(images["""url"""] + """\n""" )
fa.write(F"{class_data_dir}/images/{total}.jpg" + """\n""" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def lowercase__ ( )-> str:
UpperCamelCase = argparse.ArgumentParser("""""" , add_help=__UpperCamelCase )
parser.add_argument("""--class_prompt""" , help="""text prompt to retrieve images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--class_data_dir""" , help="""path to save images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--num_class_images""" , help="""number of images to download""" , default=200 , type=__UpperCamelCase )
return parser.parse_args()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 321 | 1 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [0, 2, 4, 6, 8]
SCREAMING_SNAKE_CASE__ = [1, 3, 5, 7, 9]
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
UpperCamelCase = 0
for digit in range(10 ):
UpperCamelCase = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , __UpperCamelCase , __UpperCamelCase )
return result
UpperCamelCase = 0
for digita in range(10 ):
UpperCamelCase = digita
if (remainder + digita) % 2 == 0:
UpperCamelCase = ODD_DIGITS
else:
UpperCamelCase = EVEN_DIGITS
for digita in other_parity_digits:
UpperCamelCase = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , __UpperCamelCase , __UpperCamelCase , )
return result
def lowercase__ ( __UpperCamelCase = 9 )-> int:
UpperCamelCase = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(__UpperCamelCase , 0 , [0] * length , __UpperCamelCase )
return result
if __name__ == "__main__":
print(f'{solution() = }')
| 321 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class a_ :
lowercase = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The column name of the images in the files."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the training data."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the validation data."""} )
lowercase = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = {}
if self.train_dir is not None:
UpperCamelCase = self.train_dir
if self.validation_dir is not None:
UpperCamelCase = self.validation_dir
UpperCamelCase = data_files if data_files else None
@dataclass
class a_ :
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowercase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class a_ ( lowerCamelCase ):
lowercase = field(
default=1E-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowercase__ ( )-> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , __UpperCamelCase , __UpperCamelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCamelCase = training_args.get_process_log_level()
logger.setLevel(__UpperCamelCase )
transformers.utils.logging.set_verbosity(__UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
UpperCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCamelCase = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __UpperCamelCase ) and data_args.train_val_split > 0.0:
UpperCamelCase = ds["""train"""].train_test_split(data_args.train_val_split )
UpperCamelCase = split["""train"""]
UpperCamelCase = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.config_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
UpperCamelCase = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase = ViTMAEForPreTraining(__UpperCamelCase )
if training_args.do_train:
UpperCamelCase = ds["""train"""].column_names
else:
UpperCamelCase = ds["""validation"""].column_names
if data_args.image_column_name is not None:
UpperCamelCase = data_args.image_column_name
elif "image" in column_names:
UpperCamelCase = """image"""
elif "img" in column_names:
UpperCamelCase = """img"""
else:
UpperCamelCase = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
UpperCamelCase = image_processor.size["""shortest_edge"""]
else:
UpperCamelCase = (image_processor.size["""height"""], image_processor.size["""width"""])
UpperCamelCase = Compose(
[
Lambda(lambda __UpperCamelCase : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(__UpperCamelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(__UpperCamelCase ):
UpperCamelCase = [transforms(__UpperCamelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
UpperCamelCase = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
UpperCamelCase = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__UpperCamelCase )
# Compute absolute learning rate
UpperCamelCase = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
UpperCamelCase = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
UpperCamelCase = Trainer(
model=__UpperCamelCase , args=__UpperCamelCase , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=__UpperCamelCase , data_collator=__UpperCamelCase , )
# Training
if training_args.do_train:
UpperCamelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCamelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCamelCase = last_checkpoint
UpperCamelCase = trainer.train(resume_from_checkpoint=__UpperCamelCase )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCamelCase = trainer.evaluate()
trainer.log_metrics("""eval""" , __UpperCamelCase )
trainer.save_metrics("""eval""" , __UpperCamelCase )
# Write model card and (optionally) push to hub
UpperCamelCase = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__UpperCamelCase )
else:
trainer.create_model_card(**__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> List[str]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
AccelerateTestCase,
TempDirTestCase,
execute_subprocess_async,
require_cuda,
require_fsdp,
require_multi_gpu,
slow,
)
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_BACKWARD_PREFETCH,
FSDP_SHARDING_STRATEGY,
FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment
set_seed(4_2)
SCREAMING_SNAKE_CASE__ = 'bert-base-cased'
SCREAMING_SNAKE_CASE__ = 'fp16'
SCREAMING_SNAKE_CASE__ = 'bf16'
SCREAMING_SNAKE_CASE__ = [FPaa, BFaa]
@require_fsdp
@require_cuda
class a_ ( lowerCamelCase ):
def A__ ( self ) -> List[str]:
"""simple docstring"""
super().setUp()
UpperCamelCase = dict(
ACCELERATE_USE_FSDP="""true""" , MASTER_ADDR="""localhost""" , MASTER_PORT="""10999""" , RANK="""0""" , LOCAL_RANK="""0""" , WORLD_SIZE="""1""" , )
def A__ ( self ) -> List[str]:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
for i, strategy in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = self.dist_env.copy()
UpperCamelCase = F"{i + 1}"
UpperCamelCase = strategy
with mockenv_context(**_SCREAMING_SNAKE_CASE ):
UpperCamelCase = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.sharding_strategy , ShardingStrategy(i + 1 ) )
def A__ ( self ) -> str:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch
for i, prefetch_policy in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = self.dist_env.copy()
UpperCamelCase = prefetch_policy
with mockenv_context(**_SCREAMING_SNAKE_CASE ):
UpperCamelCase = FullyShardedDataParallelPlugin()
if prefetch_policy == "NO_PREFETCH":
self.assertIsNone(fsdp_plugin.backward_prefetch )
else:
self.assertEqual(fsdp_plugin.backward_prefetch , BackwardPrefetch(i + 1 ) )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
for i, state_dict_type in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = self.dist_env.copy()
UpperCamelCase = state_dict_type
with mockenv_context(**_SCREAMING_SNAKE_CASE ):
UpperCamelCase = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.state_dict_type , StateDictType(i + 1 ) )
if state_dict_type == "FULL_STATE_DICT":
self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu )
self.assertTrue(fsdp_plugin.state_dict_config.ranka_only )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = AutoModel.from_pretrained(_SCREAMING_SNAKE_CASE )
for policy in FSDP_AUTO_WRAP_POLICY:
UpperCamelCase = self.dist_env.copy()
UpperCamelCase = policy
if policy == "TRANSFORMER_BASED_WRAP":
UpperCamelCase = """BertLayer"""
elif policy == "SIZE_BASED_WRAP":
UpperCamelCase = """2000"""
with mockenv_context(**_SCREAMING_SNAKE_CASE ):
UpperCamelCase = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(_SCREAMING_SNAKE_CASE )
if policy == "NO_WRAP":
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
else:
self.assertIsNotNone(fsdp_plugin.auto_wrap_policy )
UpperCamelCase = self.dist_env.copy()
UpperCamelCase = """TRANSFORMER_BASED_WRAP"""
UpperCamelCase = """T5Layer"""
with mockenv_context(**_SCREAMING_SNAKE_CASE ):
UpperCamelCase = FullyShardedDataParallelPlugin()
with self.assertRaises(_SCREAMING_SNAKE_CASE ) as cm:
fsdp_plugin.set_auto_wrap_policy(_SCREAMING_SNAKE_CASE )
self.assertTrue("""Could not find the transformer layer class to wrap in the model.""" in str(cm.exception ) )
UpperCamelCase = self.dist_env.copy()
UpperCamelCase = """SIZE_BASED_WRAP"""
UpperCamelCase = """0"""
with mockenv_context(**_SCREAMING_SNAKE_CASE ):
UpperCamelCase = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(_SCREAMING_SNAKE_CASE )
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
def A__ ( self ) -> Any:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
for mp_dtype in dtypes:
UpperCamelCase = self.dist_env.copy()
UpperCamelCase = mp_dtype
with mockenv_context(**_SCREAMING_SNAKE_CASE ):
UpperCamelCase = Accelerator()
if mp_dtype == "fp16":
UpperCamelCase = torch.floataa
elif mp_dtype == "bf16":
UpperCamelCase = torch.bfloataa
UpperCamelCase = MixedPrecision(param_dtype=_SCREAMING_SNAKE_CASE , reduce_dtype=_SCREAMING_SNAKE_CASE , buffer_dtype=_SCREAMING_SNAKE_CASE )
self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy , _SCREAMING_SNAKE_CASE )
if mp_dtype == FPaa:
self.assertTrue(isinstance(accelerator.scaler , _SCREAMING_SNAKE_CASE ) )
elif mp_dtype == BFaa:
self.assertIsNone(accelerator.scaler )
AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
for flag in [True, False]:
UpperCamelCase = self.dist_env.copy()
UpperCamelCase = str(_SCREAMING_SNAKE_CASE ).lower()
with mockenv_context(**_SCREAMING_SNAKE_CASE ):
UpperCamelCase = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.cpu_offload , CPUOffload(offload_params=_SCREAMING_SNAKE_CASE ) )
@require_fsdp
@require_multi_gpu
@slow
class a_ ( lowerCamelCase ):
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
UpperCamelCase = 0.8_2
UpperCamelCase = [
"""fsdp_shard_grad_op_transformer_based_wrap""",
"""fsdp_full_shard_transformer_based_wrap""",
]
UpperCamelCase = {
"""multi_gpu_fp16""": 3200,
"""fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2000,
"""fsdp_full_shard_transformer_based_wrap_fp16""": 1900,
# Disabling below test as it overwhelms the RAM memory usage
# on CI self-hosted runner leading to tests getting killed.
# "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500, # fp16 was leading to indefinite hang
}
UpperCamelCase = 160
UpperCamelCase = 160
UpperCamelCase = inspect.getfile(accelerate.test_utils )
UpperCamelCase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """external_deps"""] )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = os.path.join(self.test_scripts_folder , """test_performance.py""" )
UpperCamelCase = ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
for config in self.performance_configs:
UpperCamelCase = cmd.copy()
for i, strategy in enumerate(_SCREAMING_SNAKE_CASE ):
if strategy.lower() in config:
cmd_config.append(F"--fsdp_sharding_strategy={i+1}" )
break
if "fp32" in config:
cmd_config.append("""--mixed_precision=no""" )
else:
cmd_config.append("""--mixed_precision=fp16""" )
if "cpu_offload" in config:
cmd_config.append("""--fsdp_offload_params=True""" )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in config:
cmd_config.append(F"--fsdp_auto_wrap_policy={policy}" )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer""" )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append("""--fsdp_min_num_params=2000""" )
cmd_config.extend(
[
self.test_file_path,
F"--output_dir={self.tmpdir}",
F"--performance_lower_bound={self.performance_lower_bound}",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_SCREAMING_SNAKE_CASE , env=os.environ.copy() )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = os.path.join(self.test_scripts_folder , """test_checkpointing.py""" )
UpperCamelCase = [
"""accelerate""",
"""launch""",
"""--num_processes=2""",
"""--num_machines=1""",
"""--machine_rank=0""",
"""--use_fsdp""",
"""--mixed_precision=fp16""",
"""--fsdp_transformer_layer_cls_to_wrap=BertLayer""",
]
for i, strategy in enumerate(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = cmd.copy()
cmd_config.append(F"--fsdp_sharding_strategy={i+1}" )
if strategy != "FULL_SHARD":
continue
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
for state_dict_type in FSDP_STATE_DICT_TYPE:
UpperCamelCase = cmd_config[:state_dict_config_index]
cmd_config.append(F"--fsdp_state_dict_type={state_dict_type}" )
cmd_config.extend(
[
self.test_file_path,
F"--output_dir={self.tmpdir}",
"""--partial_train_epoch=1""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_SCREAMING_SNAKE_CASE , env=os.environ.copy() )
UpperCamelCase = cmd_config[:-1]
UpperCamelCase = os.path.join(self.tmpdir , """epoch_0""" )
cmd_config.extend(
[
F"--resume_from_checkpoint={resume_from_checkpoint}",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_SCREAMING_SNAKE_CASE , env=os.environ.copy() )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = os.path.join(self.test_scripts_folder , """test_peak_memory_usage.py""" )
UpperCamelCase = [
"""accelerate""",
"""launch""",
"""--num_processes=2""",
"""--num_machines=1""",
"""--machine_rank=0""",
]
for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
UpperCamelCase = cmd.copy()
if "fp16" in spec:
cmd_config.extend(["""--mixed_precision=fp16"""] )
else:
cmd_config.extend(["""--mixed_precision=no"""] )
if "multi_gpu" in spec:
continue
else:
cmd_config.extend(["""--use_fsdp"""] )
for i, strategy in enumerate(_SCREAMING_SNAKE_CASE ):
if strategy.lower() in spec:
cmd_config.append(F"--fsdp_sharding_strategy={i+1}" )
break
if "cpu_offload" in spec:
cmd_config.append("""--fsdp_offload_params=True""" )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in spec:
cmd_config.append(F"--fsdp_auto_wrap_policy={policy}" )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer""" )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append("""--fsdp_min_num_params=2000""" )
cmd_config.extend(
[
self.test_file_path,
F"--output_dir={self.tmpdir}",
F"--peak_memory_upper_bound={peak_mem_upper_bound}",
F"--n_train={self.n_train}",
F"--n_val={self.n_val}",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_SCREAMING_SNAKE_CASE , env=os.environ.copy() )
| 321 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE__ = 2_5_6
class a_ ( lowerCamelCase ):
lowercase = ["""melgan"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase = math.log(1e-5 ) # Matches MelGAN training.
UpperCamelCase = 4.0 # Largest value for most examples
UpperCamelCase = 128
self.register_modules(
notes_encoder=_SCREAMING_SNAKE_CASE , continuous_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , melgan=_SCREAMING_SNAKE_CASE , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = output_range
if clip:
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , self.min_value , self.max_value )
# Scale to [0, 1].
UpperCamelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = input_range
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = input_tokens > 0
UpperCamelCase ,UpperCamelCase = self.notes_encoder(
encoder_input_tokens=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.continuous_encoder(
encoder_inputs=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = noise_time
if not torch.is_tensor(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(_SCREAMING_SNAKE_CASE ) and len(timesteps.shape ) == 0:
UpperCamelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
UpperCamelCase = self.decoder(
encodings_and_masks=_SCREAMING_SNAKE_CASE , decoder_input_tokens=_SCREAMING_SNAKE_CASE , decoder_noise_time=_SCREAMING_SNAKE_CASE )
return logits
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "numpy" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(_SCREAMING_SNAKE_CASE )}." )
UpperCamelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
UpperCamelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
UpperCamelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE ):
if i == 0:
UpperCamelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase = ones
UpperCamelCase = self.scale_features(
_SCREAMING_SNAKE_CASE , output_range=[-1.0, 1.0] , clip=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=_SCREAMING_SNAKE_CASE , continuous_mask=_SCREAMING_SNAKE_CASE , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase = self.decode(
encodings_and_masks=_SCREAMING_SNAKE_CASE , input_tokens=_SCREAMING_SNAKE_CASE , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
UpperCamelCase = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = self.scale_to_features(_SCREAMING_SNAKE_CASE , input_range=[-1.0, 1.0] )
UpperCamelCase = mel[:1]
UpperCamelCase = mel.cpu().float().numpy()
UpperCamelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
logger.info("""Generated segment""" , _SCREAMING_SNAKE_CASE )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
UpperCamelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
SCREAMING_SNAKE_CASE__ = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False , )-> int:
output_path.parent.mkdir(parents=__UpperCamelCase , exist_ok=__UpperCamelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
__UpperCamelCase , __UpperCamelCase , f=output_path.as_posix() , input_names=__UpperCamelCase , output_names=__UpperCamelCase , dynamic_axes=__UpperCamelCase , do_constant_folding=__UpperCamelCase , use_external_data_format=__UpperCamelCase , enable_onnx_checker=__UpperCamelCase , opset_version=__UpperCamelCase , )
else:
export(
__UpperCamelCase , __UpperCamelCase , f=output_path.as_posix() , input_names=__UpperCamelCase , output_names=__UpperCamelCase , dynamic_axes=__UpperCamelCase , do_constant_folding=__UpperCamelCase , opset_version=__UpperCamelCase , )
@torch.no_grad()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False )-> int:
UpperCamelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
UpperCamelCase = """cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
else:
UpperCamelCase = """cpu"""
UpperCamelCase = Path(__UpperCamelCase )
# VAE DECODER
UpperCamelCase = AutoencoderKL.from_pretrained(model_path + """/vae""" )
UpperCamelCase = vae_decoder.config.latent_channels
# forward only through the decoder part
UpperCamelCase = vae_decoder.decode
onnx_export(
__UpperCamelCase , model_args=(
torch.randn(1 , __UpperCamelCase , 25 , 25 ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
False,
) , output_path=output_path / """vae_decoder""" / """model.onnx""" , ordered_input_names=["""latent_sample""", """return_dict"""] , output_names=["""sample"""] , dynamic_axes={
"""latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=__UpperCamelCase , )
del vae_decoder
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
type=str,
required=True,
help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
)
parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--opset',
default=1_4,
type=int,
help='The version of the ONNX operator set to use.',
)
parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('SD: Done: ONNX')
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = b, a + b
return sum(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> str:
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}." )
if tokenizer_name is None:
UpperCamelCase = TOKENIZER_CLASSES
else:
UpperCamelCase = {tokenizer_name: getattr(__UpperCamelCase , tokenizer_name + """Fast""" )}
logger.info(F"Loading tokenizer classes: {tokenizer_names}" )
for tokenizer_name in tokenizer_names:
UpperCamelCase = TOKENIZER_CLASSES[tokenizer_name]
UpperCamelCase = True
if checkpoint_name is None:
UpperCamelCase = list(tokenizer_class.max_model_input_sizes.keys() )
else:
UpperCamelCase = [checkpoint_name]
logger.info(F"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}" )
for checkpoint in checkpoint_names:
logger.info(F"Loading {tokenizer_class.__class__.__name__} {checkpoint}" )
# Load tokenizer
UpperCamelCase = tokenizer_class.from_pretrained(__UpperCamelCase , force_download=__UpperCamelCase )
# Save fast tokenizer
logger.info(F"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}" )
# For organization names we create sub-directories
if "/" in checkpoint:
UpperCamelCase ,UpperCamelCase = checkpoint.split("""/""" )
UpperCamelCase = os.path.join(__UpperCamelCase , __UpperCamelCase )
elif add_prefix:
UpperCamelCase = checkpoint
UpperCamelCase = dump_path
else:
UpperCamelCase = None
UpperCamelCase = dump_path
logger.info(F"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}" )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
UpperCamelCase = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
UpperCamelCase = file_path.split(__UpperCamelCase )[-1][0]
if next_char == "/":
UpperCamelCase = os.path.join(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = None
logger.info(F"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}" )
UpperCamelCase = tokenizer.save_pretrained(
__UpperCamelCase , legacy_format=__UpperCamelCase , filename_prefix=__UpperCamelCase )
logger.info(F"=> File names {file_names}" )
for file_name in file_names:
if not file_name.endswith("""tokenizer.json""" ):
os.remove(__UpperCamelCase )
logger.info(F"=> removing {file_name}" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
)
parser.add_argument(
'--tokenizer_name',
default=None,
type=str,
help=(
f'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
'download and convert all the checkpoints from AWS.'
