code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __magic_name__ :
UpperCamelCase__ = 42
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "dict"
UpperCamelCase__ = None
UpperCamelCase__ = field(default='Translation' , init=__SCREAMING_SNAKE_CASE , repr=__SCREAMING_SNAKE_CASE )
def __call__( self ):
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def _A( self ):
from .features import Value
return {k: Value('''string''' ) for k in sorted(self.languages )}
@dataclass
class __magic_name__ :
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "dict"
UpperCamelCase__ = None
UpperCamelCase__ = field(default='TranslationVariableLanguages' , init=__SCREAMING_SNAKE_CASE , repr=__SCREAMING_SNAKE_CASE )
def _A( self ):
lowercase =sorted(set(self.languages ) ) if self.languages else None
lowercase =len(self.languages ) if self.languages else None
def __call__( self ):
return pa.struct({'''language''': pa.list_(pa.string() ), '''translation''': pa.list_(pa.string() )} )
def _A( self , snake_case_ ):
lowercase =set(self.languages )
if self.languages and set(snake_case_ ) - lang_set:
raise ValueError(
f'Some languages in example ({", ".join(sorted(set(snake_case_ ) - lang_set ) )}) are not in valid set ({", ".join(snake_case_ )}).' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
lowercase =[]
for lang, text in translation_dict.items():
if isinstance(snake_case_ , snake_case_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
lowercase , lowercase =zip(*sorted(snake_case_ ) )
return {"language": languages, "translation": translations}
def _A( self ):
from .features import Sequence, Value
return {
"language": Sequence(Value('''string''' ) ),
"translation": Sequence(Value('''string''' ) ),
}
| 72 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''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
_UpperCAmelCase : str = 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 __magic_name__ :
UpperCamelCase__ = field(
default='cifar10' , metadata={'help': 'Name of a dataset from the datasets package'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'The column name of the images in the files.'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'A folder containing the training data.'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'A folder containing the validation data.'} )
UpperCamelCase__ = field(
default=0.1_5 , metadata={'help': 'Percent to split off of train for validation.'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def _A( self ):
lowercase ={}
if self.train_dir is not None:
lowercase =self.train_dir
if self.validation_dir is not None:
lowercase =self.validation_dir
lowercase =data_files if data_files else None
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
)
} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , 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'
)
} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
UpperCamelCase__ = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Name or path of preprocessor config.'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
UpperCamelCase__ = field(
default=0.7_5 , metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether or not to train with normalized pixel values as target.'} )
@dataclass
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = field(
default=1E-3 , metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )
def UpperCamelCase ( lowercase_ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
lowercase =torch.stack([example['''pixel_values'''] for example in examples] )
return {"pixel_values": pixel_values}
def UpperCamelCase ( ) -> Tuple:
'''simple docstring'''
lowercase =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.
lowercase , lowercase , lowercase =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase =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''' , lowercase_ , lowercase_ )
# 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()
lowercase =training_args.get_process_log_level()
logger.setLevel(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
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.
lowercase =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase =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.
lowercase =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.
lowercase =None if '''validation''' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , lowercase_ ) and data_args.train_val_split > 0.0:
lowercase =ds['''train'''].train_test_split(data_args.train_val_split )
lowercase =split['''train''']
lowercase =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.
lowercase ={
'''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:
lowercase =ViTMAEConfig.from_pretrained(model_args.config_name , **lowercase_ )
elif model_args.model_name_or_path:
lowercase =ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **lowercase_ )
else:
lowercase =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:
lowercase =ViTImageProcessor.from_pretrained(model_args.image_processor_name , **lowercase_ )
elif model_args.model_name_or_path:
lowercase =ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **lowercase_ )
else:
lowercase =ViTImageProcessor()
# create model
if model_args.model_name_or_path:
lowercase =ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=lowercase_ , 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''' )
lowercase =ViTMAEForPreTraining(lowercase_ )
if training_args.do_train:
lowercase =ds['''train'''].column_names
else:
lowercase =ds['''validation'''].column_names
if data_args.image_column_name is not None:
lowercase =data_args.image_column_name
elif "image" in column_names:
lowercase ='''image'''
elif "img" in column_names:
lowercase ='''img'''
else:
lowercase =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:
lowercase =image_processor.size['''shortest_edge''']
else:
lowercase =(image_processor.size['''height'''], image_processor.size['''width'''])
lowercase =Compose(
[
Lambda(lambda lowercase_ : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(lowercase_ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(lowercase_ : Union[str, Any] ):
lowercase =[transforms(lowercase_ ) 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:
lowercase =ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(lowercase_ )
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:
lowercase =(
ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(lowercase_ )
# Compute absolute learning rate
lowercase =(
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
lowercase =training_args.base_learning_rate * total_train_batch_size / 2_5_6
# Initialize our trainer
lowercase =Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=lowercase_ , data_collator=lowercase_ , )
# Training
if training_args.do_train:
lowercase =None
if training_args.resume_from_checkpoint is not None:
lowercase =training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase =last_checkpoint
lowercase =trainer.train(resume_from_checkpoint=lowercase_ )
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:
lowercase =trainer.evaluate()
trainer.log_metrics('''eval''' , lowercase_ )
trainer.save_metrics('''eval''' , lowercase_ )
# Write model card and (optionally) push to hub
lowercase ={
'''tasks''': '''masked-auto-encoding''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''masked-auto-encoding'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase_ )
else:
trainer.create_model_card(**lowercase_ )
def UpperCamelCase ( lowercase_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 72 |
'''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 __magic_name__ :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=6 , snake_case_=17 , snake_case_=23 , snake_case_=11 , snake_case_=True , ):
lowercase =parent
lowercase =batch_size
lowercase =seq_length
lowercase =act_dim
lowercase =state_dim
lowercase =hidden_size
lowercase =max_length
lowercase =is_training
def _A( self ):
lowercase =floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =ids_tensor((self.batch_size, self.seq_length) , vocab_size=10_00 )
lowercase =random_attention_mask((self.batch_size, self.seq_length) )
lowercase =self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _A( self ):
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 , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =DecisionTransformerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , 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 ):
lowercase =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) =config_and_inputs
lowercase ={
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (DecisionTransformerModel,) if is_torch_available() else ()
UpperCamelCase__ = ()
UpperCamelCase__ = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
UpperCamelCase__ = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =DecisionTransformerModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def _A( self ):
self.config_tester.run_common_tests()
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
@slow
def _A( self ):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase =DecisionTransformerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase =[*signature.parameters.keys()]
lowercase =[
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def _A( self ):
lowercase =2 # number of steps of autoregressive prediction we will perform
lowercase =10 # defined by the RL environment, may be normalized
lowercase =DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
lowercase =model.to(snake_case_ )
lowercase =model.config
torch.manual_seed(0 )
lowercase =torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ) # env.reset()
lowercase =torch.tensor(
[[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]] , device=snake_case_ )
lowercase =torch.tensor(snake_case_ , device=snake_case_ , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowercase =state
lowercase =torch.zeros(1 , 0 , config.act_dim , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.zeros(1 , 0 , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.tensor(0 , device=snake_case_ , dtype=torch.long ).reshape(1 , 1 )
for step in range(snake_case_ ):
lowercase =torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=snake_case_ )] , dim=1 )
lowercase =torch.cat([rewards, torch.zeros(1 , 1 , device=snake_case_ )] , dim=1 )
lowercase =torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowercase , lowercase , lowercase =model(
states=snake_case_ , actions=snake_case_ , rewards=snake_case_ , returns_to_go=snake_case_ , timesteps=snake_case_ , attention_mask=snake_case_ , return_dict=snake_case_ , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
lowercase , lowercase , lowercase , lowercase =( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ),
1.0,
False,
{},
)
lowercase =action_pred[0, -1]
lowercase =torch.cat([states, state] , dim=1 )
lowercase =returns_to_go[0, -1] - reward
lowercase =torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowercase =torch.cat(
[timesteps, torch.ones((1, 1) , device=snake_case_ , dtype=torch.long ) * (step + 1)] , dim=1 )
| 72 | 1 |
'''simple docstring'''
from __future__ import annotations
import bisect
def UpperCamelCase ( lowercase_ : list[int] , lowercase_ : int , lowercase_ : int = 0 , lowercase_ : int = -1 ) -> int:
'''simple docstring'''
if hi < 0:
lowercase =len(lowercase_ )
while lo < hi:
lowercase =lo + (hi - lo) // 2
if sorted_collection[mid] < item:
lowercase =mid + 1
else:
lowercase =mid
return lo
def UpperCamelCase ( lowercase_ : list[int] , lowercase_ : int , lowercase_ : int = 0 , lowercase_ : int = -1 ) -> int:
'''simple docstring'''
if hi < 0:
lowercase =len(lowercase_ )
while lo < hi:
lowercase =lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
lowercase =mid + 1
else:
lowercase =mid
return lo
def UpperCamelCase ( lowercase_ : list[int] , lowercase_ : int , lowercase_ : int = 0 , lowercase_ : int = -1 ) -> None:
'''simple docstring'''
sorted_collection.insert(bisect_left(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) , lowercase_ )
def UpperCamelCase ( lowercase_ : list[int] , lowercase_ : int , lowercase_ : int = 0 , lowercase_ : int = -1 ) -> None:
'''simple docstring'''
sorted_collection.insert(bisect_right(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) , lowercase_ )
def UpperCamelCase ( lowercase_ : list[int] , lowercase_ : int ) -> int | None:
'''simple docstring'''
lowercase =0
lowercase =len(lowercase_ ) - 1
while left <= right:
lowercase =left + (right - left) // 2
lowercase =sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
lowercase =midpoint - 1
else:
lowercase =midpoint + 1
return None
def UpperCamelCase ( lowercase_ : list[int] , lowercase_ : int ) -> int | None:
'''simple docstring'''
lowercase =bisect.bisect_left(lowercase_ , lowercase_ )
if index != len(lowercase_ ) and sorted_collection[index] == item:
return index
return None
def UpperCamelCase ( lowercase_ : list[int] , lowercase_ : int , lowercase_ : int , lowercase_ : int ) -> int | None:
'''simple docstring'''
if right < left:
return None
lowercase =left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(lowercase_ , lowercase_ , lowercase_ , midpoint - 1 )
else:
return binary_search_by_recursion(lowercase_ , lowercase_ , midpoint + 1 , lowercase_ )
if __name__ == "__main__":
_UpperCAmelCase : List[str] = input('''Enter numbers separated by comma:\n''').strip()
_UpperCAmelCase : Any = sorted(int(item) for item in user_input.split(''','''))
_UpperCAmelCase : Tuple = int(input('''Enter a single number to be found in the list:\n'''))
_UpperCAmelCase : int = binary_search(collection, target)
if result is None:
print(F"""{target} was not found in {collection}.""")
else:
print(F"""{target} was found at position {result} in {collection}.""")
| 72 |
'''simple docstring'''
from math import pi, sqrt, tan
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or breadth < 0 or height < 0:
raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
return 4 * pi * radius**2
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
return 3 * pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cone() only accepts non-negative values''' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'''surface_area_conical_frustum() only accepts non-negative values''' )
lowercase =(height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
return 2 * pi * radius * (height + radius)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if torus_radius < 0 or tube_radius < 0:
raise ValueError('''surface_area_torus() only accepts non-negative values''' )
if torus_radius < tube_radius:
raise ValueError(
'''surface_area_torus() does not support spindle or self intersecting tori''' )
return 4 * pow(lowercase_ , 2 ) * torus_radius * tube_radius
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or width < 0:
raise ValueError('''area_rectangle() only accepts non-negative values''' )
return length * width
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''area_square() only accepts non-negative values''' )
return side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_triangle() only accepts non-negative values''' )
return (base * height) / 2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('''Given three sides do not form a triangle''' )
lowercase =(sidea + sidea + sidea) / 2
lowercase =sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_parallelogram() only accepts non-negative values''' )
return base * height
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if basea < 0 or basea < 0 or height < 0:
raise ValueError('''area_trapezium() only accepts non-negative values''' )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''area_circle() only accepts non-negative values''' )
return pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_x < 0 or radius_y < 0:
raise ValueError('''area_ellipse() only accepts non-negative values''' )
return pi * radius_x * radius_y
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('''area_rhombus() only accepts non-negative values''' )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase ( lowercase_ : int , lowercase_ : float ) -> float:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or sides < 3:
raise ValueError(
'''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
elif length < 0:
raise ValueError(
'''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('''[DEMO] Areas of various geometric shapes: \n''')
print(F"""Rectangle: {area_rectangle(10, 20) = }""")
print(F"""Square: {area_square(10) = }""")
print(F"""Triangle: {area_triangle(10, 10) = }""")
print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
print(F"""Rhombus: {area_rhombus(10, 20) = }""")
print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
print(F"""Circle: {area_circle(20) = }""")
print(F"""Ellipse: {area_ellipse(10, 20) = }""")
print('''\nSurface Areas of various geometric shapes: \n''')
print(F"""Cube: {surface_area_cube(20) = }""")
print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
print(F"""Sphere: {surface_area_sphere(20) = }""")
print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
print(F"""Cone: {surface_area_cone(10, 20) = }""")
print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
print(F"""Torus: {surface_area_torus(20, 10) = }""")
print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
print(F"""Square: {area_reg_polygon(4, 10) = }""")
print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
| 72 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : int = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : int = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Dict = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_UpperCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = BarthezTokenizer
UpperCamelCase__ = BarthezTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def _A( self ):
super().setUp()
lowercase =BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case_ )
lowercase =tokenizer
def _A( self ):
lowercase ='''<pad>'''
lowercase =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def _A( self ):
lowercase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(snake_case_ ) , 10_11_22 )
def _A( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def _A( self ):
lowercase =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowercase =[0, 57, 30_18, 7_03_07, 91, 2]
lowercase =self.tokenizer(
snake_case_ , max_length=len(snake_case_ ) , padding=snake_case_ , truncation=snake_case_ , return_tensors='''pt''' )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
lowercase =batch.input_ids.tolist()[0]
self.assertListEqual(snake_case_ , snake_case_ )
def _A( self ):
if not self.test_rust_tokenizer:
return
lowercase =self.get_tokenizer()
lowercase =self.get_rust_tokenizer()
lowercase ='''I was born in 92000, and this is falsé.'''
lowercase =tokenizer.tokenize(snake_case_ )
lowercase =rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =self.get_rust_tokenizer()
lowercase =tokenizer.encode(snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
@slow
def _A( self ):
# fmt: off
lowercase ={'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase =[
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=snake_case_ , )
| 72 | 1 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
def UpperCamelCase ( lowercase_ : Optional[int]=None , lowercase_ : Union[str, Any]=None ) -> Dict:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=lowercase_ )
@dataclass
class __magic_name__ :
UpperCamelCase__ = list_field(
default=[] , metadata={
'help': (
'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
' of all available models'
)
} , )
UpperCamelCase__ = list_field(
default=[8] , metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'} )
UpperCamelCase__ = list_field(
default=[8, 32, 1_28, 5_12] , metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Use FP16 to accelerate inference.'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Benchmark training of model'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Verbose memory tracing'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
} , )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Trace memory line by line'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Save result to a CSV file'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Save all print statements in a log file'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to print environment information'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
' for debugging / testing and on TPU.'
)
} , )
UpperCamelCase__ = field(
default=f"""inference_time_{round(time() )}.csv""" , metadata={'help': 'CSV filename used if saving time results to csv.'} , )
UpperCamelCase__ = field(
default=f"""inference_memory_{round(time() )}.csv""" , metadata={'help': 'CSV filename used if saving memory results to csv.'} , )
UpperCamelCase__ = field(
default=f"""train_time_{round(time() )}.csv""" , metadata={'help': 'CSV filename used if saving time results to csv for training.'} , )
UpperCamelCase__ = field(
default=f"""train_memory_{round(time() )}.csv""" , metadata={'help': 'CSV filename used if saving memory results to csv for training.'} , )
UpperCamelCase__ = field(
default=f"""env_info_{round(time() )}.csv""" , metadata={'help': 'CSV filename used if saving environment information.'} , )
UpperCamelCase__ = field(
default=f"""log_{round(time() )}.csv""" , metadata={'help': 'Log filename used if print statements are saved in log.'} , )
UpperCamelCase__ = field(default=3 , metadata={'help': 'Times an experiment will be run.'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
' model weights.'
)
} , )
def _A( self ):
warnings.warn(
f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , snake_case_ , )
def _A( self ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def _A( self ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def _A( self ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 72 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_text_model'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=5_02_44 , snake_case_=7_68 , snake_case_=64 , snake_case_=20_48 , snake_case_=12 , snake_case_=12 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gelu_new" , snake_case_=0 , snake_case_=False , snake_case_=0 , snake_case_=1 , snake_case_=False , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =hidden_size
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =use_cache
lowercase =eos_token_id
lowercase =decoder_start_token_id
# for backwards compatibility
lowercase =dense_act_fn
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , tie_word_embeddings=snake_case_ , is_decoder=snake_case_ , **snake_case_ , )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_vision_model'
def __init__( self , snake_case_=7_68 , snake_case_=7_68 , snake_case_=20_48 , snake_case_=64 , snake_case_=12 , snake_case_=12 , snake_case_="gelu_new" , snake_case_=1E-6 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=1E-10 , snake_case_=1.0 , snake_case_=40_96 , snake_case_=32 , snake_case_=1_28 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =hidden_size
lowercase =patch_embed_hidden_size
lowercase =d_ff
lowercase =dropout_rate
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =initializer_range
lowercase =initializer_factor
lowercase =attention_dropout
lowercase =layer_norm_eps
lowercase =dense_act_fn
lowercase =seq_len
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =d_kv
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct'
UpperCamelCase__ = True
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=1.0 , snake_case_=0.02 , snake_case_=False , snake_case_=False , snake_case_=True , **snake_case_ , ):
super().__init__(tie_word_embeddings=snake_case_ , is_encoder_decoder=snake_case_ , **snake_case_ )
if text_config is None:
lowercase ={}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
lowercase ={}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
lowercase =PixaStructTextConfig(**snake_case_ )
lowercase =PixaStructVisionConfig(**snake_case_ )
lowercase =self.text_config.decoder_start_token_id
lowercase =self.text_config.pad_token_id
lowercase =self.text_config.eos_token_id
lowercase =initializer_factor
lowercase =initializer_range
lowercase =self.initializer_range
lowercase =self.initializer_range
lowercase =is_vqa
@classmethod
def _A( cls , snake_case_ , snake_case_ , **snake_case_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def _A( self ):
lowercase =copy.deepcopy(self.__dict__ )
lowercase =self.text_config.to_dict()
lowercase =self.vision_config.to_dict()
lowercase =self.__class__.model_type
return output
| 72 | 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
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[Any] = {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : List[Any] = ['''TimmBackbone''']
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
_UpperCAmelCase : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 |
'''simple docstring'''
def UpperCamelCase ( ) -> int:
'''simple docstring'''
return 1
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowercase_ )
def UpperCamelCase ( lowercase_ : int = 2_0_0 ) -> int:
'''simple docstring'''
return two_pound(lowercase_ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 72 | 1 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , **snake_case_ ):
super().__init__(**snake_case_ )
requires_backends(self , '''vision''' )
requires_backends(self , '''torch''' )
if self.framework != "pt":
raise ValueError(f'The {self.__class__} is only available in PyTorch.' )
self.check_model_type(snake_case_ )
def _A( self , **snake_case_ ):
lowercase ={}
lowercase ={}
lowercase ={}
# preprocess args
if "points_per_batch" in kwargs:
lowercase =kwargs['''points_per_batch''']
if "points_per_crop" in kwargs:
lowercase =kwargs['''points_per_crop''']
if "crops_n_layers" in kwargs:
lowercase =kwargs['''crops_n_layers''']
if "crop_overlap_ratio" in kwargs:
lowercase =kwargs['''crop_overlap_ratio''']
if "crop_n_points_downscale_factor" in kwargs:
lowercase =kwargs['''crop_n_points_downscale_factor''']
# postprocess args
if "pred_iou_thresh" in kwargs:
lowercase =kwargs['''pred_iou_thresh''']
if "stability_score_offset" in kwargs:
lowercase =kwargs['''stability_score_offset''']
if "mask_threshold" in kwargs:
lowercase =kwargs['''mask_threshold''']
if "stability_score_thresh" in kwargs:
lowercase =kwargs['''stability_score_thresh''']
if "crops_nms_thresh" in kwargs:
lowercase =kwargs['''crops_nms_thresh''']
if "output_rle_mask" in kwargs:
lowercase =kwargs['''output_rle_mask''']
if "output_bboxes_mask" in kwargs:
lowercase =kwargs['''output_bboxes_mask''']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self , snake_case_ , *snake_case_ , snake_case_=None , snake_case_=None , **snake_case_ ):
return super().__call__(snake_case_ , *snake_case_ , num_workers=snake_case_ , batch_size=snake_case_ , **snake_case_ )
def _A( self , snake_case_ , snake_case_=64 , snake_case_ = 0 , snake_case_ = 5_12 / 15_00 , snake_case_ = 32 , snake_case_ = 1 , ):
lowercase =load_image(snake_case_ )
lowercase =self.image_processor.size['''longest_edge''']
lowercase , lowercase , lowercase , lowercase =self.image_processor.generate_crop_boxes(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase =self.image_processor(images=snake_case_ , return_tensors='''pt''' )
with self.device_placement():
if self.framework == "pt":
lowercase =self.get_inference_context()
with inference_context():
lowercase =self._ensure_tensor_on_device(snake_case_ , device=self.device )
lowercase =self.model.get_image_embeddings(model_inputs.pop('''pixel_values''' ) )
lowercase =image_embeddings
lowercase =grid_points.shape[1]
lowercase =points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
'''To return all points at once, set points_per_batch to None''' )
for i in range(0 , snake_case_ , snake_case_ ):
lowercase =grid_points[:, i : i + points_per_batch, :, :]
lowercase =input_labels[:, i : i + points_per_batch]
lowercase =i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def _A( self , snake_case_ , snake_case_=0.88 , snake_case_=0.95 , snake_case_=0 , snake_case_=1 , ):
lowercase =model_inputs.pop('''input_boxes''' )
lowercase =model_inputs.pop('''is_last''' )
lowercase =model_inputs.pop('''original_sizes''' ).tolist()
lowercase =model_inputs.pop('''reshaped_input_sizes''' ).tolist()
lowercase =self.model(**snake_case_ )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
lowercase =model_outputs['''pred_masks''']
lowercase =self.image_processor.post_process_masks(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , binarize=snake_case_ )
lowercase =model_outputs['''iou_scores''']
lowercase , lowercase , lowercase =self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , snake_case_ , snake_case_ , snake_case_ , snake_case_ , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def _A( self , snake_case_ , snake_case_=False , snake_case_=False , snake_case_=0.7 , ):
lowercase =[]
lowercase =[]
lowercase =[]
for model_output in model_outputs:
all_scores.append(model_output.pop('''iou_scores''' ) )
all_masks.extend(model_output.pop('''masks''' ) )
all_boxes.append(model_output.pop('''boxes''' ) )
lowercase =torch.cat(snake_case_ )
lowercase =torch.cat(snake_case_ )
lowercase , lowercase , lowercase , lowercase =self.image_processor.post_process_for_mask_generation(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase =defaultdict(snake_case_ )
for output in model_outputs:
for k, v in output.items():
extra[k].append(snake_case_ )
lowercase ={}
if output_rle_mask:
lowercase =rle_mask
if output_bboxes_mask:
lowercase =bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 72 |
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['image_processor', 'tokenizer']
UpperCamelCase__ = 'BlipImageProcessor'
UpperCamelCase__ = 'AutoTokenizer'
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
# add QFormer tokenizer
lowercase =qformer_tokenizer
def __call__( self , snake_case_ = None , snake_case_ = None , snake_case_ = True , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = True , snake_case_ = None , **snake_case_ , ):
if images is None and text is None:
raise ValueError('''You have to specify at least images or text.''' )
lowercase =BatchFeature()
if text is not None:
lowercase =self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
encoding.update(snake_case_ )
lowercase =self.qformer_tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
lowercase =qformer_text_encoding.pop('''input_ids''' )
lowercase =qformer_text_encoding.pop('''attention_mask''' )
if images is not None:
lowercase =self.image_processor(snake_case_ , return_tensors=snake_case_ )
encoding.update(snake_case_ )
return encoding
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _A( self ):
lowercase =self.tokenizer.model_input_names
lowercase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _A( self , snake_case_ , **snake_case_ ):
if os.path.isfile(snake_case_ ):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
lowercase =os.path.join(snake_case_ , '''qformer_tokenizer''' )
self.qformer_tokenizer.save_pretrained(snake_case_ )
return super().save_pretrained(snake_case_ , **snake_case_ )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
lowercase =AutoTokenizer.from_pretrained(snake_case_ , subfolder='''qformer_tokenizer''' )
lowercase =cls._get_arguments_from_pretrained(snake_case_ , **snake_case_ )
args.append(snake_case_ )
return cls(*snake_case_ )
| 72 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase : str = logging.get_logger('''transformers.models.speecht5''')
def UpperCamelCase ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
hf_model.apply_weight_norm()
lowercase =checkpoint['''input_conv.weight_g''']
lowercase =checkpoint['''input_conv.weight_v''']
lowercase =checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
lowercase =checkpoint[f'upsamples.{i}.1.weight_g']
lowercase =checkpoint[f'upsamples.{i}.1.weight_v']
lowercase =checkpoint[f'upsamples.{i}.1.bias']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
lowercase =checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
lowercase =checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
lowercase =checkpoint[f'blocks.{i}.convs1.{j}.1.bias']
lowercase =checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
lowercase =checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
lowercase =checkpoint[f'blocks.{i}.convs2.{j}.1.bias']
lowercase =checkpoint['''output_conv.1.weight_g''']
lowercase =checkpoint['''output_conv.1.weight_v''']
lowercase =checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def UpperCamelCase ( lowercase_ : Optional[int] , lowercase_ : Any , lowercase_ : int , lowercase_ : Tuple=None , lowercase_ : Optional[Any]=None , ) -> Union[str, Any]:
'''simple docstring'''
if config_path is not None:
lowercase =SpeechTaHifiGanConfig.from_pretrained(lowercase_ )
else:
lowercase =SpeechTaHifiGanConfig()
lowercase =SpeechTaHifiGan(lowercase_ )
lowercase =torch.load(lowercase_ )
load_weights(orig_checkpoint['''model''']['''generator'''] , lowercase_ , lowercase_ )
lowercase =np.load(lowercase_ )
lowercase =stats[0].reshape(-1 )
lowercase =stats[1].reshape(-1 )
lowercase =torch.from_numpy(lowercase_ ).float()
lowercase =torch.from_numpy(lowercase_ ).float()
model.save_pretrained(lowercase_ )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(lowercase_ )
if __name__ == "__main__":
_UpperCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
parser.add_argument('''--stats_path''', required=True, default=None, type=str, help='''Path to stats.npy file''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
_UpperCAmelCase : Any = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 72 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_UpperCAmelCase : Dict = '''\
@inproceedings{lin-2004-rouge,
title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
author = "Lin, Chin-Yew",
booktitle = "Text Summarization Branches Out",
month = jul,
year = "2004",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W04-1013",
pages = "74--81",
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''
_UpperCAmelCase : Dict = '''
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
`"rougeL"`: Longest common subsequence based scoring.
`"rougeLSum"`: rougeLsum splits text using `"\n"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric(\'rouge\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
[\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
>>> print(results["rouge1"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results["rouge1"].mid.fmeasure)
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
'''https://github.com/google-research/google-research/tree/master/rouge''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=None , snake_case_=True , snake_case_=False ):
if rouge_types is None:
lowercase =['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
lowercase =rouge_scorer.RougeScorer(rouge_types=snake_case_ , use_stemmer=snake_case_ )
if use_aggregator:
lowercase =scoring.BootstrapAggregator()
else:
lowercase =[]
for ref, pred in zip(snake_case_ , snake_case_ ):
lowercase =scorer.score(snake_case_ , snake_case_ )
if use_aggregator:
aggregator.add_scores(snake_case_ )
else:
scores.append(snake_case_ )
if use_aggregator:
lowercase =aggregator.aggregate()
else:
lowercase ={}
for key in scores[0]:
lowercase =[score[key] for score in scores]
return result
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int ) -> bool:
'''simple docstring'''
lowercase =(1 + 2_4 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCamelCase ( lowercase_ : int = 5_0_0_0 ) -> int:
'''simple docstring'''
lowercase =[(i * (3 * i - 1)) // 2 for i in range(1 , lowercase_ )]
for i, pentagonal_i in enumerate(lowercase_ ):
for j in range(lowercase_ , len(lowercase_ ) ):
lowercase =pentagonal_nums[j]
lowercase =pentagonal_i + pentagonal_j
lowercase =pentagonal_j - pentagonal_i
if is_pentagonal(lowercase_ ) and is_pentagonal(lowercase_ ):
return b
return -1
if __name__ == "__main__":
print(F"""{solution() = }""")
| 72 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = '''▁'''
_UpperCAmelCase : Union[str, Any] = {'''vocab_file''': '''spiece.model'''}
_UpperCAmelCase : Union[str, Any] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
_UpperCAmelCase : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self , snake_case_ , snake_case_="<pad>" , snake_case_="</s>" , snake_case_="<unk>" , snake_case_="<mask_2>" , snake_case_="<mask_1>" , snake_case_=None , snake_case_=1_03 , snake_case_ = None , **snake_case_ , ):
lowercase =offset
if additional_special_tokens is not None:
if not isinstance(snake_case_ , snake_case_ ):
raise TypeError(
f'additional_special_tokens should be of type {type(snake_case_ )}, but is'
f' {type(snake_case_ )}' )
lowercase =(
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(snake_case_ ) , self.offset - 1 )
]
if len(set(snake_case_ ) ) != len(snake_case_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
lowercase =additional_special_tokens_extended
else:
lowercase =[mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case_ , unk_token=snake_case_ , mask_token=snake_case_ , pad_token=snake_case_ , mask_token_sent=snake_case_ , offset=snake_case_ , additional_special_tokens=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
lowercase =mask_token_sent
lowercase =vocab_file
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
# add special tokens to encoder dict
lowercase ={
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowercase ={v: k for k, v in self.encoder.items()}
@property
def _A( self ):
return len(self.sp_model ) + self.offset
def _A( self ):
lowercase ={self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
lowercase =self.__dict__.copy()
lowercase =None
return state
def __setstate__( self , snake_case_ ):
lowercase =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase ={}
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def _A( self , snake_case_ ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowercase =self.sp_model.piece_to_id(snake_case_ )
return sp_id + self.offset
def _A( self , snake_case_ ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowercase =self.sp_model.IdToPiece(index - self.offset )
return token
def _A( self , snake_case_ ):
lowercase =[]
lowercase =''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_ ) + token
lowercase =[]
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def _A( self , snake_case_=False ):
return 1
def _A( self , snake_case_ ):
lowercase =set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return self._special_token_mask(snake_case_ )
elif token_ids_a is None:
return self._special_token_mask(snake_case_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A( self , snake_case_ , snake_case_=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , '''wb''' ) as fi:
lowercase =self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : str ) -> bool:
'''simple docstring'''
lowercase =0
for ch in input_str:
lowercase =ord(lowercase_ )
lowercase =pow(2 , lowercase_ )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 72 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=5 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=False , snake_case_=True , snake_case_="None" , snake_case_=3 , snake_case_=4 , snake_case_=None , ):
lowercase =parent
lowercase =batch_size
lowercase =seq_length
lowercase =is_training
lowercase =use_input_mask
lowercase =use_token_type_ids
lowercase =use_labels
lowercase =vocab_size
lowercase =hidden_size
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =intermediate_size
lowercase =hidden_act
lowercase =hidden_dropout_prob
lowercase =attention_probs_dropout_prob
lowercase =max_position_embeddings
lowercase =type_vocab_size
lowercase =type_sequence_label_size
lowercase =initializer_range
lowercase =num_labels
lowercase =num_choices
lowercase =relative_attention
lowercase =position_biased_input
lowercase =pos_att_type
lowercase =scope
def _A( self ):
lowercase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase =None
if self.use_input_mask:
lowercase =ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
lowercase =None
if self.use_token_type_ids:
lowercase =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase =None
lowercase =None
lowercase =None
if self.use_labels:
lowercase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase =ids_tensor([self.batch_size] , self.num_choices )
lowercase =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A( self ):
return DebertaConfig(
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 , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def _A( self ):
lowercase =self.get_config()
lowercase =3_00
return config
def _A( self , snake_case_ ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =DebertaModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ )[0]
lowercase =model(snake_case_ , token_type_ids=snake_case_ )[0]
lowercase =model(snake_case_ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =DebertaForMaskedLM(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =self.num_labels
lowercase =DebertaForSequenceClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(snake_case_ )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =self.num_labels
lowercase =DebertaForTokenClassification(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =DebertaForQuestionAnswering(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _A( self ):
lowercase =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) =config_and_inputs
lowercase ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{
'feature-extraction': DebertaModel,
'fill-mask': DebertaForMaskedLM,
'question-answering': DebertaForQuestionAnswering,
'text-classification': DebertaForSequenceClassification,
'token-classification': DebertaForTokenClassification,
'zero-shot': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =DebertaModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def _A( self ):
self.config_tester.run_common_tests()
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*snake_case_ )
@slow
def _A( self ):
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase =DebertaModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
@unittest.skip(reason='''Model not available yet''' )
def _A( self ):
pass
@slow
def _A( self ):
lowercase =DebertaModel.from_pretrained('''microsoft/deberta-base''' )
lowercase =torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
lowercase =torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase =model(snake_case_ , attention_mask=snake_case_ )[0]