),
)
parser.add_argument(
'--checkpoint_name',
default=None,
type=str,
help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
)
parser.add_argument(
'--force_download',
action='store_true',
help='Re-download checkpoints.',
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
# Base Case
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] , __UpperCamelCase , __UpperCamelCase ):
# Color current vertex
UpperCamelCase = i
# Validate coloring
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , index + 1 ):
return True
# Backtrack
UpperCamelCase = -1
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[int]:
UpperCamelCase = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , 0 ):
return colored_vertices
return []
| 321 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['ConditionalDetrFeatureExtractor']
SCREAMING_SNAKE_CASE__ = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __UpperCamelCase ):
UpperCamelCase = 1
UpperCamelCase = 0
for i in range(__UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=6 , _SCREAMING_SNAKE_CASE=17 , _SCREAMING_SNAKE_CASE=23 , _SCREAMING_SNAKE_CASE=11 , _SCREAMING_SNAKE_CASE=True , ) -> Tuple:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = act_dim
UpperCamelCase = state_dim
UpperCamelCase = hidden_size
UpperCamelCase = max_length
UpperCamelCase = is_training
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
UpperCamelCase = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
UpperCamelCase = floats_tensor((self.batch_size, self.seq_length, 1) )
UpperCamelCase = floats_tensor((self.batch_size, self.seq_length, 1) )
UpperCamelCase = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1000 )
UpperCamelCase = random_attention_mask((self.batch_size, self.seq_length) )
UpperCamelCase = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> List[str]:
"""simple docstring"""
UpperCamelCase = DecisionTransformerModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {
"""states""": states,
"""actions""": actions,
"""rewards""": rewards,
"""returns_to_go""": returns_to_go,
"""timesteps""": timesteps,
"""attention_mask""": attention_mask,
}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = (DecisionTransformerModel,) if is_torch_available() else ()
lowercase = ()
lowercase = {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
lowercase = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = DecisionTransformerModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def A__ ( self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
@slow
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = DecisionTransformerModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = [
"""states""",
"""actions""",
"""rewards""",
"""returns_to_go""",
"""timesteps""",
"""attention_mask""",
]
self.assertListEqual(arg_names[: len(_SCREAMING_SNAKE_CASE )] , _SCREAMING_SNAKE_CASE )
@require_torch
class a_ ( unittest.TestCase ):
@slow
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = 2 # number of steps of autoregressive prediction we will perform
UpperCamelCase = 10 # defined by the RL environment, may be normalized
UpperCamelCase = DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert""" )
UpperCamelCase = model.to(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model.config
torch.manual_seed(0 )
UpperCamelCase = torch.randn(1 , 1 , config.state_dim ).to(device=_SCREAMING_SNAKE_CASE , dtype=torch.floataa ) # env.reset()
UpperCamelCase = torch.tensor(
[[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]] , device=_SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor(_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , dtype=torch.floataa ).reshape(1 , 1 , 1 )
UpperCamelCase = state
UpperCamelCase = torch.zeros(1 , 0 , config.act_dim , device=_SCREAMING_SNAKE_CASE , dtype=torch.floataa )
UpperCamelCase = torch.zeros(1 , 0 , device=_SCREAMING_SNAKE_CASE , dtype=torch.floataa )
UpperCamelCase = torch.tensor(0 , device=_SCREAMING_SNAKE_CASE , dtype=torch.long ).reshape(1 , 1 )
for step in range(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=_SCREAMING_SNAKE_CASE )] , dim=1 )
UpperCamelCase = torch.cat([rewards, torch.zeros(1 , 1 , device=_SCREAMING_SNAKE_CASE )] , dim=1 )
UpperCamelCase = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = model(
states=_SCREAMING_SNAKE_CASE , actions=_SCREAMING_SNAKE_CASE , rewards=_SCREAMING_SNAKE_CASE , returns_to_go=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1e-4 ) )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=_SCREAMING_SNAKE_CASE , dtype=torch.floataa ),
1.0,
False,
{},
)
UpperCamelCase = action_pred[0, -1]
UpperCamelCase = torch.cat([states, state] , dim=1 )
UpperCamelCase = returns_to_go[0, -1] - reward
UpperCamelCase = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
UpperCamelCase = torch.cat(
[timesteps, torch.ones((1, 1) , device=_SCREAMING_SNAKE_CASE , dtype=torch.long ) * (step + 1)] , dim=1 )
| 321 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 | 1 |
'''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = '▁'
SCREAMING_SNAKE_CASE__ = {'vocab_file': 'prophetnet.tokenizer'}
SCREAMING_SNAKE_CASE__ = {
'vocab_file': {
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
),
}
}
SCREAMING_SNAKE_CASE__ = {
'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}
SCREAMING_SNAKE_CASE__ = {
'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}
def lowercase__ ( __UpperCamelCase )-> List[str]:
UpperCamelCase = collections.OrderedDict()
with open(__UpperCamelCase , """r""" , encoding="""utf-8""" ) as reader:
UpperCamelCase = reader.readlines()
for index, token in enumerate(__UpperCamelCase ):
UpperCamelCase = token.rstrip("""\n""" )
UpperCamelCase = index
return vocab
class a_ ( lowerCamelCase ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ["""input_ids""", """attention_mask"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="[UNK]" , _SCREAMING_SNAKE_CASE="[PAD]" , _SCREAMING_SNAKE_CASE="[CLS]" , _SCREAMING_SNAKE_CASE="[MASK]" , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
UpperCamelCase = {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}
for i in range(10 ):
UpperCamelCase = F"[unused{i}]"
UpperCamelCase = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
UpperCamelCase = 12
UpperCamelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(_SCREAMING_SNAKE_CASE )
def __getstate__( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.__dict__.copy()
UpperCamelCase = None
return state
def __setstate__( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCamelCase = {}
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def A__ ( self ) -> Any:
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase = self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
UpperCamelCase = """""".join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE , """ """ ).strip()
return out_string
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCamelCase = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , """wb""" ) as fi:
UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
UpperCamelCase = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=50 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = initializer_range
UpperCamelCase = use_labels
UpperCamelCase = scope
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = self.get_config()
return config, input_ids, input_mask, token_labels
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
return BertGenerationConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , is_decoder=_SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = self.prepare_config_and_inputs()
UpperCamelCase = True
UpperCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ) -> Tuple:
"""simple docstring"""
UpperCamelCase = BertGenerationEncoder(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ) -> List[str]:
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = BertGenerationEncoder(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ) -> Tuple:
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = BertGenerationDecoder(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).eval()
# first forward pass
UpperCamelCase = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE , )["""hidden_states"""][0]
UpperCamelCase = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE , )["""hidden_states"""][0]
# select random slice
UpperCamelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = BertGenerationDecoder(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
lowercase = (BertGenerationDecoder,) if is_torch_available() else ()
lowercase = (
{"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
if is_torch_available()
else {}
)
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = BertGenerationEncoderTester(self )
UpperCamelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs()
UpperCamelCase = """bert"""
self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCamelCase = None
self.model_tester.create_and_check_model_as_decoder(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*_SCREAMING_SNAKE_CASE )
@slow
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@require_torch
class a_ ( unittest.TestCase ):
@slow
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
UpperCamelCase = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]] )
with torch.no_grad():
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )[0]
UpperCamelCase = torch.Size([1, 8, 1024] )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor(
[[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@require_torch
class a_ ( unittest.TestCase ):
@slow
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
UpperCamelCase = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]] )
with torch.no_grad():
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )[0]
UpperCamelCase = torch.Size([1, 8, 50358] )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor(
[[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 321 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 321 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
'processing_clap': ['ClapProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapModel',
'ClapPreTrainedModel',
'ClapTextModel',
'ClapTextModelWithProjection',
'ClapAudioModel',
'ClapAudioModelWithProjection',
]
SCREAMING_SNAKE_CASE__ = ['ClapFeatureExtractor']
if TYPE_CHECKING:
from .configuration_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioConfig,
ClapConfig,
ClapTextConfig,
)
from .processing_clap import ClapProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clap import ClapFeatureExtractor
from .modeling_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioModel,
ClapAudioModelWithProjection,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 | 1 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=None )-> Optional[Any]:
UpperCamelCase = None
if token is not None:
UpperCamelCase = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
UpperCamelCase = F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
UpperCamelCase = requests.get(__UpperCamelCase , headers=__UpperCamelCase ).json()
UpperCamelCase = {}
try:
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
UpperCamelCase = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(__UpperCamelCase ):
UpperCamelCase = requests.get(url + F"&page={i + 2}" , headers=__UpperCamelCase ).json()
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return job_links
except Exception:
print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )
return {}
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=None )-> List[str]:
UpperCamelCase = None
if token is not None:
UpperCamelCase = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
UpperCamelCase = F"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
UpperCamelCase = requests.get(__UpperCamelCase , headers=__UpperCamelCase ).json()
UpperCamelCase = {}
try:
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
UpperCamelCase = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(__UpperCamelCase ):
UpperCamelCase = requests.get(url + F"&page={i + 2}" , headers=__UpperCamelCase ).json()
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
return artifacts
except Exception:
print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )
return {}
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> str:
UpperCamelCase = None
if token is not None:
UpperCamelCase = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
UpperCamelCase = requests.get(__UpperCamelCase , headers=__UpperCamelCase , allow_redirects=__UpperCamelCase )
UpperCamelCase = result.headers["""Location"""]
UpperCamelCase = requests.get(__UpperCamelCase , allow_redirects=__UpperCamelCase )
UpperCamelCase = os.path.join(__UpperCamelCase , F"{artifact_name}.zip" )
with open(__UpperCamelCase , """wb""" ) as fp:
fp.write(response.content )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=None )-> str:
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = None
with zipfile.ZipFile(__UpperCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(__UpperCamelCase ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(__UpperCamelCase ) as f:
for line in f:
UpperCamelCase = line.decode("""UTF-8""" ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
UpperCamelCase = line[: line.index(""": """ )]
UpperCamelCase = line[line.index(""": """ ) + len(""": """ ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith("""FAILED """ ):
# `test` is the test method that failed
UpperCamelCase = line[len("""FAILED """ ) :]
failed_tests.append(__UpperCamelCase )
elif filename == "job_name.txt":
UpperCamelCase = line
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError(
F"`errors` and `failed_tests` should have the same number of elements. Got {len(__UpperCamelCase )} for `errors` "
F"and {len(__UpperCamelCase )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"
""" problem.""" )
UpperCamelCase = None
if job_name and job_links:
UpperCamelCase = job_links.get(__UpperCamelCase , __UpperCamelCase )
# A list with elements of the form (line of error, error, failed test)
UpperCamelCase = [x + [y] + [job_link] for x, y in zip(__UpperCamelCase , __UpperCamelCase )]
return result
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=None )-> int:
UpperCamelCase = []
UpperCamelCase = [os.path.join(__UpperCamelCase , __UpperCamelCase ) for p in os.listdir(__UpperCamelCase ) if p.endswith(""".zip""" )]
for p in paths:
errors.extend(get_errors_from_single_artifact(__UpperCamelCase , job_links=__UpperCamelCase ) )
return errors
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=None )-> Optional[Any]:
UpperCamelCase = Counter()
counter.update([x[1] for x in logs] )
UpperCamelCase = counter.most_common()
UpperCamelCase = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
UpperCamelCase = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}
UpperCamelCase = dict(sorted(r.items() , key=lambda __UpperCamelCase : item[1]["count"] , reverse=__UpperCamelCase ) )
return r
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = test.split("""::""" )[0]
if test.startswith("""tests/models/""" ):
UpperCamelCase = test.split("""/""" )[2]
else:
UpperCamelCase = None
return test
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=None )-> Optional[Any]:
UpperCamelCase = [(x[0], x[1], get_model(x[2] )) for x in logs]
UpperCamelCase = [x for x in logs if x[2] is not None]
UpperCamelCase = {x[2] for x in logs}
UpperCamelCase = {}
for test in tests:
UpperCamelCase = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
UpperCamelCase = counter.most_common()
UpperCamelCase = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
UpperCamelCase = sum(error_counts.values() )
if n_errors > 0:
UpperCamelCase = {"""count""": n_errors, """errors""": error_counts}
UpperCamelCase = dict(sorted(r.items() , key=lambda __UpperCamelCase : item[1]["count"] , reverse=__UpperCamelCase ) )
return r
def lowercase__ ( __UpperCamelCase )-> Optional[int]:
UpperCamelCase = """| no. | error | status |"""
UpperCamelCase = """|-:|:-|:-|"""
UpperCamelCase = [header, sep]
for error in reduced_by_error:
UpperCamelCase = reduced_by_error[error]["""count"""]
UpperCamelCase = F"| {count} | {error[:100]} | |"
lines.append(__UpperCamelCase )
return "\n".join(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = """| model | no. of errors | major error | count |"""
UpperCamelCase = """|-:|-:|-:|-:|"""
UpperCamelCase = [header, sep]
for model in reduced_by_model:
UpperCamelCase = reduced_by_model[model]["""count"""]
UpperCamelCase ,UpperCamelCase = list(reduced_by_model[model]["""errors"""].items() )[0]
UpperCamelCase = F"| {model} | {count} | {error[:60]} | {_count} |"
lines.append(__UpperCamelCase )
return "\n".join(__UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
parser.add_argument(
'--output_dir',
type=str,
required=True,
help='Where to store the downloaded artifacts and other result files.',
)
parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
SCREAMING_SNAKE_CASE__ = get_job_links(args.workflow_run_id, token=args.token)
SCREAMING_SNAKE_CASE__ = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
SCREAMING_SNAKE_CASE__ = k.find(' / ')
SCREAMING_SNAKE_CASE__ = k[index + len(' / ') :]
SCREAMING_SNAKE_CASE__ = v
with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
SCREAMING_SNAKE_CASE__ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
SCREAMING_SNAKE_CASE__ = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
SCREAMING_SNAKE_CASE__ = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
SCREAMING_SNAKE_CASE__ = counter.most_common(3_0)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
SCREAMING_SNAKE_CASE__ = reduce_by_error(errors)
SCREAMING_SNAKE_CASE__ = reduce_by_model(errors)
SCREAMING_SNAKE_CASE__ = make_github_table(reduced_by_error)
SCREAMING_SNAKE_CASE__ = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
fp.write(sa)
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a_ ( unittest.TestCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=224 , _SCREAMING_SNAKE_CASE=30 , _SCREAMING_SNAKE_CASE=400 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , ) -> Any:
"""simple docstring"""
UpperCamelCase = size if size is not None else {"""height""": 18, """width""": 18}
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = image_size
UpperCamelCase = min_resolution
UpperCamelCase = max_resolution
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = do_normalize
UpperCamelCase = image_mean
UpperCamelCase = image_std
def A__ ( self ) -> Dict:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a_ ( lowerCamelCase , unittest.TestCase ):
lowercase = ViTImageProcessor if is_vision_available() else None
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = EfficientFormerImageProcessorTester(self )
@property
def A__ ( self ) -> int:
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """image_mean""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """image_std""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """do_normalize""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """do_resize""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """size""" ) )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
pass
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
UpperCamelCase = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
UpperCamelCase = image_processor(_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=_SCREAMING_SNAKE_CASE , numpify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
UpperCamelCase = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
UpperCamelCase = image_processor(_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=_SCREAMING_SNAKE_CASE , torchify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
UpperCamelCase = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
UpperCamelCase = image_processor(_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
| 321 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCamelCase = mf_knapsack(i - 1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
else:
UpperCamelCase = max(
mf_knapsack(i - 1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) , mf_knapsack(i - 1 , __UpperCamelCase , __UpperCamelCase , j - wt[i - 1] ) + val[i - 1] , )
UpperCamelCase = val
return f[i][j]
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
UpperCamelCase = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
UpperCamelCase = dp[i - 1][w_]
return dp[n][w_], dp
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
if not (isinstance(__UpperCamelCase , (list, tuple) ) and isinstance(__UpperCamelCase , (list, tuple) )):
raise ValueError(
"""Both the weights and values vectors must be either lists or tuples""" )
UpperCamelCase = len(__UpperCamelCase )
if num_items != len(__UpperCamelCase ):
UpperCamelCase = (
"""The number of weights must be the same as the number of values.\n"""
F"But got {num_items} weights and {len(__UpperCamelCase )} values"
)
raise ValueError(__UpperCamelCase )
for i in range(__UpperCamelCase ):
if not isinstance(wt[i] , __UpperCamelCase ):
UpperCamelCase = (
"""All weights must be integers but got weight of """
F"type {type(wt[i] )} at index {i}"
)
raise TypeError(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = knapsack(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
UpperCamelCase = set()
_construct_solution(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return optimal_val, example_optional_set
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(__UpperCamelCase , __UpperCamelCase , i - 1 , __UpperCamelCase , __UpperCamelCase )
else:
optimal_set.add(__UpperCamelCase )
_construct_solution(__UpperCamelCase , __UpperCamelCase , i - 1 , j - wt[i - 1] , __UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [3, 2, 4, 4]
SCREAMING_SNAKE_CASE__ = [4, 3, 2, 3]
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 6
SCREAMING_SNAKE_CASE__ = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset)
| 321 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = min(__UpperCamelCase ) # min() finds the minimum value
UpperCamelCase = max(__UpperCamelCase ) # max() finds the maximum value
UpperCamelCase = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCamelCase = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCamelCase = 0
for count in range(__UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
UpperCamelCase = count + min_val
i += 1
def lowercase__ ( )-> Any:
UpperCamelCase = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__UpperCamelCase )
print("""Sorted order is:""" , """ """.join(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class a_ ( lowerCamelCase ):
lowercase = """M-CLIP"""
def __init__( self , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=768 , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = transformerDimSize
UpperCamelCase = imageDimSize
super().__init__(**_SCREAMING_SNAKE_CASE )
class a_ ( lowerCamelCase ):
lowercase = MCLIPConfig
def __init__( self , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = XLMRobertaModel(_SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.transformer(input_ids=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )[0]
UpperCamelCase = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(_SCREAMING_SNAKE_CASE ), embs
| 321 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 1 |
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = 'ybelkada/fonts'
def lowercase__ ( )-> int:
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F"You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use "
"""Pix2StructImageProcessor. Please upgrade torch.""" )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[Any]:
requires_backends(__UpperCamelCase , ["""torch"""] )
_check_torch_version()
UpperCamelCase = image_tensor.unsqueeze(0 )
UpperCamelCase = torch.nn.functional.unfold(__UpperCamelCase , (patch_height, patch_width) , stride=(patch_height, patch_width) )
UpperCamelCase = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , __UpperCamelCase , __UpperCamelCase , -1 )
UpperCamelCase = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase = 36 , __UpperCamelCase = "black" , __UpperCamelCase = "white" , __UpperCamelCase = 5 , __UpperCamelCase = 5 , __UpperCamelCase = 5 , __UpperCamelCase = 5 , __UpperCamelCase = None , __UpperCamelCase = None , )-> Image.Image:
requires_backends(__UpperCamelCase , """vision""" )
# Add new lines so that each line is no more than 80 characters.
UpperCamelCase = textwrap.TextWrapper(width=80 )
UpperCamelCase = wrapper.wrap(text=__UpperCamelCase )
UpperCamelCase = """\n""".join(__UpperCamelCase )
if font_bytes is not None and font_path is None:
UpperCamelCase = io.BytesIO(__UpperCamelCase )
elif font_path is not None:
UpperCamelCase = font_path
else:
UpperCamelCase = hf_hub_download(__UpperCamelCase , """Arial.TTF""" )
UpperCamelCase = ImageFont.truetype(__UpperCamelCase , encoding="""UTF-8""" , size=__UpperCamelCase )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
UpperCamelCase = ImageDraw.Draw(Image.new("""RGB""" , (1, 1) , __UpperCamelCase ) )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = temp_draw.textbbox((0, 0) , __UpperCamelCase , __UpperCamelCase )
# Create the actual image with a bit of padding around the text.
UpperCamelCase = text_width + left_padding + right_padding
UpperCamelCase = text_height + top_padding + bottom_padding
UpperCamelCase = Image.new("""RGB""" , (image_width, image_height) , __UpperCamelCase )
UpperCamelCase = ImageDraw.Draw(__UpperCamelCase )
draw.text(xy=(left_padding, top_padding) , text=__UpperCamelCase , fill=__UpperCamelCase , font=__UpperCamelCase )
return image
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )-> List[str]:
requires_backends(__UpperCamelCase , """vision""" )
# Convert to PIL image if necessary
UpperCamelCase = to_pil_image(__UpperCamelCase )
UpperCamelCase = render_text(__UpperCamelCase , **__UpperCamelCase )
UpperCamelCase = max(header_image.width , image.width )
UpperCamelCase = int(image.height * (new_width / image.width) )
UpperCamelCase = int(header_image.height * (new_width / header_image.width) )
UpperCamelCase = Image.new("""RGB""" , (new_width, new_height + new_header_height) , """white""" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
UpperCamelCase = to_numpy_array(__UpperCamelCase )
if infer_channel_dimension_format(__UpperCamelCase ) == ChannelDimension.LAST:
UpperCamelCase = to_channel_dimension_format(__UpperCamelCase , ChannelDimension.LAST )
return new_image
class a_ ( lowerCamelCase ):
lowercase = ["""flattened_patches"""]
def __init__( self , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 2048 , _SCREAMING_SNAKE_CASE = False , **_SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
UpperCamelCase = do_normalize
UpperCamelCase = do_convert_rgb
UpperCamelCase = max_patches
UpperCamelCase = is_vqa
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> np.ndarray:
"""simple docstring"""
requires_backends(self.extract_flattened_patches , """torch""" )
_check_torch_version()
# convert to torch
UpperCamelCase = to_channel_dimension_format(_SCREAMING_SNAKE_CASE , ChannelDimension.FIRST )
UpperCamelCase = torch.from_numpy(_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = patch_size["""height"""], patch_size["""width"""]
UpperCamelCase ,UpperCamelCase = get_image_size(_SCREAMING_SNAKE_CASE )
# maximize scale s.t.
UpperCamelCase = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
UpperCamelCase = max(min(math.floor(scale * image_height / patch_height ) , _SCREAMING_SNAKE_CASE ) , 1 )
UpperCamelCase = max(min(math.floor(scale * image_width / patch_width ) , _SCREAMING_SNAKE_CASE ) , 1 )
UpperCamelCase = max(num_feasible_rows * patch_height , 1 )
UpperCamelCase = max(num_feasible_cols * patch_width , 1 )
UpperCamelCase = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="""bilinear""" , align_corners=_SCREAMING_SNAKE_CASE , antialias=_SCREAMING_SNAKE_CASE , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
UpperCamelCase = torch_extract_patches(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = patches.shape
UpperCamelCase = patches_shape[1]
UpperCamelCase = patches_shape[2]
UpperCamelCase = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
UpperCamelCase = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE ).reshape([rows, 1] ).repeat(1 , _SCREAMING_SNAKE_CASE ).reshape([rows * columns, 1] )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE ).reshape([1, columns] ).repeat(_SCREAMING_SNAKE_CASE , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
UpperCamelCase = row_ids.to(torch.floataa )
UpperCamelCase = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
UpperCamelCase = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
UpperCamelCase = torch.nn.functional.pad(_SCREAMING_SNAKE_CASE , [0, 0, 0, max_patches - (rows * columns)] ).float()
UpperCamelCase = to_numpy_array(_SCREAMING_SNAKE_CASE )
return result
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE ) -> np.ndarray:
"""simple docstring"""
if image.dtype == np.uinta:
UpperCamelCase = image.astype(np.floataa )
# take mean across the whole `image`
UpperCamelCase = np.mean(_SCREAMING_SNAKE_CASE )
UpperCamelCase = np.std(_SCREAMING_SNAKE_CASE )
UpperCamelCase = max(_SCREAMING_SNAKE_CASE , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE , ) -> ImageInput:
"""simple docstring"""
UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCamelCase = patch_size if patch_size is not None else self.patch_size
UpperCamelCase = max_patches if max_patches is not None else self.max_patches
UpperCamelCase = self.is_vqa
if kwargs.get("""data_format""" , _SCREAMING_SNAKE_CASE ) is not None:
raise ValueError("""data_format is not an accepted input as the outputs are """ )
UpperCamelCase = make_list_of_images(_SCREAMING_SNAKE_CASE )
if not valid_images(_SCREAMING_SNAKE_CASE ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCamelCase = [convert_to_rgb(_SCREAMING_SNAKE_CASE ) for image in images]