# compare the actual values for a slice.
lowercase =torch.tensor(
[[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , snake_case_ , atol=1E-4 ) , f'{output[:, 1:4, 1:4]}' )
| 72 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Tuple ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase_ , lowercase_ ):
lowercase =np.full((len(lowercase_ ), sequence_length, 2) , lowercase_ )
else:
lowercase =np.full((len(lowercase_ ), sequence_length) , lowercase_ )
for i, tensor in enumerate(lowercase_ ):
if padding_side == "right":
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
else:
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
return out_tensor.tolist()
def UpperCamelCase ( lowercase_ : Optional[Any] ) -> str:
'''simple docstring'''
lowercase =ord(lowercase_ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
lowercase =unicodedata.category(lowercase_ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -1_00
UpperCamelCase__ = "pt"
def _A( self , snake_case_ ):
import torch
lowercase ='''label''' if '''label''' in features[0].keys() else '''labels'''
lowercase =[feature[label_name] for feature in features] if label_name in features[0].keys() else None
lowercase =self.tokenizer.pad(
snake_case_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
lowercase =torch.tensor(batch['''entity_ids'''] ).shape[1]
lowercase =self.tokenizer.padding_side
if padding_side == "right":
lowercase =[
list(snake_case_ ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_ )) for label in labels
]
else:
lowercase =[
[self.label_pad_token_id] * (sequence_length - len(snake_case_ )) + list(snake_case_ ) for label in labels
]
lowercase =[feature['''ner_tags'''] for feature in features]
lowercase =padding_tensor(snake_case_ , -1 , snake_case_ , snake_case_ )
lowercase =[feature['''original_entity_spans'''] for feature in features]
lowercase =padding_tensor(snake_case_ , (-1, -1) , snake_case_ , snake_case_ )
lowercase ={k: torch.tensor(snake_case_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 72 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : str = {
'''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}
# fmt: off
_UpperCAmelCase : Tuple = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
_UpperCAmelCase : Union[str, Any] = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'whisper'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , snake_case_=5_18_65 , snake_case_=80 , snake_case_=6 , snake_case_=4 , snake_case_=6 , snake_case_=4 , snake_case_=15_36 , snake_case_=15_36 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=5_02_57 , snake_case_=True , snake_case_=True , snake_case_="gelu" , snake_case_=2_56 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=False , snake_case_=15_00 , snake_case_=4_48 , snake_case_=5_02_56 , snake_case_=5_02_56 , snake_case_=5_02_56 , snake_case_=None , snake_case_=[2_20, 5_02_56] , snake_case_=False , snake_case_=2_56 , snake_case_=False , snake_case_=0.05 , snake_case_=10 , snake_case_=2 , snake_case_=0.0 , snake_case_=10 , snake_case_=0 , snake_case_=7 , **snake_case_ , ):
lowercase =vocab_size
lowercase =num_mel_bins
lowercase =d_model
lowercase =encoder_layers
lowercase =encoder_attention_heads
lowercase =decoder_layers
lowercase =decoder_attention_heads
lowercase =decoder_ffn_dim
lowercase =encoder_ffn_dim
lowercase =dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =activation_function
lowercase =init_std
lowercase =encoder_layerdrop
lowercase =decoder_layerdrop
lowercase =use_cache
lowercase =encoder_layers
lowercase =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase =max_source_positions
lowercase =max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
lowercase =classifier_proj_size
lowercase =use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase =apply_spec_augment
lowercase =mask_time_prob
lowercase =mask_time_length
lowercase =mask_time_min_masks
lowercase =mask_feature_prob
lowercase =mask_feature_length
lowercase =mask_feature_min_masks
lowercase =median_filter_width
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , decoder_start_token_id=snake_case_ , suppress_tokens=snake_case_ , begin_suppress_tokens=snake_case_ , **snake_case_ , )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
def _A( self ):
lowercase =OrderedDict(
[
('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase ={0: '''batch'''}
else:
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , snake_case_ = 2_20_50 , snake_case_ = 5.0 , snake_case_ = 2_20 , ):
lowercase =OrderedDict()
lowercase =OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=snake_case_ , framework=snake_case_ , sampling_rate=snake_case_ , time_duration=snake_case_ , frequency=snake_case_ , )
lowercase =encoder_inputs['''input_features'''].shape[2]
lowercase =encoder_sequence_length // 2 if self.use_past else seq_length
lowercase =super().generate_dummy_inputs(
preprocessor.tokenizer , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase =encoder_inputs.pop('''input_features''' )
lowercase =decoder_inputs.pop('''decoder_input_ids''' )
if "past_key_values" in decoder_inputs:
lowercase =decoder_inputs.pop('''past_key_values''' )
return dummy_inputs
@property
def _A( self ):
return 1E-3
| 72 |
'''simple docstring'''
_UpperCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase ( lowercase_ : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'a bytes-like object is required, not \'{data.__class__.__name__}\''
raise TypeError(lowercase_ )
lowercase =''''''.join(bin(lowercase_ )[2:].zfill(8 ) for byte in data )
lowercase =len(lowercase_ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase =b'''=''' * ((6 - len(lowercase_ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(lowercase_ ) % 6)
else:
lowercase =b''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(lowercase_ ) , 6 ) ).encode()
+ padding
)
def UpperCamelCase ( lowercase_ : str ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
lowercase =(
'''argument should be a bytes-like object or ASCII string, '''
f'not \'{encoded_data.__class__.__name__}\''
)
raise TypeError(lowercase_ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(lowercase_ , lowercase_ ):
try:
lowercase =encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
lowercase =encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(lowercase_ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase =encoded_data[:-padding]
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase =[
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(lowercase_ ) , 8 )
]
return bytes(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
from maths.prime_check import is_prime
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'Input value of [number={number}] must be an integer'
raise TypeError(lowercase_ )
if is_prime(lowercase_ ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_UpperCAmelCase : Union[str, Any] = datasets.logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = '''\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
author = "Moosavi, Nafise Sadat and
Strube, Michael",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1060",
doi = "10.18653/v1/P16-1060",
pages = "632--642",
}
'''
_UpperCAmelCase : str = '''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''
_UpperCAmelCase : Optional[int] = '''
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting \'keep_singletons=False\', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
\'mentions\': mentions
\'muc\': MUC metric [Vilain et al, 1995]
\'bcub\': B-cubed [Bagga and Baldwin, 1998]
\'ceafe\': CEAFe [Luo et al., 2005]
\'lea\': LEA [Moosavi and Strube, 2016]
\'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric(\'coval\')
>>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',
... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',
... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',
... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',
... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',
... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''
def UpperCamelCase ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=True , lowercase_ : Optional[Any]=False , lowercase_ : int="dummy_doc" ) -> str:
'''simple docstring'''
lowercase ={doc: key_lines}
lowercase ={doc: sys_lines}
lowercase ={}
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase , lowercase =reader.get_doc_mentions(lowercase_ , key_doc_lines[doc] , lowercase_ )
key_singletons_num += singletons_num
if NP_only or min_span:
lowercase =reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
lowercase , lowercase =reader.get_doc_mentions(lowercase_ , sys_doc_lines[doc] , lowercase_ )
sys_singletons_num += singletons_num
if NP_only or min_span:
lowercase =reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
if remove_nested:
lowercase , lowercase =reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
lowercase , lowercase =reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
lowercase =reader.get_mention_assignments(lowercase_ , lowercase_ )
lowercase =reader.get_mention_assignments(lowercase_ , lowercase_ )
lowercase =(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
f'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}' )
logger.info(
'''Number of resulting singleton clusters in the key '''
f'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}' )
if not keep_singletons:
logger.info(
f'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
'''files, respectively''' )
return doc_coref_infos
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Tuple ) -> Dict:
'''simple docstring'''
lowercase =get_coref_infos(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase ={}
lowercase =0
lowercase =0
for name, metric in metrics:
lowercase , lowercase , lowercase =evaluator.evaluate_documents(lowercase_ , lowercase_ , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({f'{name}/recall': recall, f'{name}/precision': precision, f'{name}/f1': fa} )
logger.info(
name.ljust(1_0 ) , f'Recall: {recall * 1_0_0:.2f}' , f' Precision: {precision * 1_0_0:.2f}' , f' F1: {fa * 1_0_0:.2f}' , )
if conll_subparts_num == 3:
lowercase =(conll / 3) * 1_0_0
logger.info(f'CoNLL score: {conll:.2f}' )
output_scores.update({'''conll_score''': conll} )
return output_scores
def UpperCamelCase ( lowercase_ : Any ) -> List[Any]:
'''simple docstring'''
lowercase =False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
lowercase =line.split()[5]
if not parse_col == "-":
lowercase =True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Sequence(datasets.Value('''string''' ) ),
} ) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False ):
lowercase =[
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
lowercase =util.check_gold_parse_annotation(snake_case_ )
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''' )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
lowercase =evaluate(
key_lines=snake_case_ , sys_lines=snake_case_ , metrics=snake_case_ , NP_only=snake_case_ , remove_nested=snake_case_ , keep_singletons=snake_case_ , min_span=snake_case_ , )
return score
| 72 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
raise ValueError('''Inductance cannot be negative''' )
if frequency < 0:
raise ValueError('''Frequency cannot be negative''' )
if reactance < 0:
raise ValueError('''Inductive reactance cannot be negative''' )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(lowercase_ , lowercase_ ):
return 0
elif n == 2:
return 1
else:
lowercase =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
lowercase =0
lowercase =2
while digits < n:
index += 1
lowercase =len(str(fibonacci(lowercase_ ) ) )
return index
def UpperCamelCase ( lowercase_ : int = 1_0_0_0 ) -> int:
'''simple docstring'''
return fibonacci_digits_index(lowercase_ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 72 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_UpperCAmelCase : Tuple = '''examples/'''
_UpperCAmelCase : Union[str, Any] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
'''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
_UpperCAmelCase : Tuple = {
'''init''': '''src/transformers/__init__.py''',
'''setup''': '''setup.py''',
}
_UpperCAmelCase : List[str] = '''README.md'''
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : Tuple , lowercase_ : int ) -> Any:
'''simple docstring'''
with open(lowercase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase =f.read()
lowercase , lowercase =REPLACE_PATTERNS[pattern]
lowercase =replace.replace('''VERSION''' , lowercase_ )
lowercase =re_pattern.sub(lowercase_ , lowercase_ )
with open(lowercase_ , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(lowercase_ )
def UpperCamelCase ( lowercase_ : Tuple ) -> int:
'''simple docstring'''
for folder, directories, fnames in os.walk(lowercase_ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('''research_projects''' )
if "legacy" in directories:
directories.remove('''legacy''' )
for fname in fnames:
if fname.endswith('''.py''' ):
update_version_in_file(os.path.join(lowercase_ , lowercase_ ) , lowercase_ , pattern='''examples''' )
def UpperCamelCase ( lowercase_ : int , lowercase_ : Any=False ) -> Optional[Any]:
'''simple docstring'''
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(lowercase_ , lowercase_ , lowercase_ )
if not patch:
update_version_in_examples(lowercase_ )
def UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
lowercase ='''🤗 Transformers currently provides the following architectures'''
lowercase ='''1. Want to contribute a new model?'''
with open(lowercase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
lowercase =f.readlines()
# Find the start of the list.
lowercase =0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowercase =start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
lowercase =lines[index].replace(
'''https://huggingface.co/docs/transformers/main/model_doc''' , '''https://huggingface.co/docs/transformers/model_doc''' , )
index += 1
with open(lowercase_ , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(lowercase_ )
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
lowercase =f.read()
lowercase =REPLACE_PATTERNS['''init'''][0].search(lowercase_ ).groups()[0]
return packaging.version.parse(lowercase_ )
def UpperCamelCase ( lowercase_ : int=False ) -> Dict:
'''simple docstring'''
lowercase =get_version()
if patch and default_version.is_devrelease:
raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' )
if default_version.is_devrelease:
lowercase =default_version.base_version
elif patch:
lowercase =f'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
lowercase =f'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
lowercase =input(f'Which version are you releasing? [{default_version}]' )
if len(lowercase_ ) == 0:
lowercase =default_version
print(f'Updating version to {version}.' )
global_version_update(lowercase_ , patch=lowercase_ )
if not patch:
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
def UpperCamelCase ( ) -> int:
'''simple docstring'''
lowercase =get_version()
lowercase =f'{current_version.major}.{current_version.minor + 1}.0.dev0'
lowercase =current_version.base_version
# Check with the user we got that right.
lowercase =input(f'Which version are we developing now? [{dev_version}]' )
if len(lowercase_ ) == 0:
lowercase =dev_version
print(f'Updating version to {version}.' )
global_version_update(lowercase_ )
print('''Cleaning main README, don\'t forget to run `make fix-copies`.''' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
_UpperCAmelCase : int = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
_UpperCAmelCase : int = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 72 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Any = {
'''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'marian'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , snake_case_=5_81_01 , snake_case_=None , snake_case_=10_24 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=True , snake_case_=True , snake_case_="gelu" , snake_case_=10_24 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=5_81_00 , snake_case_=False , snake_case_=5_81_00 , snake_case_=0 , snake_case_=0 , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =decoder_vocab_size or vocab_size
lowercase =max_position_embeddings
lowercase =d_model
lowercase =encoder_ffn_dim
lowercase =encoder_layers
lowercase =encoder_attention_heads
lowercase =decoder_ffn_dim
lowercase =decoder_layers
lowercase =decoder_attention_heads
lowercase =dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =activation_function
lowercase =init_std
lowercase =encoder_layerdrop
lowercase =decoder_layerdrop
lowercase =use_cache
lowercase =encoder_layers
lowercase =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase =share_encoder_decoder_embeddings
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , decoder_start_token_id=snake_case_ , forced_eos_token_id=snake_case_ , **snake_case_ , )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase ={0: '''batch'''}
lowercase ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
else:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super().outputs
else:
lowercase =super(snake_case_ , self ).outputs
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Generate decoder inputs
lowercase =seq_length if not self.use_past else 1
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
lowercase =dict(**snake_case_ , **snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
lowercase =common_inputs['''decoder_input_ids'''].shape[1]
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =decoder_seq_length + 3
lowercase =(
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase =torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_ , snake_case_ )] , dim=1 )
lowercase =[]
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase , lowercase =self.num_layers
lowercase =min(snake_case_ , snake_case_ )
lowercase =max(snake_case_ , snake_case_ ) - min_num_layers
lowercase ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(snake_case_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
) )
# TODO: test this.
lowercase =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(snake_case_ , snake_case_ ):
common_inputs["past_key_values"].append((torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase =seqlen + 2
lowercase , lowercase =self.num_layers
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =common_inputs['''attention_mask'''].dtype
lowercase =torch.cat(
[common_inputs['''attention_mask'''], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
lowercase =[
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(snake_case_ )
]
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase =tokenizer.num_special_tokens_to_add(snake_case_ )
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
lowercase =[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase =dict(tokenizer(snake_case_ , return_tensors=snake_case_ ) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
else:
lowercase =self._generate_dummy_inputs_for_causal_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
return common_inputs
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super()._flatten_past_key_values_(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
lowercase =super(snake_case_ , self )._flatten_past_key_values_(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
@property
def _A( self ):
return 1E-4
| 72 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __magic_name__ ( unittest.TestCase ):
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=99 , snake_case_=32 , snake_case_=5 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=16 , snake_case_=2 , snake_case_=0.02 , snake_case_=4 , ):
lowercase =parent
lowercase =batch_size
lowercase =seq_length
lowercase =is_training
lowercase =use_attention_mask
lowercase =use_token_type_ids
lowercase =use_labels
lowercase =vocab_size
lowercase =hidden_size
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =intermediate_size
lowercase =hidden_act
lowercase =hidden_dropout_prob
lowercase =attention_probs_dropout_prob
lowercase =max_position_embeddings
lowercase =type_vocab_size
lowercase =type_sequence_label_size
lowercase =initializer_range
lowercase =num_choices
def _A( self ):
lowercase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase =None
if self.use_attention_mask:
lowercase =random_attention_mask([self.batch_size, self.seq_length] )
lowercase =None
if self.use_token_type_ids:
lowercase =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase =RoFormerConfig(
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 , type_vocab_size=self.type_vocab_size , is_decoder=snake_case_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _A( self ):
lowercase =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase =config_and_inputs
lowercase ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _A( self ):
lowercase =FlaxRoFormerModelTester(self )
@slow
def _A( self ):
for model_class_name in self.all_model_classes:
lowercase =model_class_name.from_pretrained('''junnyu/roformer_chinese_small''' , from_pt=snake_case_ )
lowercase =model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case_ )
@require_flax
class __magic_name__ ( unittest.TestCase ):
@slow
def _A( self ):
lowercase =FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
lowercase =jnp.array([[0, 1, 2, 3, 4, 5]] )
lowercase =model(snake_case_ )[0]
lowercase =5_00_00
lowercase =(1, 6, vocab_size)
self.assertEqual(output.shape , snake_case_ )
lowercase =jnp.array(
[[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , snake_case_ , atol=1E-4 ) )
| 72 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCAmelCase : Dict = parse(importlib.metadata.version('''torch'''))
def UpperCamelCase ( lowercase_ : Union[str, Version] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
'''simple docstring'''
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f'`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}' )
lowercase =STR_OPERATION_TO_FUNC[operation]
if isinstance(lowercase_ , lowercase_ ):
lowercase =parse(importlib.metadata.version(lowercase_ ) )
return operation(lowercase_ , parse(lowercase_ ) )
def UpperCamelCase ( lowercase_ : str , lowercase_ : str ) -> Union[str, Any]:
'''simple docstring'''
return compare_versions(lowercase_ , lowercase_ , lowercase_ )
| 72 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=32 , snake_case_=2 , snake_case_=3 , snake_case_=16 , snake_case_=[1, 2, 1] , snake_case_=[2, 2, 4] , snake_case_=2 , snake_case_=2.0 , snake_case_=True , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.1 , snake_case_="gelu" , snake_case_=False , snake_case_=True , snake_case_=0.02 , snake_case_=1E-5 , snake_case_=True , snake_case_=None , snake_case_=True , snake_case_=10 , snake_case_=8 , ):
lowercase =parent
lowercase =batch_size
lowercase =image_size
lowercase =patch_size
lowercase =num_channels
lowercase =embed_dim
lowercase =depths
lowercase =num_heads
lowercase =window_size
lowercase =mlp_ratio
lowercase =qkv_bias
lowercase =hidden_dropout_prob
lowercase =attention_probs_dropout_prob
lowercase =drop_path_rate
lowercase =hidden_act
lowercase =use_absolute_embeddings
lowercase =patch_norm
lowercase =layer_norm_eps
lowercase =initializer_range
lowercase =is_training
lowercase =scope
lowercase =use_labels
lowercase =type_sequence_label_size
lowercase =encoder_stride
def _A( self ):
lowercase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase =None
if self.use_labels:
lowercase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase =self.get_config()
return config, pixel_values, labels
def _A( self ):
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
lowercase =SwinvaModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ )
lowercase =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase =int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
lowercase =SwinvaForMaskedImageModeling(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowercase =1
lowercase =SwinvaForMaskedImageModeling(snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase =model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
lowercase =self.type_sequence_label_size
lowercase =SwinvaForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _A( self ):
lowercase =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase =config_and_inputs
lowercase ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
UpperCamelCase__ = (
{'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =SwinvaModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , embed_dim=37 )
def _A( self ):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''' )
def _A( self ):
pass
@unittest.skip(reason='''Swinv2 does not use inputs_embeds''' )
def _A( self ):
pass
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case_ , nn.Linear ) )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase =[*signature.parameters.keys()]
lowercase =['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
lowercase =True
for model_class in self.all_model_classes:
lowercase =True
lowercase =False
lowercase =True
lowercase =model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
lowercase =model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
lowercase =outputs.attentions
lowercase =len(self.model_tester.depths )
self.assertEqual(len(snake_case_ ) , snake_case_ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase =True
lowercase =config.window_size**2
lowercase =model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
lowercase =model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
lowercase =outputs.attentions
self.assertEqual(len(snake_case_ ) , snake_case_ )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
lowercase =len(snake_case_ )
# Check attention is always last and order is fine
lowercase =True
lowercase =True
lowercase =model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
lowercase =model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
if hasattr(self.model_tester , '''num_hidden_states_types''' ):
lowercase =self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
lowercase =2
self.assertEqual(out_len + added_hidden_states , len(snake_case_ ) )
lowercase =outputs.attentions
self.assertEqual(len(snake_case_ ) , snake_case_ )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
lowercase =model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
lowercase =outputs.hidden_states
lowercase =getattr(
self.model_tester , '''expected_num_hidden_layers''' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(snake_case_ ) , snake_case_ )
# Swinv2 has a different seq_length
lowercase =(
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
lowercase =outputs.reshaped_hidden_states
self.assertEqual(len(snake_case_ ) , snake_case_ )
lowercase , lowercase , lowercase , lowercase =reshaped_hidden_states[0].shape
lowercase =(
reshaped_hidden_states[0].view(snake_case_ , snake_case_ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
lowercase =(
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
lowercase =True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase =True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
lowercase =3
lowercase =(
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
lowercase =(
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowercase =True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase =True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , (padded_height, padded_width) )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
@slow
def _A( self ):
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase =SwinvaModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
lowercase =_config_zero_init(snake_case_ )
for model_class in self.all_model_classes:
lowercase =model_class(config=snake_case_ )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase ):
@cached_property
def _A( self ):
return (
AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''' )
if is_vision_available()
else None
)
@slow
def _A( self ):
lowercase =SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''' ).to(
snake_case_ )
lowercase =self.default_image_processor
lowercase =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
lowercase =image_processor(images=snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
# forward pass
with torch.no_grad():
lowercase =model(**snake_case_ )
# verify the logits
lowercase =torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , snake_case_ )
lowercase =torch.tensor([-0.39_47, -0.43_06, 0.00_26] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1E-4 ) )
| 72 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
_UpperCAmelCase : int = [8, 5, 9, 7]
_UpperCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_UpperCAmelCase : Union[str, Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =claim_vector
lowercase =allocated_resources_table
lowercase =maximum_claim_table
def _A( self ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _A( self ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _A( self ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(snake_case_ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _A( self ):
return {self.__need().index(snake_case_ ): i for i in self.__need()}
def _A( self , **snake_case_ ):
lowercase =self.__need()
lowercase =self.__allocated_resources_table
lowercase =self.__available_resources()
lowercase =self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('''_''' * 50 + '''\n''' )
while need_list:
lowercase =False
for each_need in need_list:
lowercase =True
for index, need in enumerate(snake_case_ ):
if need > available_resources[index]:
lowercase =False
break
if execution:
lowercase =True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowercase =original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(snake_case_ )
# update available/freed resources stack
lowercase =np.array(snake_case_ ) + np.array(
alloc_resources_table[process_number] )
print(
'''Updated available resource stack for processes: '''
+ ''' '''.join([str(snake_case_ ) for x in available_resources] ) )
break
if safe:
print('''The process is in a safe state.\n''' )
else:
print('''System in unsafe state. Aborting...\n''' )
break
def _A( self ):
print(''' ''' * 9 + '''Allocated Resource Table''' )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(snake_case_ ) + 1}'
+ ''' '''.join(f'{it:>8}' for it in item )
+ '''\n''' )
print(''' ''' * 9 + '''System Resource Table''' )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(snake_case_ ) + 1}'
+ ''' '''.join(f'{it:>8}' for it in item )
+ '''\n''' )
print(
'''Current Usage by Active Processes: '''
+ ''' '''.join(str(snake_case_ ) for x in self.__claim_vector ) )
print(
'''Initial Available Resources: '''
+ ''' '''.join(str(snake_case_ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
'''openai/imagegpt-small''': '''''',
'''openai/imagegpt-medium''': '''''',
'''openai/imagegpt-large''': '''''',
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'imagegpt'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , snake_case_=5_12 + 1 , snake_case_=32 * 32 , snake_case_=5_12 , snake_case_=24 , snake_case_=8 , snake_case_=None , snake_case_="quick_gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1E-5 , snake_case_=0.02 , snake_case_=True , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False , **snake_case_ , ):
lowercase =vocab_size
lowercase =n_positions
lowercase =n_embd
lowercase =n_layer
lowercase =n_head
lowercase =n_inner
lowercase =activation_function
lowercase =resid_pdrop
lowercase =embd_pdrop
lowercase =attn_pdrop
lowercase =layer_norm_epsilon
lowercase =initializer_range
lowercase =scale_attn_weights
lowercase =use_cache
lowercase =scale_attn_by_inverse_layer_idx
lowercase =reorder_and_upcast_attn
lowercase =tie_word_embeddings
super().__init__(tie_word_embeddings=snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
def _A( self ):
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
] )
def _A( self , snake_case_ , snake_case_ = 1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , snake_case_ = 3 , snake_case_ = 32 , snake_case_ = 32 , ):
lowercase =self._generate_dummy_images(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase =dict(preprocessor(images=snake_case_ , return_tensors=snake_case_ ) )
return inputs
| 72 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase : Dict = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
_UpperCAmelCase : Dict = '''\
@inproceedings{banarjee2005,
title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
author = {Banerjee, Satanjeev and Lavie, Alon},
booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
month = jun,
year = {2005},
address = {Ann Arbor, Michigan},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W05-0909},
pages = {65--72},
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.
METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''
_UpperCAmelCase : Tuple = '''
Computes METEOR score of translated segments against one or more references.
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
\'meteor\': meteor score.
Examples:
>>> meteor = datasets.load_metric(\'meteor\')
>>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
>>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
>>> results = meteor.compute(predictions=predictions, references=references)
>>> print(round(results["meteor"], 4))
0.6944
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''] , reference_urls=[
'''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
'''https://en.wikipedia.org/wiki/METEOR''',
] , )
def _A( self , snake_case_ ):
import nltk
nltk.download('''wordnet''' )
if NLTK_VERSION >= version.Version('''3.6.5''' ):
nltk.download('''punkt''' )
if NLTK_VERSION >= version.Version('''3.6.6''' ):
nltk.download('''omw-1.4''' )
def _A( self , snake_case_ , snake_case_ , snake_case_=0.9 , snake_case_=3 , snake_case_=0.5 ):
if NLTK_VERSION >= version.Version('''3.6.5''' ):
lowercase =[
meteor_score.single_meteor_score(
word_tokenize(snake_case_ ) , word_tokenize(snake_case_ ) , alpha=snake_case_ , beta=snake_case_ , gamma=snake_case_ )
for ref, pred in zip(snake_case_ , snake_case_ )
]
else:
lowercase =[
meteor_score.single_meteor_score(snake_case_ , snake_case_ , alpha=snake_case_ , beta=snake_case_ , gamma=snake_case_ )
for ref, pred in zip(snake_case_ , snake_case_ )
]
return {"meteor": np.mean(snake_case_ )}
| 72 | 1 |
'''simple docstring'''
import numpy as np
import datasets
_UpperCAmelCase : Optional[int] = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''
_UpperCAmelCase : Optional[int] = '''\
@article{de2000mahalanobis,
title={The mahalanobis distance},
author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
journal={Chemometrics and intelligent laboratory systems},
volume={50},
number={1},
pages={1--18},
year={2000},
publisher={Elsevier}
}
'''
_UpperCAmelCase : List[Any] = '''
Args:
X: List of datapoints to be compared with the `reference_distribution`.
reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:
>>> mahalanobis_metric = datasets.load_metric("mahalanobis")
>>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
>>> print(results)
{\'mahalanobis\': array([0.5])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''X''': datasets.Sequence(datasets.Value('''float''' , id='''sequence''' ) , id='''X''' ),
} ) , )
def _A( self , snake_case_ , snake_case_ ):
# convert to numpy arrays
lowercase =np.array(snake_case_ )
lowercase =np.array(snake_case_ )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError('''Expected `X` to be a 2D vector''' )
if len(reference_distribution.shape ) != 2:
raise ValueError('''Expected `reference_distribution` to be a 2D vector''' )
if reference_distribution.shape[0] < 2:
raise ValueError(
'''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''' )
# Get mahalanobis distance for each prediction
lowercase =X - np.mean(snake_case_ )
lowercase =np.cov(reference_distribution.T )
try:
lowercase =np.linalg.inv(snake_case_ )
except np.linalg.LinAlgError:
lowercase =np.linalg.pinv(snake_case_ )
lowercase =np.dot(snake_case_ , snake_case_ )
lowercase =np.dot(snake_case_ , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 72 |
'''simple docstring'''
import sys
_UpperCAmelCase : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCamelCase ( lowercase_ : str = N ) -> int:
'''simple docstring'''
lowercase =-sys.maxsize - 1
for i in range(len(lowercase_ ) - 1_2 ):
lowercase =1
for j in range(1_3 ):
product *= int(n[i + j] )
if product > largest_product:
lowercase =product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 72 | 1 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = BarthezTokenizer
UpperCamelCase__ = BarthezTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def _A( self ):
super().setUp()
lowercase =BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case_ )
lowercase =tokenizer
def _A( self ):
lowercase ='''<pad>'''
lowercase =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def _A( self ):
lowercase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(snake_case_ ) , 10_11_22 )
def _A( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def _A( self ):
lowercase =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowercase =[0, 57, 30_18, 7_03_07, 91, 2]
lowercase =self.tokenizer(
snake_case_ , max_length=len(snake_case_ ) , padding=snake_case_ , truncation=snake_case_ , return_tensors='''pt''' )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
lowercase =batch.input_ids.tolist()[0]
self.assertListEqual(snake_case_ , snake_case_ )
def _A( self ):
if not self.test_rust_tokenizer:
return
lowercase =self.get_tokenizer()
lowercase =self.get_rust_tokenizer()
lowercase ='''I was born in 92000, and this is falsé.'''
lowercase =tokenizer.tokenize(snake_case_ )
lowercase =rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =self.get_rust_tokenizer()
lowercase =tokenizer.encode(snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
@slow
def _A( self ):
# fmt: off
lowercase ={'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase =[
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=snake_case_ , )
| 72 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 72 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( lowercase_ : list[float] ) -> bool:
'''simple docstring'''
if len(lowercase_ ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All values must be greater than 0''' )
lowercase =nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
'''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'encodec'
def __init__( self , snake_case_=[1.5, 3.0, 6.0, 12.0, 24.0] , snake_case_=2_40_00 , snake_case_=1 , snake_case_=False , snake_case_=None , snake_case_=None , snake_case_=1_28 , snake_case_=32 , snake_case_=1 , snake_case_=[8, 5, 4, 2] , snake_case_="weight_norm" , snake_case_=7 , snake_case_=7 , snake_case_=3 , snake_case_=2 , snake_case_=True , snake_case_="reflect" , snake_case_=2 , snake_case_=2 , snake_case_=1.0 , snake_case_=10_24 , snake_case_=None , snake_case_=True , **snake_case_ , ):
lowercase =target_bandwidths
lowercase =sampling_rate
lowercase =audio_channels
lowercase =normalize
lowercase =chunk_length_s
lowercase =overlap
lowercase =hidden_size
lowercase =num_filters
lowercase =num_residual_layers
lowercase =upsampling_ratios
lowercase =norm_type
lowercase =kernel_size
lowercase =last_kernel_size
lowercase =residual_kernel_size
lowercase =dilation_growth_rate
lowercase =use_causal_conv
lowercase =pad_mode
lowercase =compress
lowercase =num_lstm_layers
lowercase =trim_right_ratio
lowercase =codebook_size
lowercase =codebook_dim if codebook_dim is not None else hidden_size
lowercase =use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' )
super().__init__(**snake_case_ )
@property
def _A( self ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A( self ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A( self ):
lowercase =np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A( self ):
return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 72 | 1 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=30 , snake_case_=2 , snake_case_=3 , snake_case_=True , snake_case_=True , snake_case_=32 , snake_case_=5 , snake_case_=4 , snake_case_=37 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=10 , snake_case_=0.02 , snake_case_=3 , snake_case_=0.6 , snake_case_=None , ):
lowercase =parent
lowercase =batch_size
lowercase =image_size
lowercase =patch_size
lowercase =num_channels
lowercase =is_training
lowercase =use_labels
lowercase =hidden_size
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =intermediate_size
lowercase =hidden_act
lowercase =hidden_dropout_prob
lowercase =attention_probs_dropout_prob
lowercase =type_sequence_label_size
lowercase =initializer_range
lowercase =mask_ratio
lowercase =scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowercase =(image_size // patch_size) ** 2
lowercase =int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _A( self ):
lowercase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase =None
if self.use_labels:
lowercase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase =self.get_config()
return config, pixel_values, labels
def _A( self ):
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=snake_case_ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
lowercase =ViTMAEModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
lowercase =ViTMAEForPreTraining(snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ )
lowercase =(self.image_size // self.patch_size) ** 2
lowercase =self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
lowercase =1
lowercase =ViTMAEForPreTraining(snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase =model(snake_case_ )
lowercase =self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _A( self ):
lowercase =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase =config_and_inputs
lowercase ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
UpperCamelCase__ = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =ViTMAEModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ , hidden_size=37 )
def _A( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def _A( self ):
pass
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case_ , nn.Linear ) )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase =[*signature.parameters.keys()]
lowercase =['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*snake_case_ )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
# make masks reproducible
np.random.seed(2 )
lowercase =int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
lowercase =np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowercase =torch.from_numpy(snake_case_ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowercase =pt_noise
super().check_pt_tf_models(snake_case_ , snake_case_ , snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowercase =model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
lowercase =outputs[0].cpu().numpy()
lowercase =0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
lowercase =model_class.from_pretrained(snake_case_ )
model.to(snake_case_ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowercase =model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
# Make sure we don't have nans
lowercase =after_outputs[0].cpu().numpy()
lowercase =0
lowercase =np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(snake_case_ , 1E-5 )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def _A( self ):
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def _A( self ):
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def _A( self ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def _A( self ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def _A( self ):
pass
@slow
def _A( self ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase =ViTMAEModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def UpperCamelCase ( ) -> Any:
'''simple docstring'''
lowercase =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase ):
@cached_property
def _A( self ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def _A( self ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
lowercase =ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' ).to(snake_case_ )
lowercase =self.default_image_processor
lowercase =prepare_img()
lowercase =image_processor(images=snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowercase =ViTMAEConfig()
lowercase =int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
lowercase =np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
lowercase =model(**snake_case_ , noise=torch.from_numpy(snake_case_ ).to(device=snake_case_ ) )
# verify the logits
lowercase =torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , snake_case_ )
lowercase =torch.tensor(
[[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(snake_case_ ) , atol=1E-4 ) )
| 72 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : int = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : int = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Dict = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_UpperCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 | 1 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_UpperCAmelCase : List[str] = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
'''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
''' body.''',
'''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
''' punishment.''',
]
_UpperCAmelCase : int = [
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
'''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
''' Israelis .''',
'''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
''' sentences up by 28% .''',
]
def UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
lowercase =calculate_rouge(lowercase_ , lowercase_ , bootstrap_aggregation=lowercase_ , rouge_keys=['''rouge2''', '''rougeL'''] )
assert isinstance(lowercase_ , lowercase_ )
lowercase =calculate_rouge(lowercase_ , lowercase_ , bootstrap_aggregation=lowercase_ , rouge_keys=['''rouge2'''] )
assert (
pd.DataFrame(no_aggregation['''rouge2'''] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra['''rouge2'''] ).fmeasure.mean()
)
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
lowercase ='''rougeLsum'''
lowercase =calculate_rouge(lowercase_ , lowercase_ , newline_sep=lowercase_ , rouge_keys=[k] )[k]
lowercase =calculate_rouge(lowercase_ , lowercase_ , newline_sep=lowercase_ , rouge_keys=[k] )[k]
assert score > score_no_sep
def UpperCamelCase ( ) -> Any:
'''simple docstring'''
lowercase =['''rouge1''', '''rouge2''', '''rougeL''']
lowercase =calculate_rouge(lowercase_ , lowercase_ , newline_sep=lowercase_ , rouge_keys=lowercase_ )
lowercase =calculate_rouge(lowercase_ , lowercase_ , newline_sep=lowercase_ , rouge_keys=lowercase_ )
assert score_sep == score_no_sep
def UpperCamelCase ( ) -> Dict:
'''simple docstring'''
lowercase =[
'''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
]
lowercase =[
'''Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
''' the final seconds on board Flight 9525.''',
]
assert calculate_rouge(lowercase_ , lowercase_ , newline_sep=lowercase_ ) == calculate_rouge(lowercase_ , lowercase_ , newline_sep=lowercase_ )
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
lowercase =[
'''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
]
lowercase =[
''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
]
lowercase =calculate_rouge(lowercase_ , lowercase_ , rouge_keys=['''rougeLsum'''] , newline_sep=lowercase_ )['''rougeLsum''']
lowercase =calculate_rouge(lowercase_ , lowercase_ , rouge_keys=['''rougeLsum'''] )['''rougeLsum''']
assert new_score > prev_score
def UpperCamelCase ( ) -> Dict:
'''simple docstring'''
lowercase =Path('''examples/seq2seq/test_data/wmt_en_ro''' )
lowercase =calculate_rouge_path(data_dir.joinpath('''test.source''' ) , data_dir.joinpath('''test.target''' ) )
assert isinstance(lowercase_ , lowercase_ )
lowercase =calculate_rouge_path(
data_dir.joinpath('''test.source''' ) , data_dir.joinpath('''test.target''' ) , bootstrap_aggregation=lowercase_ )
assert isinstance(lowercase_ , lowercase_ )
| 72 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 72 |
'''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 __magic_name__ :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=6 , snake_case_=17 , snake_case_=23 , snake_case_=11 , snake_case_=True , ):
lowercase =parent
lowercase =batch_size
lowercase =seq_length
lowercase =act_dim
lowercase =state_dim
lowercase =hidden_size
lowercase =max_length
lowercase =is_training
def _A( self ):
lowercase =floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =ids_tensor((self.batch_size, self.seq_length) , vocab_size=10_00 )
lowercase =random_attention_mask((self.batch_size, self.seq_length) )
lowercase =self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _A( self ):
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 , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =DecisionTransformerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , 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 ):
lowercase =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) =config_and_inputs
lowercase ={
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (DecisionTransformerModel,) if is_torch_available() else ()
UpperCamelCase__ = ()
UpperCamelCase__ = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
UpperCamelCase__ = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =DecisionTransformerModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def _A( self ):
self.config_tester.run_common_tests()
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
@slow
def _A( self ):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase =DecisionTransformerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase =[*signature.parameters.keys()]
lowercase =[
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def _A( self ):
lowercase =2 # number of steps of autoregressive prediction we will perform
lowercase =10 # defined by the RL environment, may be normalized
lowercase =DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
lowercase =model.to(snake_case_ )
lowercase =model.config
torch.manual_seed(0 )
lowercase =torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ) # env.reset()
lowercase =torch.tensor(
[[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]] , device=snake_case_ )
lowercase =torch.tensor(snake_case_ , device=snake_case_ , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowercase =state
lowercase =torch.zeros(1 , 0 , config.act_dim , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.zeros(1 , 0 , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.tensor(0 , device=snake_case_ , dtype=torch.long ).reshape(1 , 1 )
for step in range(snake_case_ ):
lowercase =torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=snake_case_ )] , dim=1 )
lowercase =torch.cat([rewards, torch.zeros(1 , 1 , device=snake_case_ )] , dim=1 )
lowercase =torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowercase , lowercase , lowercase =model(
states=snake_case_ , actions=snake_case_ , rewards=snake_case_ , returns_to_go=snake_case_ , timesteps=snake_case_ , attention_mask=snake_case_ , return_dict=snake_case_ , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
lowercase , lowercase , lowercase , lowercase =( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ),
1.0,
False,
{},
)
lowercase =action_pred[0, -1]
lowercase =torch.cat([states, state] , dim=1 )
lowercase =returns_to_go[0, -1] - reward
lowercase =torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowercase =torch.cat(
[timesteps, torch.ones((1, 1) , device=snake_case_ , dtype=torch.long ) * (step + 1)] , dim=1 )
| 72 | 1 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_UpperCAmelCase : Dict = '''\
@inproceedings{lin-2004-rouge,
title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
author = "Lin, Chin-Yew",
booktitle = "Text Summarization Branches Out",
month = jul,
year = "2004",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W04-1013",
pages = "74--81",
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''
_UpperCAmelCase : Dict = '''
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
`"rougeL"`: Longest common subsequence based scoring.