# All transformations expect numpy arrays.
UpperCamelCase = [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("""A header text must be provided for VQA models.""" )
UpperCamelCase = kwargs.pop("""font_bytes""" , _SCREAMING_SNAKE_CASE )
UpperCamelCase = kwargs.pop("""font_path""" , _SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = [header_text] * len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = [
render_header(_SCREAMING_SNAKE_CASE , header_text[i] , font_bytes=_SCREAMING_SNAKE_CASE , font_path=_SCREAMING_SNAKE_CASE )
for i, image in enumerate(_SCREAMING_SNAKE_CASE )
]
if do_normalize:
UpperCamelCase = [self.normalize(image=_SCREAMING_SNAKE_CASE ) for image in images]
# convert to torch tensor and permute
UpperCamelCase = [
self.extract_flattened_patches(image=_SCREAMING_SNAKE_CASE , max_patches=_SCREAMING_SNAKE_CASE , patch_size=_SCREAMING_SNAKE_CASE )
for image in images
]
# create attention mask in numpy
UpperCamelCase = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
UpperCamelCase = BatchFeature(
data={"""flattened_patches""": images, """attention_mask""": attention_masks} , tensor_type=_SCREAMING_SNAKE_CASE )
return encoded_outputs
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 1000 )-> int:
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCamelCase = n - a - b
if c * c == (a * a + b * b):
UpperCamelCase = a * b * c
if candidate >= product:
UpperCamelCase = candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
'SwinForImageClassification',
'SwinForMaskedImageModeling',
'SwinModel',
'SwinPreTrainedModel',
'SwinBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFSwinForImageClassification',
'TFSwinForMaskedImageModeling',
'TFSwinModel',
'TFSwinPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE__ = _symbol_database.Default()
SCREAMING_SNAKE_CASE__ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
SCREAMING_SNAKE_CASE__ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
SCREAMING_SNAKE_CASE__ = 4_5
SCREAMING_SNAKE_CASE__ = 1_5_8_1
SCREAMING_SNAKE_CASE__ = 1_5_1_7
SCREAMING_SNAKE_CASE__ = 1_5_7_0
SCREAMING_SNAKE_CASE__ = 1_5_8_4
SCREAMING_SNAKE_CASE__ = 1_7_9_3
SCREAMING_SNAKE_CASE__ = 1_7_9_5
SCREAMING_SNAKE_CASE__ = 1_9_1_6
SCREAMING_SNAKE_CASE__ = 1_8_6_4
SCREAMING_SNAKE_CASE__ = 1_9_0_5
SCREAMING_SNAKE_CASE__ = 1_9_1_9
SCREAMING_SNAKE_CASE__ = 2_4_2_9
SCREAMING_SNAKE_CASE__ = 2_2_0_8
SCREAMING_SNAKE_CASE__ = 2_4_1_8
SCREAMING_SNAKE_CASE__ = 2_3_2_3
SCREAMING_SNAKE_CASE__ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> List[Any]:
UpperCamelCase = """"""
for i in table:
res += inp[i - 1]
return res
def lowercase__ ( __UpperCamelCase )-> List[str]:
return data[1:] + data[0]
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
UpperCamelCase = """"""
for i in range(len(__UpperCamelCase ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Tuple:
UpperCamelCase = int("""0b""" + data[0] + data[-1] , 2 )
UpperCamelCase = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Tuple:
UpperCamelCase = message[:4]
UpperCamelCase = message[4:]
UpperCamelCase = apply_table(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = xor(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = apply_sbox(__UpperCamelCase , temp[:4] ) # noqa: E741
UpperCamelCase = apply_sbox(__UpperCamelCase , temp[4:] )
UpperCamelCase = """0""" * (2 - len(__UpperCamelCase )) + l # noqa: E741
UpperCamelCase = """0""" * (2 - len(__UpperCamelCase )) + r
UpperCamelCase = apply_table(l + r , __UpperCamelCase )
UpperCamelCase = xor(__UpperCamelCase , __UpperCamelCase )
return temp + right
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = input('Enter 10 bit key: ')
SCREAMING_SNAKE_CASE__ = input('Enter 8 bit message: ')
SCREAMING_SNAKE_CASE__ = [6, 3, 7, 4, 8, 5, 1_0, 9]
SCREAMING_SNAKE_CASE__ = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
SCREAMING_SNAKE_CASE__ = [2, 4, 3, 1]
SCREAMING_SNAKE_CASE__ = [2, 6, 3, 1, 4, 8, 5, 7]
SCREAMING_SNAKE_CASE__ = [4, 1, 3, 5, 7, 2, 8, 6]
SCREAMING_SNAKE_CASE__ = [4, 1, 2, 3, 2, 3, 4, 1]
SCREAMING_SNAKE_CASE__ = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
SCREAMING_SNAKE_CASE__ = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
SCREAMING_SNAKE_CASE__ = apply_table(key, paa_table)
SCREAMING_SNAKE_CASE__ = temp[:5]
SCREAMING_SNAKE_CASE__ = temp[5:]
SCREAMING_SNAKE_CASE__ = left_shift(left)
SCREAMING_SNAKE_CASE__ = left_shift(right)
SCREAMING_SNAKE_CASE__ = apply_table(left + right, pa_table)
SCREAMING_SNAKE_CASE__ = left_shift(left)
SCREAMING_SNAKE_CASE__ = left_shift(right)
SCREAMING_SNAKE_CASE__ = left_shift(left)
SCREAMING_SNAKE_CASE__ = left_shift(right)
SCREAMING_SNAKE_CASE__ = apply_table(left + right, pa_table)
# encryption
SCREAMING_SNAKE_CASE__ = apply_table(message, IP)
SCREAMING_SNAKE_CASE__ = function(expansion, sa, sa, keya, temp)
SCREAMING_SNAKE_CASE__ = temp[4:] + temp[:4]
SCREAMING_SNAKE_CASE__ = function(expansion, sa, sa, keya, temp)
SCREAMING_SNAKE_CASE__ = apply_table(temp, IP_inv)
print('Cipher text is:', CT)
# decryption
SCREAMING_SNAKE_CASE__ = apply_table(CT, IP)
SCREAMING_SNAKE_CASE__ = function(expansion, sa, sa, keya, temp)
SCREAMING_SNAKE_CASE__ = temp[4:] + temp[:4]
SCREAMING_SNAKE_CASE__ = function(expansion, sa, sa, keya, temp)
SCREAMING_SNAKE_CASE__ = apply_table(temp, IP_inv)
print('Plain text after decypting is:', PT)
| 321 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE__ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __UpperCamelCase )-> str:
if "://" in dataset_path:
UpperCamelCase = dataset_path.split("""://""" )[1]
return dataset_path
def lowercase__ ( __UpperCamelCase )-> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = not is_remote_filesystem(__UpperCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__UpperCamelCase ) , fs._strip_protocol(__UpperCamelCase ) )
else:
fs.mv(__UpperCamelCase , __UpperCamelCase , recursive=__UpperCamelCase )
def lowercase__ ( )-> None:
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = threading.Lock()
| 321 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class a_ ( lowerCamelCase ):
lowercase = ["""image_processor""", """tokenizer"""]
lowercase = """BlipImageProcessor"""
lowercase = """AutoTokenizer"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# add QFormer tokenizer
UpperCamelCase = qformer_tokenizer
def __call__( self , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> BatchFeature:
"""simple docstring"""
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
UpperCamelCase = BatchFeature()
if text is not None:
UpperCamelCase = self.tokenizer(
text=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , stride=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_overflowing_tokens=_SCREAMING_SNAKE_CASE , return_special_tokens_mask=_SCREAMING_SNAKE_CASE , return_offsets_mapping=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , return_length=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
encoding.update(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.qformer_tokenizer(
text=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , stride=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_overflowing_tokens=_SCREAMING_SNAKE_CASE , return_special_tokens_mask=_SCREAMING_SNAKE_CASE , return_offsets_mapping=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , return_length=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase = qformer_text_encoding.pop("""input_ids""" )
UpperCamelCase = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
UpperCamelCase = self.image_processor(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE )
encoding.update(_SCREAMING_SNAKE_CASE )
return encoding
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.tokenizer.model_input_names
UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def A__ ( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
if os.path.isfile(_SCREAMING_SNAKE_CASE ):
raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file" )
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
UpperCamelCase = os.path.join(_SCREAMING_SNAKE_CASE , """qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
return super().save_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@classmethod
def A__ ( cls , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE , subfolder="""qformer_tokenizer""" )
UpperCamelCase = cls._get_arguments_from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
args.append(_SCREAMING_SNAKE_CASE )
return cls(*_SCREAMING_SNAKE_CASE )
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 321 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class a_ ( unittest.TestCase ):
@slow
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = XLMRobertaModel.from_pretrained("""xlm-roberta-base""" )
UpperCamelCase = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
UpperCamelCase = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
UpperCamelCase = torch.tensor(
[[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
@slow
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = XLMRobertaModel.from_pretrained("""xlm-roberta-large""" )
UpperCamelCase = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
# The dog is cute and lives in the garden house
UpperCamelCase = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim
UpperCamelCase = torch.tensor(
[[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
| 321 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = 'docs/source/en/_toctree.yml'
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = defaultdict(__UpperCamelCase )
UpperCamelCase = []
UpperCamelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__UpperCamelCase )
UpperCamelCase = new_doc_list
UpperCamelCase = [key for key, value in counts.items() if value > 1]
UpperCamelCase = []
for duplicate_key in duplicates:
UpperCamelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCamelCase = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowercase__ ( __UpperCamelCase=False )-> List[str]:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCamelCase = api_doc[scheduler_idx]["""sections"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
UpperCamelCase = False
if new_scheduler_doc != scheduler_doc:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_scheduler_doc
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase__ ( __UpperCamelCase=False )-> Tuple:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCamelCase = False
UpperCamelCase = api_doc[pipeline_idx]["""sections"""]
UpperCamelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCamelCase = pipeline_doc["""section"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCamelCase = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_pipeline_docs
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 321 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = '▁'
SCREAMING_SNAKE_CASE__ = {'vocab_file': 'sentencepiece.bpe.model'}
SCREAMING_SNAKE_CASE__ = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
),
}
}
SCREAMING_SNAKE_CASE__ = {
'facebook/nllb-200-distilled-600M': 1_0_2_4,
}
# fmt: off
SCREAMING_SNAKE_CASE__ = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class a_ ( lowerCamelCase ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = ["""input_ids""", """attention_mask"""]
lowercase = []
lowercase = []
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else mask_token
UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCamelCase = legacy_behaviour
super().__init__(
bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , src_lang=_SCREAMING_SNAKE_CASE , tgt_lang=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , legacy_behaviour=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCamelCase = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCamelCase = 1
UpperCamelCase = len(self.sp_model )
UpperCamelCase = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE )
}
UpperCamelCase = {v: k for k, v in self.lang_code_to_id.items()}
UpperCamelCase = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
UpperCamelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
UpperCamelCase = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
UpperCamelCase = src_lang if src_lang is not None else """eng_Latn"""
UpperCamelCase = self.lang_code_to_id[self._src_lang]
UpperCamelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.__dict__.copy()
UpperCamelCase = None
UpperCamelCase = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCamelCase = {}
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def A__ ( self ) -> str:
"""simple docstring"""
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def A__ ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
UpperCamelCase = [1] * len(self.prefix_tokens )
UpperCamelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE )) + suffix_ones
return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE )) + ([0] * len(_SCREAMING_SNAKE_CASE )) + suffix_ones
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
UpperCamelCase = src_lang
UpperCamelCase = self(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE )
UpperCamelCase = tgt_lang_id
return inputs
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase = self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = """""".join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE , """ """ ).strip()
return out_string
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCamelCase = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , """wb""" ) as fi:
UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "eng_Latn" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "fra_Latn" , **_SCREAMING_SNAKE_CASE , ) -> BatchEncoding:
"""simple docstring"""
UpperCamelCase = src_lang
UpperCamelCase = tgt_lang
return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
UpperCamelCase = []
UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
else:
UpperCamelCase = [self.cur_lang_code]
UpperCamelCase = [self.eos_token_id]
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = self.lang_code_to_id[lang]
if self.legacy_behaviour:
UpperCamelCase = []
UpperCamelCase = [self.eos_token_id, self.cur_lang_code]
else:
UpperCamelCase = [self.cur_lang_code]
UpperCamelCase = [self.eos_token_id]
| 321 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCamelCase = 1.5
UpperCamelCase = int(factor * num_class_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 )
os.makedirs(F"{class_data_dir}/images" , exist_ok=__UpperCamelCase )
if len(list(Path(F"{class_data_dir}/images" ).iterdir() ) ) >= num_class_images:
return
while True:
UpperCamelCase = client.query(text=__UpperCamelCase )
if len(__UpperCamelCase ) >= factor * num_class_images or num_images > 1E4:
break
else:
UpperCamelCase = int(factor * num_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 , )
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = tqdm(desc="""downloading real regularization images""" , total=__UpperCamelCase )
with open(F"{class_data_dir}/caption.txt" , """w""" ) as fa, open(F"{class_data_dir}/urls.txt" , """w""" ) as fa, open(
F"{class_data_dir}/images.txt" , """w""" ) as fa:
while total < num_class_images:
UpperCamelCase = class_images[count]
count += 1
try:
UpperCamelCase = requests.get(images["""url"""] )
if img.status_code == 200:
UpperCamelCase = Image.open(BytesIO(img.content ) )
with open(F"{class_data_dir}/images/{total}.jpg" , """wb""" ) as f:
f.write(img.content )
fa.write(images["""caption"""] + """\n""" )
fa.write(images["""url"""] + """\n""" )
fa.write(F"{class_data_dir}/images/{total}.jpg" + """\n""" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def lowercase__ ( )-> str:
UpperCamelCase = argparse.ArgumentParser("""""" , add_help=__UpperCamelCase )
parser.add_argument("""--class_prompt""" , help="""text prompt to retrieve images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--class_data_dir""" , help="""path to save images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--num_class_images""" , help="""number of images to download""" , default=200 , type=__UpperCamelCase )
return parser.parse_args()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class a_ :
lowercase = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The column name of the images in the files."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the training data."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the validation data."""} )
lowercase = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = {}
if self.train_dir is not None:
UpperCamelCase = self.train_dir
if self.validation_dir is not None:
UpperCamelCase = self.validation_dir
UpperCamelCase = data_files if data_files else None
@dataclass
class a_ :
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowercase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class a_ ( lowerCamelCase ):
lowercase = field(
default=1E-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowercase__ ( )-> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , __UpperCamelCase , __UpperCamelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCamelCase = training_args.get_process_log_level()
logger.setLevel(__UpperCamelCase )
transformers.utils.logging.set_verbosity(__UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
UpperCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCamelCase = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __UpperCamelCase ) and data_args.train_val_split > 0.0:
UpperCamelCase = ds["""train"""].train_test_split(data_args.train_val_split )
UpperCamelCase = split["""train"""]
UpperCamelCase = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.config_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
UpperCamelCase = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase = ViTMAEForPreTraining(__UpperCamelCase )
if training_args.do_train:
UpperCamelCase = ds["""train"""].column_names
else:
UpperCamelCase = ds["""validation"""].column_names
if data_args.image_column_name is not None:
UpperCamelCase = data_args.image_column_name
elif "image" in column_names:
UpperCamelCase = """image"""
elif "img" in column_names:
UpperCamelCase = """img"""
else:
UpperCamelCase = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
UpperCamelCase = image_processor.size["""shortest_edge"""]
else:
UpperCamelCase = (image_processor.size["""height"""], image_processor.size["""width"""])
UpperCamelCase = Compose(
[
Lambda(lambda __UpperCamelCase : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(__UpperCamelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(__UpperCamelCase ):
UpperCamelCase = [transforms(__UpperCamelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
UpperCamelCase = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
UpperCamelCase = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__UpperCamelCase )
# Compute absolute learning rate
UpperCamelCase = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
UpperCamelCase = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
UpperCamelCase = Trainer(
model=__UpperCamelCase , args=__UpperCamelCase , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=__UpperCamelCase , data_collator=__UpperCamelCase , )
# Training
if training_args.do_train:
UpperCamelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCamelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCamelCase = last_checkpoint
UpperCamelCase = trainer.train(resume_from_checkpoint=__UpperCamelCase )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCamelCase = trainer.evaluate()
trainer.log_metrics("""eval""" , __UpperCamelCase )
trainer.save_metrics("""eval""" , __UpperCamelCase )
# Write model card and (optionally) push to hub
UpperCamelCase = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__UpperCamelCase )
else:
trainer.create_model_card(**__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> List[str]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 321 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''timm_backbone'''
def __init__( self : Optional[Any] , __UpperCAmelCase : Any=None , __UpperCAmelCase : Any=3 , __UpperCAmelCase : str=True , __UpperCAmelCase : int=True , __UpperCAmelCase : Optional[int]=None , **__UpperCAmelCase : Dict , ) ->Tuple:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
a = backbone
a = num_channels
a = features_only
a = use_pretrained_backbone
a = True
a = out_indices if out_indices is not None else (-1,)
| 0 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE__ = 2_5_6
class a_ ( lowerCamelCase ):
lowercase = ["""melgan"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase = math.log(1e-5 ) # Matches MelGAN training.
UpperCamelCase = 4.0 # Largest value for most examples
UpperCamelCase = 128
self.register_modules(
notes_encoder=_SCREAMING_SNAKE_CASE , continuous_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , melgan=_SCREAMING_SNAKE_CASE , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = output_range
if clip:
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , self.min_value , self.max_value )
# Scale to [0, 1].
UpperCamelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = input_range
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = input_tokens > 0
UpperCamelCase ,UpperCamelCase = self.notes_encoder(
encoder_input_tokens=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.continuous_encoder(
encoder_inputs=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = noise_time
if not torch.is_tensor(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(_SCREAMING_SNAKE_CASE ) and len(timesteps.shape ) == 0:
UpperCamelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
UpperCamelCase = self.decoder(
encodings_and_masks=_SCREAMING_SNAKE_CASE , decoder_input_tokens=_SCREAMING_SNAKE_CASE , decoder_noise_time=_SCREAMING_SNAKE_CASE )
return logits
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "numpy" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(_SCREAMING_SNAKE_CASE )}." )
UpperCamelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
UpperCamelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
UpperCamelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE ):
if i == 0:
UpperCamelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase = ones
UpperCamelCase = self.scale_features(
_SCREAMING_SNAKE_CASE , output_range=[-1.0, 1.0] , clip=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=_SCREAMING_SNAKE_CASE , continuous_mask=_SCREAMING_SNAKE_CASE , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase = self.decode(
encodings_and_masks=_SCREAMING_SNAKE_CASE , input_tokens=_SCREAMING_SNAKE_CASE , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
UpperCamelCase = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = self.scale_to_features(_SCREAMING_SNAKE_CASE , input_range=[-1.0, 1.0] )
UpperCamelCase = mel[:1]
UpperCamelCase = mel.cpu().float().numpy()
UpperCamelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
logger.info("""Generated segment""" , _SCREAMING_SNAKE_CASE )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
UpperCamelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE )
| 321 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_: Dict ={
'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: Tuple =['AlbertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: str =['AlbertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: Optional[int] =[
'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'AlbertForMaskedLM',
'AlbertForMultipleChoice',
'AlbertForPreTraining',
'AlbertForQuestionAnswering',
'AlbertForSequenceClassification',
'AlbertForTokenClassification',
'AlbertModel',
'AlbertPreTrainedModel',
'load_tf_weights_in_albert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: List[str] =[
'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAlbertForMaskedLM',
'TFAlbertForMultipleChoice',
'TFAlbertForPreTraining',
'TFAlbertForQuestionAnswering',
'TFAlbertForSequenceClassification',
'TFAlbertForTokenClassification',
'TFAlbertMainLayer',
'TFAlbertModel',
'TFAlbertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: Optional[Any] =[
'FlaxAlbertForMaskedLM',
'FlaxAlbertForMultipleChoice',
'FlaxAlbertForPreTraining',
'FlaxAlbertForQuestionAnswering',
'FlaxAlbertForSequenceClassification',
'FlaxAlbertForTokenClassification',
'FlaxAlbertModel',
'FlaxAlbertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_: Tuple =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = b, a + b
return sum(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def _SCREAMING_SNAKE_CASE () -> int:
"""simple docstring"""
lowercase__ = '''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
lowercase__ = Image.open(requests.get(A , stream=A ).raw ).convert('''RGB''' )
return image
def _SCREAMING_SNAKE_CASE (A ) -> Any:
"""simple docstring"""
lowercase__ = []
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias") )
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias") )
rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",) )
rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias") )
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias") )
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight") )
rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias") )
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''') )
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''') )
# fmt: on
return rename_keys
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Tuple:
"""simple docstring"""
lowercase__ = dct.pop(A )
lowercase__ = val
def _SCREAMING_SNAKE_CASE (A , A ) -> Union[str, Any]:
"""simple docstring"""
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
lowercase__ = state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias" )
lowercase__ = state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias" )
# next, set bias in the state dict
lowercase__ = torch.cat((q_bias, torch.zeros_like(A , requires_grad=A ), v_bias) )
lowercase__ = qkv_bias
def _SCREAMING_SNAKE_CASE (A ) -> List[Any]:
"""simple docstring"""
lowercase__ = 364 if '''coco''' in model_name else 224
lowercase__ = InstructBlipVisionConfig(image_size=A ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
lowercase__ = TaConfig.from_pretrained('''google/flan-t5-xl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
lowercase__ = TaConfig.from_pretrained('''google/flan-t5-xxl''' , dense_act_fn='''gelu''' , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
lowercase__ = LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf''' , vocab_size=32_001 ).to_dict()
elif "vicuna-13b" in model_name:
lowercase__ = LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf''' , vocab_size=32_001 ).to_dict()
else:
raise ValueError('''Model name not supported''' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
lowercase__ = InstructBlipQFormerConfig(vocab_size=30_523 ).to_dict()
lowercase__ = InstructBlipConfig(vision_config=A , text_config=A , qformer_config=A )
return config, image_size
@torch.no_grad()
def _SCREAMING_SNAKE_CASE (A , A=None , A=False ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = AutoTokenizer.from_pretrained('''bert-base-uncased''' , truncation_side='''left''' )
qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''} )
if "t5" in model_name:
lowercase__ = TaTokenizerFast.from_pretrained('''google/flan-t5-xl''' , truncation_side='''left''' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
lowercase__ = LlamaTokenizerFast.from_pretrained(
'''huggyllama/llama-7b''' , truncation_side='''left''' , bos_token='''</s>''' , unk_token='''</s>''' )
tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''} )
lowercase__ ,lowercase__ = get_blipa_config(A )
lowercase__ = InstructBlipForConditionalGeneration(A ).eval()
lowercase__ = {
'''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
'''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
'''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
'''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
}
lowercase__ ,lowercase__ = model_name_to_original[model_name]
# load original model
print('''Loading original model...''' )
lowercase__ = '''cuda:1''' if torch.cuda.is_available() else '''cpu'''
lowercase__ = '''cuda:2''' if torch.cuda.is_available() else '''cpu'''
lowercase__ ,lowercase__ ,lowercase__ = load_model_and_preprocess(
name=A , model_type=A , is_eval=A , device=A )
original_model.eval()
print('''Done!''' )
# update state dict keys
lowercase__ = original_model.state_dict()
lowercase__ = create_rename_keys(A )
for src, dest in rename_keys:
rename_key(A , A , A )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
lowercase__ = state_dict.pop(A )
if key.startswith('''Qformer.bert''' ):
lowercase__ = key.replace('''Qformer.bert''' , '''qformer''' )
if "attention.self" in key:
lowercase__ = key.replace('''self''' , '''attention''' )
if "llm_proj" in key:
lowercase__ = key.replace('''llm_proj''' , '''language_projection''' )
if "t5_proj" in key:
lowercase__ = key.replace('''t5_proj''' , '''language_projection''' )
if key.startswith('''llm_model''' ):
lowercase__ = key.replace('''llm_model''' , '''language_model''' )
if key.startswith('''t5''' ):
lowercase__ = key.replace('''t5''' , '''language''' )
lowercase__ = val
# read in qv biases
read_in_q_v_bias(A , A )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(A , strict=A )
lowercase__ = load_demo_image()
lowercase__ = '''What is unusual about this image?'''