`"rougeLSum"`: rougeLsum splits text using `"\n"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric(\'rouge\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
[\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
>>> print(results["rouge1"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results["rouge1"].mid.fmeasure)
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
'''https://github.com/google-research/google-research/tree/master/rouge''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=None , snake_case_=True , snake_case_=False ):
if rouge_types is None:
lowercase =['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
lowercase =rouge_scorer.RougeScorer(rouge_types=snake_case_ , use_stemmer=snake_case_ )
if use_aggregator:
lowercase =scoring.BootstrapAggregator()
else:
lowercase =[]
for ref, pred in zip(snake_case_ , snake_case_ ):
lowercase =scorer.score(snake_case_ , snake_case_ )
if use_aggregator:
aggregator.add_scores(snake_case_ )
else:
scores.append(snake_case_ )
if use_aggregator:
lowercase =aggregator.aggregate()
else:
lowercase ={}
for key in scores[0]:
lowercase =[score[key] for score in scores]
return result
| 72 |
'''simple docstring'''
from math import pi, sqrt, tan
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or breadth < 0 or height < 0:
raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
return 4 * pi * radius**2
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
return 3 * pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cone() only accepts non-negative values''' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'''surface_area_conical_frustum() only accepts non-negative values''' )
lowercase =(height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
return 2 * pi * radius * (height + radius)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if torus_radius < 0 or tube_radius < 0:
raise ValueError('''surface_area_torus() only accepts non-negative values''' )
if torus_radius < tube_radius:
raise ValueError(
'''surface_area_torus() does not support spindle or self intersecting tori''' )
return 4 * pow(lowercase_ , 2 ) * torus_radius * tube_radius
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or width < 0:
raise ValueError('''area_rectangle() only accepts non-negative values''' )
return length * width
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''area_square() only accepts non-negative values''' )
return side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_triangle() only accepts non-negative values''' )
return (base * height) / 2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('''Given three sides do not form a triangle''' )
lowercase =(sidea + sidea + sidea) / 2
lowercase =sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_parallelogram() only accepts non-negative values''' )
return base * height
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if basea < 0 or basea < 0 or height < 0:
raise ValueError('''area_trapezium() only accepts non-negative values''' )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''area_circle() only accepts non-negative values''' )
return pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_x < 0 or radius_y < 0:
raise ValueError('''area_ellipse() only accepts non-negative values''' )
return pi * radius_x * radius_y
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('''area_rhombus() only accepts non-negative values''' )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase ( lowercase_ : int , lowercase_ : float ) -> float:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or sides < 3:
raise ValueError(
'''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
elif length < 0:
raise ValueError(
'''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('''[DEMO] Areas of various geometric shapes: \n''')
print(F"""Rectangle: {area_rectangle(10, 20) = }""")
print(F"""Square: {area_square(10) = }""")
print(F"""Triangle: {area_triangle(10, 10) = }""")
print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
print(F"""Rhombus: {area_rhombus(10, 20) = }""")
print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
print(F"""Circle: {area_circle(20) = }""")
print(F"""Ellipse: {area_ellipse(10, 20) = }""")
print('''\nSurface Areas of various geometric shapes: \n''')
print(F"""Cube: {surface_area_cube(20) = }""")
print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
print(F"""Sphere: {surface_area_sphere(20) = }""")
print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
print(F"""Cone: {surface_area_cone(10, 20) = }""")
print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
print(F"""Torus: {surface_area_torus(20, 10) = }""")
print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
print(F"""Square: {area_reg_polygon(4, 10) = }""")
print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
| 72 | 1 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
# See all BART models at https://huggingface.co/models?filter=bart
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'bart'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , snake_case_=5_02_65 , snake_case_=10_24 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=0.0 , snake_case_=0.0 , snake_case_="gelu" , snake_case_=10_24 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=0.0 , snake_case_=False , snake_case_=True , snake_case_=3 , snake_case_=1 , snake_case_=0 , snake_case_=2 , snake_case_=True , snake_case_=2 , snake_case_=2 , **snake_case_ , ):
lowercase =vocab_size
lowercase =max_position_embeddings
lowercase =d_model
lowercase =encoder_ffn_dim
lowercase =encoder_layers
lowercase =encoder_attention_heads
lowercase =decoder_ffn_dim
lowercase =decoder_layers
lowercase =decoder_attention_heads
lowercase =dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =activation_function
lowercase =init_std
lowercase =encoder_layerdrop
lowercase =decoder_layerdrop
lowercase =classifier_dropout
lowercase =use_cache
lowercase =encoder_layers
lowercase =scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=snake_case_ , pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , decoder_start_token_id=snake_case_ , forced_eos_token_id=snake_case_ , **snake_case_ , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , snake_case_ ):
lowercase =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.''' )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase ={0: '''batch'''}
lowercase ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
else:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super().outputs
else:
lowercase =super(snake_case_ , self ).outputs
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Generate decoder inputs
lowercase =seq_length if not self.use_past else 1
lowercase =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
lowercase =dict(**snake_case_ , **snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
lowercase =common_inputs['''decoder_input_ids'''].shape[1]
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =decoder_seq_length + 3
lowercase =(
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase =torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_ , snake_case_ )] , dim=1 )
lowercase =[]
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase , lowercase =self.num_layers
lowercase =min(snake_case_ , snake_case_ )
lowercase =max(snake_case_ , snake_case_ ) - min_num_layers
lowercase ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(snake_case_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
) )
# TODO: test this.
lowercase =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(snake_case_ , snake_case_ ):
common_inputs["past_key_values"].append((torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase =seqlen + 2
lowercase , lowercase =self.num_layers
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =common_inputs['''attention_mask'''].dtype
lowercase =torch.cat(
[common_inputs['''attention_mask'''], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
lowercase =[
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(snake_case_ )
]
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase =tokenizer.num_special_tokens_to_add(snake_case_ )
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
lowercase =[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase =dict(tokenizer(snake_case_ , return_tensors=snake_case_ ) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
elif self.task == "causal-lm":
lowercase =self._generate_dummy_inputs_for_causal_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
else:
lowercase =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
return common_inputs
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super()._flatten_past_key_values_(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
lowercase =super(snake_case_ , self )._flatten_past_key_values_(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
| 72 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = BarthezTokenizer
UpperCamelCase__ = BarthezTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def _A( self ):
super().setUp()
lowercase =BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case_ )
lowercase =tokenizer
def _A( self ):
lowercase ='''<pad>'''
lowercase =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def _A( self ):
lowercase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(snake_case_ ) , 10_11_22 )
def _A( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def _A( self ):
lowercase =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowercase =[0, 57, 30_18, 7_03_07, 91, 2]
lowercase =self.tokenizer(
snake_case_ , max_length=len(snake_case_ ) , padding=snake_case_ , truncation=snake_case_ , return_tensors='''pt''' )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
lowercase =batch.input_ids.tolist()[0]
self.assertListEqual(snake_case_ , snake_case_ )
def _A( self ):
if not self.test_rust_tokenizer:
return
lowercase =self.get_tokenizer()
lowercase =self.get_rust_tokenizer()
lowercase ='''I was born in 92000, and this is falsé.'''
lowercase =tokenizer.tokenize(snake_case_ )
lowercase =rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =self.get_rust_tokenizer()
lowercase =tokenizer.encode(snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
@slow
def _A( self ):
# fmt: off
lowercase ={'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase =[
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=snake_case_ , )
| 72 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 72 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_text_model'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=5_02_44 , snake_case_=7_68 , snake_case_=64 , snake_case_=20_48 , snake_case_=12 , snake_case_=12 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gelu_new" , snake_case_=0 , snake_case_=False , snake_case_=0 , snake_case_=1 , snake_case_=False , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =hidden_size
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =use_cache
lowercase =eos_token_id
lowercase =decoder_start_token_id
# for backwards compatibility
lowercase =dense_act_fn
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , tie_word_embeddings=snake_case_ , is_decoder=snake_case_ , **snake_case_ , )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_vision_model'
def __init__( self , snake_case_=7_68 , snake_case_=7_68 , snake_case_=20_48 , snake_case_=64 , snake_case_=12 , snake_case_=12 , snake_case_="gelu_new" , snake_case_=1E-6 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=1E-10 , snake_case_=1.0 , snake_case_=40_96 , snake_case_=32 , snake_case_=1_28 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =hidden_size
lowercase =patch_embed_hidden_size
lowercase =d_ff
lowercase =dropout_rate
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =initializer_range
lowercase =initializer_factor
lowercase =attention_dropout
lowercase =layer_norm_eps
lowercase =dense_act_fn
lowercase =seq_len
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =d_kv
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct'
UpperCamelCase__ = True
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=1.0 , snake_case_=0.02 , snake_case_=False , snake_case_=False , snake_case_=True , **snake_case_ , ):
super().__init__(tie_word_embeddings=snake_case_ , is_encoder_decoder=snake_case_ , **snake_case_ )
if text_config is None:
lowercase ={}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
lowercase ={}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
lowercase =PixaStructTextConfig(**snake_case_ )
lowercase =PixaStructVisionConfig(**snake_case_ )
lowercase =self.text_config.decoder_start_token_id
lowercase =self.text_config.pad_token_id
lowercase =self.text_config.eos_token_id
lowercase =initializer_factor
lowercase =initializer_range
lowercase =self.initializer_range
lowercase =self.initializer_range
lowercase =is_vqa
@classmethod
def _A( cls , snake_case_ , snake_case_ , **snake_case_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def _A( self ):
lowercase =copy.deepcopy(self.__dict__ )
lowercase =self.text_config.to_dict()
lowercase =self.vision_config.to_dict()
lowercase =self.__class__.model_type
return output
| 72 | 1 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : Any = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
'''
_UpperCAmelCase : str = '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''
_UpperCAmelCase : List[str] = '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
\'google_bleu\': google_bleu score
Examples:
Example 1:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.44
Example 2:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.61
Example 3:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results["google_bleu"], 2))
0.53
Example 4:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results["google_bleu"], 2))
0.4
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , )
def _A( self , snake_case_ , snake_case_ , snake_case_ = 1 , snake_case_ = 4 , ):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=snake_case_ , hypotheses=snake_case_ , min_len=snake_case_ , max_len=snake_case_ )
}
| 72 |
'''simple docstring'''
def UpperCamelCase ( ) -> int:
'''simple docstring'''
return 1
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowercase_ )
def UpperCamelCase ( lowercase_ : int = 2_0_0 ) -> int:
'''simple docstring'''
return two_pound(lowercase_ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 72 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : int = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_UpperCAmelCase : List[str] = {
'''tokenizer_file''': {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
},
}
_UpperCAmelCase : int = {
'''gpt-neox-20b''': 20_48,
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_="<|endoftext|>" , snake_case_="<|endoftext|>" , snake_case_="<|endoftext|>" , snake_case_=False , **snake_case_ , ):
super().__init__(
snake_case_ , snake_case_ , tokenizer_file=snake_case_ , unk_token=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , add_prefix_space=snake_case_ , **snake_case_ , )
lowercase =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , snake_case_ ) != add_prefix_space:
lowercase =getattr(snake_case_ , pre_tok_state.pop('''type''' ) )
lowercase =add_prefix_space
lowercase =pre_tok_class(**snake_case_ )
lowercase =add_prefix_space
def _A( self , snake_case_ , snake_case_ = None ):
lowercase =self._tokenizer.model.save(snake_case_ , name=snake_case_ )
return tuple(snake_case_ )
def _A( self , snake_case_ ):
lowercase =[]
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(snake_case_ , add_special_tokens=snake_case_ ) + [self.eos_token_id] )
if len(snake_case_ ) > self.model_max_length:
lowercase =input_ids[-self.model_max_length :]
return input_ids
| 72 |
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['image_processor', 'tokenizer']
UpperCamelCase__ = 'BlipImageProcessor'
UpperCamelCase__ = 'AutoTokenizer'
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
# add QFormer tokenizer
lowercase =qformer_tokenizer
def __call__( self , snake_case_ = None , snake_case_ = None , snake_case_ = True , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = True , snake_case_ = None , **snake_case_ , ):
if images is None and text is None:
raise ValueError('''You have to specify at least images or text.''' )
lowercase =BatchFeature()
if text is not None:
lowercase =self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
encoding.update(snake_case_ )
lowercase =self.qformer_tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
lowercase =qformer_text_encoding.pop('''input_ids''' )
lowercase =qformer_text_encoding.pop('''attention_mask''' )
if images is not None:
lowercase =self.image_processor(snake_case_ , return_tensors=snake_case_ )
encoding.update(snake_case_ )
return encoding
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _A( self ):
lowercase =self.tokenizer.model_input_names
lowercase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _A( self , snake_case_ , **snake_case_ ):
if os.path.isfile(snake_case_ ):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
lowercase =os.path.join(snake_case_ , '''qformer_tokenizer''' )
self.qformer_tokenizer.save_pretrained(snake_case_ )
return super().save_pretrained(snake_case_ , **snake_case_ )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
lowercase =AutoTokenizer.from_pretrained(snake_case_ , subfolder='''qformer_tokenizer''' )
lowercase =cls._get_arguments_from_pretrained(snake_case_ , **snake_case_ )
args.append(snake_case_ )
return cls(*snake_case_ )
| 72 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : str = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_sentencepiece_available():
import sentencepiece as sp
_UpperCAmelCase : Optional[int] = 5
_UpperCAmelCase : Any = 10
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = SpeechaTextTokenizer
UpperCamelCase__ = False
UpperCamelCase__ = True
def _A( self ):
super().setUp()
lowercase =sp.SentencePieceProcessor()
spm_model.Load(snake_case_ )
lowercase =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(snake_case_ ) )]
lowercase =dict(zip(snake_case_ , range(len(snake_case_ ) ) ) )
lowercase =Path(self.tmpdirname )
save_json(snake_case_ , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(snake_case_ , save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowercase =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def _A( self ):
lowercase ='''<pad>'''
lowercase =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def _A( self ):
lowercase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(snake_case_ ) , 10_01 )
def _A( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_01 )
def _A( self ):
lowercase =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowercase =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(snake_case_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case_ ) , [2_89, 50, 14, 1_74, 3_86] , )
lowercase =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
snake_case_ , [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''', '''.'''] , )
lowercase =tokenizer.convert_tokens_to_ids(snake_case_ )
self.assertListEqual(snake_case_ , [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowercase =tokenizer.convert_ids_to_tokens(snake_case_ )
self.assertListEqual(
snake_case_ , [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>''', '''.'''] , )
@slow
def _A( self ):
# fmt: off
lowercase ={'''input_ids''': [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name='''facebook/s2t-small-mustc-en-de-st''' , revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''' , )
@require_sentencepiece
class __magic_name__ ( unittest.TestCase ):
UpperCamelCase__ = 'valhalla/s2t_mustc_multilinguial_medium'
UpperCamelCase__ = 'C\'est trop cool'
UpperCamelCase__ = 'Esto es genial'
@classmethod
def _A( cls ):
lowercase =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def _A( self ):
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''] , 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''] , 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''] , 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''] , 11 )
def _A( self ):
self.assertEqual(self.tokenizer.vocab_size , 1_00_00 )
def _A( self ):
self.assertIn(snake_case_ , self.tokenizer.all_special_ids )
lowercase =[ES_CODE, 4, 16_01, 47, 76_47, 2]
lowercase =self.tokenizer.decode(snake_case_ , skip_special_tokens=snake_case_ )
lowercase =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
self.assertNotIn(self.tokenizer.eos_token , snake_case_ )
def _A( self ):
lowercase ='''fr'''
lowercase =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0] , snake_case_ )
self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id )
def _A( self ):
lowercase ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE] )
lowercase ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE] )
| 72 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_UpperCAmelCase : Dict = '''\
@inproceedings{lin-2004-rouge,
title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
author = "Lin, Chin-Yew",
booktitle = "Text Summarization Branches Out",
month = jul,
year = "2004",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W04-1013",
pages = "74--81",
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''
_UpperCAmelCase : Dict = '''
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
`"rougeL"`: Longest common subsequence based scoring.
`"rougeLSum"`: rougeLsum splits text using `"\n"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric(\'rouge\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
[\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
>>> print(results["rouge1"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results["rouge1"].mid.fmeasure)
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
'''https://github.com/google-research/google-research/tree/master/rouge''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=None , snake_case_=True , snake_case_=False ):
if rouge_types is None:
lowercase =['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
lowercase =rouge_scorer.RougeScorer(rouge_types=snake_case_ , use_stemmer=snake_case_ )
if use_aggregator:
lowercase =scoring.BootstrapAggregator()
else:
lowercase =[]
for ref, pred in zip(snake_case_ , snake_case_ ):
lowercase =scorer.score(snake_case_ , snake_case_ )
if use_aggregator:
aggregator.add_scores(snake_case_ )
else:
scores.append(snake_case_ )
if use_aggregator:
lowercase =aggregator.aggregate()
else:
lowercase ={}
for key in scores[0]:
lowercase =[score[key] for score in scores]
return result
| 72 | 1 |
'''simple docstring'''
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_UpperCAmelCase : List[str] = {
'''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
'''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
'''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
'''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
'''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
'''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
'''mask_downscaling.0''': '''mask_embed.conv1''',
'''mask_downscaling.1''': '''mask_embed.layer_norm1''',
'''mask_downscaling.3''': '''mask_embed.conv2''',
'''mask_downscaling.4''': '''mask_embed.layer_norm2''',
'''mask_downscaling.6''': '''mask_embed.conv3''',
'''point_embeddings''': '''point_embed''',
'''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
'''image_encoder''': '''vision_encoder''',
'''neck.0''': '''neck.conv1''',
'''neck.1''': '''neck.layer_norm1''',
'''neck.2''': '''neck.conv2''',
'''neck.3''': '''neck.layer_norm2''',
'''patch_embed.proj''': '''patch_embed.projection''',
'''.norm''': '''.layer_norm''',
'''blocks''': '''layers''',
}
def UpperCamelCase ( lowercase_ : Any ) -> int:
'''simple docstring'''
lowercase ={}
state_dict.pop('''pixel_mean''' , lowercase_ )
state_dict.pop('''pixel_std''' , lowercase_ )
lowercase =R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
lowercase =key.replace(lowercase_ , lowercase_ )
if re.match(lowercase_ , lowercase_ ):
lowercase =int(re.match(lowercase_ , lowercase_ ).group(2 ) )
if layer_nb == 0:
lowercase =key.replace('''layers.0''' , '''proj_in''' )
elif layer_nb == 1:
lowercase =key.replace('''layers.1''' , '''layers.0''' )
elif layer_nb == 2:
lowercase =key.replace('''layers.2''' , '''proj_out''' )
lowercase =value
lowercase =model_state_dict[
'''prompt_encoder.shared_embedding.positional_embedding'''
]
return model_state_dict
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : Any="ybelkada/segment-anything" ) -> Tuple:
'''simple docstring'''
lowercase =hf_hub_download(lowercase_ , f'checkpoints/{model_name}.pth' )
if "sam_vit_b" in model_name:
lowercase =SamConfig()
elif "sam_vit_l" in model_name:
lowercase =SamVisionConfig(
hidden_size=1_0_2_4 , num_hidden_layers=2_4 , num_attention_heads=1_6 , global_attn_indexes=[5, 1_1, 1_7, 2_3] , )
lowercase =SamConfig(
vision_config=lowercase_ , )
elif "sam_vit_h" in model_name:
lowercase =SamVisionConfig(
hidden_size=1_2_8_0 , num_hidden_layers=3_2 , num_attention_heads=1_6 , global_attn_indexes=[7, 1_5, 2_3, 3_1] , )
lowercase =SamConfig(
vision_config=lowercase_ , )
lowercase =torch.load(lowercase_ , map_location='''cpu''' )
lowercase =replace_keys(lowercase_ )
lowercase =SamImageProcessor()
lowercase =SamProcessor(image_processor=lowercase_ )
lowercase =SamModel(lowercase_ )
hf_model.load_state_dict(lowercase_ )
lowercase =hf_model.to('''cuda''' )
lowercase ='''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
lowercase =Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw ).convert('''RGB''' )
lowercase =[[[4_0_0, 6_5_0]]]
lowercase =[[1]]
lowercase =processor(images=np.array(lowercase_ ) , return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
lowercase =hf_model(**lowercase_ )
lowercase =output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8
lowercase =processor(
images=np.array(lowercase_ ) , input_points=lowercase_ , input_labels=lowercase_ , return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
lowercase =hf_model(**lowercase_ )
lowercase =output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4
lowercase =((7_5, 2_7_5, 1_7_2_5, 8_5_0),)
lowercase =processor(images=np.array(lowercase_ ) , input_boxes=lowercase_ , return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
lowercase =hf_model(**lowercase_ )