# create processor
lowercase__ = BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size} , image_mean=A , image_std=A )
lowercase__ = InstructBlipProcessor(
image_processor=A , tokenizer=A , qformer_tokenizer=A , )
lowercase__ = processor(images=A , text=A , return_tensors='''pt''' ).to(A )
# make sure processor creates exact same pixel values
lowercase__ = vis_processors['''eval'''](A ).unsqueeze(0 ).to(A )
lowercase__ = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , A )
original_model.to(A )
hf_model.to(A )
with torch.no_grad():
if "vicuna" in model_name:
lowercase__ = original_model({'''image''': original_pixel_values, '''text_input''': [prompt]} ).logits
lowercase__ = hf_model(**A ).logits
else:
lowercase__ = original_model(
{'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']} ).logits
lowercase__ = tokenizer('''\n''' , return_tensors='''pt''' ).input_ids.to(A )
lowercase__ = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
lowercase__ = hf_model(**A , labels=A ).logits
print('''First values of original logits:''' , original_logits[0, :3, :3] )
print('''First values of HF logits:''' , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
lowercase__ = 1E-4 if '''vicuna''' in model_name else 1E-5
assert torch.allclose(original_logits.to(logits.device ) , A , atol=A )
print('''Looks ok!''' )
print('''Generating with original model...''' )
lowercase__ = original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('''Generating with HF model...''' )
lowercase__ = hf_model.generate(
**A , do_sample=A , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
lowercase__ = 2
print('''Original generation:''' , A )
lowercase__ = processor.batch_decode(A , skip_special_tokens=A )
lowercase__ = [text.strip() for text in output_text]
print('''HF generation:''' , A )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(A )
hf_model.save_pretrained(A )
if push_to_hub:
processor.push_to_hub(f"Salesforce/{model_name}" )
hf_model.push_to_hub(f"Salesforce/{model_name}" )
if __name__ == "__main__":
lowerCamelCase : List[Any] = argparse.ArgumentParser()
lowerCamelCase : Dict = [
'instructblip-vicuna-7b',
'instructblip-vicuna-13b',
'instructblip-flan-t5-xl',
'instructblip-flan-t5-xxl',
]
parser.add_argument(
'--model_name',
default='instructblip-flan-t5-xl',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
lowerCamelCase : Union[str, Any] = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 2 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
# Base Case
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] , __UpperCamelCase , __UpperCamelCase ):
# Color current vertex
UpperCamelCase = i
# Validate coloring
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , index + 1 ):
return True
# Backtrack
UpperCamelCase = -1
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[int]:
UpperCamelCase = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , 0 ):
return colored_vertices
return []
| 321 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[Any] = 0
A : Any = len(snake_case__ )
for i in range(n - 1 ):
for j in range(i + 1 , snake_case__ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return arr, 0
A : Any = len(snake_case__ ) // 2
A : List[Any] = arr[0:mid]
A : Union[str, Any] = arr[mid:]
A, A : List[str] = count_inversions_recursive(snake_case__ )
A, A : Dict = count_inversions_recursive(snake_case__ )
A, A : Dict = _count_cross_inversions(snake_case__ , snake_case__ )
A : Optional[int] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : Tuple = []
A : Optional[Any] = 0
while i < len(snake_case__ ) and j < len(snake_case__ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(snake_case__ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(snake_case__ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : Tuple = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
A : List[Any] = count_inversions_bf(snake_case__ )
A, A : int = count_inversions_recursive(snake_case__ )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''' , snake_case__ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
A : Tuple = count_inversions_bf(snake_case__ )
A, A : Optional[int] = count_inversions_recursive(snake_case__ )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , snake_case__ )
# an empty list should also have zero inversions
A : int = []
A : List[Any] = count_inversions_bf(snake_case__ )
A, A : Optional[int] = count_inversions_recursive(snake_case__ )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''' , snake_case__ )
if __name__ == "__main__":
main()
| 3 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __UpperCamelCase ):
UpperCamelCase = 1
UpperCamelCase = 0
for i in range(__UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
'''simple docstring'''
class UpperCAmelCase_ :
def __init__( self : List[str] , UpperCAmelCase__ : list[int] ) -> None:
lowerCAmelCase = len(UpperCAmelCase__ )
lowerCAmelCase = [0] * len_array
if len_array > 0:
lowerCAmelCase = array[0]
for i in range(1 , UpperCAmelCase__ ):
lowerCAmelCase = self.prefix_sum[i - 1] + array[i]
def __UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> int:
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def __UpperCAmelCase ( self : int , UpperCAmelCase__ : int ) -> bool:
lowerCAmelCase = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(UpperCAmelCase__ )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 | 0 |
from typing import Any
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> list:
"""simple docstring"""
_validation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# Creates data structures and fill initial step
_lowercase ={}
_lowercase ={}
for state in states_space:
_lowercase =observations_space[0]
_lowercase =(
initial_probabilities[state] * emission_probabilities[state][observation]
)
_lowercase =None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__snake_case ) ):
_lowercase =observations_space[o]
_lowercase =observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
_lowercase =''''''
_lowercase =-1
for k_state in states_space:
_lowercase =(
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
_lowercase =probability
_lowercase =k_state
# Update probabilities and pointers dicts
_lowercase =(
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
_lowercase =arg_max
# The final observation
_lowercase =observations_space[len(__snake_case ) - 1]
# argmax for given final observation
_lowercase =''''''
_lowercase =-1
for k_state in states_space:
_lowercase =probabilities[(k_state, final_observation)]
if probability > max_probability:
_lowercase =probability
_lowercase =k_state
_lowercase =arg_max
# Process pointers backwards
_lowercase =last_state
_lowercase =[]
for o in range(len(__snake_case ) - 1 , -1 , -1 ):
result.append(__snake_case )
_lowercase =pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
_validate_not_empty(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
_validate_lists(__snake_case , __snake_case )
_validate_dicts(
__snake_case , __snake_case , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('''There\'s an empty parameter''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
_validate_list(__snake_case , '''observations_space''' )
_validate_list(__snake_case , '''states_space''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
if not isinstance(_object , __snake_case ):
_lowercase =F"{var_name} must be a list"
raise ValueError(__snake_case )
else:
for x in _object:
if not isinstance(__snake_case , __snake_case ):
_lowercase =F"{var_name} must be a list of strings"
raise ValueError(__snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
_validate_dict(__snake_case , '''initial_probabilities''' , __snake_case )
_validate_nested_dict(__snake_case , '''transition_probabilities''' )
_validate_nested_dict(__snake_case , '''emission_probabilities''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
_validate_dict(_object , __snake_case , __snake_case )
for x in _object.values():
_validate_dict(__snake_case , __snake_case , __snake_case , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case = False ) -> None:
"""simple docstring"""
if not isinstance(_object , __snake_case ):
_lowercase =F"{var_name} must be a dict"
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object ):
_lowercase =F"{var_name} all keys must be strings"
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ):
_lowercase ='''nested dictionary ''' if nested else ''''''
_lowercase =F"{var_name} {nested_text}all values must be {value_type.__name__}"
raise ValueError(__snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 5 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : List[Any] = logging.get_logger(__name__)
A : Union[str, Any] = {
'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
# See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class __A( a ):
snake_case_ = '''deformable_detr'''
snake_case_ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self , _snake_case=True , _snake_case=None , _snake_case=3 , _snake_case=300 , _snake_case=1_024 , _snake_case=6 , _snake_case=1_024 , _snake_case=8 , _snake_case=6 , _snake_case=1_024 , _snake_case=8 , _snake_case=0.0 , _snake_case=True , _snake_case="relu" , _snake_case=256 , _snake_case=0.1 , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=1.0 , _snake_case=True , _snake_case=False , _snake_case="sine" , _snake_case="resnet50" , _snake_case=True , _snake_case=False , _snake_case=4 , _snake_case=4 , _snake_case=4 , _snake_case=False , _snake_case=300 , _snake_case=False , _snake_case=1 , _snake_case=5 , _snake_case=2 , _snake_case=1 , _snake_case=1 , _snake_case=5 , _snake_case=2 , _snake_case=0.1 , _snake_case=0.25 , _snake_case=False , **_snake_case , ) -> Any:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
__a = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_snake_case , _snake_case ):
__a = backbone_config.get('''model_type''' )
__a = CONFIG_MAPPING[backbone_model_type]
__a = config_class.from_dict(_snake_case )
__a = use_timm_backbone
__a = backbone_config
__a = num_channels
__a = num_queries
__a = max_position_embeddings
__a = d_model
__a = encoder_ffn_dim
__a = encoder_layers
__a = encoder_attention_heads
__a = decoder_ffn_dim
__a = decoder_layers
__a = decoder_attention_heads
__a = dropout
__a = attention_dropout
__a = activation_dropout
__a = activation_function
__a = init_std
__a = init_xavier_std
__a = encoder_layerdrop
__a = auxiliary_loss
__a = position_embedding_type
__a = backbone
__a = use_pretrained_backbone
__a = dilation
# deformable attributes
__a = num_feature_levels
__a = encoder_n_points
__a = decoder_n_points
__a = two_stage
__a = two_stage_num_proposals
__a = with_box_refine
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
__a = class_cost
__a = bbox_cost
__a = giou_cost
# Loss coefficients
__a = mask_loss_coefficient
__a = dice_loss_coefficient
__a = bbox_loss_coefficient
__a = giou_loss_coefficient
__a = eos_coefficient
__a = focal_alpha
__a = disable_custom_kernels
super().__init__(is_encoder_decoder=_snake_case , **_snake_case )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
return self.d_model
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
__a = self.backbone_config.to_dict()
__a = self.__class__.model_type
return output | 6 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 321 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'biogpt'
def __init__( self : str,lowercase_ : Union[str, Any]=4_2_3_8_4,lowercase_ : List[str]=1_0_2_4,lowercase_ : Dict=2_4,lowercase_ : str=1_6,lowercase_ : Dict=4_0_9_6,lowercase_ : Optional[Any]="gelu",lowercase_ : List[str]=0.1,lowercase_ : List[Any]=0.1,lowercase_ : List[str]=1_0_2_4,lowercase_ : Optional[Any]=0.02,lowercase_ : str=1E-12,lowercase_ : List[Any]=True,lowercase_ : Dict=True,lowercase_ : Union[str, Any]=0.0,lowercase_ : Optional[Any]=0.0,lowercase_ : Union[str, Any]=1,lowercase_ : List[Any]=0,lowercase_ : Dict=2,**lowercase_ : List[str],)-> Dict:
'''simple docstring'''
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = initializer_range
A__ = layer_norm_eps
A__ = scale_embedding
A__ = use_cache
A__ = layerdrop
A__ = activation_dropout
super().__init__(pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,**lowercase_ )
| 7 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class snake_case_ ( __A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = "canine"
def __init__( self : str , _UpperCamelCase : Optional[Any]=7_6_8 , _UpperCamelCase : Optional[int]=1_2 , _UpperCamelCase : int=1_2 , _UpperCamelCase : Tuple=3_0_7_2 , _UpperCamelCase : Tuple="gelu" , _UpperCamelCase : Optional[Any]=0.1 , _UpperCamelCase : Union[str, Any]=0.1 , _UpperCamelCase : Dict=1_6_3_8_4 , _UpperCamelCase : List[Any]=1_6 , _UpperCamelCase : Tuple=0.02 , _UpperCamelCase : Optional[Any]=1e-12 , _UpperCamelCase : Union[str, Any]=0 , _UpperCamelCase : Union[str, Any]=0xe_0_0_0 , _UpperCamelCase : Dict=0xe_0_0_1 , _UpperCamelCase : Any=4 , _UpperCamelCase : Tuple=4 , _UpperCamelCase : List[str]=8 , _UpperCamelCase : Any=1_6_3_8_4 , _UpperCamelCase : List[str]=1_2_8 , **_UpperCamelCase : str , ) ->List[Any]:
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase )
snake_case_ = max_position_embeddings
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = initializer_range
snake_case_ = type_vocab_size
snake_case_ = layer_norm_eps
# Character config:
snake_case_ = downsampling_rate
snake_case_ = upsampling_kernel_size
snake_case_ = num_hash_functions
snake_case_ = num_hash_buckets
snake_case_ = local_transformer_stride | 8 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
from importlib import import_module
from .logging import get_logger
__lowerCAmelCase : str =get_logger(__name__)
class _lowercase :
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :str=None ) -> int:
__SCREAMING_SNAKE_CASE : List[str] = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('''__''' ):
setattr(self , lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : Optional[Any] = module._original_module if isinstance(lowerCAmelCase__ , _PatchedModuleObj ) else module
class _lowercase :
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
def __init__( self :Tuple , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :str , lowerCAmelCase__ :Any , lowerCAmelCase__ :Dict=None ) -> List[Any]:
__SCREAMING_SNAKE_CASE : Optional[int] = obj
__SCREAMING_SNAKE_CASE : str = target
__SCREAMING_SNAKE_CASE : Dict = new
__SCREAMING_SNAKE_CASE : Union[str, Any] = target.split('''.''' )[0]
__SCREAMING_SNAKE_CASE : List[str] = {}
__SCREAMING_SNAKE_CASE : Tuple = attrs or []
def __enter__( self :int ) -> Dict:
*__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.target.split('''.''' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCAmelCase__ ) ):
try:
__SCREAMING_SNAKE_CASE : Any = import_module('''.'''.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
__SCREAMING_SNAKE_CASE : Union[str, Any] = getattr(self.obj , lowerCAmelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCAmelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
__SCREAMING_SNAKE_CASE : int = obj_attr
# patch at top level
setattr(self.obj , lowerCAmelCase__ , _PatchedModuleObj(lowerCAmelCase__ , attrs=self.attrs ) )
__SCREAMING_SNAKE_CASE : List[str] = getattr(self.obj , lowerCAmelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCAmelCase__ , lowerCAmelCase__ , _PatchedModuleObj(getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , attrs=self.attrs ) )
__SCREAMING_SNAKE_CASE : Tuple = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
# finally set the target attribute
setattr(lowerCAmelCase__ , lowerCAmelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
__SCREAMING_SNAKE_CASE : Union[str, Any] = getattr(import_module('''.'''.join(lowerCAmelCase__ ) ) , lowerCAmelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCAmelCase__ ) is attr_value:
__SCREAMING_SNAKE_CASE : Any = getattr(self.obj , lowerCAmelCase__ )
setattr(self.obj , lowerCAmelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
__SCREAMING_SNAKE_CASE : Union[str, Any] = globals()['''__builtins__'''][target_attr]
setattr(self.obj , lowerCAmelCase__ , self.new )
else:
raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.''' )
def __exit__( self :str , *lowerCAmelCase__ :Union[str, Any] ) -> Optional[int]:
for attr in list(self.original ):
setattr(self.obj , lowerCAmelCase__ , self.original.pop(lowerCAmelCase__ ) )
def __magic_name__( self :List[Any] ) -> List[Any]:
self.__enter__()
self._active_patches.append(self )
def __magic_name__( self :Optional[int] ) -> int:
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 9 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = {
"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",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "ctc_proj",
"mask_emb": "masked_spec_embed",
}
__A = [
"ctc_proj",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , __a ) -> Optional[Any]:
"""simple docstring"""
for attribute in key.split("." ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
lowerCamelCase__: Optional[int] ="lm_head"
lowerCamelCase__: Dict =getattr(__a , __a )
if weight_type is not None:
lowerCamelCase__: str =getattr(__a , __a ).shape
else:
lowerCamelCase__: int =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":
lowerCamelCase__: Dict =value
elif weight_type == "weight_g":
lowerCamelCase__: Optional[Any] =value
elif weight_type == "weight_v":
lowerCamelCase__: int =value
elif weight_type == "bias":
lowerCamelCase__: List[str] =value
else:
lowerCamelCase__: Union[str, Any] =value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def lowerCAmelCase_ ( __a , __a , __a ) -> Any:
"""simple docstring"""
lowerCamelCase__: List[Any] =[]
lowerCamelCase__: List[str] =fairseq_model.state_dict()
lowerCamelCase__: Optional[int] =hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
lowerCamelCase__: int =False
if "conv_layers" in name:
load_conv_layer(
__a , __a , __a , __a , hf_model.config.feat_extract_norm == "group" , )
lowerCamelCase__: str =True
else:
for key, mapped_key in MAPPING.items():
lowerCamelCase__: List[str] ="unispeech." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
lowerCamelCase__: Optional[Any] =True
if "*" in mapped_key:
lowerCamelCase__: Optional[Any] =name.split(__a )[0].split("." )[-2]
lowerCamelCase__: List[str] =mapped_key.replace("*" , __a )
if "weight_g" in name:
lowerCamelCase__: List[str] ="weight_g"
elif "weight_v" in name:
lowerCamelCase__: Union[str, Any] ="weight_v"
elif "bias" in name:
lowerCamelCase__: Dict ="bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowerCamelCase__: Tuple ="weight"
else:
lowerCamelCase__: List[Any] =None
set_recursively(__a , __a , __a , __a , __a , __a )
continue
if not is_used:
unused_weights.append(__a )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCAmelCase_ ( __a , __a , __a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__: Tuple =full_name.split("conv_layers." )[-1]
lowerCamelCase__: List[str] =name.split("." )
lowerCamelCase__: str =int(items[0] )
lowerCamelCase__: Union[str, Any] =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."""
)
lowerCamelCase__: List[str] =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."""
)
lowerCamelCase__: Dict =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."
)
lowerCamelCase__: List[Any] =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."""