lowercase =output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4
# Test with 2 points and 1 image.
lowercase =[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
lowercase =[[1, 1]]
lowercase =processor(
images=np.array(lowercase_ ) , input_points=lowercase_ , input_labels=lowercase_ , return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
lowercase =hf_model(**lowercase_ )
lowercase =output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2
if __name__ == "__main__":
_UpperCAmelCase : Tuple = argparse.ArgumentParser()
_UpperCAmelCase : Optional[Any] = ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
parser.add_argument(
'''--model_name''',
default='''sam_vit_h_4b8939''',
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''',
)
parser.add_argument(
'''--model_hub_id''',
default='''ybelkada/segment-anything''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
_UpperCAmelCase : List[str] = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 72 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = '''▁'''
_UpperCAmelCase : Union[str, Any] = {'''vocab_file''': '''spiece.model'''}
_UpperCAmelCase : Union[str, Any] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
_UpperCAmelCase : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self , snake_case_ , snake_case_="<pad>" , snake_case_="</s>" , snake_case_="<unk>" , snake_case_="<mask_2>" , snake_case_="<mask_1>" , snake_case_=None , snake_case_=1_03 , snake_case_ = None , **snake_case_ , ):
lowercase =offset
if additional_special_tokens is not None:
if not isinstance(snake_case_ , snake_case_ ):
raise TypeError(
f'additional_special_tokens should be of type {type(snake_case_ )}, but is'
f' {type(snake_case_ )}' )
lowercase =(
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(snake_case_ ) , self.offset - 1 )
]
if len(set(snake_case_ ) ) != len(snake_case_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
lowercase =additional_special_tokens_extended
else:
lowercase =[mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case_ , unk_token=snake_case_ , mask_token=snake_case_ , pad_token=snake_case_ , mask_token_sent=snake_case_ , offset=snake_case_ , additional_special_tokens=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
lowercase =mask_token_sent
lowercase =vocab_file
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
# add special tokens to encoder dict
lowercase ={
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowercase ={v: k for k, v in self.encoder.items()}
@property
def _A( self ):
return len(self.sp_model ) + self.offset
def _A( self ):
lowercase ={self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
lowercase =self.__dict__.copy()
lowercase =None
return state
def __setstate__( self , snake_case_ ):
lowercase =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase ={}
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def _A( self , snake_case_ ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowercase =self.sp_model.piece_to_id(snake_case_ )
return sp_id + self.offset
def _A( self , snake_case_ ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowercase =self.sp_model.IdToPiece(index - self.offset )
return token
def _A( self , snake_case_ ):
lowercase =[]
lowercase =''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_ ) + token
lowercase =[]
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def _A( self , snake_case_=False ):
return 1
def _A( self , snake_case_ ):
lowercase =set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return self._special_token_mask(snake_case_ )
elif token_ids_a is None:
return self._special_token_mask(snake_case_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A( self , snake_case_ , snake_case_=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , '''wb''' ) as fi:
lowercase =self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
| 72 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Any = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all BART models at https://huggingface.co/models?filter=bart
_UpperCAmelCase : Optional[int] = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
}
_UpperCAmelCase : Union[str, Any] = {
'''facebook/bart-base''': 10_24,
'''facebook/bart-large''': 10_24,
'''facebook/bart-large-mnli''': 10_24,
'''facebook/bart-large-cnn''': 10_24,
'''facebook/bart-large-xsum''': 10_24,
'''yjernite/bart_eli5''': 10_24,
}
@lru_cache()
def UpperCamelCase ( ) -> int:
'''simple docstring'''
lowercase =(
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
lowercase =bs[:]
lowercase =0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase_ )
cs.append(2**8 + n )
n += 1
lowercase =[chr(lowercase_ ) for n in cs]
return dict(zip(lowercase_ , lowercase_ ) )
def UpperCamelCase ( lowercase_ : int ) -> Optional[int]:
'''simple docstring'''
lowercase =set()
lowercase =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowercase =char
return pairs
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self , snake_case_ , snake_case_ , snake_case_="replace" , snake_case_="<s>" , snake_case_="</s>" , snake_case_="</s>" , snake_case_="<s>" , snake_case_="<unk>" , snake_case_="<pad>" , snake_case_="<mask>" , snake_case_=False , **snake_case_ , ):
lowercase =AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else bos_token
lowercase =AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else eos_token
lowercase =AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else sep_token
lowercase =AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else cls_token
lowercase =AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else unk_token
lowercase =AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowercase =AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
super().__init__(
errors=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , add_prefix_space=snake_case_ , **snake_case_ , )
with open(snake_case_ , encoding='''utf-8''' ) as vocab_handle:
lowercase =json.load(snake_case_ )
lowercase ={v: k for k, v in self.encoder.items()}
lowercase =errors # how to handle errors in decoding
lowercase =bytes_to_unicode()
lowercase ={v: k for k, v in self.byte_encoder.items()}
with open(snake_case_ , encoding='''utf-8''' ) as merges_handle:
lowercase =merges_handle.read().split('''\n''' )[1:-1]
lowercase =[tuple(merge.split() ) for merge in bpe_merges]
lowercase =dict(zip(snake_case_ , range(len(snake_case_ ) ) ) )
lowercase ={}
lowercase =add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowercase =re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def _A( self ):
return len(self.encoder )
def _A( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def _A( self , snake_case_ ):
if token in self.cache:
return self.cache[token]
lowercase =tuple(snake_case_ )
lowercase =get_pairs(snake_case_ )
if not pairs:
return token
while True:
lowercase =min(snake_case_ , key=lambda snake_case_ : self.bpe_ranks.get(snake_case_ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowercase , lowercase =bigram
lowercase =[]
lowercase =0
while i < len(snake_case_ ):
try:
lowercase =word.index(snake_case_ , snake_case_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowercase =j
if word[i] == first and i < len(snake_case_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowercase =tuple(snake_case_ )
lowercase =new_word
if len(snake_case_ ) == 1:
break
else:
lowercase =get_pairs(snake_case_ )
lowercase =''' '''.join(snake_case_ )
lowercase =word
return word
def _A( self , snake_case_ ):
lowercase =[]
for token in re.findall(self.pat , snake_case_ ):
lowercase =''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case_ ).split(''' ''' ) )
return bpe_tokens
def _A( self , snake_case_ ):
return self.encoder.get(snake_case_ , self.encoder.get(self.unk_token ) )
def _A( self , snake_case_ ):
return self.decoder.get(snake_case_ )
def _A( self , snake_case_ ):
lowercase =''''''.join(snake_case_ )
lowercase =bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def _A( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(snake_case_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=snake_case_ , ensure_ascii=snake_case_ ) + '''\n''' )
lowercase =0
with open(snake_case_ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda snake_case_ : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
''' Please check that the tokenizer is not corrupted!''' )
lowercase =token_index
writer.write(''' '''.join(snake_case_ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def _A( self , snake_case_ , snake_case_ = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase =[self.cls_token_id]
lowercase =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case_ )) + [1]
return [1] + ([0] * len(snake_case_ )) + [1, 1] + ([0] * len(snake_case_ )) + [1]
def _A( self , snake_case_ , snake_case_ = None ):
lowercase =[self.sep_token_id]
lowercase =[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 , snake_case_ , snake_case_=False , **snake_case_ ):
lowercase =kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(snake_case_ ) > 0 and not text[0].isspace()):
lowercase =''' ''' + text
return (text, kwargs)
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 72 | 1 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( lowercase_ : Tuple , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowercase =StableDiffusionPipeline.from_pretrained(lowercase_ , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
lowercase =load_file(lowercase_ )
lowercase =[]
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
lowercase =key.split('''.''' )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_''' )[-1].split('''_''' )
lowercase =pipeline.text_encoder
else:
lowercase =key.split('''.''' )[0].split(LORA_PREFIX_UNET + '''_''' )[-1].split('''_''' )
lowercase =pipeline.unet
# find the target layer
lowercase =layer_infos.pop(0 )
while len(lowercase_ ) > -1:
try:
lowercase =curr_layer.__getattr__(lowercase_ )
if len(lowercase_ ) > 0:
lowercase =layer_infos.pop(0 )
elif len(lowercase_ ) == 0:
break
except Exception:
if len(lowercase_ ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
lowercase =layer_infos.pop(0 )
lowercase =[]
if "lora_down" in key:
pair_keys.append(key.replace('''lora_down''' , '''lora_up''' ) )
pair_keys.append(lowercase_ )
else:
pair_keys.append(lowercase_ )
pair_keys.append(key.replace('''lora_up''' , '''lora_down''' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
lowercase =state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
lowercase =state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase_ , lowercase_ ).unsqueeze(2 ).unsqueeze(3 )
else:
lowercase =state_dict[pair_keys[0]].to(torch.floataa )
lowercase =state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase_ , lowercase_ )
# update visited list
for item in pair_keys:
visited.append(lowercase_ )
return pipeline
if __name__ == "__main__":
_UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
)
parser.add_argument(
'''--lora_prefix_text_encoder''',
default='''lora_te''',
type=str,
help='''The prefix of text encoder weight in safetensors''',
)
parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
parser.add_argument(
'''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
)
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
_UpperCAmelCase : Tuple = parser.parse_args()
_UpperCAmelCase : Any = args.base_model_path
_UpperCAmelCase : Any = args.checkpoint_path
_UpperCAmelCase : Optional[int] = args.dump_path
_UpperCAmelCase : Optional[int] = args.lora_prefix_unet
_UpperCAmelCase : Dict = args.lora_prefix_text_encoder
_UpperCAmelCase : str = args.alpha
_UpperCAmelCase : Optional[int] = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
_UpperCAmelCase : Tuple = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 72 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Tuple ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase_ , lowercase_ ):
lowercase =np.full((len(lowercase_ ), sequence_length, 2) , lowercase_ )
else:
lowercase =np.full((len(lowercase_ ), sequence_length) , lowercase_ )
for i, tensor in enumerate(lowercase_ ):
if padding_side == "right":
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
else:
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
return out_tensor.tolist()
def UpperCamelCase ( lowercase_ : Optional[Any] ) -> str:
'''simple docstring'''
lowercase =ord(lowercase_ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
lowercase =unicodedata.category(lowercase_ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -1_00
UpperCamelCase__ = "pt"
def _A( self , snake_case_ ):
import torch
lowercase ='''label''' if '''label''' in features[0].keys() else '''labels'''
lowercase =[feature[label_name] for feature in features] if label_name in features[0].keys() else None
lowercase =self.tokenizer.pad(
snake_case_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
lowercase =torch.tensor(batch['''entity_ids'''] ).shape[1]
lowercase =self.tokenizer.padding_side
if padding_side == "right":
lowercase =[
list(snake_case_ ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_ )) for label in labels
]
else:
lowercase =[
[self.label_pad_token_id] * (sequence_length - len(snake_case_ )) + list(snake_case_ ) for label in labels
]
lowercase =[feature['''ner_tags'''] for feature in features]
lowercase =padding_tensor(snake_case_ , -1 , snake_case_ , snake_case_ )
lowercase =[feature['''original_entity_spans'''] for feature in features]
lowercase =padding_tensor(snake_case_ , (-1, -1) , snake_case_ , snake_case_ )
lowercase ={k: torch.tensor(snake_case_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 72 | 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 PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = '''▁'''
_UpperCAmelCase : Union[str, Any] = {'''vocab_file''': '''spiece.model'''}
_UpperCAmelCase : Union[str, Any] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
_UpperCAmelCase : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self , snake_case_ , snake_case_="<pad>" , snake_case_="</s>" , snake_case_="<unk>" , snake_case_="<mask_2>" , snake_case_="<mask_1>" , snake_case_=None , snake_case_=1_03 , snake_case_ = None , **snake_case_ , ):
lowercase =offset
if additional_special_tokens is not None:
if not isinstance(snake_case_ , snake_case_ ):
raise TypeError(
f'additional_special_tokens should be of type {type(snake_case_ )}, but is'
f' {type(snake_case_ )}' )
lowercase =(
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(snake_case_ ) , self.offset - 1 )
]
if len(set(snake_case_ ) ) != len(snake_case_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
lowercase =additional_special_tokens_extended
else:
lowercase =[mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case_ , unk_token=snake_case_ , mask_token=snake_case_ , pad_token=snake_case_ , mask_token_sent=snake_case_ , offset=snake_case_ , additional_special_tokens=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
lowercase =mask_token_sent
lowercase =vocab_file
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
# add special tokens to encoder dict
lowercase ={
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowercase ={v: k for k, v in self.encoder.items()}
@property
def _A( self ):
return len(self.sp_model ) + self.offset
def _A( self ):
lowercase ={self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
lowercase =self.__dict__.copy()
lowercase =None
return state
def __setstate__( self , snake_case_ ):
lowercase =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase ={}
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def _A( self , snake_case_ ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowercase =self.sp_model.piece_to_id(snake_case_ )
return sp_id + self.offset
def _A( self , snake_case_ ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowercase =self.sp_model.IdToPiece(index - self.offset )
return token
def _A( self , snake_case_ ):
lowercase =[]
lowercase =''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_ ) + token
lowercase =[]
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def _A( self , snake_case_=False ):
return 1
def _A( self , snake_case_ ):
lowercase =set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return self._special_token_mask(snake_case_ )
elif token_ids_a is None:
return self._special_token_mask(snake_case_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A( self , snake_case_ , snake_case_=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , '''wb''' ) as fi:
lowercase =self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
| 72 |
'''simple docstring'''
_UpperCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase ( lowercase_ : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'a bytes-like object is required, not \'{data.__class__.__name__}\''
raise TypeError(lowercase_ )
lowercase =''''''.join(bin(lowercase_ )[2:].zfill(8 ) for byte in data )
lowercase =len(lowercase_ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase =b'''=''' * ((6 - len(lowercase_ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(lowercase_ ) % 6)
else:
lowercase =b''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(lowercase_ ) , 6 ) ).encode()
+ padding
)
def UpperCamelCase ( lowercase_ : str ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
lowercase =(
'''argument should be a bytes-like object or ASCII string, '''
f'not \'{encoded_data.__class__.__name__}\''
)
raise TypeError(lowercase_ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(lowercase_ , lowercase_ ):
try:
lowercase =encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
lowercase =encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(lowercase_ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase =encoded_data[:-padding]
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase =[
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(lowercase_ ) , 8 )
]
return bytes(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'xglm'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'num_attention_heads': 'attention_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=25_60_08 , snake_case_=20_48 , snake_case_=10_24 , snake_case_=40_96 , snake_case_=24 , snake_case_=16 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=True , snake_case_=True , snake_case_=2 , snake_case_=1 , snake_case_=0 , snake_case_=2 , **snake_case_ , ):
lowercase =vocab_size
lowercase =max_position_embeddings
lowercase =d_model
lowercase =ffn_dim
lowercase =num_layers
lowercase =attention_heads
lowercase =activation_function
lowercase =dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =layerdrop
lowercase =init_std
lowercase =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase =use_cache
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , **snake_case_ , )
| 72 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_UpperCAmelCase : Union[str, Any] = datasets.logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = '''\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
author = "Moosavi, Nafise Sadat and
Strube, Michael",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1060",
doi = "10.18653/v1/P16-1060",
pages = "632--642",
}
'''
_UpperCAmelCase : str = '''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''
_UpperCAmelCase : Optional[int] = '''
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting \'keep_singletons=False\', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
\'mentions\': mentions
\'muc\': MUC metric [Vilain et al, 1995]
\'bcub\': B-cubed [Bagga and Baldwin, 1998]
\'ceafe\': CEAFe [Luo et al., 2005]
\'lea\': LEA [Moosavi and Strube, 2016]
\'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric(\'coval\')
>>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',
... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',
... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',
... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',
... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',
... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''
def UpperCamelCase ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=True , lowercase_ : Optional[Any]=False , lowercase_ : int="dummy_doc" ) -> str:
'''simple docstring'''
lowercase ={doc: key_lines}
lowercase ={doc: sys_lines}
lowercase ={}
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase , lowercase =reader.get_doc_mentions(lowercase_ , key_doc_lines[doc] , lowercase_ )
key_singletons_num += singletons_num
if NP_only or min_span:
lowercase =reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
lowercase , lowercase =reader.get_doc_mentions(lowercase_ , sys_doc_lines[doc] , lowercase_ )
sys_singletons_num += singletons_num
if NP_only or min_span:
lowercase =reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
if remove_nested:
lowercase , lowercase =reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
lowercase , lowercase =reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
lowercase =reader.get_mention_assignments(lowercase_ , lowercase_ )
lowercase =reader.get_mention_assignments(lowercase_ , lowercase_ )
lowercase =(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
f'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}' )
logger.info(
'''Number of resulting singleton clusters in the key '''
f'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}' )
if not keep_singletons:
logger.info(
f'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
'''files, respectively''' )
return doc_coref_infos
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Tuple ) -> Dict:
'''simple docstring'''
lowercase =get_coref_infos(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase ={}
lowercase =0
lowercase =0
for name, metric in metrics:
lowercase , lowercase , lowercase =evaluator.evaluate_documents(lowercase_ , lowercase_ , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({f'{name}/recall': recall, f'{name}/precision': precision, f'{name}/f1': fa} )
logger.info(
name.ljust(1_0 ) , f'Recall: {recall * 1_0_0:.2f}' , f' Precision: {precision * 1_0_0:.2f}' , f' F1: {fa * 1_0_0:.2f}' , )
if conll_subparts_num == 3:
lowercase =(conll / 3) * 1_0_0
logger.info(f'CoNLL score: {conll:.2f}' )
output_scores.update({'''conll_score''': conll} )
return output_scores
def UpperCamelCase ( lowercase_ : Any ) -> List[Any]:
'''simple docstring'''
lowercase =False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
lowercase =line.split()[5]
if not parse_col == "-":
lowercase =True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Sequence(datasets.Value('''string''' ) ),
} ) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False ):
lowercase =[
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
lowercase =util.check_gold_parse_annotation(snake_case_ )
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''' )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
lowercase =evaluate(
key_lines=snake_case_ , sys_lines=snake_case_ , metrics=snake_case_ , NP_only=snake_case_ , remove_nested=snake_case_ , keep_singletons=snake_case_ , min_span=snake_case_ , )
return score
| 72 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_UpperCAmelCase : List[Any] = '''docs/source/en/_toctree.yml'''
def UpperCamelCase ( lowercase_ : int ) -> Dict:
'''simple docstring'''
lowercase =defaultdict(lowercase_ )
lowercase =[]
lowercase =[]
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(lowercase_ )
lowercase =new_doc_list
lowercase =[key for key, value in counts.items() if value > 1]
lowercase =[]
for duplicate_key in duplicates:
lowercase =list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key} )
if len(lowercase_ ) > 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] )
lowercase =sorted(lowercase_ , key=lambda lowercase_ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(lowercase_ ) > 1:
raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.''' )
overview_doc.extend(lowercase_ )
# Sort
return overview_doc
def UpperCamelCase ( lowercase_ : Union[str, Any]=False ) -> List[str]:
'''simple docstring'''
with open(lowercase_ , encoding='''utf-8''' ) as f:
lowercase =yaml.safe_load(f.read() )
# Get to the API doc
lowercase =0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase =content[api_idx]['''sections''']
# Then to the model doc
lowercase =0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
lowercase =api_doc[scheduler_idx]['''sections''']
lowercase =clean_doc_toc(lowercase_ )
lowercase =False
if new_scheduler_doc != scheduler_doc:
lowercase =True
if overwrite:
lowercase =new_scheduler_doc
if diff:
if overwrite:
lowercase =api_doc
with open(lowercase_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(lowercase_ , allow_unicode=lowercase_ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
def UpperCamelCase ( lowercase_ : str=False ) -> Any:
'''simple docstring'''
with open(lowercase_ , encoding='''utf-8''' ) as f:
lowercase =yaml.safe_load(f.read() )
# Get to the API doc
lowercase =0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase =content[api_idx]['''sections''']
# Then to the model doc
lowercase =0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
lowercase =False
lowercase =api_doc[pipeline_idx]['''sections''']
lowercase =[]
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
lowercase =pipeline_doc['''section''']
lowercase =clean_doc_toc(lowercase_ )
if overwrite:
lowercase =new_sub_pipeline_doc
new_pipeline_docs.append(lowercase_ )
# sort overall pipeline doc
lowercase =clean_doc_toc(lowercase_ )
if new_pipeline_docs != pipeline_docs:
lowercase =True
if overwrite:
lowercase =new_pipeline_docs
if diff:
if overwrite:
lowercase =api_doc
with open(lowercase_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(lowercase_ , allow_unicode=lowercase_ ) )
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__":
_UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_UpperCAmelCase : str = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(lowercase_ , lowercase_ ):
return 0
elif n == 2:
return 1
else:
lowercase =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
lowercase =0
lowercase =2
while digits < n:
index += 1
lowercase =len(str(fibonacci(lowercase_ ) ) )
return index
def UpperCamelCase ( lowercase_ : int = 1_0_0_0 ) -> int:
'''simple docstring'''
return fibonacci_digits_index(lowercase_ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 72 | 1 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def UpperCamelCase ( lowercase_ : Dict , lowercase_ : Union[str, Any] ) -> Dict:
'''simple docstring'''
lowercase ='''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
lowercase =Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw ).convert('''RGB''' )
lowercase =transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3) , (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1) ),
] )
lowercase =transform(lowercase_ ).unsqueeze(0 ).to(lowercase_ )
return image
def UpperCamelCase ( lowercase_ : Any ) -> int:
'''simple docstring'''
if "visual_encoder" in key:
lowercase =re.sub('''visual_encoder*''' , '''vision_model.encoder''' , lowercase_ )
if "blocks" in key:
lowercase =re.sub(R'''blocks''' , '''layers''' , lowercase_ )
if "attn" in key:
lowercase =re.sub(R'''attn''' , '''self_attn''' , lowercase_ )
if "norm1" in key:
lowercase =re.sub(R'''norm1''' , '''layer_norm1''' , lowercase_ )
if "norm2" in key:
lowercase =re.sub(R'''norm2''' , '''layer_norm2''' , lowercase_ )
if "encoder.norm" in key:
lowercase =re.sub(R'''encoder.norm''' , '''post_layernorm''' , lowercase_ )
if "encoder.patch_embed.proj" in key:
lowercase =re.sub(R'''encoder.patch_embed.proj''' , '''embeddings.patch_embedding''' , lowercase_ )
if "encoder.pos_embed" in key:
lowercase =re.sub(R'''encoder.pos_embed''' , '''embeddings.position_embedding''' , lowercase_ )
if "encoder.cls_token" in key:
lowercase =re.sub(R'''encoder.cls_token''' , '''embeddings.class_embedding''' , lowercase_ )
if "self_attn" in key:
lowercase =re.sub(R'''self_attn.proj''' , '''self_attn.projection''' , lowercase_ )
return key
@torch.no_grad()
def UpperCamelCase ( lowercase_ : Dict , lowercase_ : Tuple=None ) -> str:
'''simple docstring'''
if config_path is not None:
lowercase =BlipConfig.from_pretrained(lowercase_ )
else:
lowercase =BlipConfig(projection_dim=5_1_2 , text_config={} , vision_config={} )
lowercase =BlipForConditionalGeneration(lowercase_ ).eval()
lowercase ='''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''
lowercase =blip_decoder(pretrained=lowercase_ , image_size=3_8_4 , vit='''base''' )
lowercase =pt_model.eval()
lowercase =pt_model.state_dict()
for key in modified_state_dict.copy():
lowercase =modified_state_dict.pop(lowercase_ )
lowercase =rename_key(lowercase_ )
lowercase =value
hf_model.load_state_dict(lowercase_ )
lowercase =3_8_4
lowercase =load_demo_image(image_size=lowercase_ , device='''cpu''' )
lowercase =BertTokenizer.from_pretrained('''bert-base-uncased''' )
lowercase =tokenizer(['''a picture of'''] ).input_ids
lowercase =hf_model.generate(lowercase_ , lowercase_ )
assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]
lowercase =hf_model.generate(lowercase_ )
assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(lowercase_ )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
lowercase =(
'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
)
lowercase =blip_vqa(pretrained=lowercase_ , image_size=lowercase_ , vit='''base''' )
vqa_model.eval()
lowercase =vqa_model.state_dict()
for key in modified_state_dict.copy():
lowercase =modified_state_dict.pop(lowercase_ )
lowercase =rename_key(lowercase_ )
lowercase =value
lowercase =BlipForQuestionAnswering(lowercase_ )
hf_vqa_model.load_state_dict(lowercase_ )
lowercase =['''How many dogs are in this image?''']
lowercase =tokenizer(lowercase_ , return_tensors='''pt''' ).input_ids
lowercase =hf_vqa_model.generate(lowercase_ , lowercase_ )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa''' )
lowercase ='''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''
lowercase =blip_itm(pretrained=lowercase_ , image_size=lowercase_ , vit='''base''' )
itm_model.eval()
lowercase =itm_model.state_dict()
for key in modified_state_dict.copy():
lowercase =modified_state_dict.pop(lowercase_ )
lowercase =rename_key(lowercase_ )
lowercase =value
lowercase =BlipForImageTextRetrieval(lowercase_ )
lowercase =['''A picture of a woman with a dog sitting in a beach''']
lowercase =tokenizer(
lowercase_ , return_tensors='''pt''' , padding='''max_length''' , truncation=lowercase_ , max_length=3_5 , ).input_ids
hf_itm_model.load_state_dict(lowercase_ )
hf_itm_model.eval()
lowercase =hf_itm_model(lowercase_ , lowercase_ , use_itm_head=lowercase_ )
lowercase =hf_itm_model(lowercase_ , lowercase_ , use_itm_head=lowercase_ )
assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm''' )
if __name__ == "__main__":
_UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
_UpperCAmelCase : Tuple = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 72 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Any = {
'''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'marian'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , snake_case_=5_81_01 , snake_case_=None , snake_case_=10_24 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=True , snake_case_=True , snake_case_="gelu" , snake_case_=10_24 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=5_81_00 , snake_case_=False , snake_case_=5_81_00 , snake_case_=0 , snake_case_=0 , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =decoder_vocab_size or vocab_size
lowercase =max_position_embeddings
lowercase =d_model
lowercase =encoder_ffn_dim
lowercase =encoder_layers
lowercase =encoder_attention_heads
lowercase =decoder_ffn_dim
lowercase =decoder_layers
lowercase =decoder_attention_heads
lowercase =dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =activation_function
lowercase =init_std
lowercase =encoder_layerdrop
lowercase =decoder_layerdrop
lowercase =use_cache
lowercase =encoder_layers
lowercase =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase =share_encoder_decoder_embeddings
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , decoder_start_token_id=snake_case_ , forced_eos_token_id=snake_case_ , **snake_case_ , )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase ={0: '''batch'''}
lowercase ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
else:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super().outputs
else:
lowercase =super(snake_case_ , self ).outputs
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Generate decoder inputs
lowercase =seq_length if not self.use_past else 1
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
lowercase =dict(**snake_case_ , **snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
lowercase =common_inputs['''decoder_input_ids'''].shape[1]
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =decoder_seq_length + 3
lowercase =(
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase =torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_ , snake_case_ )] , dim=1 )
lowercase =[]
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase , lowercase =self.num_layers
lowercase =min(snake_case_ , snake_case_ )
lowercase =max(snake_case_ , snake_case_ ) - min_num_layers
lowercase ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(snake_case_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
) )
# TODO: test this.
lowercase =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(snake_case_ , snake_case_ ):
common_inputs["past_key_values"].append((torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase =seqlen + 2
lowercase , lowercase =self.num_layers
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =common_inputs['''attention_mask'''].dtype
lowercase =torch.cat(
[common_inputs['''attention_mask'''], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
lowercase =[
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(snake_case_ )
]
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase =tokenizer.num_special_tokens_to_add(snake_case_ )
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
lowercase =[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase =dict(tokenizer(snake_case_ , return_tensors=snake_case_ ) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
else:
lowercase =self._generate_dummy_inputs_for_causal_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
return common_inputs
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super()._flatten_past_key_values_(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
lowercase =super(snake_case_ , self )._flatten_past_key_values_(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
@property
def _A( self ):
return 1E-4
| 72 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
_UpperCAmelCase : Optional[int] = re.compile(r'''\s+''')
def UpperCamelCase ( lowercase_ : Dict ) -> Any:
'''simple docstring'''
return {"hash": hashlib.mda(re.sub(lowercase_ , '''''' , example['''content'''] ).encode('''utf-8''' ) ).hexdigest()}
def UpperCamelCase ( lowercase_ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowercase =[len(lowercase_ ) for line in example['''content'''].splitlines()]
return {"line_mean": np.mean(lowercase_ ), "line_max": max(lowercase_ )}
def UpperCamelCase ( lowercase_ : Union[str, Any] ) -> str:
'''simple docstring'''
lowercase =np.mean([c.isalnum() for c in example['''content''']] )
return {"alpha_frac": alpha_frac}
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : int ) -> Optional[Any]:
'''simple docstring'''
if example["hash"] in uniques:
uniques.remove(example['''hash'''] )
return True
else:
return False
def UpperCamelCase ( lowercase_ : Optional[int] , lowercase_ : Optional[Any]=5 ) -> int:
'''simple docstring'''
lowercase =['''auto-generated''', '''autogenerated''', '''automatically generated''']
lowercase =example['''content'''].splitlines()
for _, line in zip(range(lowercase_ ) , lowercase_ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def UpperCamelCase ( lowercase_ : Any , lowercase_ : Optional[Any]=5 , lowercase_ : List[Any]=0.0_5 ) -> Optional[Any]:
'''simple docstring'''
lowercase =['''unit tests''', '''test file''', '''configuration file''']
lowercase =example['''content'''].splitlines()
lowercase =0
lowercase =0
# first test
for _, line in zip(range(lowercase_ ) , lowercase_ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
lowercase =example['''content'''].count('''\n''' )
lowercase =int(coeff * nlines )
for line in lines:
count_config += line.lower().count('''config''' )
count_test += line.lower().count('''test''' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def UpperCamelCase ( lowercase_ : str ) -> List[str]:
'''simple docstring'''
lowercase =['''def ''', '''class ''', '''for ''', '''while ''']
lowercase =example['''content'''].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : str=4 ) -> int:
'''simple docstring'''
lowercase =example['''content'''].splitlines()
lowercase =0
for line in lines:
counter += line.lower().count('''=''' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def UpperCamelCase ( lowercase_ : Union[str, Any] ) -> int:
'''simple docstring'''
lowercase =tokenizer(example['''content'''] , truncation=lowercase_ )['''input_ids''']
lowercase =len(example['''content'''] ) / len(lowercase_ )
return {"ratio": ratio}
def UpperCamelCase ( lowercase_ : List[Any] ) -> int:
'''simple docstring'''
lowercase ={}
results.update(get_hash(lowercase_ ) )
results.update(line_stats(lowercase_ ) )
results.update(alpha_stats(lowercase_ ) )
results.update(char_token_ratio(lowercase_ ) )
results.update(is_autogenerated(lowercase_ ) )
results.update(is_config_or_test(lowercase_ ) )
results.update(has_no_keywords(lowercase_ ) )
results.update(has_few_assignments(lowercase_ ) )
return results
def UpperCamelCase ( lowercase_ : Any , lowercase_ : Any , lowercase_ : Dict ) -> Optional[int]:
'''simple docstring'''
if not check_uniques(lowercase_ , lowercase_ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def UpperCamelCase ( lowercase_ : str ) -> List[str]:
'''simple docstring'''
with open(lowercase_ , '''rb''' ) as f_in:
with gzip.open(str(lowercase_ ) + '''.gz''' , '''wb''' , compresslevel=6 ) as f_out:
shutil.copyfileobj(lowercase_ , lowercase_ )
os.unlink(lowercase_ )
# Settings
_UpperCAmelCase : Any = HfArgumentParser(PreprocessingArguments)
_UpperCAmelCase : Tuple = parser.parse_args()
if args.num_workers is None:
_UpperCAmelCase : str = multiprocessing.cpu_count()
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
_UpperCAmelCase : int = time.time()
_UpperCAmelCase : Optional[Any] = load_dataset(args.dataset_name, split='''train''')
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
_UpperCAmelCase : int = time.time()
_UpperCAmelCase : Optional[Any] = ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
_UpperCAmelCase : Tuple = set(ds.unique('''hash'''))
_UpperCAmelCase : Optional[int] = len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
_UpperCAmelCase : Dict = time.time()
_UpperCAmelCase : Tuple = ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(F"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
_UpperCAmelCase : str = time.time()
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(F"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
_UpperCAmelCase : Tuple = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
json.dump(duplicate_clusters, f)
_UpperCAmelCase : List[str] = output_dir / '''data'''
data_dir.mkdir(exist_ok=True)
_UpperCAmelCase : List[str] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
_UpperCAmelCase : Dict = str(data_dir / F"""file-{file_number+1:012}.json""")
_UpperCAmelCase : List[str] = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"""Time to save dataset: {time.time()-t_start:.2f}""")
| 72 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCAmelCase : Dict = parse(importlib.metadata.version('''torch'''))
def UpperCamelCase ( lowercase_ : Union[str, Version] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
'''simple docstring'''
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f'`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}' )
lowercase =STR_OPERATION_TO_FUNC[operation]
if isinstance(lowercase_ , lowercase_ ):
lowercase =parse(importlib.metadata.version(lowercase_ ) )
return operation(lowercase_ , parse(lowercase_ ) )
def UpperCamelCase ( lowercase_ : str , lowercase_ : str ) -> Union[str, Any]:
'''simple docstring'''
return compare_versions(lowercase_ , lowercase_ , lowercase_ )
| 72 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __magic_name__ :
def __init__( self , snake_case_ ):
lowercase =data
lowercase =[0X6745_2301, 0XEFCD_AB89, 0X98BA_DCFE, 0X1032_5476, 0XC3D2_E1F0]
@staticmethod
def _A( snake_case_ , snake_case_ ):
return ((n << b) | (n >> (32 - b))) & 0XFFFF_FFFF
def _A( self ):
lowercase =B'''\x80''' + B'''\x00''' * (63 - (len(self.data ) + 8) % 64)
lowercase =self.data + padding + struct.pack('''>Q''' , 8 * len(self.data ) )
return padded_data
def _A( self ):
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def _A( self , snake_case_ ):
lowercase =list(struct.unpack('''>16L''' , snake_case_ ) ) + [0] * 64
for i in range(16 , 80 ):
lowercase =self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def _A( self ):
lowercase =self.padding()
lowercase =self.split_blocks()
for block in self.blocks:
lowercase =self.expand_block(snake_case_ )
lowercase , lowercase , lowercase , lowercase , lowercase =self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
lowercase =(b & c) | ((~b) & d)
lowercase =0X5A82_7999
elif 20 <= i < 40:
lowercase =b ^ c ^ d
lowercase =0X6ED9_EBA1
elif 40 <= i < 60:
lowercase =(b & c) | (b & d) | (c & d)
lowercase =0X8F1B_BCDC
elif 60 <= i < 80:
lowercase =b ^ c ^ d
lowercase =0XCA62_C1D6
lowercase , lowercase , lowercase , lowercase , lowercase =(
self.rotate(snake_case_ , 5 ) + f + e + k + expanded_block[i] & 0XFFFF_FFFF,
a,
self.rotate(snake_case_ , 30 ),
c,
d,
)
lowercase =(
self.h[0] + a & 0XFFFF_FFFF,
self.h[1] + b & 0XFFFF_FFFF,
self.h[2] + c & 0XFFFF_FFFF,
self.h[3] + d & 0XFFFF_FFFF,
self.h[4] + e & 0XFFFF_FFFF,
)
return ("{:08x}" * 5).format(*self.h )
def UpperCamelCase ( ) -> int:
'''simple docstring'''
lowercase =b'''Test String'''
assert SHAaHash(lowercase_ ).final_hash() == hashlib.shaa(lowercase_ ).hexdigest() # noqa: S324
def UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
lowercase =argparse.ArgumentParser(description='''Process some strings or files''' )
parser.add_argument(
'''--string''' , dest='''input_string''' , default='''Hello World!! Welcome to Cryptography''' , help='''Hash the string''' , )
parser.add_argument('''--file''' , dest='''input_file''' , help='''Hash contents of a file''' )
lowercase =parser.parse_args()
lowercase =args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , '''rb''' ) as f:
lowercase =f.read()
else:
lowercase =bytes(lowercase_ , '''utf-8''' )
print(SHAaHash(lowercase_ ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
_UpperCAmelCase : int = [8, 5, 9, 7]
_UpperCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_UpperCAmelCase : Union[str, Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =claim_vector
lowercase =allocated_resources_table
lowercase =maximum_claim_table
def _A( self ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _A( self ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _A( self ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(snake_case_ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _A( self ):
return {self.__need().index(snake_case_ ): i for i in self.__need()}
def _A( self , **snake_case_ ):
lowercase =self.__need()
lowercase =self.__allocated_resources_table
lowercase =self.__available_resources()
lowercase =self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('''_''' * 50 + '''\n''' )
while need_list:
lowercase =False
for each_need in need_list:
lowercase =True
for index, need in enumerate(snake_case_ ):
if need > available_resources[index]:
lowercase =False
break
if execution:
lowercase =True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowercase =original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(snake_case_ )
# update available/freed resources stack
lowercase =np.array(snake_case_ ) + np.array(
alloc_resources_table[process_number] )
print(
'''Updated available resource stack for processes: '''
+ ''' '''.join([str(snake_case_ ) for x in available_resources] ) )
break
if safe:
print('''The process is in a safe state.\n''' )
else:
print('''System in unsafe state. Aborting...\n''' )
break
def _A( self ):
print(''' ''' * 9 + '''Allocated Resource Table''' )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(snake_case_ ) + 1}'
+ ''' '''.join(f'{it:>8}' for it in item )
+ '''\n''' )
print(''' ''' * 9 + '''System Resource Table''' )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(snake_case_ ) + 1}'
+ ''' '''.join(f'{it:>8}' for it in item )
+ '''\n''' )
print(
'''Current Usage by Active Processes: '''
+ ''' '''.join(str(snake_case_ ) for x in self.__claim_vector ) )
print(
'''Initial Available Resources: '''
+ ''' '''.join(str(snake_case_ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : int = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'efficientnet'
def __init__( self , snake_case_ = 3 , snake_case_ = 6_00 , snake_case_ = 2.0 , snake_case_ = 3.1 , snake_case_ = 8 , snake_case_ = [3, 3, 5, 3, 5, 5, 3] , snake_case_ = [32, 16, 24, 40, 80, 1_12, 1_92] , snake_case_ = [16, 24, 40, 80, 1_12, 1_92, 3_20] , snake_case_ = [] , snake_case_ = [1, 2, 2, 2, 1, 2, 1] , snake_case_ = [1, 2, 2, 3, 3, 4, 1] , snake_case_ = [1, 6, 6, 6, 6, 6, 6] , snake_case_ = 0.25 , snake_case_ = "swish" , snake_case_ = 25_60 , snake_case_ = "mean" , snake_case_ = 0.02 , snake_case_ = 0.0_01 , snake_case_ = 0.99 , snake_case_ = 0.5 , snake_case_ = 0.2 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =num_channels
lowercase =image_size
lowercase =width_coefficient
lowercase =depth_coefficient
lowercase =depth_divisor
lowercase =kernel_sizes
lowercase =in_channels
lowercase =out_channels
lowercase =depthwise_padding
lowercase =strides
lowercase =num_block_repeats
lowercase =expand_ratios
lowercase =squeeze_expansion_ratio
lowercase =hidden_act
lowercase =hidden_dim
lowercase =pooling_type
lowercase =initializer_range
lowercase =batch_norm_eps
lowercase =batch_norm_momentum
lowercase =dropout_rate
lowercase =drop_connect_rate
lowercase =sum(snake_case_ ) * 4
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = version.parse('1.11' )
@property
def _A( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _A( self ):
return 1E-5
| 72 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase : Dict = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
_UpperCAmelCase : Dict = '''\
@inproceedings{banarjee2005,
title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
author = {Banerjee, Satanjeev and Lavie, Alon},
booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
month = jun,
year = {2005},
address = {Ann Arbor, Michigan},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W05-0909},
pages = {65--72},
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.
METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''
_UpperCAmelCase : Tuple = '''
Computes METEOR score of translated segments against one or more references.
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
\'meteor\': meteor score.