)
lowerCamelCase__: List[str] =value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__a )
@torch.no_grad()
def lowerCAmelCase_ ( __a , __a , __a=None , __a=None , __a=True ) -> int:
"""simple docstring"""
if config_path is not None:
lowerCamelCase__: str =UniSpeechConfig.from_pretrained(__a )
else:
lowerCamelCase__: List[Any] =UniSpeechConfig()
if is_finetuned:
if dict_path:
lowerCamelCase__: str =Dictionary.load_from_json(__a )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCamelCase__: Any =target_dict.pad_index
lowerCamelCase__: int =target_dict.bos_index
lowerCamelCase__: Any =target_dict.eos_index
lowerCamelCase__: Dict =len(target_dict.symbols )
lowerCamelCase__: Optional[int] =os.path.join(__a , "vocab.json" )
if not os.path.isdir(__a ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__a ) )
return
os.makedirs(__a , exist_ok=__a )
lowerCamelCase__: Optional[Any] =target_dict.indices
# fairseq has the <pad> and <s> switched
lowerCamelCase__: Optional[Any] =42
lowerCamelCase__: List[Any] =43
with open(__a , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(__a , __a )
lowerCamelCase__: List[str] =WavaVecaPhonemeCTCTokenizer(
__a , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=__a , )
lowerCamelCase__: Dict =True if config.feat_extract_norm == "layer" else False
lowerCamelCase__: Tuple =WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__a , return_attention_mask=__a , )
lowerCamelCase__: List[Any] =WavaVecaProcessor(feature_extractor=__a , tokenizer=__a )
processor.save_pretrained(__a )
lowerCamelCase__: int =UniSpeechForCTC(__a )
else:
lowerCamelCase__: int =UniSpeechForPreTraining(__a )
if is_finetuned:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Optional[int] =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] ), "w2v_path": checkpoint_path} )
else:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Tuple =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
lowerCamelCase__: List[str] =model[0].eval()
recursively_load_weights(__a , __a , __a )
hf_unispeech.save_pretrained(__a )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
__A = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 10 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = min(__UpperCamelCase ) # min() finds the minimum value
UpperCamelCase = max(__UpperCamelCase ) # max() finds the maximum value
UpperCamelCase = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCamelCase = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCamelCase = 0
for count in range(__UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
UpperCamelCase = count + min_val
i += 1
def lowercase__ ( )-> Any:
UpperCamelCase = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__UpperCamelCase )
print("""Sorted order is:""" , """ """.join(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 321 | 0 |
from __future__ import annotations
def _UpperCAmelCase (UpperCamelCase__ : tuple[int, int] , UpperCamelCase__ : int ):
_A , _A : Union[str, Any] = position
_A : str = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
_A : Dict = []
for position in positions:
_A , _A : List[str] = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(UpperCamelCase__ )
return permissible_positions
def _UpperCAmelCase (UpperCamelCase__ : list[list[int]] ):
return not any(elem == 0 for row in board for elem in row )
def _UpperCAmelCase (UpperCamelCase__ : list[list[int]] , UpperCamelCase__ : tuple[int, int] , UpperCamelCase__ : int ):
if is_complete(UpperCamelCase__ ):
return True
for position in get_valid_pos(UpperCamelCase__ , len(UpperCamelCase__ ) ):
_A , _A : Union[str, Any] = position
if board[y][x] == 0:
_A : Optional[Any] = curr + 1
if open_knight_tour_helper(UpperCamelCase__ , UpperCamelCase__ , curr + 1 ):
return True
_A : Union[str, Any] = 0
return False
def _UpperCAmelCase (UpperCamelCase__ : int ):
_A : Tuple = [[0 for i in range(UpperCamelCase__ )] for j in range(UpperCamelCase__ )]
for i in range(UpperCamelCase__ ):
for j in range(UpperCamelCase__ ):
_A : Union[str, Any] = 1
if open_knight_tour_helper(UpperCamelCase__ , (i, j) , 1 ):
return board
_A : Any = 0
_A : Union[str, Any] = f"Open Kight Tour cannot be performed on a board of size {n}"
raise ValueError(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
def lowerCamelCase__ ( A__ : Any , A__ : Union[str, Any]=False ):
'''simple docstring'''
__lowerCamelCase = OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith("""head""" ):
__lowerCamelCase = """segformer.encoder.""" + key
if key.startswith("""backbone""" ):
__lowerCamelCase = key.replace("""backbone""" , """segformer.encoder""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
__lowerCamelCase = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
__lowerCamelCase = key.replace(f'patch_embed{idx}' , f'patch_embeddings.{int(A__ )-1}' )
if "norm" in key:
__lowerCamelCase = key.replace("""norm""" , """layer_norm""" )
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
__lowerCamelCase = key[key.find("""segformer.encoder.layer_norm""" ) + len("""segformer.encoder.layer_norm""" )]
__lowerCamelCase = key.replace(f'layer_norm{idx}' , f'layer_norm.{int(A__ )-1}' )
if "layer_norm1" in key:
__lowerCamelCase = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
__lowerCamelCase = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
__lowerCamelCase = key[key.find("""block""" ) + len("""block""" )]
__lowerCamelCase = key.replace(f'block{idx}' , f'block.{int(A__ )-1}' )
if "attn.q" in key:
__lowerCamelCase = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
__lowerCamelCase = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
__lowerCamelCase = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
__lowerCamelCase = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
__lowerCamelCase = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
__lowerCamelCase = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
__lowerCamelCase = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
__lowerCamelCase = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
__lowerCamelCase = key[key.find("""linear_c""" ) + len("""linear_c""" )]
__lowerCamelCase = key.replace(f'linear_c{idx}' , f'linear_c.{int(A__ )-1}' )
if key.startswith("""head""" ):
__lowerCamelCase = key.replace("""head""" , """classifier""" )
__lowerCamelCase = value
return new_state_dict
def lowerCamelCase__ ( A__ : List[Any] , A__ : int ):
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
__lowerCamelCase = state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight' )
__lowerCamelCase = state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
__lowerCamelCase = kv_weight[
: config.hidden_sizes[i], :
]
__lowerCamelCase = kv_bias[: config.hidden_sizes[i]]
__lowerCamelCase = kv_weight[
config.hidden_sizes[i] :, :
]
__lowerCamelCase = kv_bias[
config.hidden_sizes[i] :
]
def lowerCamelCase__ ( ):
'''simple docstring'''
__lowerCamelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCamelCase = Image.open(requests.get(A__ , stream=A__ ).raw )
return image
@torch.no_grad()
def lowerCamelCase__ ( A__ : int , A__ : Optional[Any] , A__ : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase = SegformerConfig()
__lowerCamelCase = False
# set attributes based on model_name
__lowerCamelCase = """huggingface/label-files"""
if "segformer" in model_name:
__lowerCamelCase = model_name[len("""segformer.""" ) : len("""segformer.""" ) + 2]
if "ade" in model_name:
__lowerCamelCase = 150
__lowerCamelCase = """ade20k-id2label.json"""
__lowerCamelCase = (1, 150, 128, 128)
elif "city" in model_name:
__lowerCamelCase = 19
__lowerCamelCase = """cityscapes-id2label.json"""
__lowerCamelCase = (1, 19, 128, 128)
else:
raise ValueError(f'Model {model_name} not supported' )
elif "mit" in model_name:
__lowerCamelCase = True
__lowerCamelCase = model_name[4:6]
__lowerCamelCase = 1000
__lowerCamelCase = """imagenet-1k-id2label.json"""
__lowerCamelCase = (1, 1000)
else:
raise ValueError(f'Model {model_name} not supported' )
# set config attributes
__lowerCamelCase = json.load(open(hf_hub_download(A__ , A__ , repo_type="""dataset""" ) , """r""" ) )
__lowerCamelCase = {int(A__ ): v for k, v in idalabel.items()}
__lowerCamelCase = idalabel
__lowerCamelCase = {v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
__lowerCamelCase = [64, 128, 320, 512]
__lowerCamelCase = 256
elif size == "b2":
__lowerCamelCase = [64, 128, 320, 512]
__lowerCamelCase = 768
__lowerCamelCase = [3, 4, 6, 3]
elif size == "b3":
__lowerCamelCase = [64, 128, 320, 512]
__lowerCamelCase = 768
__lowerCamelCase = [3, 4, 18, 3]
elif size == "b4":
__lowerCamelCase = [64, 128, 320, 512]
__lowerCamelCase = 768
__lowerCamelCase = [3, 8, 27, 3]
elif size == "b5":
__lowerCamelCase = [64, 128, 320, 512]
__lowerCamelCase = 768
__lowerCamelCase = [3, 6, 40, 3]
else:
raise ValueError(f'Size {size} not supported' )
# load image processor (only resize + normalize)
__lowerCamelCase = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=A__ , align=A__ , do_random_crop=A__ )
# prepare image
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(images=A__ , return_tensors="""pt""" ).pixel_values
logger.info(f'Converting model {model_name}...' )
# load original state dict
if encoder_only:
__lowerCamelCase = torch.load(A__ , map_location=torch.device("""cpu""" ) )
else:
__lowerCamelCase = torch.load(A__ , map_location=torch.device("""cpu""" ) )["""state_dict"""]
# rename keys
__lowerCamelCase = rename_keys(A__ , encoder_only=A__ )
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(A__ , A__ )
# create HuggingFace model and load state dict
if encoder_only:
__lowerCamelCase = False
__lowerCamelCase = SegformerForImageClassification(A__ )
else:
__lowerCamelCase = SegformerForSemanticSegmentation(A__ )
model.load_state_dict(A__ )
model.eval()
# forward pass
__lowerCamelCase = model(A__ )
__lowerCamelCase = outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
__lowerCamelCase = torch.tensor(
[
[[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
[[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
[[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
] )
elif model_name == "segformer.b1.512x512.ade.160k":
__lowerCamelCase = torch.tensor(
[
[[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
[[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
[[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
] )
elif model_name == "segformer.b2.512x512.ade.160k":
__lowerCamelCase = torch.tensor(
[
[[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
[[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
[[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
] )
elif model_name == "segformer.b3.512x512.ade.160k":
__lowerCamelCase = torch.tensor(
[
[[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
[[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
[[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
] )
elif model_name == "segformer.b4.512x512.ade.160k":
__lowerCamelCase = torch.tensor(
[
[[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
[[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
[[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
] )
elif model_name == "segformer.b5.640x640.ade.160k":
__lowerCamelCase = torch.tensor(
[
[[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
[[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
[[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
] )
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
__lowerCamelCase = torch.tensor(
[
[[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
[[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
[[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
] )
elif model_name == "segformer.b0.512x1024.city.160k":
__lowerCamelCase = torch.tensor(
[
[[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
[[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
[[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
] )
elif model_name == "segformer.b0.640x1280.city.160k":
__lowerCamelCase = torch.tensor(
[
[
[-1.1_3_7_2E0_1, -1.2_7_8_7E0_1, -1.3_4_7_7E0_1],
[-1.2_5_3_6E0_1, -1.4_1_9_4E0_1, -1.4_4_0_9E0_1],
[-1.3_2_1_7E0_1, -1.4_8_8_8E0_1, -1.5_3_2_7E0_1],
],
[
[-1.4_7_9_1E0_1, -1.7_1_2_2E0_1, -1.8_2_7_7E0_1],
[-1.7_1_6_3E0_1, -1.9_1_9_2E0_1, -1.9_5_3_3E0_1],
[-1.7_8_9_7E0_1, -1.9_9_9_1E0_1, -2.0_3_1_5E0_1],
],
[
[7.6_7_2_3E-0_1, 4.1_9_2_1E-0_1, -7.7_8_7_8E-0_2],
[4.7_7_7_2E-0_1, 9.5_5_5_7E-0_3, -2.8_0_8_2E-0_1],
[3.6_0_3_2E-0_1, -2.4_8_2_6E-0_1, -5.1_1_6_8E-0_1],
],
] )
elif model_name == "segformer.b0.768x768.city.160k":
__lowerCamelCase = torch.tensor(
[
[[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
[[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
[[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
] )
elif model_name == "segformer.b1.1024x1024.city.160k":
__lowerCamelCase = torch.tensor(
[
[[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
[[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
[[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
] )
elif model_name == "segformer.b2.1024x1024.city.160k":
__lowerCamelCase = torch.tensor(
[
[[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
[[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
[[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
] )
elif model_name == "segformer.b3.1024x1024.city.160k":
__lowerCamelCase = torch.tensor(
[
[[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
[[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
[[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
] )
elif model_name == "segformer.b4.1024x1024.city.160k":
__lowerCamelCase = torch.tensor(
[
[[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
[[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
[[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
] )
elif model_name == "segformer.b5.1024x1024.city.160k":
__lowerCamelCase = torch.tensor(
[
[[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
[[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
[[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
] )
else:
__lowerCamelCase = logits.argmax(-1 ).item()
print("""Predicted class:""" , model.config.idalabel[predicted_class_idx] )
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3] , A__ , atol=1E-2 )
# finally, save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(A__ ).mkdir(exist_ok=A__ )
model.save_pretrained(A__ )
image_processor.save_pretrained(A__ )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='segformer.b0.512x512.ade.160k',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
UpperCAmelCase_ = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 12 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 0 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase : int = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , *lowerCAmelCase__ : Union[str, Any] , **lowerCAmelCase__ : Any):
warnings.warn(
"The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use CLIPImageProcessor instead." , lowerCAmelCase__ , )
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__)
| 13 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 1000 )-> int:
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCamelCase = n - a - b
if c * c == (a * a + b * b):
UpperCamelCase = a * b * c
if candidate >= product:
UpperCamelCase = candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
_lowerCamelCase : Tuple = """CompVis/stable-diffusion-v1-1"""
_lowerCamelCase : Tuple = """CompVis/stable-diffusion-v1-2"""
_lowerCamelCase : List[Any] = """CompVis/stable-diffusion-v1-3"""
_lowerCamelCase : Optional[Any] = """CompVis/stable-diffusion-v1-4"""
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase__ : AutoencoderKL , UpperCAmelCase__ : CLIPTextModel , UpperCAmelCase__ : CLIPTokenizer , UpperCAmelCase__ : UNetaDConditionModel , UpperCAmelCase__ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , UpperCAmelCase__ : StableDiffusionSafetyChecker , UpperCAmelCase__ : CLIPImageProcessor , UpperCAmelCase__ : bool = True , ) ->List[Any]:
'''simple docstring'''
super()._init_()
A__ = StableDiffusionPipeline.from_pretrained(UpperCAmelCase__)
A__ = StableDiffusionPipeline.from_pretrained(UpperCAmelCase__)
A__ = StableDiffusionPipeline.from_pretrained(UpperCAmelCase__)
A__ = StableDiffusionPipeline(
vae=UpperCAmelCase__ , text_encoder=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ , safety_checker=UpperCAmelCase__ , feature_extractor=UpperCAmelCase__ , requires_safety_checker=UpperCAmelCase__ , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea)
@property
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Dict[str, Any]:
'''simple docstring'''
return {k: getattr(self , UpperCAmelCase__) for k in self.config.keys() if not k.startswith('''_''')}
def SCREAMING_SNAKE_CASE ( self : str , UpperCAmelCase__ : Optional[Union[str, int]] = "auto") ->List[Any]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
A__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Dict) ->int:
'''simple docstring'''
self.enable_attention_slicing(UpperCAmelCase__)
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( self : Optional[int] , UpperCAmelCase__ : Union[str, List[str]] , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : float = 7.5 , UpperCAmelCase__ : Optional[Union[str, List[str]]] = None , UpperCAmelCase__ : Optional[int] = 1 , UpperCAmelCase__ : float = 0.0 , UpperCAmelCase__ : Optional[torch.Generator] = None , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[str] = "pil" , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase__ : int = 1 , **UpperCAmelCase__ : str , ) ->Tuple:
'''simple docstring'''
return self.pipea(
prompt=UpperCAmelCase__ , height=UpperCAmelCase__ , width=UpperCAmelCase__ , num_inference_steps=UpperCAmelCase__ , guidance_scale=UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ , num_images_per_prompt=UpperCAmelCase__ , eta=UpperCAmelCase__ , generator=UpperCAmelCase__ , latents=UpperCAmelCase__ , output_type=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , callback=UpperCAmelCase__ , callback_steps=UpperCAmelCase__ , **UpperCAmelCase__ , )
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( self : int , UpperCAmelCase__ : Union[str, List[str]] , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : float = 7.5 , UpperCAmelCase__ : Optional[Union[str, List[str]]] = None , UpperCAmelCase__ : Optional[int] = 1 , UpperCAmelCase__ : float = 0.0 , UpperCAmelCase__ : Optional[torch.Generator] = None , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[str] = "pil" , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase__ : int = 1 , **UpperCAmelCase__ : str , ) ->Optional[int]:
'''simple docstring'''
return self.pipea(
prompt=UpperCAmelCase__ , height=UpperCAmelCase__ , width=UpperCAmelCase__ , num_inference_steps=UpperCAmelCase__ , guidance_scale=UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ , num_images_per_prompt=UpperCAmelCase__ , eta=UpperCAmelCase__ , generator=UpperCAmelCase__ , latents=UpperCAmelCase__ , output_type=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , callback=UpperCAmelCase__ , callback_steps=UpperCAmelCase__ , **UpperCAmelCase__ , )
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , UpperCAmelCase__ : Union[str, List[str]] , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : float = 7.5 , UpperCAmelCase__ : Optional[Union[str, List[str]]] = None , UpperCAmelCase__ : Optional[int] = 1 , UpperCAmelCase__ : float = 0.0 , UpperCAmelCase__ : Optional[torch.Generator] = None , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[str] = "pil" , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase__ : int = 1 , **UpperCAmelCase__ : List[str] , ) ->List[Any]:
'''simple docstring'''
return self.pipea(
prompt=UpperCAmelCase__ , height=UpperCAmelCase__ , width=UpperCAmelCase__ , num_inference_steps=UpperCAmelCase__ , guidance_scale=UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ , num_images_per_prompt=UpperCAmelCase__ , eta=UpperCAmelCase__ , generator=UpperCAmelCase__ , latents=UpperCAmelCase__ , output_type=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , callback=UpperCAmelCase__ , callback_steps=UpperCAmelCase__ , **UpperCAmelCase__ , )
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , UpperCAmelCase__ : Union[str, List[str]] , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : float = 7.5 , UpperCAmelCase__ : Optional[Union[str, List[str]]] = None , UpperCAmelCase__ : Optional[int] = 1 , UpperCAmelCase__ : float = 0.0 , UpperCAmelCase__ : Optional[torch.Generator] = None , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[str] = "pil" , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase__ : int = 1 , **UpperCAmelCase__ : Union[str, Any] , ) ->Optional[Any]:
'''simple docstring'''
return self.pipea(
prompt=UpperCAmelCase__ , height=UpperCAmelCase__ , width=UpperCAmelCase__ , num_inference_steps=UpperCAmelCase__ , guidance_scale=UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ , num_images_per_prompt=UpperCAmelCase__ , eta=UpperCAmelCase__ , generator=UpperCAmelCase__ , latents=UpperCAmelCase__ , output_type=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , callback=UpperCAmelCase__ , callback_steps=UpperCAmelCase__ , **UpperCAmelCase__ , )
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( self : int , UpperCAmelCase__ : Union[str, List[str]] , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 50 , UpperCAmelCase__ : float = 7.5 , UpperCAmelCase__ : Optional[Union[str, List[str]]] = None , UpperCAmelCase__ : Optional[int] = 1 , UpperCAmelCase__ : float = 0.0 , UpperCAmelCase__ : Optional[torch.Generator] = None , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[str] = "pil" , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase__ : int = 1 , **UpperCAmelCase__ : Union[str, Any] , ) ->Tuple:
'''simple docstring'''
A__ = '''cuda''' if torch.cuda.is_available() else '''cpu'''
self.to(UpperCAmelCase__)
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"""`height` and `width` must be divisible by 8 but are {height} and {width}.""")
# Get first result from Stable Diffusion Checkpoint v1.1
A__ = self.textaimg_sda_a(
prompt=UpperCAmelCase__ , height=UpperCAmelCase__ , width=UpperCAmelCase__ , num_inference_steps=UpperCAmelCase__ , guidance_scale=UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ , num_images_per_prompt=UpperCAmelCase__ , eta=UpperCAmelCase__ , generator=UpperCAmelCase__ , latents=UpperCAmelCase__ , output_type=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , callback=UpperCAmelCase__ , callback_steps=UpperCAmelCase__ , **UpperCAmelCase__ , )
# Get first result from Stable Diffusion Checkpoint v1.2
A__ = self.textaimg_sda_a(
prompt=UpperCAmelCase__ , height=UpperCAmelCase__ , width=UpperCAmelCase__ , num_inference_steps=UpperCAmelCase__ , guidance_scale=UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ , num_images_per_prompt=UpperCAmelCase__ , eta=UpperCAmelCase__ , generator=UpperCAmelCase__ , latents=UpperCAmelCase__ , output_type=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , callback=UpperCAmelCase__ , callback_steps=UpperCAmelCase__ , **UpperCAmelCase__ , )
# Get first result from Stable Diffusion Checkpoint v1.3
A__ = self.textaimg_sda_a(
prompt=UpperCAmelCase__ , height=UpperCAmelCase__ , width=UpperCAmelCase__ , num_inference_steps=UpperCAmelCase__ , guidance_scale=UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ , num_images_per_prompt=UpperCAmelCase__ , eta=UpperCAmelCase__ , generator=UpperCAmelCase__ , latents=UpperCAmelCase__ , output_type=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , callback=UpperCAmelCase__ , callback_steps=UpperCAmelCase__ , **UpperCAmelCase__ , )
# Get first result from Stable Diffusion Checkpoint v1.4
A__ = self.textaimg_sda_a(
prompt=UpperCAmelCase__ , height=UpperCAmelCase__ , width=UpperCAmelCase__ , num_inference_steps=UpperCAmelCase__ , guidance_scale=UpperCAmelCase__ , negative_prompt=UpperCAmelCase__ , num_images_per_prompt=UpperCAmelCase__ , eta=UpperCAmelCase__ , generator=UpperCAmelCase__ , latents=UpperCAmelCase__ , output_type=UpperCAmelCase__ , return_dict=UpperCAmelCase__ , callback=UpperCAmelCase__ , callback_steps=UpperCAmelCase__ , **UpperCAmelCase__ , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]])
| 14 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Any = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "mvp"
snake_case_ = ["past_key_values"]
snake_case_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : str ,A : Optional[Any]=5_02_67 ,A : int=10_24 ,A : List[Any]=12 ,A : Any=40_96 ,A : Dict=16 ,A : Any=12 ,A : Optional[int]=40_96 ,A : Optional[int]=16 ,A : List[Any]=0.0 ,A : List[Any]=0.0 ,A : Optional[Any]="gelu" ,A : int=10_24 ,A : int=0.1 ,A : Tuple=0.0 ,A : Optional[Any]=0.0 ,A : Optional[Any]=0.02 ,A : str=0.0 ,A : Any=False ,A : Optional[Any]=True ,A : str=1 ,A : Optional[Any]=0 ,A : Optional[Any]=2 ,A : List[Any]=True ,A : int=2 ,A : str=2 ,A : List[Any]=False ,A : str=1_00 ,A : Any=8_00 ,**A : str ,):
__A = vocab_size
__A = max_position_embeddings
__A = d_model
__A = encoder_ffn_dim
__A = encoder_layers
__A = encoder_attention_heads
__A = decoder_ffn_dim
__A = decoder_layers
__A = decoder_attention_heads
__A = dropout
__A = attention_dropout
__A = activation_dropout
__A = activation_function
__A = init_std
__A = encoder_layerdrop
__A = decoder_layerdrop
__A = classifier_dropout
__A = use_cache
__A = encoder_layers
__A = scale_embedding # scale factor will be sqrt(d_model) if True
__A = use_prompt
__A = prompt_length
__A = prompt_mid_dim
super().__init__(
pad_token_id=A ,bos_token_id=A ,eos_token_id=A ,is_encoder_decoder=A ,decoder_start_token_id=A ,forced_eos_token_id=A ,**A ,)
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated" ,A ):
__A = self.bos_token_id
warnings.warn(
f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
"The config can simply be saved and uploaded again to be fixed." )
| 15 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE__ = _symbol_database.Default()
SCREAMING_SNAKE_CASE__ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
SCREAMING_SNAKE_CASE__ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
SCREAMING_SNAKE_CASE__ = 4_5
SCREAMING_SNAKE_CASE__ = 1_5_8_1
SCREAMING_SNAKE_CASE__ = 1_5_1_7
SCREAMING_SNAKE_CASE__ = 1_5_7_0
SCREAMING_SNAKE_CASE__ = 1_5_8_4
SCREAMING_SNAKE_CASE__ = 1_7_9_3
SCREAMING_SNAKE_CASE__ = 1_7_9_5
SCREAMING_SNAKE_CASE__ = 1_9_1_6
SCREAMING_SNAKE_CASE__ = 1_8_6_4
SCREAMING_SNAKE_CASE__ = 1_9_0_5
SCREAMING_SNAKE_CASE__ = 1_9_1_9
SCREAMING_SNAKE_CASE__ = 2_4_2_9
SCREAMING_SNAKE_CASE__ = 2_2_0_8
SCREAMING_SNAKE_CASE__ = 2_4_1_8
SCREAMING_SNAKE_CASE__ = 2_3_2_3
SCREAMING_SNAKE_CASE__ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 321 | 0 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
lowerCAmelCase_ = '\\n Text data.\n Second line of data.'