Examples:
>>> meteor = datasets.load_metric(\'meteor\')
>>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
>>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
>>> results = meteor.compute(predictions=predictions, references=references)
>>> print(round(results["meteor"], 4))
0.6944
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''] , reference_urls=[
'''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
'''https://en.wikipedia.org/wiki/METEOR''',
] , )
def _A( self , snake_case_ ):
import nltk
nltk.download('''wordnet''' )
if NLTK_VERSION >= version.Version('''3.6.5''' ):
nltk.download('''punkt''' )
if NLTK_VERSION >= version.Version('''3.6.6''' ):
nltk.download('''omw-1.4''' )
def _A( self , snake_case_ , snake_case_ , snake_case_=0.9 , snake_case_=3 , snake_case_=0.5 ):
if NLTK_VERSION >= version.Version('''3.6.5''' ):
lowercase =[
meteor_score.single_meteor_score(
word_tokenize(snake_case_ ) , word_tokenize(snake_case_ ) , alpha=snake_case_ , beta=snake_case_ , gamma=snake_case_ )
for ref, pred in zip(snake_case_ , snake_case_ )
]
else:
lowercase =[
meteor_score.single_meteor_score(snake_case_ , snake_case_ , alpha=snake_case_ , beta=snake_case_ , gamma=snake_case_ )
for ref, pred in zip(snake_case_ , snake_case_ )
]
return {"meteor": np.mean(snake_case_ )}
| 72 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Any = {
'''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:
_UpperCAmelCase : Optional[int] = [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapModel''',
'''ClapPreTrainedModel''',
'''ClapTextModel''',
'''ClapTextModelWithProjection''',
'''ClapAudioModel''',
'''ClapAudioModelWithProjection''',
]
_UpperCAmelCase : Optional[Any] = ['''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
_UpperCAmelCase : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 |
'''simple docstring'''
import sys
_UpperCAmelCase : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCamelCase ( lowercase_ : str = N ) -> int:
'''simple docstring'''
lowercase =-sys.maxsize - 1
for i in range(len(lowercase_ ) - 1_2 ):
lowercase =1
for j in range(1_3 ):
product *= int(n[i + j] )
if product > largest_product:
lowercase =product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 72 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowercase =FunnelConfig.from_json_file(lowercase_ )
print(f'Building PyTorch model from configuration: {config}' )
lowercase =FunnelBaseModel(lowercase_ ) if base_model else FunnelModel(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_UpperCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
)
_UpperCAmelCase : List[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 72 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(lowercase_ , lowercase_ ):
return 0
elif n == 2:
return 1
else:
lowercase =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
lowercase =0
lowercase =2
while digits < n:
index += 1
lowercase =len(str(fibonacci(lowercase_ ) ) )
return index
def UpperCamelCase ( lowercase_ : int = 1_0_0_0 ) -> int:
'''simple docstring'''
return fibonacci_digits_index(lowercase_ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 72 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
'''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'encodec'
def __init__( self , snake_case_=[1.5, 3.0, 6.0, 12.0, 24.0] , snake_case_=2_40_00 , snake_case_=1 , snake_case_=False , snake_case_=None , snake_case_=None , snake_case_=1_28 , snake_case_=32 , snake_case_=1 , snake_case_=[8, 5, 4, 2] , snake_case_="weight_norm" , snake_case_=7 , snake_case_=7 , snake_case_=3 , snake_case_=2 , snake_case_=True , snake_case_="reflect" , snake_case_=2 , snake_case_=2 , snake_case_=1.0 , snake_case_=10_24 , snake_case_=None , snake_case_=True , **snake_case_ , ):
lowercase =target_bandwidths
lowercase =sampling_rate
lowercase =audio_channels
lowercase =normalize
lowercase =chunk_length_s
lowercase =overlap
lowercase =hidden_size
lowercase =num_filters
lowercase =num_residual_layers
lowercase =upsampling_ratios
lowercase =norm_type
lowercase =kernel_size
lowercase =last_kernel_size
lowercase =residual_kernel_size
lowercase =dilation_growth_rate
lowercase =use_causal_conv
lowercase =pad_mode
lowercase =compress
lowercase =num_lstm_layers
lowercase =trim_right_ratio
lowercase =codebook_size
lowercase =codebook_dim if codebook_dim is not None else hidden_size
lowercase =use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' )
super().__init__(**snake_case_ )
@property
def _A( self ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A( self ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A( self ):
lowercase =np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A( self ):
return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 72 | 1 |
'''simple docstring'''
import sys
_UpperCAmelCase : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCamelCase ( lowercase_ : str = N ) -> int:
'''simple docstring'''
lowercase =-sys.maxsize - 1
for i in range(len(lowercase_ ) - 1_2 ):
lowercase =1
for j in range(1_3 ):
product *= int(n[i + j] )
if product > largest_product:
lowercase =product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 72 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : int = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : int = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Dict = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_UpperCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def _A( snake_case_ ):
raise NotImplementedError()
@abstractmethod
def _A( self ):
raise NotImplementedError()
| 72 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 42
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = True
@register_to_config
def __init__( self , snake_case_ = 3 , snake_case_ = 3 , snake_case_ = ("DownEncoderBlock2D",) , snake_case_ = ("UpDecoderBlock2D",) , snake_case_ = (64,) , snake_case_ = 1 , snake_case_ = "silu" , snake_case_ = 4 , snake_case_ = 32 , snake_case_ = 32 , snake_case_ = 0.1_82_15 , ):
super().__init__()
# pass init params to Encoder
lowercase =Encoder(
in_channels=snake_case_ , out_channels=snake_case_ , down_block_types=snake_case_ , block_out_channels=snake_case_ , layers_per_block=snake_case_ , act_fn=snake_case_ , norm_num_groups=snake_case_ , double_z=snake_case_ , )
# pass init params to Decoder
lowercase =Decoder(
in_channels=snake_case_ , out_channels=snake_case_ , up_block_types=snake_case_ , block_out_channels=snake_case_ , layers_per_block=snake_case_ , norm_num_groups=snake_case_ , act_fn=snake_case_ , )
lowercase =nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
lowercase =nn.Convad(snake_case_ , snake_case_ , 1 )
lowercase =False
lowercase =False
# only relevant if vae tiling is enabled
lowercase =self.config.sample_size
lowercase =(
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
lowercase =int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
lowercase =0.25
def _A( self , snake_case_ , snake_case_=False ):
if isinstance(snake_case_ , (Encoder, Decoder) ):
lowercase =value
def _A( self , snake_case_ = True ):
lowercase =use_tiling
def _A( self ):
self.enable_tiling(snake_case_ )
def _A( self ):
lowercase =True
def _A( self ):
lowercase =False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def _A( self ):
lowercase ={}
def fn_recursive_add_processors(snake_case_ , snake_case_ , snake_case_ ):
if hasattr(snake_case_ , '''set_processor''' ):
lowercase =module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'{name}.{sub_name}' , snake_case_ , snake_case_ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(snake_case_ , snake_case_ , snake_case_ )
return processors
def _A( self , snake_case_ ):
lowercase =len(self.attn_processors.keys() )
if isinstance(snake_case_ , snake_case_ ) and len(snake_case_ ) != count:
raise ValueError(
f'A dict of processors was passed, but the number of processors {len(snake_case_ )} does not match the'
f' number of attention layers: {count}. Please make sure to pass {count} processor classes.' )
def fn_recursive_attn_processor(snake_case_ , snake_case_ , snake_case_ ):
if hasattr(snake_case_ , '''set_processor''' ):
if not isinstance(snake_case_ , snake_case_ ):
module.set_processor(snake_case_ )
else:
module.set_processor(processor.pop(f'{name}.processor' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'{name}.{sub_name}' , snake_case_ , snake_case_ )
for name, module in self.named_children():
fn_recursive_attn_processor(snake_case_ , snake_case_ , snake_case_ )
def _A( self ):
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def _A( self , snake_case_ , snake_case_ = True ):
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(snake_case_ , return_dict=snake_case_ )
if self.use_slicing and x.shape[0] > 1:
lowercase =[self.encoder(snake_case_ ) for x_slice in x.split(1 )]
lowercase =torch.cat(snake_case_ )
else:
lowercase =self.encoder(snake_case_ )
lowercase =self.quant_conv(snake_case_ )
lowercase =DiagonalGaussianDistribution(snake_case_ )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=snake_case_ )
def _A( self , snake_case_ , snake_case_ = True ):
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(snake_case_ , return_dict=snake_case_ )
lowercase =self.post_quant_conv(snake_case_ )
lowercase =self.decoder(snake_case_ )
if not return_dict:
return (dec,)
return DecoderOutput(sample=snake_case_ )
@apply_forward_hook
def _A( self , snake_case_ , snake_case_ = True ):
if self.use_slicing and z.shape[0] > 1:
lowercase =[self._decode(snake_case_ ).sample for z_slice in z.split(1 )]
lowercase =torch.cat(snake_case_ )
else:
lowercase =self._decode(snake_case_ ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=snake_case_ )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
lowercase =min(a.shape[2] , b.shape[2] , snake_case_ )
for y in range(snake_case_ ):
lowercase =a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
lowercase =min(a.shape[3] , b.shape[3] , snake_case_ )
for x in range(snake_case_ ):
lowercase =a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def _A( self , snake_case_ , snake_case_ = True ):
lowercase =int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
lowercase =int(self.tile_latent_min_size * self.tile_overlap_factor )
lowercase =self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
lowercase =[]
for i in range(0 , x.shape[2] , snake_case_ ):
lowercase =[]
for j in range(0 , x.shape[3] , snake_case_ ):
lowercase =x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
lowercase =self.encoder(snake_case_ )
lowercase =self.quant_conv(snake_case_ )
row.append(snake_case_ )
rows.append(snake_case_ )
lowercase =[]
for i, row in enumerate(snake_case_ ):
lowercase =[]
for j, tile in enumerate(snake_case_ ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
lowercase =self.blend_v(rows[i - 1][j] , snake_case_ , snake_case_ )
if j > 0:
lowercase =self.blend_h(row[j - 1] , snake_case_ , snake_case_ )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(snake_case_ , dim=3 ) )
lowercase =torch.cat(snake_case_ , dim=2 )
lowercase =DiagonalGaussianDistribution(snake_case_ )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=snake_case_ )
def _A( self , snake_case_ , snake_case_ = True ):
lowercase =int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
lowercase =int(self.tile_sample_min_size * self.tile_overlap_factor )
lowercase =self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
lowercase =[]
for i in range(0 , z.shape[2] , snake_case_ ):
lowercase =[]
for j in range(0 , z.shape[3] , snake_case_ ):
lowercase =z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
lowercase =self.post_quant_conv(snake_case_ )
lowercase =self.decoder(snake_case_ )
row.append(snake_case_ )
rows.append(snake_case_ )
lowercase =[]
for i, row in enumerate(snake_case_ ):
lowercase =[]
for j, tile in enumerate(snake_case_ ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
lowercase =self.blend_v(rows[i - 1][j] , snake_case_ , snake_case_ )
if j > 0:
lowercase =self.blend_h(row[j - 1] , snake_case_ , snake_case_ )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(snake_case_ , dim=3 ) )
lowercase =torch.cat(snake_case_ , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=snake_case_ )
def _A( self , snake_case_ , snake_case_ = False , snake_case_ = True , snake_case_ = None , ):
lowercase =sample
lowercase =self.encode(snake_case_ ).latent_dist
if sample_posterior:
lowercase =posterior.sample(generator=snake_case_ )
else:
lowercase =posterior.mode()
lowercase =self.decode(snake_case_ ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=snake_case_ )
| 72 |
'''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 __magic_name__ :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=6 , snake_case_=17 , snake_case_=23 , snake_case_=11 , snake_case_=True , ):
lowercase =parent
lowercase =batch_size
lowercase =seq_length
lowercase =act_dim
lowercase =state_dim
lowercase =hidden_size
lowercase =max_length
lowercase =is_training
def _A( self ):
lowercase =floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =ids_tensor((self.batch_size, self.seq_length) , vocab_size=10_00 )
lowercase =random_attention_mask((self.batch_size, self.seq_length) )
lowercase =self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _A( self ):
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 , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =DecisionTransformerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , 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 ):
lowercase =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) =config_and_inputs
lowercase ={
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (DecisionTransformerModel,) if is_torch_available() else ()
UpperCamelCase__ = ()
UpperCamelCase__ = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
UpperCamelCase__ = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =DecisionTransformerModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def _A( self ):
self.config_tester.run_common_tests()
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
@slow
def _A( self ):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase =DecisionTransformerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase =[*signature.parameters.keys()]
lowercase =[
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def _A( self ):
lowercase =2 # number of steps of autoregressive prediction we will perform
lowercase =10 # defined by the RL environment, may be normalized
lowercase =DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
lowercase =model.to(snake_case_ )
lowercase =model.config
torch.manual_seed(0 )
lowercase =torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ) # env.reset()
lowercase =torch.tensor(
[[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]] , device=snake_case_ )
lowercase =torch.tensor(snake_case_ , device=snake_case_ , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowercase =state
lowercase =torch.zeros(1 , 0 , config.act_dim , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.zeros(1 , 0 , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.tensor(0 , device=snake_case_ , dtype=torch.long ).reshape(1 , 1 )
for step in range(snake_case_ ):
lowercase =torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=snake_case_ )] , dim=1 )
lowercase =torch.cat([rewards, torch.zeros(1 , 1 , device=snake_case_ )] , dim=1 )
lowercase =torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowercase , lowercase , lowercase =model(
states=snake_case_ , actions=snake_case_ , rewards=snake_case_ , returns_to_go=snake_case_ , timesteps=snake_case_ , attention_mask=snake_case_ , return_dict=snake_case_ , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
lowercase , lowercase , lowercase , lowercase =( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ),
1.0,
False,
{},
)
lowercase =action_pred[0, -1]
lowercase =torch.cat([states, state] , dim=1 )
lowercase =returns_to_go[0, -1] - reward
lowercase =torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowercase =torch.cat(
[timesteps, torch.ones((1, 1) , device=snake_case_ , dtype=torch.long ) * (step + 1)] , dim=1 )
| 72 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = KandinskyVaaPipeline
UpperCamelCase__ = [
'image_embeds',
'negative_image_embeds',
]
UpperCamelCase__ = ['image_embeds', 'negative_image_embeds']
UpperCamelCase__ = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
UpperCamelCase__ = False
@property
def _A( self ):
return 32
@property
def _A( self ):
return 32
@property
def _A( self ):
return self.time_input_dim
@property
def _A( self ):
return self.time_input_dim * 4
@property
def _A( self ):
return 1_00
@property
def _A( self ):
torch.manual_seed(0 )
lowercase ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase =UNetaDConditionModel(**snake_case_ )
return model
@property
def _A( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _A( self ):
torch.manual_seed(0 )
lowercase =VQModel(**self.dummy_movq_kwargs )
return model
def _A( self ):
lowercase =self.dummy_unet
lowercase =self.dummy_movq
lowercase =DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='''linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=snake_case_ , )
lowercase ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _A( self , snake_case_ , snake_case_=0 ):
lowercase =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
lowercase =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case_ )
if str(snake_case_ ).startswith('''mps''' ):
lowercase =torch.manual_seed(snake_case_ )
else:
lowercase =torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
lowercase ={
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def _A( self ):
lowercase ='''cpu'''
lowercase =self.get_dummy_components()
lowercase =self.pipeline_class(**snake_case_ )
lowercase =pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
lowercase =pipe(**self.get_dummy_inputs(snake_case_ ) )
lowercase =output.images
lowercase =pipe(
**self.get_dummy_inputs(snake_case_ ) , return_dict=snake_case_ , )[0]
lowercase =image[0, -3:, -3:, -1]
lowercase =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase =np.array(
[0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase ):
def _A( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A( self ):
lowercase =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy''' )
lowercase =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case_ )
lowercase =KandinskyVaaPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
lowercase =pipeline.to(snake_case_ )
pipeline.set_progress_bar_config(disable=snake_case_ )
lowercase ='''red cat, 4k photo'''
lowercase =torch.Generator(device='''cuda''' ).manual_seed(0 )
lowercase , lowercase =pipe_prior(
snake_case_ , generator=snake_case_ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
lowercase =torch.Generator(device='''cuda''' ).manual_seed(0 )
lowercase =pipeline(
image_embeds=snake_case_ , negative_image_embeds=snake_case_ , generator=snake_case_ , num_inference_steps=1_00 , output_type='''np''' , )
lowercase =output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ )
| 72 |
'''simple docstring'''
from math import pi, sqrt, tan
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or breadth < 0 or height < 0:
raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
return 4 * pi * radius**2
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
return 3 * pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cone() only accepts non-negative values''' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'''surface_area_conical_frustum() only accepts non-negative values''' )
lowercase =(height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
return 2 * pi * radius * (height + radius)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if torus_radius < 0 or tube_radius < 0:
raise ValueError('''surface_area_torus() only accepts non-negative values''' )
if torus_radius < tube_radius:
raise ValueError(
'''surface_area_torus() does not support spindle or self intersecting tori''' )
return 4 * pow(lowercase_ , 2 ) * torus_radius * tube_radius
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or width < 0:
raise ValueError('''area_rectangle() only accepts non-negative values''' )
return length * width
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''area_square() only accepts non-negative values''' )
return side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_triangle() only accepts non-negative values''' )
return (base * height) / 2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('''Given three sides do not form a triangle''' )
lowercase =(sidea + sidea + sidea) / 2
lowercase =sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_parallelogram() only accepts non-negative values''' )
return base * height
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if basea < 0 or basea < 0 or height < 0:
raise ValueError('''area_trapezium() only accepts non-negative values''' )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''area_circle() only accepts non-negative values''' )
return pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_x < 0 or radius_y < 0:
raise ValueError('''area_ellipse() only accepts non-negative values''' )
return pi * radius_x * radius_y
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('''area_rhombus() only accepts non-negative values''' )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase ( lowercase_ : int , lowercase_ : float ) -> float:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or sides < 3:
raise ValueError(
'''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
elif length < 0:
raise ValueError(
'''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('''[DEMO] Areas of various geometric shapes: \n''')
print(F"""Rectangle: {area_rectangle(10, 20) = }""")
print(F"""Square: {area_square(10) = }""")
print(F"""Triangle: {area_triangle(10, 10) = }""")
print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
print(F"""Rhombus: {area_rhombus(10, 20) = }""")
print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
print(F"""Circle: {area_circle(20) = }""")
print(F"""Ellipse: {area_ellipse(10, 20) = }""")
print('''\nSurface Areas of various geometric shapes: \n''')
print(F"""Cube: {surface_area_cube(20) = }""")
print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
print(F"""Sphere: {surface_area_sphere(20) = }""")
print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
print(F"""Cone: {surface_area_cone(10, 20) = }""")
print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
print(F"""Torus: {surface_area_torus(20, 10) = }""")
print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
print(F"""Square: {area_reg_polygon(4, 10) = }""")
print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
| 72 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
_UpperCAmelCase : List[Any] = None
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Any = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
_UpperCAmelCase : Optional[int] = {
'''vocab_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
),
},
}
_UpperCAmelCase : List[str] = {
'''facebook/nllb-large-en-ro''': 10_24,
'''facebook/nllb-200-distilled-600M''': 10_24,
}
# fmt: off
_UpperCAmelCase : Dict = ['''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 __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = ['input_ids', 'attention_mask']
UpperCamelCase__ = NllbTokenizer
UpperCamelCase__ = []
UpperCamelCase__ = []
def __init__( self , snake_case_=None , snake_case_=None , snake_case_="<s>" , snake_case_="</s>" , snake_case_="</s>" , snake_case_="<s>" , snake_case_="<unk>" , snake_case_="<pad>" , snake_case_="<mask>" , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=False , **snake_case_ , ):
# Mask token behave like a normal word, i.e. include the space before it
lowercase =AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
lowercase =legacy_behaviour
super().__init__(
vocab_file=snake_case_ , tokenizer_file=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , src_lang=snake_case_ , tgt_lang=snake_case_ , additional_special_tokens=snake_case_ , legacy_behaviour=snake_case_ , **snake_case_ , )
lowercase =vocab_file
lowercase =False if not self.vocab_file else True
lowercase =FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
lowercase ={
lang_code: self.convert_tokens_to_ids(snake_case_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowercase =src_lang if src_lang is not None else '''eng_Latn'''
lowercase =self.convert_tokens_to_ids(self._src_lang )
lowercase =tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _A( self ):
return self._src_lang
@src_lang.setter
def _A( self , snake_case_ ):
lowercase =new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _A( self , snake_case_ , snake_case_ = None ):
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 , snake_case_ , snake_case_ = None ):
lowercase =[self.sep_token_id]
lowercase =[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 , snake_case_ , snake_case_ , snake_case_ , snake_case_ , **snake_case_ ):
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
lowercase =src_lang
lowercase =self(snake_case_ , add_special_tokens=snake_case_ , return_tensors=snake_case_ , **snake_case_ )
lowercase =self.convert_tokens_to_ids(snake_case_ )
lowercase =tgt_lang_id
return inputs
def _A( self , snake_case_ , snake_case_ = "eng_Latn" , snake_case_ = None , snake_case_ = "fra_Latn" , **snake_case_ , ):
lowercase =src_lang
lowercase =tgt_lang
return super().prepare_seqaseq_batch(snake_case_ , snake_case_ , **snake_case_ )
def _A( self ):
return self.set_src_lang_special_tokens(self.src_lang )
def _A( self ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _A( self , snake_case_ ):
lowercase =self.convert_tokens_to_ids(snake_case_ )
if self.legacy_behaviour:
lowercase =[]
lowercase =[self.eos_token_id, self.cur_lang_code]
else:
lowercase =[self.cur_lang_code]
lowercase =[self.eos_token_id]
lowercase =self.convert_ids_to_tokens(self.prefix_tokens )
lowercase =self.convert_ids_to_tokens(self.suffix_tokens )
lowercase =processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _A( self , snake_case_ ):
lowercase =self.convert_tokens_to_ids(snake_case_ )
if self.legacy_behaviour:
lowercase =[]
lowercase =[self.eos_token_id, self.cur_lang_code]
else:
lowercase =[self.cur_lang_code]
lowercase =[self.eos_token_id]
lowercase =self.convert_ids_to_tokens(self.prefix_tokens )
lowercase =self.convert_ids_to_tokens(self.suffix_tokens )
lowercase =processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _A( self , snake_case_ , snake_case_ = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
return
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 72 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = BarthezTokenizer
UpperCamelCase__ = BarthezTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def _A( self ):
super().setUp()
lowercase =BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case_ )
lowercase =tokenizer
def _A( self ):
lowercase ='''<pad>'''
lowercase =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def _A( self ):
lowercase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(snake_case_ ) , 10_11_22 )
def _A( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def _A( self ):
lowercase =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowercase =[0, 57, 30_18, 7_03_07, 91, 2]
lowercase =self.tokenizer(
snake_case_ , max_length=len(snake_case_ ) , padding=snake_case_ , truncation=snake_case_ , return_tensors='''pt''' )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
lowercase =batch.input_ids.tolist()[0]
self.assertListEqual(snake_case_ , snake_case_ )
def _A( self ):
if not self.test_rust_tokenizer:
return
lowercase =self.get_tokenizer()
lowercase =self.get_rust_tokenizer()
lowercase ='''I was born in 92000, and this is falsé.'''
lowercase =tokenizer.tokenize(snake_case_ )
lowercase =rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =self.get_rust_tokenizer()
lowercase =tokenizer.encode(snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
@slow
def _A( self ):
# fmt: off
lowercase ={'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase =[
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=snake_case_ , )
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
lowercase =[]
lowercase =set({'''(''', '''[''', '''{'''} )
lowercase =set({''')''', ''']''', '''}'''} )
lowercase ={'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(lowercase_ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowercase_ ) == 0 or (len(lowercase_ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowercase_ ) == 0
def UpperCamelCase ( ) -> Any:
'''simple docstring'''
lowercase =input('''Enter sequence of brackets: ''' )
if is_balanced(lowercase_ ):
print(lowercase_ , '''is balanced''' )
else:
print(lowercase_ , '''is not balanced''' )
if __name__ == "__main__":
main()
| 72 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_text_model'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=5_02_44 , snake_case_=7_68 , snake_case_=64 , snake_case_=20_48 , snake_case_=12 , snake_case_=12 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gelu_new" , snake_case_=0 , snake_case_=False , snake_case_=0 , snake_case_=1 , snake_case_=False , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =hidden_size
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =use_cache
lowercase =eos_token_id
lowercase =decoder_start_token_id
# for backwards compatibility
lowercase =dense_act_fn
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , tie_word_embeddings=snake_case_ , is_decoder=snake_case_ , **snake_case_ , )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_vision_model'
def __init__( self , snake_case_=7_68 , snake_case_=7_68 , snake_case_=20_48 , snake_case_=64 , snake_case_=12 , snake_case_=12 , snake_case_="gelu_new" , snake_case_=1E-6 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=1E-10 , snake_case_=1.0 , snake_case_=40_96 , snake_case_=32 , snake_case_=1_28 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =hidden_size
lowercase =patch_embed_hidden_size
lowercase =d_ff
lowercase =dropout_rate
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =initializer_range
lowercase =initializer_factor
lowercase =attention_dropout
lowercase =layer_norm_eps
lowercase =dense_act_fn
lowercase =seq_len
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =d_kv
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct'
UpperCamelCase__ = True
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=1.0 , snake_case_=0.02 , snake_case_=False , snake_case_=False , snake_case_=True , **snake_case_ , ):
super().__init__(tie_word_embeddings=snake_case_ , is_encoder_decoder=snake_case_ , **snake_case_ )
if text_config is None:
lowercase ={}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
lowercase ={}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
lowercase =PixaStructTextConfig(**snake_case_ )
lowercase =PixaStructVisionConfig(**snake_case_ )
lowercase =self.text_config.decoder_start_token_id
lowercase =self.text_config.pad_token_id
lowercase =self.text_config.eos_token_id
lowercase =initializer_factor
lowercase =initializer_range
lowercase =self.initializer_range
lowercase =self.initializer_range
lowercase =is_vqa
@classmethod
def _A( cls , snake_case_ , snake_case_ , **snake_case_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def _A( self ):
lowercase =copy.deepcopy(self.__dict__ )
lowercase =self.text_config.to_dict()
lowercase =self.vision_config.to_dict()
lowercase =self.__class__.model_type
return output
| 72 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCAmelCase : Optional[Any] = (7_20, 12_80) # Height, Width
_UpperCAmelCase : str = (0.4, 0.6) # if height or width lower than this scale, drop it.
_UpperCAmelCase : Any = 1 / 1_00
_UpperCAmelCase : Tuple = ''''''
_UpperCAmelCase : Union[str, Any] = ''''''
_UpperCAmelCase : Any = ''''''
_UpperCAmelCase : Tuple = 2_50
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase , lowercase =get_dataset(lowercase_ , lowercase_ )
for index in range(lowercase_ ):
lowercase =random.sample(range(len(lowercase_ ) ) , 4 )
lowercase , lowercase , lowercase =update_image_and_anno(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , filter_scale=lowercase_ , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
lowercase =random_chars(3_2 )
lowercase =path.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
lowercase =f'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
cva.imwrite(f'{file_root}.jpg' , lowercase_ , [cva.IMWRITE_JPEG_QUALITY, 8_5] )
print(f'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}' )
lowercase =[]
for anno in new_annos:
lowercase =anno[3] - anno[1]
lowercase =anno[4] - anno[2]
lowercase =anno[1] + width / 2
lowercase =anno[2] + height / 2
lowercase =f'{anno[0]} {x_center} {y_center} {width} {height}'
annos_list.append(lowercase_ )
with open(f'{file_root}.txt' , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def UpperCamelCase ( lowercase_ : str , lowercase_ : str ) -> tuple[list, list]:
'''simple docstring'''
lowercase =[]
lowercase =[]
for label_file in glob.glob(os.path.join(lowercase_ , '''*.txt''' ) ):
lowercase =label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(lowercase_ ) as in_file:
lowercase =in_file.readlines()
lowercase =os.path.join(lowercase_ , f'{label_name}.jpg' )
lowercase =[]
for obj_list in obj_lists:
lowercase =obj_list.rstrip('''\n''' ).split(''' ''' )
lowercase =float(obj[1] ) - float(obj[3] ) / 2
lowercase =float(obj[2] ) - float(obj[4] ) / 2
lowercase =float(obj[1] ) + float(obj[3] ) / 2
lowercase =float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(lowercase_ )
labels.append(lowercase_ )
return img_paths, labels
def UpperCamelCase ( lowercase_ : list , lowercase_ : list , lowercase_ : list[int] , lowercase_ : tuple[int, int] , lowercase_ : tuple[float, float] , lowercase_ : float = 0.0 , ) -> tuple[list, list, str]:
'''simple docstring'''
lowercase =np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
lowercase =scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
lowercase =scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
lowercase =int(scale_x * output_size[1] )
lowercase =int(scale_y * output_size[0] )
lowercase =[]
lowercase =[]
for i, index in enumerate(lowercase_ ):
lowercase =all_img_list[index]
path_list.append(lowercase_ )
lowercase =all_annos[index]
lowercase =cva.imread(lowercase_ )
if i == 0: # top-left
lowercase =cva.resize(lowercase_ , (divid_point_x, divid_point_y) )
lowercase =img
for bbox in img_annos:
lowercase =bbox[1] * scale_x
lowercase =bbox[2] * scale_y
lowercase =bbox[3] * scale_x
lowercase =bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
lowercase =cva.resize(lowercase_ , (output_size[1] - divid_point_x, divid_point_y) )
lowercase =img
for bbox in img_annos:
lowercase =scale_x + bbox[1] * (1 - scale_x)
lowercase =bbox[2] * scale_y
lowercase =scale_x + bbox[3] * (1 - scale_x)
lowercase =bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
lowercase =cva.resize(lowercase_ , (divid_point_x, output_size[0] - divid_point_y) )
lowercase =img
for bbox in img_annos:
lowercase =bbox[1] * scale_x
lowercase =scale_y + bbox[2] * (1 - scale_y)
lowercase =bbox[3] * scale_x
lowercase =scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
lowercase =cva.resize(
lowercase_ , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
lowercase =img
for bbox in img_annos:
lowercase =scale_x + bbox[1] * (1 - scale_x)
lowercase =scale_y + bbox[2] * (1 - scale_y)
lowercase =scale_x + bbox[3] * (1 - scale_x)
lowercase =scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
lowercase =[
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def UpperCamelCase ( lowercase_ : int ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
lowercase =ascii_lowercase + digits
return "".join(random.choice(lowercase_ ) for _ in range(lowercase_ ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 72 |
'''simple docstring'''
def UpperCamelCase ( ) -> int:
'''simple docstring'''
return 1
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowercase_ )
def UpperCamelCase ( lowercase_ : int = 2_0_0 ) -> int:
'''simple docstring'''
return two_pound(lowercase_ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 72 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
_UpperCAmelCase : int = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def UpperCamelCase ( ) -> Tuple:
'''simple docstring'''
lowercase =os.path.dirname(os.path.realpath(lowercase_ ) )
lowercase =os.path.join(lowercase_ , '''words.txt''' )
lowercase =''''''
with open(lowercase_ ) as f:
lowercase =f.readline()
lowercase =[word.strip('''"''' ) for word in words.strip('''\r\n''' ).split(''',''' )]
lowercase =[
word
for word in [sum(ord(lowercase_ ) - 6_4 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(lowercase_ )
if __name__ == "__main__":
print(solution())
| 72 |
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['image_processor', 'tokenizer']
UpperCamelCase__ = 'BlipImageProcessor'
UpperCamelCase__ = 'AutoTokenizer'
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
# add QFormer tokenizer
lowercase =qformer_tokenizer
def __call__( self , snake_case_ = None , snake_case_ = None , snake_case_ = True , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = True , snake_case_ = None , **snake_case_ , ):
if images is None and text is None:
raise ValueError('''You have to specify at least images or text.''' )
lowercase =BatchFeature()
if text is not None:
lowercase =self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
encoding.update(snake_case_ )
lowercase =self.qformer_tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
lowercase =qformer_text_encoding.pop('''input_ids''' )
lowercase =qformer_text_encoding.pop('''attention_mask''' )
if images is not None:
lowercase =self.image_processor(snake_case_ , return_tensors=snake_case_ )
encoding.update(snake_case_ )
return encoding
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _A( self ):
lowercase =self.tokenizer.model_input_names
lowercase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _A( self , snake_case_ , **snake_case_ ):
if os.path.isfile(snake_case_ ):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
lowercase =os.path.join(snake_case_ , '''qformer_tokenizer''' )
self.qformer_tokenizer.save_pretrained(snake_case_ )
return super().save_pretrained(snake_case_ , **snake_case_ )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
lowercase =AutoTokenizer.from_pretrained(snake_case_ , subfolder='''qformer_tokenizer''' )
lowercase =cls._get_arguments_from_pretrained(snake_case_ , **snake_case_ )
args.append(snake_case_ )
return cls(*snake_case_ )
| 72 | 1 |
'''simple docstring'''
from typing import Any
def UpperCamelCase ( lowercase_ : list ) -> list[Any]:
'''simple docstring'''
if not input_list:
return []
lowercase =[input_list.count(lowercase_ ) for value in input_list]
lowercase =max(lowercase_ ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(lowercase_ ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_UpperCAmelCase : Dict = '''\
@inproceedings{lin-2004-rouge,
title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
author = "Lin, Chin-Yew",
booktitle = "Text Summarization Branches Out",
month = jul,
year = "2004",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W04-1013",
pages = "74--81",
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''
_UpperCAmelCase : Dict = '''
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
`"rougeL"`: Longest common subsequence based scoring.
`"rougeLSum"`: rougeLsum splits text using `"\n"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric(\'rouge\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
[\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
>>> print(results["rouge1"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results["rouge1"].mid.fmeasure)
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
'''https://github.com/google-research/google-research/tree/master/rouge''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=None , snake_case_=True , snake_case_=False ):
if rouge_types is None:
lowercase =['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
lowercase =rouge_scorer.RougeScorer(rouge_types=snake_case_ , use_stemmer=snake_case_ )
if use_aggregator:
lowercase =scoring.BootstrapAggregator()
else:
lowercase =[]
for ref, pred in zip(snake_case_ , snake_case_ ):
lowercase =scorer.score(snake_case_ , snake_case_ )
if use_aggregator:
aggregator.add_scores(snake_case_ )
else:
scores.append(snake_case_ )
if use_aggregator:
lowercase =aggregator.aggregate()
else:
lowercase ={}
for key in scores[0]:
lowercase =[score[key] for score in scores]
return result
| 72 | 1 |
'''simple docstring'''
import os
from pathlib import Path
def UpperCamelCase ( ) -> Any:
'''simple docstring'''
from torch.utils.cpp_extension import load
lowercase =Path(lowercase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
lowercase =[
root / filename
for filename in [
'''vision.cpp''',
os.path.join('''cpu''' , '''ms_deform_attn_cpu.cpp''' ),
os.path.join('''cuda''' , '''ms_deform_attn_cuda.cu''' ),
]
]
load(
'''MultiScaleDeformableAttention''' , lowercase_ , with_cuda=lowercase_ , extra_include_paths=[str(lowercase_ )] , extra_cflags=['''-DWITH_CUDA=1'''] , extra_cuda_cflags=[
'''-DCUDA_HAS_FP16=1''',
'''-D__CUDA_NO_HALF_OPERATORS__''',
'''-D__CUDA_NO_HALF_CONVERSIONS__''',
'''-D__CUDA_NO_HALF2_OPERATORS__''',
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 72 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = '''▁'''
_UpperCAmelCase : Union[str, Any] = {'''vocab_file''': '''spiece.model'''}
_UpperCAmelCase : Union[str, Any] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
_UpperCAmelCase : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self , snake_case_ , snake_case_="<pad>" , snake_case_="</s>" , snake_case_="<unk>" , snake_case_="<mask_2>" , snake_case_="<mask_1>" , snake_case_=None , snake_case_=1_03 , snake_case_ = None , **snake_case_ , ):
lowercase =offset
if additional_special_tokens is not None:
if not isinstance(snake_case_ , snake_case_ ):
raise TypeError(
f'additional_special_tokens should be of type {type(snake_case_ )}, but is'
f' {type(snake_case_ )}' )
lowercase =(
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(snake_case_ ) , self.offset - 1 )
]
if len(set(snake_case_ ) ) != len(snake_case_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
lowercase =additional_special_tokens_extended
else:
lowercase =[mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case_ , unk_token=snake_case_ , mask_token=snake_case_ , pad_token=snake_case_ , mask_token_sent=snake_case_ , offset=snake_case_ , additional_special_tokens=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
lowercase =mask_token_sent
lowercase =vocab_file
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
# add special tokens to encoder dict
lowercase ={
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowercase ={v: k for k, v in self.encoder.items()}
@property
def _A( self ):
return len(self.sp_model ) + self.offset
def _A( self ):
lowercase ={self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
lowercase =self.__dict__.copy()
lowercase =None
return state
def __setstate__( self , snake_case_ ):
lowercase =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase ={}
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def _A( self , snake_case_ ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowercase =self.sp_model.piece_to_id(snake_case_ )
return sp_id + self.offset
def _A( self , snake_case_ ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowercase =self.sp_model.IdToPiece(index - self.offset )
return token
def _A( self , snake_case_ ):
lowercase =[]
lowercase =''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_ ) + token
lowercase =[]
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def _A( self , snake_case_=False ):
return 1
def _A( self , snake_case_ ):
lowercase =set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return self._special_token_mask(snake_case_ )
elif token_ids_a is None:
return self._special_token_mask(snake_case_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A( self , snake_case_ , snake_case_=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , '''wb''' ) as fi:
lowercase =self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
| 72 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'''pipelines_utils''',
'''0.22.0''',
'''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
standard_warn=False,
stacklevel=3,
)
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 72 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def _A( snake_case_ ):
raise NotImplementedError()
@abstractmethod
def _A( self ):
raise NotImplementedError()
| 72 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Tuple ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase_ , lowercase_ ):
lowercase =np.full((len(lowercase_ ), sequence_length, 2) , lowercase_ )
else:
lowercase =np.full((len(lowercase_ ), sequence_length) , lowercase_ )
for i, tensor in enumerate(lowercase_ ):
if padding_side == "right":
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
else:
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
return out_tensor.tolist()
def UpperCamelCase ( lowercase_ : Optional[Any] ) -> str:
'''simple docstring'''
lowercase =ord(lowercase_ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
lowercase =unicodedata.category(lowercase_ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -1_00
UpperCamelCase__ = "pt"
def _A( self , snake_case_ ):
import torch
lowercase ='''label''' if '''label''' in features[0].keys() else '''labels'''
lowercase =[feature[label_name] for feature in features] if label_name in features[0].keys() else None
lowercase =self.tokenizer.pad(
snake_case_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
lowercase =torch.tensor(batch['''entity_ids'''] ).shape[1]
lowercase =self.tokenizer.padding_side
if padding_side == "right":
lowercase =[
list(snake_case_ ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_ )) for label in labels
]
else:
lowercase =[
[self.label_pad_token_id] * (sequence_length - len(snake_case_ )) + list(snake_case_ ) for label in labels
]
lowercase =[feature['''ner_tags'''] for feature in features]
lowercase =padding_tensor(snake_case_ , -1 , snake_case_ , snake_case_ )
lowercase =[feature['''original_entity_spans'''] for feature in features]
lowercase =padding_tensor(snake_case_ , (-1, -1) , snake_case_ , snake_case_ )
lowercase ={k: torch.tensor(snake_case_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 72 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : str = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'roc_bert'
def __init__( self , snake_case_=3_05_22 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_=30_72 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_12 , snake_case_=2 , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=True , snake_case_=0 , snake_case_="absolute" , snake_case_=None , snake_case_=True , snake_case_=True , snake_case_=7_68 , snake_case_=9_10 , snake_case_=5_12 , snake_case_=2_48_58 , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =max_position_embeddings
lowercase =hidden_size
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =intermediate_size
lowercase =hidden_act
lowercase =hidden_dropout_prob
lowercase =attention_probs_dropout_prob
lowercase =initializer_range
lowercase =type_vocab_size
lowercase =layer_norm_eps
lowercase =use_cache
lowercase =enable_pronunciation
lowercase =enable_shape
lowercase =pronunciation_embed_dim
lowercase =pronunciation_vocab_size
lowercase =shape_embed_dim
lowercase =shape_vocab_size
lowercase =concat_input
lowercase =position_embedding_type
lowercase =classifier_dropout
super().__init__(pad_token_id=snake_case_ , **snake_case_ )
| 72 |
'''simple docstring'''
_UpperCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase ( lowercase_ : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'a bytes-like object is required, not \'{data.__class__.__name__}\''
raise TypeError(lowercase_ )
lowercase =''''''.join(bin(lowercase_ )[2:].zfill(8 ) for byte in data )
lowercase =len(lowercase_ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase =b'''=''' * ((6 - len(lowercase_ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(lowercase_ ) % 6)
else:
lowercase =b''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(lowercase_ ) , 6 ) ).encode()
+ padding
)
def UpperCamelCase ( lowercase_ : str ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
lowercase =(
'''argument should be a bytes-like object or ASCII string, '''
f'not \'{encoded_data.__class__.__name__}\''
)
raise TypeError(lowercase_ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(lowercase_ , lowercase_ ):
try:
lowercase =encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
lowercase =encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(lowercase_ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase =encoded_data[:-padding]
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase =[
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(lowercase_ ) , 8 )
]
return bytes(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.17.0.dev0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')
_UpperCAmelCase : Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
UpperCamelCase__ = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
UpperCamelCase__ = field(
default=10_24 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'A csv or a json file containing the training data.'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'A csv or a json file containing the validation data.'} )
UpperCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'A csv or a json file containing the test data.'} )
def _A( self ):
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('''Need either a GLUE task, a training/validation file or a dataset name.''' )
else:
lowercase =self.train_file.split('''.''' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
lowercase =self.validation_file.split('''.''' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __magic_name__ :
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
UpperCamelCase__ = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
UpperCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
lowercase =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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.