lowerCAmelCase_ = 'file'
@pytest.fixture(scope='''session''' )
def __UpperCAmelCase ( __lowerCamelCase ) -> Union[str, Any]:
lowercase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
lowercase__ : Any = bytes(__lowerCamelCase , '''utf-8''' )
with zstd.open(__lowerCamelCase , '''wb''' ) as f:
f.write(__lowerCamelCase )
return path
@pytest.fixture
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
with open(os.path.join(tmpfs.local_root_dir , __lowerCamelCase ) , '''w''' ) as f:
f.write(__lowerCamelCase )
return FILE_PATH
@pytest.mark.parametrize('''compression_format''' , ['''gzip''', '''xz''', '''zstd'''] )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
lowercase__ : str = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
lowercase__ : str = input_paths[compression_format]
lowercase__ : Tuple = tmp_path / '''cache'''
lowercase__ : Optional[int] = DownloadConfig(cache_dir=__lowerCamelCase , extract_compressed_file=__lowerCamelCase )
lowercase__ : Union[str, Any] = cached_path(__lowerCamelCase , download_config=__lowerCamelCase )
with open(__lowerCamelCase ) as f:
lowercase__ : Dict = f.read()
with open(__lowerCamelCase ) as f:
lowercase__ : int = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('''default_extracted''' , [True, False] )
@pytest.mark.parametrize('''default_cache_dir''' , [True, False] )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
lowercase__ : Any = '''custom_cache'''
lowercase__ : Tuple = '''custom_extracted_dir'''
lowercase__ : Tuple = tmp_path / '''custom_extracted_path'''
if default_extracted:
lowercase__ : List[Any] = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR''' , __lowerCamelCase )
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(__lowerCamelCase ) )
lowercase__ : str = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
lowercase__ : int = xz_file
lowercase__ : List[Any] = (
DownloadConfig(extract_compressed_file=__lowerCamelCase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=__lowerCamelCase )
)
lowercase__ : Union[str, Any] = cached_path(__lowerCamelCase , download_config=__lowerCamelCase )
assert Path(__lowerCamelCase ).parent.parts[-2:] == expected
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
# absolute path
lowercase__ : List[Any] = str(Path(__lowerCamelCase ).resolve() )
assert cached_path(__lowerCamelCase ) == text_file
# relative path
lowercase__ : Union[str, Any] = str(Path(__lowerCamelCase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(__lowerCamelCase ) == text_file
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
# absolute path
lowercase__ : Dict = str(tmp_path.resolve() / '''__missing_file__.txt''' )
with pytest.raises(__lowerCamelCase ):
cached_path(__lowerCamelCase )
# relative path
lowercase__ : Dict = '''./__missing_file__.txt'''
with pytest.raises(__lowerCamelCase ):
cached_path(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase ) -> int:
lowercase__ : List[str] = get_from_cache(f"""tmp://{tmpfs_file}""" )
with open(__lowerCamelCase ) as f:
lowercase__ : int = f.read()
assert output_file_content == FILE_CONTENT
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , __lowerCamelCase )
def __UpperCAmelCase ( ) -> Tuple:
with pytest.raises(__lowerCamelCase ):
cached_path('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , __lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
lowercase__ : str = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(__lowerCamelCase ):
http_get('''https://huggingface.co''' , temp_file=__lowerCamelCase )
with pytest.raises(__lowerCamelCase ):
http_head('''https://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , __lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase ) -> List[str]:
lowercase__ : List[str] = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(__lowerCamelCase ):
ftp_get('''ftp://huggingface.co''' , temp_file=__lowerCamelCase )
with pytest.raises(__lowerCamelCase ):
ftp_head('''ftp://huggingface.co''' )
@patch('''datasets.config.HF_DATASETS_OFFLINE''' , __lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase ) -> Union[str, Any]:
lowercase__ : List[Any] = tmp_path_factory.mktemp('''data''' ) / '''file.html'''
with pytest.raises(__lowerCamelCase ):
fsspec_get('''s3://huggingface.co''' , temp_file=__lowerCamelCase )
with pytest.raises(__lowerCamelCase ):
fsspec_head('''s3://huggingface.co''' )
| 16 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 | 0 |
"""simple docstring"""
import cva
import numpy as np
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Tuple, UpperCAmelCase__ : float, UpperCAmelCase__ : int ):
if k in (0.04, 0.06):
__lowercase = k
__lowercase = window_size
else:
raise ValueError("invalid k value" )
def __str__( self : Any ):
return str(self.k )
def _lowercase ( self : Optional[int], UpperCAmelCase__ : str ):
__lowercase = cva.imread(UpperCAmelCase__, 0 )
__lowercase ,__lowercase = img.shape
__lowercase = []
__lowercase = img.copy()
__lowercase = cva.cvtColor(UpperCAmelCase__, cva.COLOR_GRAY2RGB )
__lowercase ,__lowercase = np.gradient(UpperCAmelCase__ )
__lowercase = dx**2
__lowercase = dy**2
__lowercase = dx * dy
__lowercase = 0.04
__lowercase = self.window_size // 2
for y in range(UpperCAmelCase__, h - offset ):
for x in range(UpperCAmelCase__, w - offset ):
__lowercase = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
__lowercase = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
__lowercase = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
__lowercase = (wxx * wyy) - (wxy**2)
__lowercase = wxx + wyy
__lowercase = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0), 0 )
color_img.itemset((y, x, 1), 0 )
color_img.itemset((y, x, 2), 2_5_5 )
return color_img, corner_list
if __name__ == "__main__":
_a = HarrisCorner(0.04, 3)
_a , _a = edge_detect.detect('path_to_image')
cva.imwrite('detect.png', color_img)
| 17 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE__ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __UpperCamelCase )-> str:
if "://" in dataset_path:
UpperCamelCase = dataset_path.split("""://""" )[1]
return dataset_path
def lowercase__ ( __UpperCamelCase )-> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = not is_remote_filesystem(__UpperCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__UpperCamelCase ) , fs._strip_protocol(__UpperCamelCase ) )
else:
fs.mv(__UpperCamelCase , __UpperCamelCase , recursive=__UpperCamelCase )
def lowercase__ ( )-> None:
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = threading.Lock()
| 321 | 0 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class a__ ( A__ ):
def __init__( self : Dict,_A : List[Any],_A : Any ):
"""simple docstring"""
super().__init__()
self.register_modules(unet=_A,scheduler=_A )
@torch.no_grad()
def __call__( self : Dict,_A : int = 1,_A : Optional[torch.Generator] = None,_A : int = 50,_A : Optional[str] = "pil",_A : bool = True,**_A : str,):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),generator=_A,)
SCREAMING_SNAKE_CASE_ : int = image.to(self.device )
# set step values
self.scheduler.set_timesteps(_A )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
SCREAMING_SNAKE_CASE_ : Tuple = self.unet(_A,_A ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
SCREAMING_SNAKE_CASE_ : Tuple = self.scheduler.step(_A,_A,_A ).prev_sample
SCREAMING_SNAKE_CASE_ : str = (image / 2 + 0.5).clamp(0,1 )
SCREAMING_SNAKE_CASE_ : Tuple = image.cpu().permute(0,2,3,1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE_ : Dict = self.numpy_to_pil(_A )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=_A ), "This is a local test"
| 18 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 321 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A ={
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas''': ['''TapasTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TapasForMaskedLM''',
'''TapasForQuestionAnswering''',
'''TapasForSequenceClassification''',
'''TapasModel''',
'''TapasPreTrainedModel''',
'''load_tf_weights_in_tapas''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFTapasForMaskedLM''',
'''TFTapasForQuestionAnswering''',
'''TFTapasForSequenceClassification''',
'''TFTapasModel''',
'''TFTapasPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
__A =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 19 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = 'docs/source/en/_toctree.yml'
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = defaultdict(__UpperCamelCase )
UpperCamelCase = []
UpperCamelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__UpperCamelCase )
UpperCamelCase = new_doc_list
UpperCamelCase = [key for key, value in counts.items() if value > 1]
UpperCamelCase = []
for duplicate_key in duplicates:
UpperCamelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCamelCase = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowercase__ ( __UpperCamelCase=False )-> List[str]:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCamelCase = api_doc[scheduler_idx]["""sections"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
UpperCamelCase = False
if new_scheduler_doc != scheduler_doc:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_scheduler_doc
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase__ ( __UpperCamelCase=False )-> Tuple:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCamelCase = False
UpperCamelCase = api_doc[pipeline_idx]["""sections"""]
UpperCamelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCamelCase = pipeline_doc["""section"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCamelCase = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_pipeline_docs
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 321 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : Optional[int] = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Union[str, Any] = ["""FNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : List[str] = ["""FNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : str = [
"""FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FNetForMaskedLM""",
"""FNetForMultipleChoice""",
"""FNetForNextSentencePrediction""",
"""FNetForPreTraining""",
"""FNetForQuestionAnswering""",
"""FNetForSequenceClassification""",
"""FNetForTokenClassification""",
"""FNetLayer""",
"""FNetModel""",
"""FNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
lowercase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 20 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCamelCase = 1.5
UpperCamelCase = int(factor * num_class_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 )
os.makedirs(F"{class_data_dir}/images" , exist_ok=__UpperCamelCase )
if len(list(Path(F"{class_data_dir}/images" ).iterdir() ) ) >= num_class_images:
return
while True:
UpperCamelCase = client.query(text=__UpperCamelCase )
if len(__UpperCamelCase ) >= factor * num_class_images or num_images > 1E4:
break
else:
UpperCamelCase = int(factor * num_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 , )
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = tqdm(desc="""downloading real regularization images""" , total=__UpperCamelCase )
with open(F"{class_data_dir}/caption.txt" , """w""" ) as fa, open(F"{class_data_dir}/urls.txt" , """w""" ) as fa, open(
F"{class_data_dir}/images.txt" , """w""" ) as fa:
while total < num_class_images:
UpperCamelCase = class_images[count]
count += 1
try:
UpperCamelCase = requests.get(images["""url"""] )
if img.status_code == 200:
UpperCamelCase = Image.open(BytesIO(img.content ) )
with open(F"{class_data_dir}/images/{total}.jpg" , """wb""" ) as f:
f.write(img.content )
fa.write(images["""caption"""] + """\n""" )
fa.write(images["""url"""] + """\n""" )
fa.write(F"{class_data_dir}/images/{total}.jpg" + """\n""" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def lowercase__ ( )-> str:
UpperCamelCase = argparse.ArgumentParser("""""" , add_help=__UpperCamelCase )
parser.add_argument("""--class_prompt""" , help="""text prompt to retrieve images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--class_data_dir""" , help="""path to save images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--num_class_images""" , help="""number of images to download""" , default=200 , type=__UpperCamelCase )
return parser.parse_args()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 321 | 0 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class _lowerCamelCase( unittest.TestCase ):
@parameterized.expand([(None,), ('foo.json',)])
def UpperCamelCase ( self, lowerCamelCase) -> Optional[Any]:
"""simple docstring"""
_lowercase : Tuple = GenerationConfig(
do_sample=lowerCamelCase, temperature=0.7, length_penalty=1.0, bad_words_ids=[[1, 2, 3], [4, 5]], )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase, config_name=lowerCamelCase)
_lowercase : List[str] = GenerationConfig.from_pretrained(lowerCamelCase, config_name=lowerCamelCase)
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample, lowerCamelCase)
self.assertEqual(loaded_config.temperature, 0.7)
self.assertEqual(loaded_config.length_penalty, 1.0)
self.assertEqual(loaded_config.bad_words_ids, [[1, 2, 3], [4, 5]])
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k, 50)
self.assertEqual(loaded_config.max_length, 20)
self.assertEqual(loaded_config.max_time, lowerCamelCase)
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : int = AutoConfig.from_pretrained('gpt2')
_lowercase : List[str] = GenerationConfig.from_model_config(lowerCamelCase)
_lowercase : Union[str, Any] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowerCamelCase, lowerCamelCase)
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id, default_generation_config.eos_token_id)
self.assertEqual(generation_config_from_model.eos_token_id, model_config.eos_token_id)
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : Union[str, Any] = GenerationConfig()
_lowercase : List[Any] = {
'max_new_tokens': 10_24,
'foo': 'bar',
}
_lowercase : Any = copy.deepcopy(lowerCamelCase)
_lowercase : List[Any] = generation_config.update(**lowerCamelCase)
# update_kwargs was not modified (no side effects)
self.assertEqual(lowerCamelCase, lowerCamelCase)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens, 10_24)
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowerCamelCase, {'foo': 'bar'})
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
_lowercase : List[Any] = GenerationConfig()
_lowercase : List[Any] = 'bar'
with tempfile.TemporaryDirectory('test-generation-config') as tmp_dir:
generation_config.save_pretrained(lowerCamelCase)
_lowercase : List[Any] = GenerationConfig.from_pretrained(lowerCamelCase)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo, 'bar')
_lowercase : int = GenerationConfig.from_model_config(lowerCamelCase)
assert not hasattr(lowerCamelCase, 'foo') # no new kwargs should be initialized if from config
def UpperCamelCase ( self) -> int:
"""simple docstring"""
_lowercase : Optional[int] = GenerationConfig()
self.assertEqual(default_config.temperature, 1.0)
self.assertEqual(default_config.do_sample, lowerCamelCase)
self.assertEqual(default_config.num_beams, 1)
_lowercase : Optional[Any] = GenerationConfig(
do_sample=lowerCamelCase, temperature=0.7, length_penalty=1.0, bad_words_ids=[[1, 2, 3], [4, 5]], )
self.assertEqual(config.temperature, 0.7)
self.assertEqual(config.do_sample, lowerCamelCase)
self.assertEqual(config.num_beams, 1)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase)
_lowercase : List[Any] = GenerationConfig.from_pretrained(lowerCamelCase, temperature=1.0)
self.assertEqual(loaded_config.temperature, 1.0)
self.assertEqual(loaded_config.do_sample, lowerCamelCase)
self.assertEqual(loaded_config.num_beams, 1) # default value
@is_staging_test
class _lowerCamelCase( unittest.TestCase ):
@classmethod
def UpperCamelCase ( cls) -> str:
"""simple docstring"""
_lowercase : Union[str, Any] = TOKEN
HfFolder.save_token(lowerCamelCase)
@classmethod
def UpperCamelCase ( cls) -> Optional[int]:
"""simple docstring"""
try:
delete_repo(token=cls._token, repo_id='test-generation-config')
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='valid_org/test-generation-config-org')
except HTTPError:
pass
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : List[Any] = GenerationConfig(
do_sample=lowerCamelCase, temperature=0.7, length_penalty=1.0, )
config.push_to_hub('test-generation-config', use_auth_token=self._token)
_lowercase : Any = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase, getattr(lowerCamelCase, lowerCamelCase))
# Reset repo
delete_repo(token=self._token, repo_id='test-generation-config')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase, repo_id='test-generation-config', push_to_hub=lowerCamelCase, use_auth_token=self._token)
_lowercase : Tuple = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase, getattr(lowerCamelCase, lowerCamelCase))
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Dict = GenerationConfig(
do_sample=lowerCamelCase, temperature=0.7, length_penalty=1.0, )
config.push_to_hub('valid_org/test-generation-config-org', use_auth_token=self._token)
_lowercase : Any = GenerationConfig.from_pretrained('valid_org/test-generation-config-org')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase, getattr(lowerCamelCase, lowerCamelCase))
# Reset repo
delete_repo(token=self._token, repo_id='valid_org/test-generation-config-org')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase, repo_id='valid_org/test-generation-config-org', push_to_hub=lowerCamelCase, use_auth_token=self._token)
_lowercase : Optional[Any] = GenerationConfig.from_pretrained('valid_org/test-generation-config-org')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase, getattr(lowerCamelCase, lowerCamelCase))
| 21 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class a_ :
lowercase = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The column name of the images in the files."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the training data."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the validation data."""} )
lowercase = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = {}
if self.train_dir is not None:
UpperCamelCase = self.train_dir
if self.validation_dir is not None:
UpperCamelCase = self.validation_dir
UpperCamelCase = data_files if data_files else None
@dataclass
class a_ :
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowercase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class a_ ( lowerCamelCase ):
lowercase = field(
default=1E-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowercase__ ( )-> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , __UpperCamelCase , __UpperCamelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCamelCase = training_args.get_process_log_level()
logger.setLevel(__UpperCamelCase )
transformers.utils.logging.set_verbosity(__UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
UpperCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCamelCase = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __UpperCamelCase ) and data_args.train_val_split > 0.0:
UpperCamelCase = ds["""train"""].train_test_split(data_args.train_val_split )
UpperCamelCase = split["""train"""]
UpperCamelCase = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.config_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
UpperCamelCase = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase = ViTMAEForPreTraining(__UpperCamelCase )
if training_args.do_train:
UpperCamelCase = ds["""train"""].column_names
else:
UpperCamelCase = ds["""validation"""].column_names
if data_args.image_column_name is not None:
UpperCamelCase = data_args.image_column_name
elif "image" in column_names:
UpperCamelCase = """image"""
elif "img" in column_names:
UpperCamelCase = """img"""
else:
UpperCamelCase = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
UpperCamelCase = image_processor.size["""shortest_edge"""]
else:
UpperCamelCase = (image_processor.size["""height"""], image_processor.size["""width"""])
UpperCamelCase = Compose(
[
Lambda(lambda __UpperCamelCase : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(__UpperCamelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(__UpperCamelCase ):
UpperCamelCase = [transforms(__UpperCamelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
UpperCamelCase = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
UpperCamelCase = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__UpperCamelCase )
# Compute absolute learning rate
UpperCamelCase = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
UpperCamelCase = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
UpperCamelCase = Trainer(
model=__UpperCamelCase , args=__UpperCamelCase , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=__UpperCamelCase , data_collator=__UpperCamelCase , )
# Training
if training_args.do_train:
UpperCamelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCamelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCamelCase = last_checkpoint
UpperCamelCase = trainer.train(resume_from_checkpoint=__UpperCamelCase )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCamelCase = trainer.evaluate()
trainer.log_metrics("""eval""" , __UpperCamelCase )
trainer.save_metrics("""eval""" , __UpperCamelCase )
# Write model card and (optionally) push to hub
UpperCamelCase = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__UpperCamelCase )
else:
trainer.create_model_card(**__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> List[str]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 321 | 0 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A_ ( tf.keras.layers.Layer ):
def __init__( self : Tuple , snake_case_ : Dict[str, int] , snake_case_ : List[str] , snake_case_ : int = None , snake_case_ : int = None ):
super().__init__()
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = max_length
_UpperCAmelCase = vocab
_UpperCAmelCase = merges
_UpperCAmelCase = BytePairTokenizer(snake_case_ , snake_case_ , sequence_length=snake_case_ )
@classmethod
def lowercase ( cls : Optional[int] , snake_case_ : GPTaTokenizer , *snake_case_ : List[Any] , **snake_case_ : Any ):
_UpperCAmelCase = [" ".join(snake_case_ ) for m in tokenizer.bpe_ranks.keys()]
_UpperCAmelCase = tokenizer.get_vocab()
return cls(snake_case_ , snake_case_ , *snake_case_ , **snake_case_ )
@classmethod
def lowercase ( cls : Optional[int] , snake_case_ : Union[str, os.PathLike] , *snake_case_ : Union[str, Any] , **snake_case_ : List[Any] ):
_UpperCAmelCase = GPTaTokenizer.from_pretrained(snake_case_ , *snake_case_ , **snake_case_ )
return cls.from_tokenizer(snake_case_ , *snake_case_ , **snake_case_ )
@classmethod
def lowercase ( cls : int , snake_case_ : List[Any] ):
return cls(**snake_case_ )
def lowercase ( self : str ):
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowercase ( self : List[str] , snake_case_ : Union[str, Any] , snake_case_ : int = None ):
_UpperCAmelCase = self.tf_tokenizer(snake_case_ )
_UpperCAmelCase = tf.ones_like(snake_case_ )
if self.pad_token_id is not None:
# pad the tokens up to max length
_UpperCAmelCase = max_length if max_length is not None else self.max_length
if max_length is not None:
_UpperCAmelCase , _UpperCAmelCase = pad_model_inputs(
snake_case_ , max_seq_length=snake_case_ , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 22 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE__ = 2_5_6
class a_ ( lowerCamelCase ):
lowercase = ["""melgan"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase = math.log(1e-5 ) # Matches MelGAN training.