lowercase , lowercase , lowercase =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase =parser.parse_args_into_dataclasses()
# 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 )] , )
lowercase =training_args.get_process_log_level()
logger.setLevel(lowercase_ )
datasets.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
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.
lowercase =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase =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.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowercase =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
lowercase ={'''train''': data_args.train_file, '''validation''': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
lowercase =data_args.train_file.split('''.''' )[-1]
lowercase =data_args.test_file.split('''.''' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
lowercase =data_args.test_file
else:
raise ValueError('''Need either a GLUE task or a test file for `do_predict`.''' )
for key in data_files.keys():
logger.info(f'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith('''.csv''' ):
# Loading a dataset from local csv files
lowercase =load_dataset('''csv''' , data_files=lowercase_ , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
lowercase =load_dataset('''json''' , data_files=lowercase_ , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
lowercase =raw_datasets['''train'''].features['''label'''].names
lowercase =len(lowercase_ )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
lowercase =TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowercase_ , )
lowercase =BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
lowercase ='''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowercase =False
# Some models have set the order of the labels to use, so let's make sure we do use it.
lowercase ={'''Refused''': 0, '''Entailed''': 1}
lowercase ={0: '''Refused''', 1: '''Entailed'''}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
lowercase =min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowercase_ : List[str] ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowercase_ : int ):
lowercase =[_table_row.split('''#''' ) for _table_row in _table_text.strip('''\n''' ).split('''\n''' )]
lowercase =pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
lowercase =examples['''statement''']
lowercase =list(map(_convert_table_text_to_pandas , examples['''table_text'''] ) )
lowercase =tokenizer(lowercase_ , lowercase_ , padding=lowercase_ , max_length=lowercase_ , truncation=lowercase_ )
lowercase =examples['''label''']
return result
with training_args.main_process_first(desc='''dataset map pre-processing''' ):
lowercase =raw_datasets.map(
lowercase_ , batched=lowercase_ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on dataset''' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
lowercase =raw_datasets['''train''']
if data_args.max_train_samples is not None:
lowercase =train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
lowercase =raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
lowercase =eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('''--do_predict requires a test dataset''' )
lowercase =raw_datasets['''test''']
if data_args.max_predict_samples is not None:
lowercase =predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowercase_ ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase_ : EvalPrediction ):
lowercase =p.predictions[0] if isinstance(p.predictions , lowercase_ ) else p.predictions
lowercase =np.argmax(lowercase_ , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowercase =default_data_collator
elif training_args.fpaa:
lowercase =DataCollatorWithPadding(lowercase_ , pad_to_multiple_of=8 )
else:
lowercase =None
# Initialize our Trainer
lowercase =Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase_ , tokenizer=lowercase_ , data_collator=lowercase_ , )
# Training
if training_args.do_train:
lowercase =None
if training_args.resume_from_checkpoint is not None:
lowercase =training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase =last_checkpoint
lowercase =trainer.train(resume_from_checkpoint=lowercase_ )
lowercase =train_result.metrics
lowercase =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_ )
)
lowercase =min(lowercase_ , len(lowercase_ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''' , lowercase_ )
trainer.save_metrics('''train''' , lowercase_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase =trainer.evaluate(eval_dataset=lowercase_ )
lowercase =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase_ )
lowercase =min(lowercase_ , len(lowercase_ ) )
trainer.log_metrics('''eval''' , lowercase_ )
trainer.save_metrics('''eval''' , lowercase_ )
if training_args.do_predict:
logger.info('''*** Predict ***''' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
lowercase =predict_dataset.remove_columns('''label''' )
lowercase =trainer.predict(lowercase_ , metric_key_prefix='''predict''' ).predictions
lowercase =np.argmax(lowercase_ , axis=1 )
lowercase =os.path.join(training_args.output_dir , '''predict_results_tabfact.txt''' )
if trainer.is_world_process_zero():
with open(lowercase_ , '''w''' ) as writer:
logger.info('''***** Predict Results *****''' )
writer.write('''index\tprediction\n''' )
for index, item in enumerate(lowercase_ ):
lowercase =label_list[item]
writer.write(f'{index}\t{item}\n' )
lowercase ={'''finetuned_from''': model_args.model_name_or_path, '''tasks''': '''text-classification'''}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase_ )
else:
trainer.create_model_card(**lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> str:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 72 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_UpperCAmelCase : Union[str, Any] = datasets.logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = '''\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
author = "Moosavi, Nafise Sadat and
Strube, Michael",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1060",
doi = "10.18653/v1/P16-1060",
pages = "632--642",
}
'''
_UpperCAmelCase : str = '''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''
_UpperCAmelCase : Optional[int] = '''
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting \'keep_singletons=False\', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
\'mentions\': mentions
\'muc\': MUC metric [Vilain et al, 1995]
\'bcub\': B-cubed [Bagga and Baldwin, 1998]
\'ceafe\': CEAFe [Luo et al., 2005]
\'lea\': LEA [Moosavi and Strube, 2016]
\'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric(\'coval\')
>>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',
... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',
... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',
... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',
... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',
... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''
def UpperCamelCase ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=True , lowercase_ : Optional[Any]=False , lowercase_ : int="dummy_doc" ) -> str:
'''simple docstring'''
lowercase ={doc: key_lines}
lowercase ={doc: sys_lines}
lowercase ={}
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase , lowercase =reader.get_doc_mentions(lowercase_ , key_doc_lines[doc] , lowercase_ )
key_singletons_num += singletons_num
if NP_only or min_span:
lowercase =reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
lowercase , lowercase =reader.get_doc_mentions(lowercase_ , sys_doc_lines[doc] , lowercase_ )
sys_singletons_num += singletons_num
if NP_only or min_span:
lowercase =reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
if remove_nested:
lowercase , lowercase =reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
lowercase , lowercase =reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
lowercase =reader.get_mention_assignments(lowercase_ , lowercase_ )
lowercase =reader.get_mention_assignments(lowercase_ , lowercase_ )
lowercase =(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
f'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}' )
logger.info(
'''Number of resulting singleton clusters in the key '''
f'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}' )
if not keep_singletons:
logger.info(
f'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
'''files, respectively''' )
return doc_coref_infos
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Tuple ) -> Dict:
'''simple docstring'''
lowercase =get_coref_infos(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase ={}
lowercase =0
lowercase =0
for name, metric in metrics:
lowercase , lowercase , lowercase =evaluator.evaluate_documents(lowercase_ , lowercase_ , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({f'{name}/recall': recall, f'{name}/precision': precision, f'{name}/f1': fa} )
logger.info(
name.ljust(1_0 ) , f'Recall: {recall * 1_0_0:.2f}' , f' Precision: {precision * 1_0_0:.2f}' , f' F1: {fa * 1_0_0:.2f}' , )
if conll_subparts_num == 3:
lowercase =(conll / 3) * 1_0_0
logger.info(f'CoNLL score: {conll:.2f}' )
output_scores.update({'''conll_score''': conll} )
return output_scores
def UpperCamelCase ( lowercase_ : Any ) -> List[Any]:
'''simple docstring'''
lowercase =False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
lowercase =line.split()[5]
if not parse_col == "-":
lowercase =True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Sequence(datasets.Value('''string''' ) ),
} ) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False ):
lowercase =[
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
lowercase =util.check_gold_parse_annotation(snake_case_ )
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''' )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
lowercase =evaluate(
key_lines=snake_case_ , sys_lines=snake_case_ , metrics=snake_case_ , NP_only=snake_case_ , remove_nested=snake_case_ , keep_singletons=snake_case_ , min_span=snake_case_ , )
return score
| 72 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Union[str, Any]=1_0 ) -> List[Any]:
'''simple docstring'''
lowercase =[]
for _ in range(lowercase_ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : str=1_0 ) -> Dict:
'''simple docstring'''
lowercase =[]
for step in range(lowercase_ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase =os.path.join(lowercase_ , '''schedule.bin''' )
torch.save(scheduler.state_dict() , lowercase_ )
lowercase =torch.load(lowercase_ )
scheduler.load_state_dict(lowercase_ )
return lrs
@require_torch
class __magic_name__ ( unittest.TestCase ):
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for a, b in zip(snake_case_ , snake_case_ ):
self.assertAlmostEqual(snake_case_ , snake_case_ , delta=snake_case_ )
def _A( self ):
lowercase =torch.tensor([0.1, -0.2, -0.1] , requires_grad=snake_case_ )
lowercase =torch.tensor([0.4, 0.2, -0.5] )
lowercase =nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
lowercase =AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(1_00 ):
lowercase =criterion(snake_case_ , snake_case_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def _A( self ):
lowercase =torch.tensor([0.1, -0.2, -0.1] , requires_grad=snake_case_ )
lowercase =torch.tensor([0.4, 0.2, -0.5] )
lowercase =nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
lowercase =Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=snake_case_ , weight_decay=0.0 , relative_step=snake_case_ , scale_parameter=snake_case_ , warmup_init=snake_case_ , )
for _ in range(10_00 ):
lowercase =criterion(snake_case_ , snake_case_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class __magic_name__ ( unittest.TestCase ):
UpperCamelCase__ = nn.Linear(50 , 50 ) if is_torch_available() else None
UpperCamelCase__ = AdamW(m.parameters() , lr=1_0.0 ) if is_torch_available() else None
UpperCamelCase__ = 10
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for a, b in zip(snake_case_ , snake_case_ ):
self.assertAlmostEqual(snake_case_ , snake_case_ , delta=snake_case_ , msg=snake_case_ )
def _A( self ):
lowercase ={'''num_warmup_steps''': 2, '''num_training_steps''': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
lowercase ={
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'''num_warmup_steps''': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, '''num_cycles''': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
[0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
),
get_inverse_sqrt_schedule: (
{'''num_warmup_steps''': 2},
[0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
),
}
for scheduler_func, data in scheds.items():
lowercase , lowercase =data
lowercase =scheduler_func(self.optimizer , **snake_case_ )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
lowercase =unwrap_schedule(snake_case_ , self.num_steps )
self.assertListAlmostEqual(
snake_case_ , snake_case_ , tol=1E-2 , msg=f'failed for {scheduler_func} in normal scheduler' , )
lowercase =scheduler_func(self.optimizer , **snake_case_ )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(snake_case_ ) # wrap to test picklability of the schedule
lowercase =unwrap_and_save_reload_schedule(snake_case_ , self.num_steps )
self.assertListEqual(snake_case_ , snake_case_ , msg=f'failed for {scheduler_func} in save and reload' )
class __magic_name__ :
def __init__( self , snake_case_ ):
lowercase =fn
def __call__( self , *snake_case_ , **snake_case_ ):
return self.fn(*snake_case_ , **snake_case_ )
@classmethod
def _A( self , snake_case_ ):
lowercase =list(map(self , scheduler.lr_lambdas ) )
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(lowercase_ , lowercase_ ):
return 0
elif n == 2:
return 1
else:
lowercase =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
lowercase =0
lowercase =2
while digits < n:
index += 1
lowercase =len(str(fibonacci(lowercase_ ) ) )
return index
def UpperCamelCase ( lowercase_ : int = 1_0_0_0 ) -> int:
'''simple docstring'''
return fibonacci_digits_index(lowercase_ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 72 | 1 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def UpperCamelCase ( lowercase_ : str ) -> Dict:
'''simple docstring'''
def decorator(lowercase_ : Optional[Any] ):
lowercase =getattr(lowercase_ , '''handle_key''' , [] )
handle += [key]
setattr(lowercase_ , '''handle_key''' , lowercase_ )
return func
return decorator
def UpperCamelCase ( *lowercase_ : List[str] ) -> List[Any]:
'''simple docstring'''
def decorator(lowercase_ : Optional[int] ):
lowercase =getattr(lowercase_ , '''handle_key''' , [] )
handle += keys
setattr(lowercase_ , '''handle_key''' , lowercase_ )
return func
return decorator
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __new__( cls , snake_case_ , snake_case_ , snake_case_ ):
lowercase =super().__new__(cls , snake_case_ , snake_case_ , snake_case_ )
if not hasattr(snake_case_ , '''key_handler''' ):
setattr(snake_case_ , '''key_handler''' , {} )
setattr(snake_case_ , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
lowercase =getattr(snake_case_ , '''handle_key''' , [] )
for key in handled_keys:
lowercase =value
return new_cls
@staticmethod
def _A( cls ):
lowercase =get_character()
if char != KEYMAP["undefined"]:
lowercase =ord(snake_case_ )
lowercase =cls.key_handler.get(snake_case_ )
if handler:
lowercase =char
return handler(cls )
else:
return None
def UpperCamelCase ( cls : Optional[Any] ) -> Any:
'''simple docstring'''
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 72 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Any = {
'''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'marian'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , snake_case_=5_81_01 , snake_case_=None , snake_case_=10_24 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=True , snake_case_=True , snake_case_="gelu" , snake_case_=10_24 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=5_81_00 , snake_case_=False , snake_case_=5_81_00 , snake_case_=0 , snake_case_=0 , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =decoder_vocab_size or vocab_size
lowercase =max_position_embeddings
lowercase =d_model
lowercase =encoder_ffn_dim
lowercase =encoder_layers
lowercase =encoder_attention_heads
lowercase =decoder_ffn_dim
lowercase =decoder_layers
lowercase =decoder_attention_heads
lowercase =dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =activation_function
lowercase =init_std
lowercase =encoder_layerdrop
lowercase =decoder_layerdrop
lowercase =use_cache
lowercase =encoder_layers
lowercase =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase =share_encoder_decoder_embeddings
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , decoder_start_token_id=snake_case_ , forced_eos_token_id=snake_case_ , **snake_case_ , )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase ={0: '''batch'''}
lowercase ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
else:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super().outputs
else:
lowercase =super(snake_case_ , self ).outputs
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Generate decoder inputs
lowercase =seq_length if not self.use_past else 1
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
lowercase =dict(**snake_case_ , **snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
lowercase =common_inputs['''decoder_input_ids'''].shape[1]
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =decoder_seq_length + 3
lowercase =(
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase =torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_ , snake_case_ )] , dim=1 )
lowercase =[]
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase , lowercase =self.num_layers
lowercase =min(snake_case_ , snake_case_ )
lowercase =max(snake_case_ , snake_case_ ) - min_num_layers
lowercase ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(snake_case_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
) )
# TODO: test this.
lowercase =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(snake_case_ , snake_case_ ):
common_inputs["past_key_values"].append((torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase =seqlen + 2
lowercase , lowercase =self.num_layers
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =common_inputs['''attention_mask'''].dtype
lowercase =torch.cat(
[common_inputs['''attention_mask'''], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
lowercase =[
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(snake_case_ )
]
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase =tokenizer.num_special_tokens_to_add(snake_case_ )
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
lowercase =[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase =dict(tokenizer(snake_case_ , return_tensors=snake_case_ ) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
else:
lowercase =self._generate_dummy_inputs_for_causal_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
return common_inputs
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super()._flatten_past_key_values_(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
lowercase =super(snake_case_ , self )._flatten_past_key_values_(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
@property
def _A( self ):
return 1E-4
| 72 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'canine'
def __init__( self , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_=30_72 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1_63_84 , snake_case_=16 , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=0 , snake_case_=0XE000 , snake_case_=0XE001 , snake_case_=4 , snake_case_=4 , snake_case_=8 , snake_case_=1_63_84 , snake_case_=1_28 , **snake_case_ , ):
super().__init__(pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
lowercase =max_position_embeddings
lowercase =hidden_size
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =intermediate_size
lowercase =hidden_act
lowercase =hidden_dropout_prob
lowercase =attention_probs_dropout_prob
lowercase =initializer_range
lowercase =type_vocab_size
lowercase =layer_norm_eps
# Character config:
lowercase =downsampling_rate
lowercase =upsampling_kernel_size
lowercase =num_hash_functions
lowercase =num_hash_buckets
lowercase =local_transformer_stride
| 72 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCAmelCase : Dict = parse(importlib.metadata.version('''torch'''))
def UpperCamelCase ( lowercase_ : Union[str, Version] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
'''simple docstring'''
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f'`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}' )
lowercase =STR_OPERATION_TO_FUNC[operation]
if isinstance(lowercase_ , lowercase_ ):
lowercase =parse(importlib.metadata.version(lowercase_ ) )
return operation(lowercase_ , parse(lowercase_ ) )
def UpperCamelCase ( lowercase_ : str , lowercase_ : str ) -> Union[str, Any]:
'''simple docstring'''
return compare_versions(lowercase_ , lowercase_ , lowercase_ )
| 72 | 1 |
'''simple docstring'''
from __future__ import annotations
_UpperCAmelCase : str = '''Muhammad Umer Farooq'''
_UpperCAmelCase : Any = '''MIT'''
_UpperCAmelCase : Tuple = '''1.0.0'''
_UpperCAmelCase : List[str] = '''Muhammad Umer Farooq'''
_UpperCAmelCase : Optional[int] = '''contact@muhammadumerfarooq.me'''
_UpperCAmelCase : str = '''Alpha'''
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , snake_case_ ):
super().__init__()
lowercase =[]
lowercase =domain
def _A( self , snake_case_ , snake_case_ ):
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
lowercase =parse.urljoin(self.domain , snake_case_ )
self.urls.append(snake_case_ )
def UpperCamelCase ( lowercase_ : str ) -> str:
'''simple docstring'''
return ".".join(get_sub_domain_name(lowercase_ ).split('''.''' )[-2:] )
def UpperCamelCase ( lowercase_ : str ) -> str:
'''simple docstring'''
return parse.urlparse(lowercase_ ).netloc
def UpperCamelCase ( lowercase_ : str = "https://github.com" ) -> list[str]:
'''simple docstring'''
lowercase =get_domain_name(lowercase_ )
# Initialize the parser
lowercase =Parser(lowercase_ )
try:
# Open URL
lowercase =requests.get(lowercase_ )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
lowercase =set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
lowercase =requests.get(lowercase_ )
# Get the valid email.
lowercase =re.findall('''[a-zA-Z0-9]+@''' + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(lowercase_ )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(lowercase_ )
if __name__ == "__main__":
_UpperCAmelCase : List[str] = emails_from_url('''https://github.com''')
print(F"""{len(emails)} emails found:""")
print('''\n'''.join(sorted(emails)))
| 72 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
_UpperCAmelCase : int = [8, 5, 9, 7]
_UpperCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_UpperCAmelCase : Union[str, Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =claim_vector
lowercase =allocated_resources_table
lowercase =maximum_claim_table
def _A( self ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _A( self ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _A( self ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(snake_case_ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _A( self ):
return {self.__need().index(snake_case_ ): i for i in self.__need()}
def _A( self , **snake_case_ ):
lowercase =self.__need()
lowercase =self.__allocated_resources_table
lowercase =self.__available_resources()
lowercase =self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('''_''' * 50 + '''\n''' )
while need_list:
lowercase =False
for each_need in need_list:
lowercase =True
for index, need in enumerate(snake_case_ ):
if need > available_resources[index]:
lowercase =False
break
if execution:
lowercase =True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowercase =original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(snake_case_ )
# update available/freed resources stack
lowercase =np.array(snake_case_ ) + np.array(
alloc_resources_table[process_number] )
print(
'''Updated available resource stack for processes: '''
+ ''' '''.join([str(snake_case_ ) for x in available_resources] ) )
break
if safe:
print('''The process is in a safe state.\n''' )
else:
print('''System in unsafe state. Aborting...\n''' )
break
def _A( self ):
print(''' ''' * 9 + '''Allocated Resource Table''' )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(snake_case_ ) + 1}'
+ ''' '''.join(f'{it:>8}' for it in item )
+ '''\n''' )
print(''' ''' * 9 + '''System Resource Table''' )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(snake_case_ ) + 1}'
+ ''' '''.join(f'{it:>8}' for it in item )
+ '''\n''' )
print(
'''Current Usage by Active Processes: '''
+ ''' '''.join(str(snake_case_ ) for x in self.__claim_vector ) )
print(
'''Initial Available Resources: '''
+ ''' '''.join(str(snake_case_ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''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 __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self , snake_case_ , snake_case_ , snake_case_ = None , snake_case_ = None ):
super().__init__()
lowercase =pad_token_id
lowercase =max_length
lowercase =vocab
lowercase =merges
lowercase =BytePairTokenizer(snake_case_ , snake_case_ , sequence_length=snake_case_ )
@classmethod
def _A( cls , snake_case_ , *snake_case_ , **snake_case_ ):
lowercase =[''' '''.join(snake_case_ ) for m in tokenizer.bpe_ranks.keys()]
lowercase =tokenizer.get_vocab()
return cls(snake_case_ , snake_case_ , *snake_case_ , **snake_case_ )
@classmethod
def _A( cls , snake_case_ , *snake_case_ , **snake_case_ ):
lowercase =GPTaTokenizer.from_pretrained(snake_case_ , *snake_case_ , **snake_case_ )
return cls.from_tokenizer(snake_case_ , *snake_case_ , **snake_case_ )
@classmethod
def _A( cls , snake_case_ ):
return cls(**snake_case_ )
def _A( self ):
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def _A( self , snake_case_ , snake_case_ = None ):
lowercase =self.tf_tokenizer(snake_case_ )
lowercase =tf.ones_like(snake_case_ )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowercase =max_length if max_length is not None else self.max_length
if max_length is not None:
lowercase , lowercase =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}
| 72 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase : Dict = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
_UpperCAmelCase : Dict = '''\
@inproceedings{banarjee2005,
title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
author = {Banerjee, Satanjeev and Lavie, Alon},
booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
month = jun,
year = {2005},
address = {Ann Arbor, Michigan},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W05-0909},
pages = {65--72},
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.
METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''
_UpperCAmelCase : Tuple = '''
Computes METEOR score of translated segments against one or more references.
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
\'meteor\': meteor score.
Examples:
>>> meteor = datasets.load_metric(\'meteor\')
>>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
>>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
>>> results = meteor.compute(predictions=predictions, references=references)
>>> print(round(results["meteor"], 4))
0.6944
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''] , reference_urls=[
'''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
'''https://en.wikipedia.org/wiki/METEOR''',
] , )
def _A( self , snake_case_ ):
import nltk
nltk.download('''wordnet''' )
if NLTK_VERSION >= version.Version('''3.6.5''' ):
nltk.download('''punkt''' )
if NLTK_VERSION >= version.Version('''3.6.6''' ):
nltk.download('''omw-1.4''' )
def _A( self , snake_case_ , snake_case_ , snake_case_=0.9 , snake_case_=3 , snake_case_=0.5 ):
if NLTK_VERSION >= version.Version('''3.6.5''' ):
lowercase =[
meteor_score.single_meteor_score(
word_tokenize(snake_case_ ) , word_tokenize(snake_case_ ) , alpha=snake_case_ , beta=snake_case_ , gamma=snake_case_ )
for ref, pred in zip(snake_case_ , snake_case_ )
]
else:
lowercase =[
meteor_score.single_meteor_score(snake_case_ , snake_case_ , alpha=snake_case_ , beta=snake_case_ , gamma=snake_case_ )
for ref, pred in zip(snake_case_ , snake_case_ )
]
return {"meteor": np.mean(snake_case_ )}
| 72 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = OrderedDict(
[
('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
('''beit''', '''BeitFeatureExtractor'''),
('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
('''clap''', '''ClapFeatureExtractor'''),
('''clip''', '''CLIPFeatureExtractor'''),
('''clipseg''', '''ViTFeatureExtractor'''),
('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
('''convnext''', '''ConvNextFeatureExtractor'''),
('''cvt''', '''ConvNextFeatureExtractor'''),
('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
('''data2vec-vision''', '''BeitFeatureExtractor'''),
('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
('''deit''', '''DeiTFeatureExtractor'''),
('''detr''', '''DetrFeatureExtractor'''),
('''dinat''', '''ViTFeatureExtractor'''),
('''donut-swin''', '''DonutFeatureExtractor'''),
('''dpt''', '''DPTFeatureExtractor'''),
('''encodec''', '''EncodecFeatureExtractor'''),
('''flava''', '''FlavaFeatureExtractor'''),
('''glpn''', '''GLPNFeatureExtractor'''),
('''groupvit''', '''CLIPFeatureExtractor'''),
('''hubert''', '''Wav2Vec2FeatureExtractor'''),
('''imagegpt''', '''ImageGPTFeatureExtractor'''),
('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
('''levit''', '''LevitFeatureExtractor'''),
('''maskformer''', '''MaskFormerFeatureExtractor'''),
('''mctct''', '''MCTCTFeatureExtractor'''),
('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
('''mobilevit''', '''MobileViTFeatureExtractor'''),
('''nat''', '''ViTFeatureExtractor'''),
('''owlvit''', '''OwlViTFeatureExtractor'''),
('''perceiver''', '''PerceiverFeatureExtractor'''),
('''poolformer''', '''PoolFormerFeatureExtractor'''),
('''regnet''', '''ConvNextFeatureExtractor'''),
('''resnet''', '''ConvNextFeatureExtractor'''),
('''segformer''', '''SegformerFeatureExtractor'''),
('''sew''', '''Wav2Vec2FeatureExtractor'''),
('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
('''speecht5''', '''SpeechT5FeatureExtractor'''),
('''swiftformer''', '''ViTFeatureExtractor'''),
('''swin''', '''ViTFeatureExtractor'''),
('''swinv2''', '''ViTFeatureExtractor'''),
('''table-transformer''', '''DetrFeatureExtractor'''),
('''timesformer''', '''VideoMAEFeatureExtractor'''),
('''tvlt''', '''TvltFeatureExtractor'''),
('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
('''van''', '''ConvNextFeatureExtractor'''),
('''videomae''', '''VideoMAEFeatureExtractor'''),
('''vilt''', '''ViltFeatureExtractor'''),
('''vit''', '''ViTFeatureExtractor'''),
('''vit_mae''', '''ViTFeatureExtractor'''),
('''vit_msn''', '''ViTFeatureExtractor'''),
('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
('''whisper''', '''WhisperFeatureExtractor'''),
('''xclip''', '''CLIPFeatureExtractor'''),
('''yolos''', '''YolosFeatureExtractor'''),
]
)
_UpperCAmelCase : Any = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def UpperCamelCase ( lowercase_ : str ) -> int:
'''simple docstring'''
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowercase =model_type_to_module_name(lowercase_ )
lowercase =importlib.import_module(f'.{module_name}' , '''transformers.models''' )
try:
return getattr(lowercase_ , lowercase_ )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(lowercase_ , '''__name__''' , lowercase_ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowercase =importlib.import_module('''transformers''' )
if hasattr(lowercase_ , lowercase_ ):
return getattr(lowercase_ , lowercase_ )
return None
def UpperCamelCase ( lowercase_ : Union[str, os.PathLike] , lowercase_ : Optional[Union[str, os.PathLike]] = None , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : Optional[Dict[str, str]] = None , lowercase_ : Optional[Union[bool, str]] = None , lowercase_ : Optional[str] = None , lowercase_ : bool = False , **lowercase_ : List[str] , ) -> List[str]:
'''simple docstring'''
lowercase =get_file_from_repo(
lowercase_ , lowercase_ , cache_dir=lowercase_ , force_download=lowercase_ , resume_download=lowercase_ , proxies=lowercase_ , use_auth_token=lowercase_ , revision=lowercase_ , local_files_only=lowercase_ , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(lowercase_ , encoding='''utf-8''' ) as reader:
return json.load(lowercase_ )
class __magic_name__ :
def __init__( self ):
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(snake_case_ )
def _A( cls , snake_case_ , **snake_case_ ):
lowercase =kwargs.pop('''config''' , snake_case_ )
lowercase =kwargs.pop('''trust_remote_code''' , snake_case_ )
lowercase =True
lowercase , lowercase =FeatureExtractionMixin.get_feature_extractor_dict(snake_case_ , **snake_case_ )
lowercase =config_dict.get('''feature_extractor_type''' , snake_case_ )
lowercase =None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
lowercase =config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(snake_case_ , snake_case_ ):
lowercase =AutoConfig.from_pretrained(snake_case_ , **snake_case_ )
# It could be in `config.feature_extractor_type``
lowercase =getattr(snake_case_ , '''feature_extractor_type''' , snake_case_ )
if hasattr(snake_case_ , '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
lowercase =config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
lowercase =feature_extractor_class_from_name(snake_case_ )
lowercase =feature_extractor_auto_map is not None
lowercase =feature_extractor_class is not None or type(snake_case_ ) in FEATURE_EXTRACTOR_MAPPING
lowercase =resolve_trust_remote_code(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if has_remote_code and trust_remote_code:
lowercase =get_class_from_dynamic_module(
snake_case_ , snake_case_ , **snake_case_ )
lowercase =kwargs.pop('''code_revision''' , snake_case_ )
if os.path.isdir(snake_case_ ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(snake_case_ , **snake_case_ )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(snake_case_ , **snake_case_ )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(snake_case_ ) in FEATURE_EXTRACTOR_MAPPING:
lowercase =FEATURE_EXTRACTOR_MAPPING[type(snake_case_ )]
return feature_extractor_class.from_dict(snake_case_ , **snake_case_ )
raise ValueError(
f'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
f'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}' )
@staticmethod
def _A( snake_case_ , snake_case_ ):
FEATURE_EXTRACTOR_MAPPING.register(snake_case_ , snake_case_ )
| 72 |
'''simple docstring'''
import sys
_UpperCAmelCase : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCamelCase ( lowercase_ : str = N ) -> int:
'''simple docstring'''
lowercase =-sys.maxsize - 1
for i in range(len(lowercase_ ) - 1_2 ):
lowercase =1
for j in range(1_3 ):
product *= int(n[i + j] )
if product > largest_product:
lowercase =product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : Dict ) -> List[str]: # noqa: E741
'''simple docstring'''
lowercase =len(lowercase_ )
lowercase =0
lowercase =[0] * n
lowercase =[False] * n
lowercase =[False] * n
def dfs(lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Optional[int] ):
if parent == root:
out_edge_count += 1
lowercase =True
lowercase =at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
lowercase =dfs(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase =min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
lowercase =True
# AP found via cycle
if at == low[to]:
lowercase =True
else:
lowercase =min(low[at] , lowercase_ )
return out_edge_count
for i in range(lowercase_ ):
if not visited[i]:
lowercase =0
lowercase =dfs(lowercase_ , lowercase_ , -1 , lowercase_ )
lowercase =out_edge_count > 1
for x in range(len(lowercase_ ) ):
if is_art[x] is True:
print(lowercase_ )
# Adjacency list of graph
_UpperCAmelCase : Any = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 72 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 72 | 1 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def UpperCamelCase ( lowercase_ : Any , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : Union[str, Any]=None ) -> Dict:
'''simple docstring'''
lowercase =(path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
lowercase , lowercase =True, True
lowercase =dfs(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
return path
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
lowercase =0
lowercase =-1
for i in range(lowercase_ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
lowercase =i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : List[str] ) -> Any:
'''simple docstring'''
lowercase =[[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
lowercase , lowercase =check_circuit_or_path(lowercase_ , lowercase_ )
if check == 3:
print('''graph is not Eulerian''' )
print('''no path''' )
return
lowercase =1
if check == 2:
lowercase =odd_node
print('''graph has a Euler path''' )
if check == 1:
print('''graph has a Euler cycle''' )
lowercase =dfs(lowercase_ , lowercase_ , lowercase_ )
print(lowercase_ )
def UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
lowercase ={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
lowercase ={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
lowercase ={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
lowercase ={1: [2, 3], 2: [1, 3], 3: [1, 2]}
lowercase ={
1: [],
2: []
# all degree is zero
}
lowercase =1_0
check_euler(lowercase_ , lowercase_ )
check_euler(lowercase_ , lowercase_ )
check_euler(lowercase_ , lowercase_ )
check_euler(lowercase_ , lowercase_ )
check_euler(lowercase_ , lowercase_ )
if __name__ == "__main__":
main()
| 72 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
'''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'encodec'
def __init__( self , snake_case_=[1.5, 3.0, 6.0, 12.0, 24.0] , snake_case_=2_40_00 , snake_case_=1 , snake_case_=False , snake_case_=None , snake_case_=None , snake_case_=1_28 , snake_case_=32 , snake_case_=1 , snake_case_=[8, 5, 4, 2] , snake_case_="weight_norm" , snake_case_=7 , snake_case_=7 , snake_case_=3 , snake_case_=2 , snake_case_=True , snake_case_="reflect" , snake_case_=2 , snake_case_=2 , snake_case_=1.0 , snake_case_=10_24 , snake_case_=None , snake_case_=True , **snake_case_ , ):
lowercase =target_bandwidths
lowercase =sampling_rate
lowercase =audio_channels
lowercase =normalize
lowercase =chunk_length_s
lowercase =overlap
lowercase =hidden_size
lowercase =num_filters
lowercase =num_residual_layers
lowercase =upsampling_ratios
lowercase =norm_type
lowercase =kernel_size
lowercase =last_kernel_size
lowercase =residual_kernel_size
lowercase =dilation_growth_rate
lowercase =use_causal_conv
lowercase =pad_mode
lowercase =compress
lowercase =num_lstm_layers
lowercase =trim_right_ratio
lowercase =codebook_size
lowercase =codebook_dim if codebook_dim is not None else hidden_size
lowercase =use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' )
super().__init__(**snake_case_ )
@property
def _A( self ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A( self ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A( self ):
lowercase =np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A( self ):
return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 72 | 1 |
'''simple docstring'''
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 UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
assert isinstance(lowercase_ , lowercase_ )
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 UpperCamelCase ( lowercase_ : Optional[int] , lowercase_ : Dict , lowercase_ : int ) -> Tuple:
'''simple docstring'''
lowercase =tmp_path / '''cache'''
lowercase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowercase =JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@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 UpperCamelCase ( lowercase_ : int , lowercase_ : str , lowercase_ : List[Any] ) -> int:
'''simple docstring'''
lowercase =tmp_path / '''cache'''
lowercase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase =features.copy() if features else default_expected_features
lowercase =(
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase =JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def UpperCamelCase ( lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : List[str] ) -> Tuple:
'''simple docstring'''
lowercase =tmp_path / '''cache'''
lowercase ={'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
lowercase =features.copy() if features else default_expected_features
lowercase =(
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase =JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
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 UpperCamelCase ( lowercase_ : int , lowercase_ : Dict ) -> List[Any]:
'''simple docstring'''
lowercase ={'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
lowercase =features.copy()
lowercase =(
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase =tmp_path / '''cache'''
lowercase =JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
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 UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : List[Any] ) -> Tuple:
'''simple docstring'''
lowercase =tmp_path / '''cache'''
lowercase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase =JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , split=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def UpperCamelCase ( lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
if issubclass(lowercase_ , lowercase_ ):
lowercase =jsonl_path
elif issubclass(lowercase_ , lowercase_ ):
lowercase =[jsonl_path]
lowercase =tmp_path / '''cache'''
lowercase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase =JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Dict=("train",) ) -> List[Any]:
'''simple docstring'''
assert isinstance(lowercase_ , lowercase_ )
for split in splits:
lowercase =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 UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Any ) -> List[Any]:
'''simple docstring'''
lowercase =tmp_path / '''cache'''
lowercase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowercase =JsonDatasetReader({'''train''': jsonl_path} , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@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 UpperCamelCase ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : Dict ) -> Tuple:
'''simple docstring'''
lowercase =tmp_path / '''cache'''
lowercase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase =features.copy() if features else default_expected_features
lowercase =(
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase =JsonDatasetReader({'''train''': jsonl_path} , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def UpperCamelCase ( lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
if split:
lowercase ={split: jsonl_path}
else:
lowercase ='''train'''
lowercase ={'''train''': jsonl_path, '''test''': jsonl_path}
lowercase =tmp_path / '''cache'''
lowercase ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowercase =JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def UpperCamelCase ( lowercase_ : Any ) -> Tuple:
'''simple docstring'''
return json.load(lowercase_ )
def UpperCamelCase ( lowercase_ : Optional[int] ) -> str:
'''simple docstring'''
return [json.loads(lowercase_ ) for line in buffer]
class __magic_name__ :
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case_ , snake_case_ , lines=snake_case_ ).write()
buffer.seek(0 )
lowercase =load_json_function(snake_case_ )
assert isinstance(snake_case_ , snake_case_ )
assert isinstance(exported_content[0] , snake_case_ )
assert len(snake_case_ ) == 10
@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 _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case_ , snake_case_ , lines=snake_case_ , orient=snake_case_ ).write()
buffer.seek(0 )
lowercase =load_json(snake_case_ )
assert isinstance(snake_case_ , snake_case_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(snake_case_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(snake_case_ ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def _A( self , snake_case_ , snake_case_ , snake_case_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case_ , snake_case_ , lines=snake_case_ , num_proc=2 ).write()
buffer.seek(0 )
lowercase =load_json_function(snake_case_ )
assert isinstance(snake_case_ , snake_case_ )
assert isinstance(exported_content[0] , snake_case_ )
assert len(snake_case_ ) == 10
@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 _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case_ , snake_case_ , lines=snake_case_ , orient=snake_case_ , num_proc=2 ).write()
buffer.seek(0 )
lowercase =load_json(snake_case_ )
assert isinstance(snake_case_ , snake_case_ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(snake_case_ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(snake_case_ ) == 10
def _A( self , snake_case_ ):
with pytest.raises(snake_case_ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case_ , snake_case_ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =tmp_path_factory.mktemp('''data''' ) / f'test.json.{extension}'
lowercase =str(shared_datadir / f'test_file.json.{extension}' )
JsonDatasetWriter(snake_case_ , snake_case_ , compression=snake_case_ ).write()
with fsspec.open(snake_case_ , '''rb''' , compression='''infer''' ) as f:
lowercase =f.read()
with fsspec.open(snake_case_ , '''rb''' , compression='''infer''' ) as f:
lowercase =f.read()
assert exported_content == original_content
| 72 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : int = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : int = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Dict = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_UpperCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 | 1 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
_UpperCAmelCase : Dict = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Dict ) -> Tuple:
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ['''integration''', '''unit'''] ):
continue
item.add_marker(pytest.mark.unit )
def UpperCamelCase ( lowercase_ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
config.addinivalue_line('''markers''' , '''torchaudio_latest: mark test to run with torchaudio>=0.12''' )
@pytest.fixture(autouse=lowercase_ )
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Optional[int] ) -> str:
'''simple docstring'''
lowercase =tmp_path_factory.getbasetemp() / '''cache'''
lowercase =test_hf_cache_home / '''datasets'''
lowercase =test_hf_cache_home / '''metrics'''
lowercase =test_hf_cache_home / '''modules'''
monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' , str(lowercase_ ) )
monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' , str(lowercase_ ) )
monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' , str(lowercase_ ) )
lowercase =test_hf_datasets_cache / '''downloads'''
monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' , str(lowercase_ ) )
lowercase =test_hf_datasets_cache / '''downloads''' / '''extracted'''
monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' , str(lowercase_ ) )
@pytest.fixture(autouse=lowercase_ , scope='''session''' )
def UpperCamelCase ( ) -> Dict:
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=lowercase_ )
def UpperCamelCase ( lowercase_ : List[Any] ) -> Any:
'''simple docstring'''
monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' , lowercase_ )
@pytest.fixture
def UpperCamelCase ( lowercase_ : str ) -> Optional[Any]:
'''simple docstring'''
monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' , lowercase_ )
| 72 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 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
_UpperCAmelCase : str = '''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 UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : List[str]=None ) -> List[str]:
'''simple docstring'''
require_version(deps[pkg] , lowercase_ )
| 72 |
'''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 __magic_name__ :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=6 , snake_case_=17 , snake_case_=23 , snake_case_=11 , snake_case_=True , ):
lowercase =parent
lowercase =batch_size
lowercase =seq_length
lowercase =act_dim
lowercase =state_dim
lowercase =hidden_size
lowercase =max_length
lowercase =is_training
def _A( self ):
lowercase =floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =ids_tensor((self.batch_size, self.seq_length) , vocab_size=10_00 )
lowercase =random_attention_mask((self.batch_size, self.seq_length) )
lowercase =self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _A( self ):
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 , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =DecisionTransformerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , 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 ):
lowercase =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) =config_and_inputs
lowercase ={
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (DecisionTransformerModel,) if is_torch_available() else ()
UpperCamelCase__ = ()
UpperCamelCase__ = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
UpperCamelCase__ = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =DecisionTransformerModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def _A( self ):
self.config_tester.run_common_tests()
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
@slow
def _A( self ):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase =DecisionTransformerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase =[*signature.parameters.keys()]
lowercase =[
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def _A( self ):
lowercase =2 # number of steps of autoregressive prediction we will perform
lowercase =10 # defined by the RL environment, may be normalized
lowercase =DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
lowercase =model.to(snake_case_ )
lowercase =model.config
torch.manual_seed(0 )
lowercase =torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ) # env.reset()
lowercase =torch.tensor(
[[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]] , device=snake_case_ )
lowercase =torch.tensor(snake_case_ , device=snake_case_ , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowercase =state
lowercase =torch.zeros(1 , 0 , config.act_dim , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.zeros(1 , 0 , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.tensor(0 , device=snake_case_ , dtype=torch.long ).reshape(1 , 1 )
for step in range(snake_case_ ):
lowercase =torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=snake_case_ )] , dim=1 )
lowercase =torch.cat([rewards, torch.zeros(1 , 1 , device=snake_case_ )] , dim=1 )
lowercase =torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowercase , lowercase , lowercase =model(
states=snake_case_ , actions=snake_case_ , rewards=snake_case_ , returns_to_go=snake_case_ , timesteps=snake_case_ , attention_mask=snake_case_ , return_dict=snake_case_ , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
lowercase , lowercase , lowercase , lowercase =( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ),
1.0,
False,
{},
)
lowercase =action_pred[0, -1]
lowercase =torch.cat([states, state] , dim=1 )
lowercase =returns_to_go[0, -1] - reward
lowercase =torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowercase =torch.cat(
[timesteps, torch.ones((1, 1) , device=snake_case_ , dtype=torch.long ) * (step + 1)] , dim=1 )
| 72 | 1 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
_UpperCAmelCase : Dict = logging.getLogger(__name__)
def UpperCamelCase ( lowercase_ : Dict=2 , lowercase_ : List[str]=3 , lowercase_ : List[Any]=1_6 , lowercase_ : int = 1_0 , lowercase_ : int = 2 ) -> Dict:
'''simple docstring'''
def get_dataset(lowercase_ : int ):
lowercase =torch.randn(batch_size * n_batches , 1 )
return TensorDataset(lowercase_ , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
lowercase =get_dataset(lowercase_ )
lowercase =get_dataset(lowercase_ )
lowercase =DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4 )
lowercase =DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4 )
return (train_dataloader, valid_dataloader)
def UpperCamelCase ( lowercase_ : Any , lowercase_ : str , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : Union[str, Any]=None ) -> List[str]:
'''simple docstring'''
lowercase =[]
for epoch in range(lowercase_ ):
# Train quickly
model.train()
for batch in dataloader:
lowercase , lowercase =batch
lowercase =model(lowercase_ )
lowercase =torch.nn.functional.mse_loss(lowercase_ , lowercase_ )
accelerator.backward(lowercase_ )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class __magic_name__ ( nn.Module ):
def __init__( self ):
super().__init__()
lowercase =nn.Parameter(torch.randn(1 ) )
lowercase =nn.Parameter(torch.randn(1 ) )
def _A( self , snake_case_ ):
return x * self.a + self.b
class __magic_name__ ( unittest.TestCase ):
def _A( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
lowercase =DummyModel()
lowercase =torch.optim.Adam(params=model.parameters() , lr=1E-3 )
lowercase , lowercase =dummy_dataloaders()
lowercase =ProjectConfiguration(total_limit=1 , project_dir=snake_case_ , automatic_checkpoint_naming=snake_case_ )
# Train baseline
lowercase =Accelerator(project_config=snake_case_ )
lowercase , lowercase , lowercase , lowercase =accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) , 1 )
def _A( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
lowercase =DummyModel()
lowercase =torch.optim.Adam(params=model.parameters() , lr=1E-3 )
lowercase , lowercase =dummy_dataloaders()
# Train baseline
lowercase =Accelerator()
lowercase , lowercase , lowercase , lowercase =accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Save initial
lowercase =os.path.join(snake_case_ , '''initial''' )
accelerator.save_state(snake_case_ )
((lowercase) , (lowercase)) =model.a.item(), model.b.item()
lowercase =optimizer.state_dict()
lowercase =train(3 , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
((lowercase) , (lowercase)) =model.a.item(), model.b.item()
lowercase =optimizer.state_dict()
# Train partially
set_seed(42 )
lowercase =DummyModel()
lowercase =torch.optim.Adam(params=model.parameters() , lr=1E-3 )
lowercase , lowercase =dummy_dataloaders()
lowercase =Accelerator()
lowercase , lowercase , lowercase , lowercase =accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
accelerator.load_state(snake_case_ )
((lowercase) , (lowercase)) =model.a.item(), model.b.item()
lowercase =optimizer.state_dict()
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
lowercase =train(2 , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Save everything
lowercase =os.path.join(snake_case_ , '''checkpoint''' )
accelerator.save_state(snake_case_ )
# Load everything back in and make sure all states work
accelerator.load_state(snake_case_ )
test_rands += train(1 , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
((lowercase) , (lowercase)) =model.a.item(), model.b.item()
lowercase =optimizer.state_dict()
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
def _A( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
lowercase =DummyModel()
lowercase =torch.optim.Adam(params=model.parameters() , lr=1E-3 )
lowercase , lowercase =dummy_dataloaders()
lowercase =ProjectConfiguration(automatic_checkpoint_naming=snake_case_ )
# Train baseline
lowercase =Accelerator(project_dir=snake_case_ , project_config=snake_case_ )
lowercase , lowercase , lowercase , lowercase =accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Save initial
accelerator.save_state()
((lowercase) , (lowercase)) =model.a.item(), model.b.item()
lowercase =optimizer.state_dict()
lowercase =train(3 , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
((lowercase) , (lowercase)) =model.a.item(), model.b.item()
lowercase =optimizer.state_dict()
# Train partially
set_seed(42 )
lowercase =DummyModel()
lowercase =torch.optim.Adam(params=model.parameters() , lr=1E-3 )
lowercase , lowercase =dummy_dataloaders()
lowercase =ProjectConfiguration(iteration=1 , automatic_checkpoint_naming=snake_case_ )
lowercase =Accelerator(project_dir=snake_case_ , project_config=snake_case_ )
lowercase , lowercase , lowercase , lowercase =accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
accelerator.load_state(os.path.join(snake_case_ , '''checkpoints''' , '''checkpoint_0''' ) )
((lowercase) , (lowercase)) =model.a.item(), model.b.item()
lowercase =optimizer.state_dict()
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
lowercase =train(2 , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(snake_case_ , '''checkpoints''' , '''checkpoint_1''' ) )
test_rands += train(1 , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
((lowercase) , (lowercase)) =model.a.item(), model.b.item()
lowercase =optimizer.state_dict()
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
self.assertEqual(snake_case_ , snake_case_ )
def _A( self ):
lowercase =torch.tensor([1, 2, 3] )
lowercase =torch.tensor([2, 3, 4] )
lowercase =DummyModel()
lowercase =torch.optim.Adam(net.parameters() )
lowercase =Accelerator()
with self.assertRaises(snake_case_ ) as ve:
accelerator.register_for_checkpointing(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase =str(ve.exception )
self.assertTrue('''Item at index 0''' in message )
self.assertTrue('''Item at index 1''' in message )
self.assertFalse('''Item at index 2''' in message )
self.assertFalse('''Item at index 3''' in message )
def _A( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
lowercase =DummyModel()
lowercase =torch.optim.Adam(params=model.parameters() , lr=1E-3 )
lowercase =torch.optim.lr_scheduler.StepLR(snake_case_ , step_size=1 , gamma=0.99 )
lowercase , lowercase =dummy_dataloaders()
lowercase =ProjectConfiguration(automatic_checkpoint_naming=snake_case_ )
# Train baseline
lowercase =Accelerator(project_dir=snake_case_ , project_config=snake_case_ )
lowercase , lowercase , lowercase , lowercase , lowercase =accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Save initial
accelerator.save_state()
lowercase =scheduler.state_dict()
train(3 , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
self.assertNotEqual(snake_case_ , scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(snake_case_ , '''checkpoints''' , '''checkpoint_0''' ) )
self.assertEqual(snake_case_ , scheduler.state_dict() )
def _A( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
lowercase =DummyModel()
lowercase =ProjectConfiguration(automatic_checkpoint_naming=snake_case_ , total_limit=2 )
# Train baseline
lowercase =Accelerator(project_dir=snake_case_ , project_config=snake_case_ )
lowercase =accelerator.prepare(snake_case_ )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(snake_case_ , '''checkpoints''' , '''checkpoint_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case_ , '''checkpoints''' , '''checkpoint_9''' ) ) )
self.assertTrue(os.path.exists(os.path.join(snake_case_ , '''checkpoints''' , '''checkpoint_10''' ) ) )
@require_cuda
def _A( self ):
lowercase =['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
execute_subprocess_async(snake_case_ , env=os.environ.copy() )
if __name__ == "__main__":
_UpperCAmelCase : List[str] = '''/tmp/accelerate/state_checkpointing'''
_UpperCAmelCase : str = DummyModel()
_UpperCAmelCase : Optional[int] = torch.optim.Adam(params=model.parameters(), lr=1e-3)
_UpperCAmelCase : Tuple = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = dummy_dataloaders()
_UpperCAmelCase : List[str] = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
_UpperCAmelCase : Optional[int] = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
_UpperCAmelCase , _UpperCAmelCase : Tuple = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
_UpperCAmelCase : Optional[Any] = group['''params'''][0].device
break
assert param_device.type == accelerator.device.type
_UpperCAmelCase : List[Any] = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
for group in optimizer.param_groups:
_UpperCAmelCase : Any = group['''params'''][0].device
break
assert (
param_device.type == torch.device('''cpu''').type
), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
for group in optimizer.param_groups:
_UpperCAmelCase : int = group['''params'''][0].device
break
assert (
param_device.type == accelerator.device.type
), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone()
| 72 |
'''simple docstring'''
from math import pi, sqrt, tan
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or breadth < 0 or height < 0:
raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
return 4 * pi * radius**2
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
return 3 * pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cone() only accepts non-negative values''' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'''surface_area_conical_frustum() only accepts non-negative values''' )
lowercase =(height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
return 2 * pi * radius * (height + radius)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if torus_radius < 0 or tube_radius < 0:
raise ValueError('''surface_area_torus() only accepts non-negative values''' )
if torus_radius < tube_radius:
raise ValueError(
'''surface_area_torus() does not support spindle or self intersecting tori''' )
return 4 * pow(lowercase_ , 2 ) * torus_radius * tube_radius
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or width < 0:
raise ValueError('''area_rectangle() only accepts non-negative values''' )
return length * width
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''area_square() only accepts non-negative values''' )
return side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_triangle() only accepts non-negative values''' )
return (base * height) / 2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('''Given three sides do not form a triangle''' )
lowercase =(sidea + sidea + sidea) / 2
lowercase =sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_parallelogram() only accepts non-negative values''' )
return base * height
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if basea < 0 or basea < 0 or height < 0:
raise ValueError('''area_trapezium() only accepts non-negative values''' )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''area_circle() only accepts non-negative values''' )
return pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_x < 0 or radius_y < 0:
raise ValueError('''area_ellipse() only accepts non-negative values''' )
return pi * radius_x * radius_y
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('''area_rhombus() only accepts non-negative values''' )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase ( lowercase_ : int , lowercase_ : float ) -> float:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or sides < 3:
raise ValueError(
'''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
elif length < 0:
raise ValueError(
'''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('''[DEMO] Areas of various geometric shapes: \n''')
print(F"""Rectangle: {area_rectangle(10, 20) = }""")
print(F"""Square: {area_square(10) = }""")
print(F"""Triangle: {area_triangle(10, 10) = }""")
print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
print(F"""Rhombus: {area_rhombus(10, 20) = }""")
print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
print(F"""Circle: {area_circle(20) = }""")
print(F"""Ellipse: {area_ellipse(10, 20) = }""")
print('''\nSurface Areas of various geometric shapes: \n''')
print(F"""Cube: {surface_area_cube(20) = }""")
print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
print(F"""Sphere: {surface_area_sphere(20) = }""")
print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
print(F"""Cone: {surface_area_cone(10, 20) = }""")
print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
print(F"""Torus: {surface_area_torus(20, 10) = }""")
print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
print(F"""Square: {area_reg_polygon(4, 10) = }""")
print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : list[int] ) -> int:
'''simple docstring'''
if not numbers:
return 0
if not isinstance(lowercase_ , (list, tuple) ) or not all(
isinstance(lowercase_ , lowercase_ ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowercase =lowercase =lowercase =numbers[0]
for i in range(1 , len(lowercase_ ) ):
# update the maximum and minimum subarray products
lowercase =numbers[i]
if number < 0:
lowercase , lowercase =min_till_now, max_till_now
lowercase =max(lowercase_ , max_till_now * number )
lowercase =min(lowercase_ , min_till_now * number )
# update the maximum product found till now
lowercase =max(lowercase_ , lowercase_ )
return max_prod
| 72 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = BarthezTokenizer
UpperCamelCase__ = BarthezTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def _A( self ):
super().setUp()
lowercase =BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case_ )
lowercase =tokenizer
def _A( self ):
lowercase ='''<pad>'''
lowercase =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def _A( self ):
lowercase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(snake_case_ ) , 10_11_22 )
def _A( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def _A( self ):
lowercase =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowercase =[0, 57, 30_18, 7_03_07, 91, 2]
lowercase =self.tokenizer(
snake_case_ , max_length=len(snake_case_ ) , padding=snake_case_ , truncation=snake_case_ , return_tensors='''pt''' )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
lowercase =batch.input_ids.tolist()[0]
self.assertListEqual(snake_case_ , snake_case_ )
def _A( self ):
if not self.test_rust_tokenizer:
return
lowercase =self.get_tokenizer()
lowercase =self.get_rust_tokenizer()
lowercase ='''I was born in 92000, and this is falsé.'''
lowercase =tokenizer.tokenize(snake_case_ )
lowercase =rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =self.get_rust_tokenizer()
lowercase =tokenizer.encode(snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
@slow
def _A( self ):
# fmt: off
lowercase ={'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase =[
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=snake_case_ , )
| 72 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'unispeech'
def __init__( self , snake_case_=32 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_=30_72 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.02 , snake_case_=1E-5 , snake_case_="group" , snake_case_="gelu" , snake_case_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , snake_case_=(5, 2, 2, 2, 2, 2, 2) , snake_case_=(10, 3, 3, 3, 3, 2, 2) , snake_case_=False , snake_case_=1_28 , snake_case_=16 , snake_case_=False , snake_case_=True , snake_case_=0.05 , snake_case_=10 , snake_case_=2 , snake_case_=0.0 , snake_case_=10 , snake_case_=0 , snake_case_=3_20 , snake_case_=2 , snake_case_=0.1 , snake_case_=1_00 , snake_case_=2_56 , snake_case_=2_56 , snake_case_=0.1 , snake_case_="mean" , snake_case_=False , snake_case_=False , snake_case_=2_56 , snake_case_=80 , snake_case_=0 , snake_case_=1 , snake_case_=2 , snake_case_=0.5 , **snake_case_ , ):
super().__init__(**snake_case_ , pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ )
lowercase =hidden_size
lowercase =feat_extract_norm
lowercase =feat_extract_activation
lowercase =list(snake_case_ )
lowercase =list(snake_case_ )
lowercase =list(snake_case_ )
lowercase =conv_bias
lowercase =num_conv_pos_embeddings
lowercase =num_conv_pos_embedding_groups
lowercase =len(self.conv_dim )
lowercase =num_hidden_layers
lowercase =intermediate_size
lowercase =hidden_act
lowercase =num_attention_heads
lowercase =hidden_dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =feat_proj_dropout
lowercase =final_dropout
lowercase =layerdrop
lowercase =layer_norm_eps
lowercase =initializer_range
lowercase =num_ctc_classes
lowercase =vocab_size
lowercase =do_stable_layer_norm
lowercase =use_weighted_layer_sum
lowercase =classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase =apply_spec_augment
lowercase =mask_time_prob
lowercase =mask_time_length
lowercase =mask_time_min_masks
lowercase =mask_feature_prob
lowercase =mask_feature_length
lowercase =mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowercase =num_codevectors_per_group
lowercase =num_codevector_groups
lowercase =contrastive_logits_temperature
lowercase =feat_quantizer_dropout
lowercase =num_negatives
lowercase =codevector_dim
lowercase =proj_codevector_dim
lowercase =diversity_loss_weight
# ctc loss
lowercase =ctc_loss_reduction
lowercase =ctc_zero_infinity
# pretraining loss
lowercase =replace_prob
@property
def _A( self ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 72 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_text_model'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=5_02_44 , snake_case_=7_68 , snake_case_=64 , snake_case_=20_48 , snake_case_=12 , snake_case_=12 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gelu_new" , snake_case_=0 , snake_case_=False , snake_case_=0 , snake_case_=1 , snake_case_=False , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =hidden_size
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =use_cache
lowercase =eos_token_id
lowercase =decoder_start_token_id
# for backwards compatibility
lowercase =dense_act_fn
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , tie_word_embeddings=snake_case_ , is_decoder=snake_case_ , **snake_case_ , )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_vision_model'
def __init__( self , snake_case_=7_68 , snake_case_=7_68 , snake_case_=20_48 , snake_case_=64 , snake_case_=12 , snake_case_=12 , snake_case_="gelu_new" , snake_case_=1E-6 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=1E-10 , snake_case_=1.0 , snake_case_=40_96 , snake_case_=32 , snake_case_=1_28 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =hidden_size
lowercase =patch_embed_hidden_size
lowercase =d_ff
lowercase =dropout_rate
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =initializer_range
lowercase =initializer_factor
lowercase =attention_dropout
lowercase =layer_norm_eps
lowercase =dense_act_fn
lowercase =seq_len
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =d_kv
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct'
UpperCamelCase__ = True
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=1.0 , snake_case_=0.02 , snake_case_=False , snake_case_=False , snake_case_=True , **snake_case_ , ):
super().__init__(tie_word_embeddings=snake_case_ , is_encoder_decoder=snake_case_ , **snake_case_ )
if text_config is None:
lowercase ={}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
lowercase ={}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
lowercase =PixaStructTextConfig(**snake_case_ )
lowercase =PixaStructVisionConfig(**snake_case_ )
lowercase =self.text_config.decoder_start_token_id
lowercase =self.text_config.pad_token_id
lowercase =self.text_config.eos_token_id
lowercase =initializer_factor
lowercase =initializer_range
lowercase =self.initializer_range
lowercase =self.initializer_range
lowercase =is_vqa
@classmethod
def _A( cls , snake_case_ , snake_case_ , **snake_case_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def _A( self ):
lowercase =copy.deepcopy(self.__dict__ )
lowercase =self.text_config.to_dict()
lowercase =self.vision_config.to_dict()
lowercase =self.__class__.model_type
return output
| 72 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_=2 , snake_case_=True , snake_case_=False , snake_case_=10 , snake_case_=3 , snake_case_=32 * 4 , snake_case_=32 * 6 , snake_case_=4 , snake_case_=32 , ):
lowercase =parent
lowercase =batch_size
lowercase =is_training
lowercase =use_auxiliary_loss
lowercase =num_queries
lowercase =num_channels
lowercase =min_size
lowercase =max_size
lowercase =num_labels
lowercase =mask_feature_size
def _A( self ):
lowercase =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case_ )
lowercase =torch.ones([self.batch_size, self.min_size, self.max_size] , device=snake_case_ )
lowercase =(
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=snake_case_ ) > 0.5
).float()
lowercase =(torch.rand((self.batch_size, self.num_labels) , device=snake_case_ ) > 0.5).long()
lowercase =self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def _A( self ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def _A( self ):
lowercase , lowercase , lowercase , lowercase , lowercase =self.prepare_config_and_inputs()
lowercase ={'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def _A( self , snake_case_ , snake_case_ ):
lowercase =output.encoder_hidden_states
lowercase =output.pixel_decoder_hidden_states
lowercase =output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , config.decoder_config.decoder_layers )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=False ):
with torch.no_grad():
lowercase =MaskFormerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
lowercase =model(snake_case_ , output_hidden_states=snake_case_ )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(snake_case_ , snake_case_ )
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
lowercase =MaskFormerForInstanceSegmentation(config=snake_case_ )
model.to(snake_case_ )
model.eval()
def comm_check_on_output(snake_case_ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
lowercase =model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
lowercase =model(snake_case_ )
comm_check_on_output(snake_case_ )
lowercase =model(
pixel_values=snake_case_ , pixel_mask=snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
comm_check_on_output(snake_case_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
UpperCamelCase__ = (
{'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =MaskFormerModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def _A( self ):
self.config_tester.run_common_tests()
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_ )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def _A( self ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def _A( self ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def _A( self ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def _A( self ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def _A( self ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def _A( self ):
pass
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase =[*signature.parameters.keys()]
lowercase =['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case_ )
@slow
def _A( self ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
lowercase =MaskFormerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _A( self ):
lowercase =(self.model_tester.min_size,) * 2
lowercase ={
'''pixel_values''': torch.randn((2, 3, *size) , device=snake_case_ ),
'''mask_labels''': torch.randn((2, 10, *size) , device=snake_case_ ),
'''class_labels''': torch.zeros(2 , 10 , device=snake_case_ ).long(),
}
lowercase =MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(snake_case_ )
lowercase =model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ ).to(snake_case_ )
lowercase =model(**snake_case_ , output_attentions=snake_case_ )
self.assertTrue(outputs.attentions is not None )
def _A( self ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
lowercase =self.all_model_classes[1]
lowercase , lowercase , lowercase , lowercase , lowercase =self.model_tester.prepare_config_and_inputs()
lowercase =model_class(snake_case_ )
model.to(snake_case_ )
model.train()
lowercase =model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ ).loss
loss.backward()
def _A( self ):
# only MaskFormerForInstanceSegmentation has the loss
lowercase =self.all_model_classes[1]
lowercase , lowercase , lowercase , lowercase , lowercase =self.model_tester.prepare_config_and_inputs()
lowercase =True
lowercase =True
lowercase =model_class(snake_case_ )
model.to(snake_case_ )
model.train()
lowercase =model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
lowercase =outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
lowercase =outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
lowercase =outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
lowercase =outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=snake_case_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_UpperCAmelCase : List[Any] = 1e-4
def UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
lowercase =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class __magic_name__ ( unittest.TestCase ):
@cached_property
def _A( self ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def _A( self ):
lowercase =MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(snake_case_ )
lowercase =self.default_image_processor
lowercase =prepare_img()
lowercase =image_processor(snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
lowercase =inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowercase =model(**snake_case_ )
lowercase =torch.tensor(
[[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
lowercase =torch.tensor(
[[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
lowercase =torch.tensor(
[[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def _A( self ):
lowercase =(
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(snake_case_ )
.eval()
)
lowercase =self.default_image_processor
lowercase =prepare_img()
lowercase =image_processor(snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
lowercase =inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowercase =model(**snake_case_ )
# masks_queries_logits
lowercase =outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
lowercase =[
[-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
[-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
[-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
]
lowercase =torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
lowercase =outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowercase =torch.tensor(
[
[1.6_512E00, -5.2_572E00, -3.3_519E00],
[3.6_169E-02, -5.9_025E00, -2.9_313E00],
[1.0_766E-04, -7.7_630E00, -5.1_263E00],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def _A( self ):
lowercase =(
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(snake_case_ )
.eval()
)
lowercase =self.default_image_processor
lowercase =prepare_img()
lowercase =image_processor(snake_case_ , return_tensors='''pt''' ).to(snake_case_ )
lowercase =inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowercase =model(**snake_case_ )
# masks_queries_logits
lowercase =outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
lowercase =[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
lowercase =torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
lowercase =outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowercase =torch.tensor(
[[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def _A( self ):
lowercase =(
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(snake_case_ )
.eval()
)
lowercase =self.default_image_processor
lowercase =image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='''pt''' , )
lowercase =inputs['''pixel_values'''].to(snake_case_ )
lowercase =[el.to(snake_case_ ) for el in inputs['''mask_labels''']]
lowercase =[el.to(snake_case_ ) for el in inputs['''class_labels''']]
with torch.no_grad():
lowercase =model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
| 72 |
'''simple docstring'''
def UpperCamelCase ( ) -> int:
'''simple docstring'''
return 1
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowercase_ )
def UpperCamelCase ( lowercase_ : int = 2_0_0 ) -> int:
'''simple docstring'''
return two_pound(lowercase_ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 72 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'sew'
def __init__( self , snake_case_=32 , snake_case_=7_68 , snake_case_=12 , snake_case_=12 , snake_case_=30_72 , snake_case_=2 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.02 , snake_case_=1E-5 , snake_case_="group" , snake_case_="gelu" , snake_case_=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) , snake_case_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , snake_case_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , snake_case_=False , snake_case_=1_28 , snake_case_=16 , snake_case_=True , snake_case_=0.05 , snake_case_=10 , snake_case_=2 , snake_case_=0.0 , snake_case_=10 , snake_case_=0 , snake_case_="mean" , snake_case_=False , snake_case_=False , snake_case_=2_56 , snake_case_=0 , snake_case_=1 , snake_case_=2 , **snake_case_ , ):
super().__init__(**snake_case_ , pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ )
lowercase =hidden_size
lowercase =feat_extract_norm
lowercase =feat_extract_activation
lowercase =list(snake_case_ )
lowercase =list(snake_case_ )
lowercase =list(snake_case_ )
lowercase =conv_bias
lowercase =num_conv_pos_embeddings
lowercase =num_conv_pos_embedding_groups
lowercase =len(self.conv_dim )
lowercase =num_hidden_layers
lowercase =intermediate_size
lowercase =squeeze_factor
lowercase =hidden_act
lowercase =num_attention_heads
lowercase =hidden_dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =feat_proj_dropout
lowercase =final_dropout
lowercase =layerdrop
lowercase =layer_norm_eps
lowercase =initializer_range
lowercase =vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
f'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
f'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase =apply_spec_augment
lowercase =mask_time_prob
lowercase =mask_time_length
lowercase =mask_time_min_masks
lowercase =mask_feature_prob
lowercase =mask_feature_length
lowercase =mask_feature_min_masks
# ctc loss
lowercase =ctc_loss_reduction
lowercase =ctc_zero_infinity
# sequence classification
lowercase =use_weighted_layer_sum
lowercase =classifier_proj_size
@property
def _A( self ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 72 |
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['image_processor', 'tokenizer']
UpperCamelCase__ = 'BlipImageProcessor'
UpperCamelCase__ = 'AutoTokenizer'
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
# add QFormer tokenizer
lowercase =qformer_tokenizer
def __call__( self , snake_case_ = None , snake_case_ = None , snake_case_ = True , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = True , snake_case_ = None , **snake_case_ , ):
if images is None and text is None:
raise ValueError('''You have to specify at least images or text.''' )
lowercase =BatchFeature()
if text is not None:
lowercase =self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
encoding.update(snake_case_ )
lowercase =self.qformer_tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
lowercase =qformer_text_encoding.pop('''input_ids''' )
lowercase =qformer_text_encoding.pop('''attention_mask''' )
if images is not None:
lowercase =self.image_processor(snake_case_ , return_tensors=snake_case_ )
encoding.update(snake_case_ )
return encoding
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _A( self ):
lowercase =self.tokenizer.model_input_names
lowercase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _A( self , snake_case_ , **snake_case_ ):
if os.path.isfile(snake_case_ ):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
lowercase =os.path.join(snake_case_ , '''qformer_tokenizer''' )
self.qformer_tokenizer.save_pretrained(snake_case_ )
return super().save_pretrained(snake_case_ , **snake_case_ )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
lowercase =AutoTokenizer.from_pretrained(snake_case_ , subfolder='''qformer_tokenizer''' )
lowercase =cls._get_arguments_from_pretrained(snake_case_ , **snake_case_ )
args.append(snake_case_ )
return cls(*snake_case_ )
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( ) -> int:
'''simple docstring'''
return 1
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowercase_ )
def UpperCamelCase ( lowercase_ : int = 2_0_0 ) -> int:
'''simple docstring'''
return two_pound(lowercase_ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 72 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_UpperCAmelCase : Dict = '''\
@inproceedings{lin-2004-rouge,
title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
author = "Lin, Chin-Yew",
booktitle = "Text Summarization Branches Out",
month = jul,
year = "2004",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W04-1013",
pages = "74--81",
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''
_UpperCAmelCase : Dict = '''
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
`"rougeL"`: Longest common subsequence based scoring.