UpperCamelCase = 4.0 # Largest value for most examples
UpperCamelCase = 128
self.register_modules(
notes_encoder=_SCREAMING_SNAKE_CASE , continuous_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , melgan=_SCREAMING_SNAKE_CASE , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = output_range
if clip:
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , self.min_value , self.max_value )
# Scale to [0, 1].
UpperCamelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = input_range
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = input_tokens > 0
UpperCamelCase ,UpperCamelCase = self.notes_encoder(
encoder_input_tokens=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.continuous_encoder(
encoder_inputs=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = noise_time
if not torch.is_tensor(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(_SCREAMING_SNAKE_CASE ) and len(timesteps.shape ) == 0:
UpperCamelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
UpperCamelCase = self.decoder(
encodings_and_masks=_SCREAMING_SNAKE_CASE , decoder_input_tokens=_SCREAMING_SNAKE_CASE , decoder_noise_time=_SCREAMING_SNAKE_CASE )
return logits
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "numpy" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(_SCREAMING_SNAKE_CASE )}." )
UpperCamelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
UpperCamelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
UpperCamelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE ):
if i == 0:
UpperCamelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase = ones
UpperCamelCase = self.scale_features(
_SCREAMING_SNAKE_CASE , output_range=[-1.0, 1.0] , clip=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=_SCREAMING_SNAKE_CASE , continuous_mask=_SCREAMING_SNAKE_CASE , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase = self.decode(
encodings_and_masks=_SCREAMING_SNAKE_CASE , input_tokens=_SCREAMING_SNAKE_CASE , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
UpperCamelCase = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = self.scale_to_features(_SCREAMING_SNAKE_CASE , input_range=[-1.0, 1.0] )
UpperCamelCase = mel[:1]
UpperCamelCase = mel.cpu().float().numpy()
UpperCamelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
logger.info("""Generated segment""" , _SCREAMING_SNAKE_CASE )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
UpperCamelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE )
| 321 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 23 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = b, a + b
return sum(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
snake_case_ = 256047
snake_case_ = 256145
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase , unittest.TestCase ):
A_ : Any = NllbTokenizer
A_ : Tuple = NllbTokenizerFast
A_ : Union[str, Any] = True
A_ : List[str] = True
A_ : int = {}
def a (self : Any ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__snake_case = NllbTokenizer(a__ , keep_accents=a__ )
tokenizer.save_pretrained(self.tmpdirname )
def a (self : List[Any] ):
"""simple docstring"""
__snake_case = NllbTokenizer(a__ , keep_accents=a__ )
__snake_case = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(a__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(a__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__snake_case = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
a__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__snake_case = tokenizer.convert_tokens_to_ids(a__ )
self.assertListEqual(
a__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__snake_case = tokenizer.convert_ids_to_tokens(a__ )
self.assertListEqual(
a__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def a (self : Optional[Any] ):
"""simple docstring"""
__snake_case = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__snake_case = self.rust_tokenizer_class.from_pretrained(a__ , **a__ )
__snake_case = self.tokenizer_class.from_pretrained(a__ , **a__ )
__snake_case = tempfile.mkdtemp()
__snake_case = tokenizer_r.save_pretrained(a__ )
__snake_case = tokenizer_p.save_pretrained(a__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__snake_case = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(a__ , a__ )
# Checks everything loads correctly in the same way
__snake_case = tokenizer_r.from_pretrained(a__ )
__snake_case = tokenizer_p.from_pretrained(a__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(a__ , a__ ) )
shutil.rmtree(a__ )
# Save tokenizer rust, legacy_format=True
__snake_case = tempfile.mkdtemp()
__snake_case = tokenizer_r.save_pretrained(a__ , legacy_format=a__ )
__snake_case = tokenizer_p.save_pretrained(a__ )
# Checks it save with the same files
self.assertSequenceEqual(a__ , a__ )
# Checks everything loads correctly in the same way
__snake_case = tokenizer_r.from_pretrained(a__ )
__snake_case = tokenizer_p.from_pretrained(a__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(a__ , a__ ) )
shutil.rmtree(a__ )
# Save tokenizer rust, legacy_format=False
__snake_case = tempfile.mkdtemp()
__snake_case = tokenizer_r.save_pretrained(a__ , legacy_format=a__ )
__snake_case = tokenizer_p.save_pretrained(a__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__snake_case = tokenizer_r.from_pretrained(a__ )
__snake_case = tokenizer_p.from_pretrained(a__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(a__ , a__ ) )
shutil.rmtree(a__ )
@require_torch
def a (self : Optional[Any] ):
"""simple docstring"""
if not self.test_seqaseq:
return
__snake_case = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
__snake_case = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
''' will only worsen the violence and misery for millions of people.''',
]
__snake_case = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
try:
__snake_case = tokenizer.prepare_seqaseq_batch(
src_texts=a__ , tgt_texts=a__ , max_length=3 , max_target_length=10 , return_tensors='''pt''' , src_lang='''eng_Latn''' , tgt_lang='''ron_Latn''' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__snake_case = tokenizer.prepare_seqaseq_batch(
a__ , tgt_texts=a__ , max_length=3 , return_tensors='''pt''' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__snake_case = tokenizer.prepare_seqaseq_batch(
src_texts=a__ , max_length=3 , max_target_length=10 , return_tensors='''pt''' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('''decoder_input_ids''' , a__ )
@unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.''' )
def a (self : List[str] ):
"""simple docstring"""
pass
def a (self : int ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__snake_case = [AddedToken('''<special>''' , lstrip=a__ )]
__snake_case = self.rust_tokenizer_class.from_pretrained(
a__ , additional_special_tokens=a__ , **a__ )
__snake_case = tokenizer_r.encode('''Hey this is a <special> token''' )
__snake_case = tokenizer_r.encode('''<special>''' , add_special_tokens=a__ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__snake_case = self.rust_tokenizer_class.from_pretrained(
a__ , additional_special_tokens=a__ , **a__ , )
__snake_case = self.tokenizer_class.from_pretrained(
a__ , additional_special_tokens=a__ , **a__ )
__snake_case = tokenizer_p.encode('''Hey this is a <special> token''' )
__snake_case = tokenizer_cr.encode('''Hey this is a <special> token''' )
self.assertEqual(a__ , a__ )
self.assertEqual(a__ , a__ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
A_ : List[Any] = 'facebook/nllb-200-distilled-600M'
A_ : List[str] = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
A_ : List[Any] = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
A_ : List[Any] = [
256_047,
16_297,
134_408,
8_165,
248_066,
14_734,
950,
1_135,
105_721,
3_573,
83,
27_352,
108,
49_486,
2,
]
@classmethod
def a (cls : Tuple ):
"""simple docstring"""
__snake_case = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''eng_Latn''' , tgt_lang='''ron_Latn''' )
__snake_case = 1
return cls
def a (self : Optional[int] ):
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab'''] , 25_6001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn'''] , 25_6002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn'''] , 25_6057 )
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , a__ )
def a (self : Optional[Any] ):
"""simple docstring"""
self.assertIn(a__ , self.tokenizer.all_special_ids )
# fmt: off
__snake_case = [RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
# fmt: on
__snake_case = self.tokenizer.decode(a__ , skip_special_tokens=a__ )
__snake_case = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=a__ )
self.assertEqual(a__ , a__ )
self.assertNotIn(self.tokenizer.eos_token , a__ )
def a (self : Optional[int] ):
"""simple docstring"""
__snake_case = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , a__ )
__snake_case = 10
__snake_case = self.tokenizer(a__ , max_length=a__ , truncation=a__ ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , a__ )
self.assertEqual(len(a__ ) , a__ )
def a (self : Tuple ):
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [25_6203, 3] )
def a (self : Any ):
"""simple docstring"""
__snake_case = tempfile.mkdtemp()
__snake_case = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(a__ )
__snake_case = NllbTokenizer.from_pretrained(a__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , a__ )
@require_torch
def a (self : str ):
"""simple docstring"""
__snake_case = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=a__ , truncation=a__ , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
__snake_case = shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['''ron_Latn'''] )
self.assertIsInstance(a__ , a__ )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__snake_case = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , a__ )
self.assertEqual(a__ , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.tokenizer(self.src_text , padding=a__ , truncation=a__ , max_length=3 , return_tensors='''pt''' )
__snake_case = self.tokenizer(
text_target=self.tgt_text , padding=a__ , truncation=a__ , max_length=10 , return_tensors='''pt''' )
__snake_case = targets['''input_ids''']
__snake_case = shift_tokens_right(
a__ , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def a (self : str ):
"""simple docstring"""
__snake_case = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
nested_simplify(a__ ) , {
# A, test, EOS, en_XX
'''input_ids''': [[25_6047, 70, 7356, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 25_6057,
} , )
@require_torch
def a (self : Optional[Any] ):
"""simple docstring"""
__snake_case = True
__snake_case = self.tokenizer(
'''UN Chief says there is no military solution in Syria''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
inputs.input_ids , [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )
__snake_case = False
__snake_case = self.tokenizer(
'''UN Chief says there is no military solution in Syria''' , src_lang='''eng_Latn''' , tgt_lang='''fra_Latn''' )
self.assertEqual(
inputs.input_ids , [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )
| 24 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
# Base Case
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] , __UpperCamelCase , __UpperCamelCase ):
# Color current vertex
UpperCamelCase = i
# Validate coloring
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , index + 1 ):
return True
# Backtrack
UpperCamelCase = -1
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[int]:
UpperCamelCase = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , 0 ):
return colored_vertices
return []
| 321 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Optional[int] = '''audio-spectrogram-transformer'''
def __init__(self , SCREAMING_SNAKE_CASE__=7_68 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=30_72 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=10 , SCREAMING_SNAKE_CASE__=10_24 , SCREAMING_SNAKE_CASE__=1_28 , **SCREAMING_SNAKE_CASE__ , ) -> Tuple:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : str = num_hidden_layers
SCREAMING_SNAKE_CASE__ : int = num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE__ : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : int = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Dict = patch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = qkv_bias
SCREAMING_SNAKE_CASE__ : Optional[int] = frequency_stride
SCREAMING_SNAKE_CASE__ : Any = time_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = max_length
SCREAMING_SNAKE_CASE__ : Any = num_mel_bins
| 25 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __UpperCamelCase ):
UpperCamelCase = 1
UpperCamelCase = 0
for i in range(__UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class lowercase ( unittest.TestCase ):
def a__ ( self ) -> Any:
_A : str = tempfile.mkdtemp()
_A : str = BlipImageProcessor()
_A : List[Any] = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
_A : Union[str, Any] = BlipaProcessor(_a , _a )
processor.save_pretrained(self.tmpdirname )
def a__ ( self , **_a ) -> List[Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **_a ).tokenizer
def a__ ( self , **_a ) -> Optional[int]:
return AutoProcessor.from_pretrained(self.tmpdirname , **_a ).image_processor
def a__ ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
def a__ ( self ) -> Any:
_A : List[str] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_A : List[str] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def a__ ( self ) -> List[Any]:
_A : List[Any] = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A : Dict = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_A : Optional[int] = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
_A : List[Any] = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def a__ ( self ) -> str:
_A : Any = self.get_image_processor()
_A : Optional[Any] = self.get_tokenizer()
_A : List[Any] = BlipaProcessor(tokenizer=_a , image_processor=_a )
_A : List[str] = self.prepare_image_inputs()
_A : Optional[Any] = image_processor(_a , return_tensors="""np""" )
_A : Optional[int] = processor(images=_a , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def a__ ( self ) -> List[Any]:
_A : Tuple = self.get_image_processor()
_A : List[str] = self.get_tokenizer()
_A : str = BlipaProcessor(tokenizer=_a , image_processor=_a )
_A : Optional[Any] = """lower newer"""
_A : Tuple = processor(text=_a )
_A : Optional[int] = tokenizer(_a , return_token_type_ids=_a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def a__ ( self ) -> Optional[Any]:
_A : str = self.get_image_processor()
_A : List[str] = self.get_tokenizer()
_A : Dict = BlipaProcessor(tokenizer=_a , image_processor=_a )
_A : int = """lower newer"""
_A : List[str] = self.prepare_image_inputs()
_A : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def a__ ( self ) -> Optional[Any]:
_A : List[Any] = self.get_image_processor()
_A : Optional[int] = self.get_tokenizer()
_A : str = BlipaProcessor(tokenizer=_a , image_processor=_a )
_A : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A : Tuple = processor.batch_decode(_a )
_A : Optional[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def a__ ( self ) -> Any:
_A : List[str] = self.get_image_processor()
_A : Any = self.get_tokenizer()
_A : Optional[Any] = BlipaProcessor(tokenizer=_a , image_processor=_a )
_A : Tuple = """lower newer"""
_A : str = self.prepare_image_inputs()
_A : Any = processor(text=_a , images=_a )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
| 26 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus" ):
__a : List[Any] = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text , 'html.parser' )
__a : Union[str, Any] = soup.findAll('h1' )
__a : int = soup.findAll('div' , {'class': 'maincounter-number'} )
keys += soup.findAll('span' , {'class': 'panel-title'} )
values += soup.findAll('div' , {'class': 'number-table-main'} )
return {key.text.strip(): value.text.strip() for key, value in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
if __name__ == "__main__":
print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
for key, value in world_covidaa_stats().items():
print(f'''{key}\n{value}\n''')
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
@staticmethod
@abstractmethod
def A ( UpperCamelCase__ : ArgumentParser ):
"""simple docstring"""
raise NotImplementedError()
@abstractmethod
def A ( self : Any ):
"""simple docstring"""
raise NotImplementedError()
| 28 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 321 | 0 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
@add_end_docstrings(_snake_case )
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> List[Any]:
super().__init__(*_UpperCamelCase , **_UpperCamelCase )
requires_backends(self , 'decord' )
self.check_model_type(_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=None ) -> Dict:
UpperCAmelCase_ : Optional[Any] = {}
if frame_sampling_rate is not None:
UpperCAmelCase_ : Optional[Any] = frame_sampling_rate
if num_frames is not None:
UpperCAmelCase_ : Optional[Any] = num_frames
UpperCAmelCase_ : Optional[Any] = {}
if top_k is not None:
UpperCAmelCase_ : List[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , _UpperCamelCase , **_UpperCamelCase ) -> Union[str, Any]:
return super().__call__(_UpperCamelCase , **_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase=None , _UpperCamelCase=1 ) -> Optional[int]:
if num_frames is None:
UpperCAmelCase_ : Optional[Any] = self.model.config.num_frames
if video.startswith('http://' ) or video.startswith('https://' ):
UpperCAmelCase_ : List[str] = BytesIO(requests.get(_UpperCamelCase ).content )
UpperCAmelCase_ : Optional[Any] = VideoReader(_UpperCamelCase )
videoreader.seek(0 )
UpperCAmelCase_ : List[Any] = 0
UpperCAmelCase_ : Union[str, Any] = num_frames * frame_sampling_rate - 1
UpperCAmelCase_ : Optional[int] = np.linspace(_UpperCamelCase , _UpperCamelCase , num=_UpperCamelCase , dtype=np.intaa )
UpperCAmelCase_ : List[Any] = videoreader.get_batch(_UpperCamelCase ).asnumpy()
UpperCAmelCase_ : Union[str, Any] = list(_UpperCamelCase )
UpperCAmelCase_ : List[Any] = self.image_processor(_UpperCamelCase , return_tensors=self.framework )
return model_inputs
def __UpperCAmelCase ( self , _UpperCamelCase ) -> List[Any]:
UpperCAmelCase_ : Dict = self.model(**_UpperCamelCase )
return model_outputs
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase=5 ) -> List[str]:
if top_k > self.model.config.num_labels:
UpperCAmelCase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
UpperCAmelCase_ : Dict = model_outputs.logits.softmax(-1 )[0]
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = probs.topk(_UpperCamelCase )
else:
raise ValueError(f"Unsupported framework: {self.framework}" )
UpperCAmelCase_ : str = scores.tolist()
UpperCAmelCase_ : Any = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase , _UpperCamelCase )]
| 29 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 | 0 |
def a ( snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float , ):
'''simple docstring'''
lowercase_ = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('''All input parameters must be positive''' )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError('''Relative densities cannot be greater than one''' )
else:
lowercase_ = 1 - (matter_density + radiation_density + dark_energy)
lowercase_ = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
lowercase_ = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
__a = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1E-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 30 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ (metaclass=snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = ["torch", "scipy"]
def __init__( self : Any , *A : List[str] , **A : Tuple ):
requires_backends(self , ["torch", "scipy"] )
@classmethod
def _A ( cls : Dict , *A : Union[str, Any] , **A : int ):
requires_backends(cls , ["torch", "scipy"] )
@classmethod
def _A ( cls : Dict , *A : int , **A : Optional[Any] ):
requires_backends(cls , ["torch", "scipy"] )
| 31 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMING_SNAKE_CASE_ ( __A : List[str] , __A : Optional[Any] ) -> Dict:
"""simple docstring"""
assert isinstance(__A , __A )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] , __A : List[Any] , __A : Tuple ) -> Optional[Any]:
"""simple docstring"""
a_ : Any = tmp_path / 'cache'
a_ : Optional[Any] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
a_ : str = JsonDatasetReader(__A , cache_dir=__A , keep_in_memory=__A ).read()
_check_json_dataset(__A , __A )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def SCREAMING_SNAKE_CASE_ ( __A : Union[str, Any] , __A : Tuple , __A : Optional[int] ) -> Dict:
"""simple docstring"""
a_ : Any = tmp_path / 'cache'
a_ : Optional[int] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a_ : int = features.copy() if features else default_expected_features
a_ : str = (
Features({feature: Value(__A ) for feature, dtype in features.items()} ) if features is not None else None
)
a_ : Any = JsonDatasetReader(__A , features=__A , cache_dir=__A ).read()
_check_json_dataset(__A , __A )
@pytest.mark.parametrize(
'features' , [
None,
{'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
] , )
def SCREAMING_SNAKE_CASE_ ( __A : Any , __A : List[Any] , __A : str ) -> Any:
"""simple docstring"""
a_ : Tuple = tmp_path / 'cache'
a_ : str = {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
a_ : Tuple = features.copy() if features else default_expected_features
a_ : str = (
Features({feature: Value(__A ) for feature, dtype in features.items()} ) if features is not None else None
)
a_ : List[str] = JsonDatasetReader(__A , features=__A , cache_dir=__A ).read()
assert isinstance(__A , __A )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def SCREAMING_SNAKE_CASE_ ( __A : str , __A : List[str] ) -> Any:
"""simple docstring"""
a_ : Dict = {'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
a_ : Optional[Any] = features.copy()
a_ : List[str] = (
Features({feature: Value(__A ) for feature, dtype in features.items()} ) if features is not None else None
)
a_ : List[Any] = tmp_path / 'cache'
a_ : Any = JsonDatasetReader(__A , features=__A , cache_dir=__A ).read()
assert isinstance(__A , __A )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def SCREAMING_SNAKE_CASE_ ( __A : Tuple , __A : Optional[int] , __A : Any ) -> List[Any]:
"""simple docstring"""
a_ : List[Any] = tmp_path / 'cache'
a_ : str = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a_ : str = JsonDatasetReader(__A , cache_dir=__A , split=__A ).read()
_check_json_dataset(__A , __A )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' , [str, list] )
def SCREAMING_SNAKE_CASE_ ( __A : Any , __A : Optional[Any] , __A : Optional[int] ) -> List[Any]:
"""simple docstring"""
if issubclass(__A , __A ):
a_ : Dict = jsonl_path
elif issubclass(__A , __A ):
a_ : Tuple = [jsonl_path]
a_ : List[str] = tmp_path / 'cache'
a_ : List[Any] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a_ : int = JsonDatasetReader(__A , cache_dir=__A ).read()
_check_json_dataset(__A , __A )
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : Tuple , __A : Tuple=("train",) ) -> int:
"""simple docstring"""
assert isinstance(__A , __A )
for split in splits:
a_ : str = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def SCREAMING_SNAKE_CASE_ ( __A : Dict , __A : Dict , __A : Any ) -> Union[str, Any]:
"""simple docstring"""
a_ : Any = tmp_path / 'cache'
a_ : str = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
a_ : List[Any] = JsonDatasetReader({'train': jsonl_path} , cache_dir=__A , keep_in_memory=__A ).read()
_check_json_datasetdict(__A , __A )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def SCREAMING_SNAKE_CASE_ ( __A : str , __A : Any , __A : List[Any] ) -> List[str]:
"""simple docstring"""
a_ : List[Any] = tmp_path / 'cache'
a_ : Optional[int] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a_ : Union[str, Any] = features.copy() if features else default_expected_features
a_ : Tuple = (
Features({feature: Value(__A ) for feature, dtype in features.items()} ) if features is not None else None
)
a_ : Dict = JsonDatasetReader({'train': jsonl_path} , features=__A , cache_dir=__A ).read()
_check_json_datasetdict(__A , __A )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def SCREAMING_SNAKE_CASE_ ( __A : Dict , __A : str , __A : List[str] ) -> Any:
"""simple docstring"""
if split:
a_ : Union[str, Any] = {split: jsonl_path}
else:
a_ : str = 'train'
a_ : List[Any] = {'train': jsonl_path, 'test': jsonl_path}
a_ : Optional[Any] = tmp_path / 'cache'
a_ : Optional[Any] = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a_ : Optional[int] = JsonDatasetReader(__A , cache_dir=__A ).read()
_check_json_datasetdict(__A , __A , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def SCREAMING_SNAKE_CASE_ ( __A : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return json.load(__A )
def SCREAMING_SNAKE_CASE_ ( __A : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return [json.loads(__A ) for line in buffer]
class SCREAMING_SNAKE_CASE__ :
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple ) -> Dict:
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , lines=SCREAMING_SNAKE_CASE__ ).write()
buffer.seek(0 )
a_ : Union[str, Any] = load_json_function(SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert isinstance(exported_content[0] , SCREAMING_SNAKE_CASE__ )
assert len(SCREAMING_SNAKE_CASE__ ) == 1_0
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Optional[Any]:
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , lines=SCREAMING_SNAKE_CASE__ , orient=SCREAMING_SNAKE_CASE__ ).write()
buffer.seek(0 )
a_ : List[Any] = load_json(SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(SCREAMING_SNAKE_CASE__ , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(SCREAMING_SNAKE_CASE__ ) == 1_0
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Any ) -> Optional[int]:
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , lines=SCREAMING_SNAKE_CASE__ , num_proc=2 ).write()
buffer.seek(0 )
a_ : Optional[int] = load_json_function(SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert isinstance(exported_content[0] , SCREAMING_SNAKE_CASE__ )
assert len(SCREAMING_SNAKE_CASE__ ) == 1_0
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[str]:
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , lines=SCREAMING_SNAKE_CASE__ , orient=SCREAMING_SNAKE_CASE__ , num_proc=2 ).write()
buffer.seek(0 )
a_ : str = load_json(SCREAMING_SNAKE_CASE__ )
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(SCREAMING_SNAKE_CASE__ , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(SCREAMING_SNAKE_CASE__ ) == 1_0
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Dict:
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , num_proc=0 )
@pytest.mark.parametrize('compression, extension' , [('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')] )
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Optional[int]:
a_ : Optional[int] = tmp_path_factory.mktemp('data' ) / F"""test.json.{extension}"""
a_ : int = str(shared_datadir / F"""test_file.json.{extension}""" )
JsonDatasetWriter(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , compression=SCREAMING_SNAKE_CASE__ ).write()
with fsspec.open(SCREAMING_SNAKE_CASE__ , 'rb' , compression='infer' ) as f:
a_ : Optional[Any] = f.read()
with fsspec.open(SCREAMING_SNAKE_CASE__ , 'rb' , compression='infer' ) as f:
a_ : List[str] = f.read()
assert exported_content == original_content
| 32 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = min(__UpperCamelCase ) # min() finds the minimum value
UpperCamelCase = max(__UpperCamelCase ) # max() finds the maximum value
UpperCamelCase = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCamelCase = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCamelCase = 0
for count in range(__UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
UpperCamelCase = count + min_val
i += 1
def lowercase__ ( )-> Any:
UpperCamelCase = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__UpperCamelCase )
print("""Sorted order is:""" , """ """.join(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 321 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : List[Any] = logging.get_logger(__name__)
__A : List[Any] = {
'''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}
class _UpperCAmelCase ( _A , _A ):
SCREAMING_SNAKE_CASE_ : List[Any] = "resnet"
SCREAMING_SNAKE_CASE_ : Tuple = ["basic", "bottleneck"]
def __init__( self : Any , A : Tuple=3 , A : str=64 , A : Tuple=[2_56, 5_12, 10_24, 20_48] , A : List[Any]=[3, 4, 6, 3] , A : Union[str, Any]="bottleneck" , A : int="relu" , A : List[Any]=False , A : Tuple=None , A : int=None , **A : List[str] , ) -> List[Any]:
super().__init__(**A )
if layer_type not in self.layer_types:
raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' )
lowercase_ : List[Any] = num_channels
lowercase_ : Tuple = embedding_size
lowercase_ : Dict = hidden_sizes
lowercase_ : Tuple = depths
lowercase_ : Optional[int] = layer_type
lowercase_ : str = hidden_act
lowercase_ : Dict = downsample_in_first_stage
lowercase_ : str = ['''stem'''] + [F'''stage{idx}''' for idx in range(1 , len(A ) + 1 )]
lowercase_ , lowercase_ : List[str] = get_aligned_output_features_output_indices(
out_features=A , out_indices=A , stage_names=self.stage_names )
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : str = version.parse("1.11" )
@property
def A ( self : Any ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def A ( self : Union[str, Any] ) -> float:
return 1e-3
| 33 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 | 0 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _a :
def __init__( self : Any , lowercase : Any , lowercase : int , lowercase : int ):
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
UpperCAmelCase = img
UpperCAmelCase = img.shape[1]
UpperCAmelCase = img.shape[0]
UpperCAmelCase = dst_width
UpperCAmelCase = dst_height
UpperCAmelCase = self.src_w / self.dst_w
UpperCAmelCase = self.src_h / self.dst_h
UpperCAmelCase = UpperCAmelCase = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def A ( self : Union[str, Any] ):
'''simple docstring'''
for i in range(self.dst_h ):
for j in range(self.dst_w ):
UpperCAmelCase = self.img[self.get_y(lowercase )][self.get_x(lowercase )]
def A ( self : Dict , lowercase : int ):
'''simple docstring'''
return int(self.ratio_x * x )
def A ( self : List[Any] , lowercase : int ):
'''simple docstring'''
return int(self.ratio_y * y )
if __name__ == "__main__":
A , A =8_00, 6_00
A =imread('image_data/lena.jpg', 1)
A =NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
)
waitKey(0)
destroyAllWindows()
| 34 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 0 |
'''simple docstring'''
from PIL import Image
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Image:
def brightness(_lowerCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(_lowerCAmelCase )
if __name__ == "__main__":
# Load image
with Image.open("image_data/lena.jpg") as img:
# Change brightness to 100
__a = change_brightness(img, 100)
brigt_img.save("image_data/lena_brightness.png", format="png")
| 35 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 1000 )-> int:
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCamelCase = n - a - b
if c * c == (a * a + b * b):
UpperCamelCase = a * b * c
if candidate >= product:
UpperCamelCase = candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 0 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_snake_case = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
_snake_case = direct_transformers_import(PATH_TO_TRANSFORMERS)
_snake_case = transformers.models.auto.configuration_auto.CONFIG_MAPPING
_snake_case = {
# used to compute the property `self.chunk_length`
"EncodecConfig": ["overlap"],
# used as `self.bert_model = BertModel(config, ...)`
"DPRConfig": True,
# not used in modeling files, but it's an important information
"FSMTConfig": ["langs"],
# used internally in the configuration class file
"GPTNeoConfig": ["attention_types"],
# used internally in the configuration class file
"EsmConfig": ["is_folding_model"],
# used during training (despite we don't have training script for these models yet)
"Mask2FormerConfig": ["ignore_value"],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"OneFormerConfig": ["ignore_value", "norm"],
# used during preprocessing and collation, see `collating_graphormer.py`
"GraphormerConfig": ["spatial_pos_max"],
# used internally in the configuration class file
"T5Config": ["feed_forward_proj"],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"MT5Config": ["feed_forward_proj", "tokenizer_class"],
"UMT5Config": ["feed_forward_proj", "tokenizer_class"],
# used internally in the configuration class file
"LongT5Config": ["feed_forward_proj"],
# used internally in the configuration class file
"SwitchTransformersConfig": ["feed_forward_proj"],
# having default values other than `1e-5` - we can't fix them without breaking
"BioGptConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"GLPNConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"SegformerConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"CvtConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"PerceiverConfig": ["layer_norm_eps"],
# used internally to calculate the feature size
"InformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"AutoformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate `mlp_dim`
"SamVisionConfig": ["mlp_ratio"],
# For (head) training, but so far not implemented
"ClapAudioConfig": ["num_classes"],
# Not used, but providing useful information to users
"SpeechT5HifiGanConfig": ["sampling_rate"],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
"CLIPSegConfig": True,
"DeformableDetrConfig": True,
"DetaConfig": True,
"DinatConfig": True,
"DonutSwinConfig": True,
"EfficientFormerConfig": True,
"FSMTConfig": True,
"JukeboxConfig": True,
"LayoutLMv2Config": True,
"MaskFormerSwinConfig": True,
"MT5Config": True,
"NatConfig": True,
"OneFormerConfig": True,
"PerceiverConfig": True,
"RagConfig": True,
"SpeechT5Config": True,
"SwinConfig": True,
"Swin2SRConfig": True,
"Swinv2Config": True,
"SwitchTransformersConfig": True,
"TableTransformerConfig": True,
"TapasConfig": True,
"TransfoXLConfig": True,
"UniSpeechConfig": True,
"UniSpeechSatConfig": True,
"WavLMConfig": True,
"WhisperConfig": True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
"JukeboxPriorConfig": True,
# TODO: @Younes (for `is_decoder`)
"Pix2StructTextConfig": True,
}
)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
F"config.{attribute}" in modeling_source
or F"getattr(config, \"{attribute}\"" in modeling_source
or F"getattr(self.config, \"{attribute}\"" in modeling_source
):
_lowerCAmelCase : Any = True
# Deal with multi-line cases
elif (
re.search(
rF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"" , _lowerCamelCase , )
is not None
):
_lowerCAmelCase : Union[str, Any] = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
_lowerCAmelCase : Optional[int] = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
_lowerCAmelCase : Tuple = [
"bos_index",
"eos_index",
"pad_index",
"unk_index",
"mask_index",
"image_size",
"use_cache",
"out_features",
"out_indices",
]
_lowerCAmelCase : List[str] = ["encoder_no_repeat_ngram_size"]
# Special cases to be allowed
_lowerCAmelCase : Dict = True
if not attribute_used:
_lowerCAmelCase : List[str] = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
_lowerCAmelCase : Tuple = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
_lowerCAmelCase : Any = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
_lowerCAmelCase : Dict = True
elif attribute.endswith("_token_id" ):
_lowerCAmelCase : Any = True
# configuration class specific cases
if not case_allowed:
_lowerCAmelCase : Optional[int] = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
_lowerCAmelCase : Optional[Any] = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = dict(inspect.signature(config_class.__init__ ).parameters )
_lowerCAmelCase : List[Any] = [x for x in list(signature.keys() ) if x not in ["self", "kwargs"]]
_lowerCAmelCase : List[Any] = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
_lowerCAmelCase : Optional[Any] = {}
if len(config_class.attribute_map ) > 0:
_lowerCAmelCase : Any = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
_lowerCAmelCase : Optional[Any] = inspect.getsourcefile(_lowerCamelCase )
_lowerCAmelCase : str = os.path.dirname(_lowerCamelCase )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
_lowerCAmelCase : Any = [os.path.join(_lowerCamelCase , _lowerCamelCase ) for fn in os.listdir(_lowerCamelCase ) if fn.startswith("modeling_" )]
# Get the source code strings
_lowerCAmelCase : Union[str, Any] = []
for path in modeling_paths:
if os.path.isfile(_lowerCamelCase ):
with open(_lowerCamelCase ) as fp:
modeling_sources.append(fp.read() )
_lowerCAmelCase : List[str] = []
for config_param, default_value in zip(_lowerCamelCase , _lowerCamelCase ):
# `attributes` here is all the variant names for `config_param`
_lowerCAmelCase : int = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
unused_attributes.append(attributes[0] )
return sorted(_lowerCamelCase )
def A ( ):
'''simple docstring'''
_lowerCAmelCase : int = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
_lowerCAmelCase : Tuple = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda _lowerCamelCase : inspect.isclass(_lowerCamelCase )
and issubclass(_lowerCamelCase , _lowerCamelCase )
and inspect.getmodule(_lowerCamelCase ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
_lowerCAmelCase : Optional[int] = check_config_attributes_being_used(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
_lowerCAmelCase : Dict = unused_attributes
if len(_lowerCamelCase ) > 0:
_lowerCAmelCase : Dict = "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
for name, attributes in configs_with_unused_attributes.items():
error += F"{name}: {attributes}\n"
raise ValueError(_lowerCamelCase )
if __name__ == "__main__":
check_config_attributes()
| 36 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 | 0 |
'''simple docstring'''
from manim import *
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : Dict = Rectangle(height=0.5 ,width=0.5 )
lowerCAmelCase__ : Any = Rectangle(height=0.2_5 ,width=0.2_5 )
lowerCAmelCase__ : List[str] = Rectangle(height=0.4_6 ,width=0.4_6 ).set_stroke(width=0 )
lowerCAmelCase__ : Any = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : List[str] = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : str = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : Optional[Any] = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : List[Any] = VGroup(__UpperCAmelCase ,__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : Optional[Any] = Text("""CPU""" ,font_size=24 )
lowerCAmelCase__ : str = Group(__UpperCAmelCase ,__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0.5 ,aligned_edge=__UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__UpperCAmelCase )
lowerCAmelCase__ : str = [mem.copy() for i in range(4 )]
lowerCAmelCase__ : List[Any] = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : Optional[int] = Text("""GPU""" ,font_size=24 )
lowerCAmelCase__ : Union[str, Any] = Group(__UpperCAmelCase ,__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0.5 ,aligned_edge=__UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(__UpperCAmelCase )
lowerCAmelCase__ : Tuple = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : List[str] = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : Optional[Any] = Text("""Model""" ,font_size=24 )
lowerCAmelCase__ : Optional[Any] = Group(__UpperCAmelCase ,__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0.5 ,aligned_edge=__UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(__UpperCAmelCase )
lowerCAmelCase__ : Any = []
lowerCAmelCase__ : Any = []
lowerCAmelCase__ : List[Any] = []
for i, rect in enumerate(__UpperCAmelCase ):
rect.set_stroke(__UpperCAmelCase )
lowerCAmelCase__ : str = Rectangle(height=0.4_6 / 4 ,width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(__UpperCAmelCase ,opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) ,buff=0.0_2 ,direction=__UpperCAmelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] ,direction=__UpperCAmelCase ,buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] ,direction=__UpperCAmelCase ,buff=0.0 )
self.add(__UpperCAmelCase )
model_cpu_arr.append(__UpperCAmelCase )
self.add(*__UpperCAmelCase ,*__UpperCAmelCase ,*__UpperCAmelCase )
lowerCAmelCase__ : Dict = [mem.copy() for i in range(6 )]
lowerCAmelCase__ : Any = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : int = Text("""Loaded Checkpoint""" ,font_size=24 )
lowerCAmelCase__ : str = Group(__UpperCAmelCase ,__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0.5 ,aligned_edge=__UpperCAmelCase )
checkpoint.move_to([3, 0.5, 0] )
self.add(__UpperCAmelCase )
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : List[str] = []
for i, rect in enumerate(__UpperCAmelCase ):
lowerCAmelCase__ : List[Any] = fill.copy().set_fill(__UpperCAmelCase ,opacity=0.7 )
target.move_to(__UpperCAmelCase )
ckpt_arr.append(__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(__UpperCAmelCase )
self.add(*__UpperCAmelCase ,*__UpperCAmelCase )
lowerCAmelCase__ : str = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCAmelCase__ : Any = MarkupText(
F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" ,font_size=18 ,)
key_text.move_to([-5, 2.4, 0] )
self.add(__UpperCAmelCase ,__UpperCAmelCase )
lowerCAmelCase__ : int = MarkupText(
F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" ,font_size=18 ,)
blue_text.next_to(__UpperCAmelCase ,DOWN * 2.4 ,aligned_edge=key_text.get_left() )
self.add(__UpperCAmelCase )
lowerCAmelCase__ : str = MarkupText(
F"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.""" ,font_size=24 ,)
step_a.move_to([2, 2, 0] )
lowerCAmelCase__ : List[Any] = [meta_mem.copy() for i in range(6 )]
lowerCAmelCase__ : str = [meta_mem.copy() for i in range(6 )]
lowerCAmelCase__ : Union[str, Any] = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : Dict = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : Union[str, Any] = VGroup(__UpperCAmelCase ,__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0 )
lowerCAmelCase__ : Tuple = Text("""Disk""" ,font_size=24 )
lowerCAmelCase__ : Optional[Any] = Group(__UpperCAmelCase ,__UpperCAmelCase ).arrange(__UpperCAmelCase ,buff=0.5 ,aligned_edge=__UpperCAmelCase )
disk.move_to([-4.0, -1.2_5, 0] )
self.play(Write(__UpperCAmelCase ,run_time=3 ) ,Write(__UpperCAmelCase ,run_time=1 ) ,Create(__UpperCAmelCase ,run_time=1 ) )
lowerCAmelCase__ : List[Any] = []
for i, rect in enumerate(__UpperCAmelCase ):
lowerCAmelCase__ : Optional[int] = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(__UpperCAmelCase ,run_time=1.5 ) )
self.play(*__UpperCAmelCase )
self.play(FadeOut(__UpperCAmelCase ) )
lowerCAmelCase__ : Tuple = MarkupText(F"""Then, the checkpoint is removed from memory\nthrough garbage collection.""" ,font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(__UpperCAmelCase ,run_time=3 ) )
self.play(
FadeOut(__UpperCAmelCase ,__UpperCAmelCase ,*__UpperCAmelCase ,*__UpperCAmelCase ) ,)
self.wait()
| 37 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE__ = _symbol_database.Default()
SCREAMING_SNAKE_CASE__ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
SCREAMING_SNAKE_CASE__ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
SCREAMING_SNAKE_CASE__ = 4_5
SCREAMING_SNAKE_CASE__ = 1_5_8_1
SCREAMING_SNAKE_CASE__ = 1_5_1_7
SCREAMING_SNAKE_CASE__ = 1_5_7_0
SCREAMING_SNAKE_CASE__ = 1_5_8_4
SCREAMING_SNAKE_CASE__ = 1_7_9_3
SCREAMING_SNAKE_CASE__ = 1_7_9_5
SCREAMING_SNAKE_CASE__ = 1_9_1_6
SCREAMING_SNAKE_CASE__ = 1_8_6_4
SCREAMING_SNAKE_CASE__ = 1_9_0_5
SCREAMING_SNAKE_CASE__ = 1_9_1_9
SCREAMING_SNAKE_CASE__ = 2_4_2_9
SCREAMING_SNAKE_CASE__ = 2_2_0_8
SCREAMING_SNAKE_CASE__ = 2_4_1_8
SCREAMING_SNAKE_CASE__ = 2_3_2_3
SCREAMING_SNAKE_CASE__ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 321 | 0 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : list ) -> int:
"""simple docstring"""
_enforce_args(__magic_name__ , __magic_name__ )
if n == 0:
return 0
UpperCamelCase :List[Any] = float("""-inf""" )
for i in range(1 , n + 1 ):
UpperCamelCase :Optional[Any] = max(
__magic_name__ , prices[i - 1] + naive_cut_rod_recursive(n - i , __magic_name__ ) )
return max_revue
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : list ) -> Union[str, Any]:
"""simple docstring"""
_enforce_args(__magic_name__ , __magic_name__ )
UpperCamelCase :Tuple = [float("""-inf""" ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(__magic_name__ , __magic_name__ , __magic_name__ )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : list , __magic_name__ : list ) -> Union[str, Any]:
"""simple docstring"""
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
UpperCamelCase :Optional[int] = float("""-inf""" )
for i in range(1 , n + 1 ):
UpperCamelCase :List[str] = max(
__magic_name__ , prices[i - 1] + _top_down_cut_rod_recursive(n - i , __magic_name__ , __magic_name__ ) , )
UpperCamelCase :List[str] = max_revenue
return max_rev[n]
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : list ) -> str:
"""simple docstring"""
_enforce_args(__magic_name__ , __magic_name__ )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
UpperCamelCase :Tuple = [float("""-inf""" ) for _ in range(n + 1 )]
UpperCamelCase :Any = 0
for i in range(1 , n + 1 ):
UpperCamelCase :Tuple = max_rev[i]
for j in range(1 , i + 1 ):
UpperCamelCase :Union[str, Any] = max(__magic_name__ , prices[j - 1] + max_rev[i - j] )
UpperCamelCase :Dict = max_revenue_i
return max_rev[n]
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : list ) -> Optional[int]:
"""simple docstring"""
if n < 0:
UpperCamelCase :Tuple = f"""n must be greater than or equal to 0. Got n = {n}"""
raise ValueError(__magic_name__ )
if n > len(__magic_name__ ):
UpperCamelCase :Optional[int] = (
"""Each integral piece of rod must have a corresponding price. """
f"""Got n = {n} but length of prices = {len(__magic_name__ )}"""
)
raise ValueError(__magic_name__ )
def SCREAMING_SNAKE_CASE_ ( ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase :Dict = [6, 10, 12, 15, 20, 23]
UpperCamelCase :Optional[Any] = len(__magic_name__ )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
UpperCamelCase :Dict = 36
UpperCamelCase :Optional[int] = top_down_cut_rod(__magic_name__ , __magic_name__ )
UpperCamelCase :Any = bottom_up_cut_rod(__magic_name__ , __magic_name__ )
UpperCamelCase :List[Any] = naive_cut_rod_recursive(__magic_name__ , __magic_name__ )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 38 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( snake_case__ , snake_case__ , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = StableDiffusionXLImgaImgPipeline
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
UpperCamelCase__ = PipelineTesterMixin.required_optional_params - {"latents"}
UpperCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
UpperCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase , addition_embed_type='text_time' , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
_UpperCAmelCase = EulerDiscreteScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , steps_offset=1 , beta_schedule='scaled_linear' , timestep_spacing='leading' , )
torch.manual_seed(0 )
_UpperCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=32 , )
_UpperCAmelCase = CLIPTextModel(UpperCAmelCase )
_UpperCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' , local_files_only=UpperCAmelCase )
_UpperCAmelCase = CLIPTextModelWithProjection(UpperCAmelCase )
_UpperCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' , local_files_only=UpperCAmelCase )
_UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'text_encoder_2': text_encoder_a,
'tokenizer_2': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase=0 ):
"""simple docstring"""
_UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
_UpperCAmelCase = image / 2 + 0.5
if str(UpperCAmelCase ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(UpperCAmelCase )
else:
_UpperCAmelCase = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
_UpperCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 5.0,
'output_type': 'numpy',
'strength': 0.75,
}
return inputs
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase )
_UpperCAmelCase = sd_pipe.to(UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = self.get_dummy_inputs(UpperCAmelCase )
_UpperCAmelCase = sd_pipe(**UpperCAmelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_UpperCAmelCase = np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase ( self ):
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def UpperCamelCase ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def UpperCamelCase ( self ):
"""simple docstring"""
pass
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = StableDiffusionXLImgaImgPipeline(**UpperCAmelCase )
_UpperCAmelCase = sd_pipe.to(UpperCAmelCase )
_UpperCAmelCase = sd_pipe.to(UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase )
# forward without prompt embeds
_UpperCAmelCase = self.get_dummy_inputs(UpperCAmelCase )
_UpperCAmelCase = 3 * ['this is a negative prompt']
_UpperCAmelCase = negative_prompt
_UpperCAmelCase = 3 * [inputs['prompt']]
_UpperCAmelCase = sd_pipe(**UpperCAmelCase )
_UpperCAmelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
_UpperCAmelCase = self.get_dummy_inputs(UpperCAmelCase )
_UpperCAmelCase = 3 * ['this is a negative prompt']
_UpperCAmelCase = 3 * [inputs.pop('prompt' )]
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = sd_pipe.encode_prompt(UpperCAmelCase , negative_prompt=UpperCAmelCase )
_UpperCAmelCase = sd_pipe(
**UpperCAmelCase , prompt_embeds=UpperCAmelCase , negative_prompt_embeds=UpperCAmelCase , pooled_prompt_embeds=UpperCAmelCase , negative_pooled_prompt_embeds=UpperCAmelCase , )
_UpperCAmelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase="cpu" , UpperCAmelCase=torch.floataa , UpperCAmelCase=0 ):
"""simple docstring"""
_UpperCAmelCase = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
_UpperCAmelCase = np.random.RandomState(UpperCAmelCase ).standard_normal((1, 4, 64, 64) )
_UpperCAmelCase = torch.from_numpy(UpperCAmelCase ).to(device=UpperCAmelCase , dtype=UpperCAmelCase )
_UpperCAmelCase = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base' )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = self.get_inputs(UpperCAmelCase )
_UpperCAmelCase = pipe(**UpperCAmelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06] )
assert np.abs(image_slice - expected_slice ).max() < 7e-3
| 39 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE__ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __UpperCamelCase )-> str:
if "://" in dataset_path:
UpperCamelCase = dataset_path.split("""://""" )[1]
return dataset_path
def lowercase__ ( __UpperCamelCase )-> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = not is_remote_filesystem(__UpperCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__UpperCamelCase ) , fs._strip_protocol(__UpperCamelCase ) )
else:
fs.mv(__UpperCamelCase , __UpperCamelCase , recursive=__UpperCamelCase )
def lowercase__ ( )-> None:
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = threading.Lock()
| 321 | 0 |
"""simple docstring"""
import argparse
import os
import re
__lowercase = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowercase = re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__lowercase = re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")
def lowercase ( A_ , A_ = False )-> Dict:
'''simple docstring'''
with open(A_ , "r" , encoding="utf-8" ) as f:
a : List[str] = f.read()
a : Union[str, Any] = content.split("\n" )
a : Any = []
a : Tuple = 0
while line_idx < len(A_ ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
a : str = len(re.search(R"^(\s*)\S" , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(" " * indent + "(" ):
new_lines.append(lines[line_idx] )
line_idx += 1
a : int = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
a : Any = line_idx
while not lines[line_idx].startswith(" " * indent + ")" ):
line_idx += 1
blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
a : Tuple = sorted(A_ , key=lambda A_ : _re_identifier.search(A_ ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(A_ , "w" , encoding="utf-8" ) as f:
f.write("\n".join(A_ ) )
elif "\n".join(A_ ) != content:
return True
def lowercase ( A_ = False )-> List[str]:
'''simple docstring'''
a : Dict = [os.path.join(A_ , A_ ) for f in os.listdir(A_ ) if f.endswith(".py" )]
a : int = [sort_auto_mapping(A_ , overwrite=A_ ) for fname in fnames]
if not overwrite and any(A_ ):
a : Any = [f for f, d in zip(A_ , A_ ) if d]
raise ValueError(
F'''The following files have auto mappings that need sorting: {", ".join(A_ )}. Run `make style` to fix'''
" this." )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
__lowercase = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 40 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 321 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.