`"rougeLSum"`: rougeLsum splits text using `"\n"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric(\'rouge\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
[\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
>>> print(results["rouge1"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results["rouge1"].mid.fmeasure)
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
'''https://github.com/google-research/google-research/tree/master/rouge''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=None , snake_case_=True , snake_case_=False ):
if rouge_types is None:
lowercase =['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
lowercase =rouge_scorer.RougeScorer(rouge_types=snake_case_ , use_stemmer=snake_case_ )
if use_aggregator:
lowercase =scoring.BootstrapAggregator()
else:
lowercase =[]
for ref, pred in zip(snake_case_ , snake_case_ ):
lowercase =scorer.score(snake_case_ , snake_case_ )
if use_aggregator:
aggregator.add_scores(snake_case_ )
else:
scores.append(snake_case_ )
if use_aggregator:
lowercase =aggregator.aggregate()
else:
lowercase ={}
for key in scores[0]:
lowercase =[score[key] for score in scores]
return result
| 72 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase : int = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
''' Distillation'''
)
)
parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
parser.add_argument('''--vocab_transform''', action='''store_true''')
_UpperCAmelCase : Optional[int] = parser.parse_args()
if args.model_type == "roberta":
_UpperCAmelCase : Tuple = RobertaForMaskedLM.from_pretrained(args.model_name)
_UpperCAmelCase : Optional[Any] = '''roberta'''
elif args.model_type == "gpt2":
_UpperCAmelCase : List[Any] = GPTaLMHeadModel.from_pretrained(args.model_name)
_UpperCAmelCase : List[Any] = '''transformer'''
_UpperCAmelCase : List[Any] = model.state_dict()
_UpperCAmelCase : str = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
_UpperCAmelCase : Tuple = state_dict[F"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
_UpperCAmelCase : Any = F"""{prefix}.embeddings.{w}.weight"""
_UpperCAmelCase : Union[str, Any] = state_dict[param_name]
for w in ["weight", "bias"]:
_UpperCAmelCase : int = F"""{prefix}.embeddings.LayerNorm.{w}"""
_UpperCAmelCase : Tuple = state_dict[param_name]
# Transformer Blocks #
_UpperCAmelCase : Dict = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
_UpperCAmelCase : Any = state_dict[
F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
_UpperCAmelCase : List[str] = state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
_UpperCAmelCase : Optional[int] = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
_UpperCAmelCase : Optional[Any] = state_dict[F"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_UpperCAmelCase : str = state_dict[F"""lm_head.dense.{w}"""]
_UpperCAmelCase : List[str] = state_dict[F"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
_UpperCAmelCase : Optional[int] = state_dict[F"""{prefix}.ln_f.{w}"""]
_UpperCAmelCase : List[Any] = state_dict['''lm_head.weight''']
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 72 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = '''▁'''
_UpperCAmelCase : Union[str, Any] = {'''vocab_file''': '''spiece.model'''}
_UpperCAmelCase : Union[str, Any] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
_UpperCAmelCase : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self , snake_case_ , snake_case_="<pad>" , snake_case_="</s>" , snake_case_="<unk>" , snake_case_="<mask_2>" , snake_case_="<mask_1>" , snake_case_=None , snake_case_=1_03 , snake_case_ = None , **snake_case_ , ):
lowercase =offset
if additional_special_tokens is not None:
if not isinstance(snake_case_ , snake_case_ ):
raise TypeError(
f'additional_special_tokens should be of type {type(snake_case_ )}, but is'
f' {type(snake_case_ )}' )
lowercase =(
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(snake_case_ ) , self.offset - 1 )
]
if len(set(snake_case_ ) ) != len(snake_case_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
lowercase =additional_special_tokens_extended
else:
lowercase =[mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case_ , unk_token=snake_case_ , mask_token=snake_case_ , pad_token=snake_case_ , mask_token_sent=snake_case_ , offset=snake_case_ , additional_special_tokens=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
lowercase =mask_token_sent
lowercase =vocab_file
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
# add special tokens to encoder dict
lowercase ={
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowercase ={v: k for k, v in self.encoder.items()}
@property
def _A( self ):
return len(self.sp_model ) + self.offset
def _A( self ):
lowercase ={self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
lowercase =self.__dict__.copy()
lowercase =None
return state
def __setstate__( self , snake_case_ ):
lowercase =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase ={}
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def _A( self , snake_case_ ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowercase =self.sp_model.piece_to_id(snake_case_ )
return sp_id + self.offset
def _A( self , snake_case_ ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowercase =self.sp_model.IdToPiece(index - self.offset )
return token
def _A( self , snake_case_ ):
lowercase =[]
lowercase =''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_ ) + token
lowercase =[]
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def _A( self , snake_case_=False ):
return 1
def _A( self , snake_case_ ):
lowercase =set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return self._special_token_mask(snake_case_ )
elif token_ids_a is None:
return self._special_token_mask(snake_case_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A( self , snake_case_ , snake_case_=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , '''wb''' ) as fi:
lowercase =self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
| 72 | 1 |
'''simple docstring'''
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_ ):
lowercase =name
lowercase =val
def __str__( self ):
return f'{self.__class__.__name__}({self.name}, {self.val})'
def __lt__( self , snake_case_ ):
return self.val < other.val
class __magic_name__ :
def __init__( self , snake_case_ ):
lowercase ={}
lowercase ={}
lowercase =self.build_heap(snake_case_ )
def __getitem__( self , snake_case_ ):
return self.get_value(snake_case_ )
def _A( self , snake_case_ ):
return (idx - 1) // 2
def _A( self , snake_case_ ):
return idx * 2 + 1
def _A( self , snake_case_ ):
return idx * 2 + 2
def _A( self , snake_case_ ):
return self.heap_dict[key]
def _A( self , snake_case_ ):
lowercase =len(snake_case_ ) - 1
lowercase =self.get_parent_idx(snake_case_ )
for idx, i in enumerate(snake_case_ ):
lowercase =idx
lowercase =i.val
for i in range(snake_case_ , -1 , -1 ):
self.sift_down(snake_case_ , snake_case_ )
return array
def _A( self , snake_case_ , snake_case_ ):
while True:
lowercase =self.get_left_child_idx(snake_case_ ) # noqa: E741
lowercase =self.get_right_child_idx(snake_case_ )
lowercase =idx
if l < len(snake_case_ ) and array[l] < array[idx]:
lowercase =l
if r < len(snake_case_ ) and array[r] < array[smallest]:
lowercase =r
if smallest != idx:
lowercase , lowercase =array[smallest], array[idx]
(
(
lowercase
) , (
lowercase
) ,
) =(
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
lowercase =smallest
else:
break
def _A( self , snake_case_ ):
lowercase =self.get_parent_idx(snake_case_ )
while p >= 0 and self.heap[p] > self.heap[idx]:
lowercase , lowercase =self.heap[idx], self.heap[p]
lowercase , lowercase =(
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
lowercase =p
lowercase =self.get_parent_idx(snake_case_ )
def _A( self ):
return self.heap[0]
def _A( self ):
lowercase , lowercase =self.heap[-1], self.heap[0]
lowercase , lowercase =(
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
lowercase =self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def _A( self , snake_case_ ):
self.heap.append(snake_case_ )
lowercase =len(self.heap ) - 1
lowercase =node.val
self.sift_up(len(self.heap ) - 1 )
def _A( self ):
return len(self.heap ) == 0
def _A( self , snake_case_ , snake_case_ ):
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
lowercase =new_value
lowercase =new_value
self.sift_up(self.idx_of_element[node] )
_UpperCAmelCase : Any = Node('''R''', -1)
_UpperCAmelCase : Optional[int] = Node('''B''', 6)
_UpperCAmelCase : Tuple = Node('''A''', 3)
_UpperCAmelCase : Union[str, Any] = Node('''X''', 1)
_UpperCAmelCase : List[str] = Node('''E''', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
_UpperCAmelCase : Union[str, Any] = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
print(i)
print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 72 | 1 |
'''simple docstring'''
import math
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase =input('''Enter message: ''' )
lowercase =int(input(f'Enter key [2-{len(lowercase_ ) - 1}]: ' ) )
lowercase =input('''Encryption/Decryption [e/d]: ''' )
if mode.lower().startswith('''e''' ):
lowercase =encrypt_message(lowercase_ , lowercase_ )
elif mode.lower().startswith('''d''' ):
lowercase =decrypt_message(lowercase_ , lowercase_ )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f'Output:\n{text + "|"}' )
def UpperCamelCase ( lowercase_ : int , lowercase_ : str ) -> str:
'''simple docstring'''
lowercase =[''''''] * key
for col in range(lowercase_ ):
lowercase =col
while pointer < len(lowercase_ ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(lowercase_ )
def UpperCamelCase ( lowercase_ : int , lowercase_ : str ) -> str:
'''simple docstring'''
lowercase =math.ceil(len(lowercase_ ) / key )
lowercase =key
lowercase =(num_cols * num_rows) - len(lowercase_ )
lowercase =[''''''] * num_cols
lowercase =0
lowercase =0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
lowercase =0
row += 1
return "".join(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 72 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Tuple ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase_ , lowercase_ ):
lowercase =np.full((len(lowercase_ ), sequence_length, 2) , lowercase_ )
else:
lowercase =np.full((len(lowercase_ ), sequence_length) , lowercase_ )
for i, tensor in enumerate(lowercase_ ):
if padding_side == "right":
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
else:
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
return out_tensor.tolist()
def UpperCamelCase ( lowercase_ : Optional[Any] ) -> str:
'''simple docstring'''
lowercase =ord(lowercase_ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
lowercase =unicodedata.category(lowercase_ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -1_00
UpperCamelCase__ = "pt"
def _A( self , snake_case_ ):
import torch
lowercase ='''label''' if '''label''' in features[0].keys() else '''labels'''
lowercase =[feature[label_name] for feature in features] if label_name in features[0].keys() else None
lowercase =self.tokenizer.pad(
snake_case_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
lowercase =torch.tensor(batch['''entity_ids'''] ).shape[1]
lowercase =self.tokenizer.padding_side
if padding_side == "right":
lowercase =[
list(snake_case_ ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_ )) for label in labels
]
else:
lowercase =[
[self.label_pad_token_id] * (sequence_length - len(snake_case_ )) + list(snake_case_ ) for label in labels
]
lowercase =[feature['''ner_tags'''] for feature in features]
lowercase =padding_tensor(snake_case_ , -1 , snake_case_ , snake_case_ )
lowercase =[feature['''original_entity_spans'''] for feature in features]
lowercase =padding_tensor(snake_case_ , (-1, -1) , snake_case_ , snake_case_ )
lowercase ={k: torch.tensor(snake_case_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 72 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
_UpperCAmelCase : Dict = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['pixel_values']
def __init__( self , snake_case_ = True , snake_case_ = None , snake_case_ = PILImageResampling.BILINEAR , snake_case_ = True , snake_case_ = 1 / 2_55 , snake_case_ = True , snake_case_ = None , snake_case_ = True , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =size if size is not None else {'''shortest_edge''': 2_24}
lowercase =get_size_dict(snake_case_ , default_to_square=snake_case_ )
lowercase =crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
lowercase =get_size_dict(snake_case_ , param_name='''crop_size''' )
lowercase =do_resize
lowercase =size
lowercase =resample
lowercase =do_rescale
lowercase =rescale_factor
lowercase =do_center_crop
lowercase =crop_size
lowercase =do_flip_channel_order
def _A( self , snake_case_ , snake_case_ , snake_case_ = PIL.Image.BILINEAR , snake_case_ = None , **snake_case_ , ):
lowercase =get_size_dict(snake_case_ , default_to_square=snake_case_ )
if "shortest_edge" not in size:
raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}' )
lowercase =get_resize_output_image_size(snake_case_ , size=size['''shortest_edge'''] , default_to_square=snake_case_ )
return resize(snake_case_ , size=snake_case_ , resample=snake_case_ , data_format=snake_case_ , **snake_case_ )
def _A( self , snake_case_ , snake_case_ , snake_case_ = None , **snake_case_ , ):
lowercase =get_size_dict(snake_case_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(snake_case_ , size=(size['''height'''], size['''width''']) , data_format=snake_case_ , **snake_case_ )
def _A( self , snake_case_ , snake_case_ , snake_case_ = None , **snake_case_ , ):
return rescale(snake_case_ , scale=snake_case_ , data_format=snake_case_ , **snake_case_ )
def _A( self , snake_case_ , snake_case_ = None ):
return flip_channel_order(snake_case_ , data_format=snake_case_ )
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = ChannelDimension.FIRST , **snake_case_ , ):
lowercase =do_resize if do_resize is not None else self.do_resize
lowercase =resample if resample is not None else self.resample
lowercase =do_rescale if do_rescale is not None else self.do_rescale
lowercase =rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase =do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase =(
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
lowercase =size if size is not None else self.size
lowercase =get_size_dict(snake_case_ , default_to_square=snake_case_ )
lowercase =crop_size if crop_size is not None else self.crop_size
lowercase =get_size_dict(snake_case_ , param_name='''crop_size''' )
lowercase =make_list_of_images(snake_case_ )
if not valid_images(snake_case_ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
# All transformations expect numpy arrays.
lowercase =[to_numpy_array(snake_case_ ) for image in images]
if do_resize:
lowercase =[self.resize(image=snake_case_ , size=snake_case_ , resample=snake_case_ ) for image in images]
if do_center_crop:
lowercase =[self.center_crop(image=snake_case_ , size=snake_case_ ) for image in images]
if do_rescale:
lowercase =[self.rescale(image=snake_case_ , scale=snake_case_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
lowercase =[self.flip_channel_order(image=snake_case_ ) for image in images]
lowercase =[to_channel_dimension_format(snake_case_ , snake_case_ ) for image in images]
lowercase ={'''pixel_values''': images}
return BatchFeature(data=snake_case_ , tensor_type=snake_case_ )
def _A( self , snake_case_ , snake_case_ = None ):
lowercase =outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(snake_case_ ) != len(snake_case_ ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(snake_case_ ):
lowercase =target_sizes.numpy()
lowercase =[]
for idx in range(len(snake_case_ ) ):
lowercase =torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=snake_case_ )
lowercase =resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(snake_case_ )
else:
lowercase =logits.argmax(dim=1 )
lowercase =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 72 |
'''simple docstring'''
_UpperCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase ( lowercase_ : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'a bytes-like object is required, not \'{data.__class__.__name__}\''
raise TypeError(lowercase_ )
lowercase =''''''.join(bin(lowercase_ )[2:].zfill(8 ) for byte in data )
lowercase =len(lowercase_ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase =b'''=''' * ((6 - len(lowercase_ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(lowercase_ ) % 6)
else:
lowercase =b''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(lowercase_ ) , 6 ) ).encode()
+ padding
)
def UpperCamelCase ( lowercase_ : str ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
lowercase =(
'''argument should be a bytes-like object or ASCII string, '''
f'not \'{encoded_data.__class__.__name__}\''
)
raise TypeError(lowercase_ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(lowercase_ , lowercase_ ):
try:
lowercase =encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
lowercase =encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(lowercase_ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase =encoded_data[:-padding]
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase =[
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(lowercase_ ) , 8 )
]
return bytes(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 72 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_UpperCAmelCase : Union[str, Any] = datasets.logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = '''\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
author = "Moosavi, Nafise Sadat and
Strube, Michael",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1060",
doi = "10.18653/v1/P16-1060",
pages = "632--642",
}
'''
_UpperCAmelCase : str = '''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''
_UpperCAmelCase : Optional[int] = '''
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting \'keep_singletons=False\', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
\'mentions\': mentions
\'muc\': MUC metric [Vilain et al, 1995]
\'bcub\': B-cubed [Bagga and Baldwin, 1998]
\'ceafe\': CEAFe [Luo et al., 2005]
\'lea\': LEA [Moosavi and Strube, 2016]
\'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric(\'coval\')
>>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',
... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',
... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',
... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',
... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',
... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''
def UpperCamelCase ( lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=True , lowercase_ : Optional[Any]=False , lowercase_ : int="dummy_doc" ) -> str:
'''simple docstring'''
lowercase ={doc: key_lines}
lowercase ={doc: sys_lines}
lowercase ={}
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase =0
lowercase , lowercase =reader.get_doc_mentions(lowercase_ , key_doc_lines[doc] , lowercase_ )
key_singletons_num += singletons_num
if NP_only or min_span:
lowercase =reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
lowercase , lowercase =reader.get_doc_mentions(lowercase_ , sys_doc_lines[doc] , lowercase_ )
sys_singletons_num += singletons_num
if NP_only or min_span:
lowercase =reader.set_annotated_parse_trees(lowercase_ , key_doc_lines[doc] , lowercase_ , lowercase_ )
if remove_nested:
lowercase , lowercase =reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
lowercase , lowercase =reader.remove_nested_coref_mentions(lowercase_ , lowercase_ )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
lowercase =reader.get_mention_assignments(lowercase_ , lowercase_ )
lowercase =reader.get_mention_assignments(lowercase_ , lowercase_ )
lowercase =(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
f'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}' )
logger.info(
'''Number of resulting singleton clusters in the key '''
f'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}' )
if not keep_singletons:
logger.info(
f'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
'''files, respectively''' )
return doc_coref_infos
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Tuple ) -> Dict:
'''simple docstring'''
lowercase =get_coref_infos(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase ={}
lowercase =0
lowercase =0
for name, metric in metrics:
lowercase , lowercase , lowercase =evaluator.evaluate_documents(lowercase_ , lowercase_ , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({f'{name}/recall': recall, f'{name}/precision': precision, f'{name}/f1': fa} )
logger.info(
name.ljust(1_0 ) , f'Recall: {recall * 1_0_0:.2f}' , f' Precision: {precision * 1_0_0:.2f}' , f' F1: {fa * 1_0_0:.2f}' , )
if conll_subparts_num == 3:
lowercase =(conll / 3) * 1_0_0
logger.info(f'CoNLL score: {conll:.2f}' )
output_scores.update({'''conll_score''': conll} )
return output_scores
def UpperCamelCase ( lowercase_ : Any ) -> List[Any]:
'''simple docstring'''
lowercase =False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
lowercase =line.split()[5]
if not parse_col == "-":
lowercase =True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Sequence(datasets.Value('''string''' ) ),
} ) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False ):
lowercase =[
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
lowercase =util.check_gold_parse_annotation(snake_case_ )
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''' )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
lowercase =evaluate(
key_lines=snake_case_ , sys_lines=snake_case_ , metrics=snake_case_ , NP_only=snake_case_ , remove_nested=snake_case_ , keep_singletons=snake_case_ , min_span=snake_case_ , )
return score
| 72 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : List[Any] = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPConfig''',
'''CLIPOnnxConfig''',
'''CLIPTextConfig''',
'''CLIPVisionConfig''',
],
'''processing_clip''': ['''CLIPProcessor'''],
'''tokenization_clip''': ['''CLIPTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : int = ['''CLIPTokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Tuple = ['''CLIPFeatureExtractor''']
_UpperCAmelCase : List[str] = ['''CLIPImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Dict = [
'''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPModel''',
'''CLIPPreTrainedModel''',
'''CLIPTextModel''',
'''CLIPTextModelWithProjection''',
'''CLIPVisionModel''',
'''CLIPVisionModelWithProjection''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : List[str] = [
'''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCLIPModel''',
'''TFCLIPPreTrainedModel''',
'''TFCLIPTextModel''',
'''TFCLIPVisionModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[int] = [
'''FlaxCLIPModel''',
'''FlaxCLIPPreTrainedModel''',
'''FlaxCLIPTextModel''',
'''FlaxCLIPTextPreTrainedModel''',
'''FlaxCLIPVisionModel''',
'''FlaxCLIPVisionPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
_UpperCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(lowercase_ , lowercase_ ):
return 0
elif n == 2:
return 1
else:
lowercase =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
lowercase =0
lowercase =2
while digits < n:
index += 1
lowercase =len(str(fibonacci(lowercase_ ) ) )
return index
def UpperCamelCase ( lowercase_ : int = 1_0_0_0 ) -> int:
'''simple docstring'''
return fibonacci_digits_index(lowercase_ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 72 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
'''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
),
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'gptsan-japanese'
UpperCamelCase__ = [
'past_key_values',
]
UpperCamelCase__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=3_60_00 , snake_case_=12_80 , snake_case_=10_24 , snake_case_=81_92 , snake_case_=40_96 , snake_case_=1_28 , snake_case_=10 , snake_case_=0 , snake_case_=16 , snake_case_=16 , snake_case_=1_28 , snake_case_=0.0 , snake_case_=1E-5 , snake_case_=False , snake_case_=0.0 , snake_case_="float32" , snake_case_=False , snake_case_=False , snake_case_=False , snake_case_=0.0_02 , snake_case_=False , snake_case_=True , snake_case_=3_59_98 , snake_case_=3_59_95 , snake_case_=3_59_99 , **snake_case_ , ):
lowercase =vocab_size
lowercase =max_position_embeddings
lowercase =d_model
lowercase =d_ff
lowercase =d_ext
lowercase =d_spout
lowercase =num_switch_layers
lowercase =num_ext_layers
lowercase =num_switch_layers + num_ext_layers
lowercase =num_heads
lowercase =num_experts
lowercase =expert_capacity
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =router_bias
lowercase =router_jitter_noise
lowercase =router_dtype
lowercase =router_ignore_padding_tokens
lowercase =output_hidden_states
lowercase =output_attentions
lowercase =initializer_factor
lowercase =output_router_logits
lowercase =use_cache
super().__init__(
separator_token_id=snake_case_ , pad_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ , )
| 72 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : Any = {
'''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'marian'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , snake_case_=5_81_01 , snake_case_=None , snake_case_=10_24 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=12 , snake_case_=40_96 , snake_case_=16 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=True , snake_case_=True , snake_case_="gelu" , snake_case_=10_24 , snake_case_=0.1 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.02 , snake_case_=5_81_00 , snake_case_=False , snake_case_=5_81_00 , snake_case_=0 , snake_case_=0 , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =decoder_vocab_size or vocab_size
lowercase =max_position_embeddings
lowercase =d_model
lowercase =encoder_ffn_dim
lowercase =encoder_layers
lowercase =encoder_attention_heads
lowercase =decoder_ffn_dim
lowercase =decoder_layers
lowercase =decoder_attention_heads
lowercase =dropout
lowercase =attention_dropout
lowercase =activation_dropout
lowercase =activation_function
lowercase =init_std
lowercase =encoder_layerdrop
lowercase =decoder_layerdrop
lowercase =use_cache
lowercase =encoder_layers
lowercase =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase =share_encoder_decoder_embeddings
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , decoder_start_token_id=snake_case_ , forced_eos_token_id=snake_case_ , **snake_case_ , )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase ={0: '''batch'''}
lowercase ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
else:
lowercase =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def _A( self ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super().outputs
else:
lowercase =super(snake_case_ , self ).outputs
if self.use_past:
lowercase , lowercase =self.num_layers
for i in range(snake_case_ ):
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowercase ={0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Generate decoder inputs
lowercase =seq_length if not self.use_past else 1
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
lowercase =dict(**snake_case_ , **snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
lowercase =common_inputs['''decoder_input_ids'''].shape[1]
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =decoder_seq_length + 3
lowercase =(
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase =torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_ , snake_case_ )] , dim=1 )
lowercase =[]
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase , lowercase =self.num_layers
lowercase =min(snake_case_ , snake_case_ )
lowercase =max(snake_case_ , snake_case_ ) - min_num_layers
lowercase ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(snake_case_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
) )
# TODO: test this.
lowercase =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(snake_case_ , snake_case_ ):
common_inputs["past_key_values"].append((torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
lowercase =self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase =seqlen + 2
lowercase , lowercase =self.num_layers
lowercase , lowercase =self.num_attention_heads
lowercase =(
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase =common_inputs['''attention_mask'''].dtype
lowercase =torch.cat(
[common_inputs['''attention_mask'''], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
lowercase =[
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(snake_case_ )
]
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase =tokenizer.num_special_tokens_to_add(snake_case_ )
lowercase =compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
lowercase =[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase =dict(tokenizer(snake_case_ , return_tensors=snake_case_ ) )
return common_inputs
def _A( self , snake_case_ , snake_case_ = -1 , snake_case_ = -1 , snake_case_ = False , snake_case_ = None , ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
else:
lowercase =self._generate_dummy_inputs_for_causal_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
return common_inputs
def _A( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
if self.task in ["default", "seq2seq-lm"]:
lowercase =super()._flatten_past_key_values_(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
lowercase =super(snake_case_ , self )._flatten_past_key_values_(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
@property
def _A( self ):
return 1E-4
| 72 | 1 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : list , lowercase_ : int = 0 ) -> list:
'''simple docstring'''
lowercase =length or len(lowercase_ )
lowercase =False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
lowercase , lowercase =list_data[i + 1], list_data[i]
lowercase =True
return list_data if not swapped else bubble_sort(lowercase_ , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCAmelCase : Dict = parse(importlib.metadata.version('''torch'''))
def UpperCamelCase ( lowercase_ : Union[str, Version] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
'''simple docstring'''
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f'`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}' )
lowercase =STR_OPERATION_TO_FUNC[operation]
if isinstance(lowercase_ , lowercase_ ):
lowercase =parse(importlib.metadata.version(lowercase_ ) )
return operation(lowercase_ , parse(lowercase_ ) )
def UpperCamelCase ( lowercase_ : str , lowercase_ : str ) -> Union[str, Any]:
'''simple docstring'''
return compare_versions(lowercase_ , lowercase_ , lowercase_ )
| 72 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = CTRLTokenizer
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase =['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
lowercase =dict(zip(snake_case_ , range(len(snake_case_ ) ) ) )
lowercase =['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
lowercase ={'''unk_token''': '''<unk>'''}
lowercase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(snake_case_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(snake_case_ ) )
def _A( self , **snake_case_ ):
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def _A( self , snake_case_ ):
lowercase ='''adapt react readapt apt'''
lowercase ='''adapt react readapt apt'''
return input_text, output_text
def _A( self ):
lowercase =CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowercase ='''adapt react readapt apt'''
lowercase ='''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
lowercase =tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =tokens + [tokenizer.unk_token]
lowercase =[0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ ) , snake_case_ )
| 72 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
_UpperCAmelCase : int = [8, 5, 9, 7]
_UpperCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_UpperCAmelCase : Union[str, Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __magic_name__ :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =claim_vector
lowercase =allocated_resources_table
lowercase =maximum_claim_table
def _A( self ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _A( self ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _A( self ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(snake_case_ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _A( self ):
return {self.__need().index(snake_case_ ): i for i in self.__need()}
def _A( self , **snake_case_ ):
lowercase =self.__need()
lowercase =self.__allocated_resources_table
lowercase =self.__available_resources()
lowercase =self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('''_''' * 50 + '''\n''' )
while need_list:
lowercase =False
for each_need in need_list:
lowercase =True
for index, need in enumerate(snake_case_ ):
if need > available_resources[index]:
lowercase =False
break
if execution:
lowercase =True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
lowercase =original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(snake_case_ )
# update available/freed resources stack
lowercase =np.array(snake_case_ ) + np.array(
alloc_resources_table[process_number] )
print(
'''Updated available resource stack for processes: '''
+ ''' '''.join([str(snake_case_ ) for x in available_resources] ) )
break
if safe:
print('''The process is in a safe state.\n''' )
else:
print('''System in unsafe state. Aborting...\n''' )
break
def _A( self ):
print(''' ''' * 9 + '''Allocated Resource Table''' )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(snake_case_ ) + 1}'
+ ''' '''.join(f'{it:>8}' for it in item )
+ '''\n''' )
print(''' ''' * 9 + '''System Resource Table''' )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(snake_case_ ) + 1}'
+ ''' '''.join(f'{it:>8}' for it in item )
+ '''\n''' )
print(
'''Current Usage by Active Processes: '''
+ ''' '''.join(str(snake_case_ ) for x in self.__claim_vector ) )
print(
'''Initial Available Resources: '''
+ ''' '''.join(str(snake_case_ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : str = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'nat'
UpperCamelCase__ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=4 , snake_case_=3 , snake_case_=64 , snake_case_=[3, 4, 6, 5] , snake_case_=[2, 4, 8, 16] , snake_case_=7 , snake_case_=3.0 , snake_case_=True , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.1 , snake_case_="gelu" , snake_case_=0.02 , snake_case_=1E-5 , snake_case_=0.0 , snake_case_=None , snake_case_=None , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =patch_size
lowercase =num_channels
lowercase =embed_dim
lowercase =depths
lowercase =len(snake_case_ )
lowercase =num_heads
lowercase =kernel_size
lowercase =mlp_ratio
lowercase =qkv_bias
lowercase =hidden_dropout_prob
lowercase =attention_probs_dropout_prob
lowercase =drop_path_rate
lowercase =hidden_act
lowercase =layer_norm_eps
lowercase =initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowercase =int(embed_dim * 2 ** (len(snake_case_ ) - 1) )
lowercase =layer_scale_init_value
lowercase =['''stem'''] + [f'stage{idx}' for idx in range(1 , len(snake_case_ ) + 1 )]
lowercase , lowercase =get_aligned_output_features_output_indices(
out_features=snake_case_ , out_indices=snake_case_ , stage_names=self.stage_names )
| 72 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase : Dict = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
_UpperCAmelCase : Dict = '''\
@inproceedings{banarjee2005,
title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
author = {Banerjee, Satanjeev and Lavie, Alon},
booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
month = jun,
year = {2005},
address = {Ann Arbor, Michigan},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W05-0909},
pages = {65--72},
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.
METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''
_UpperCAmelCase : Tuple = '''
Computes METEOR score of translated segments against one or more references.
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
\'meteor\': meteor score.
Examples:
>>> meteor = datasets.load_metric(\'meteor\')
>>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
>>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
>>> results = meteor.compute(predictions=predictions, references=references)
>>> print(round(results["meteor"], 4))
0.6944
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''] , reference_urls=[
'''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
'''https://en.wikipedia.org/wiki/METEOR''',
] , )
def _A( self , snake_case_ ):
import nltk
nltk.download('''wordnet''' )
if NLTK_VERSION >= version.Version('''3.6.5''' ):
nltk.download('''punkt''' )
if NLTK_VERSION >= version.Version('''3.6.6''' ):
nltk.download('''omw-1.4''' )
def _A( self , snake_case_ , snake_case_ , snake_case_=0.9 , snake_case_=3 , snake_case_=0.5 ):
if NLTK_VERSION >= version.Version('''3.6.5''' ):
lowercase =[
meteor_score.single_meteor_score(
word_tokenize(snake_case_ ) , word_tokenize(snake_case_ ) , alpha=snake_case_ , beta=snake_case_ , gamma=snake_case_ )
for ref, pred in zip(snake_case_ , snake_case_ )
]
else:
lowercase =[
meteor_score.single_meteor_score(snake_case_ , snake_case_ , alpha=snake_case_ , beta=snake_case_ , gamma=snake_case_ )
for ref, pred in zip(snake_case_ , snake_case_ )
]
return {"meteor": np.mean(snake_case_ )}
| 72 | 1 |
'''simple docstring'''
import os
import numpy
import onnx
def UpperCamelCase ( lowercase_ : Any , lowercase_ : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
lowercase =a.name
lowercase =b.name
lowercase =''''''
lowercase =''''''
lowercase =a == b
lowercase =name_a
lowercase =name_b
return res
def UpperCamelCase ( lowercase_ : str , lowercase_ : Tuple , lowercase_ : int ) -> Optional[int]:
'''simple docstring'''
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(lowercase_ , lowercase_ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , lowercase_ , lowercase_ )
_graph_replace_input_with(node_proto.attribute[1].g , lowercase_ , lowercase_ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , lowercase_ , lowercase_ )
def UpperCamelCase ( lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
for n in graph_proto.node:
_node_replace_input_with(lowercase_ , lowercase_ , lowercase_ )
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : int ) -> Tuple:
'''simple docstring'''
lowercase =list(model.graph.initializer )
lowercase =list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase =inits[i].name
lowercase =inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , lowercase_ , lowercase_ )
def UpperCamelCase ( lowercase_ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowercase =os.path.dirname(lowercase_ )
lowercase =os.path.basename(lowercase_ )
lowercase =onnx.load(os.path.join(lowercase_ , lowercase_ ) )
lowercase =list(model.graph.initializer )
lowercase =set()
lowercase ={}
lowercase =[]
lowercase =0
for i in range(len(lowercase_ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(lowercase_ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(lowercase_ )
dup_set.add(lowercase_ )
lowercase =inits[j].data_type
lowercase =numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 1_1:
mem_size *= 8
else:
print('''unexpected data type: ''' , lowercase_ )
total_reduced_size += mem_size
lowercase =inits[i].name
lowercase =inits[j].name
if name_i in dup_map:
dup_map[name_i].append(lowercase_ )
else:
lowercase =[name_j]
ind_to_replace.append((j, i) )
print('''total reduced size: ''' , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 , '''GB''' )
lowercase =sorted(lowercase_ )
_remove_dup_initializers_from_model(lowercase_ , lowercase_ , lowercase_ )
lowercase ='''optimized_''' + model_file_name
lowercase =os.path.join(lowercase_ , lowercase_ )
onnx.save(lowercase_ , lowercase_ )
return new_model
| 72 |
'''simple docstring'''
import sys
_UpperCAmelCase : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCamelCase ( lowercase_ : str = N ) -> int:
'''simple docstring'''
lowercase =-sys.maxsize - 1
for i in range(len(lowercase_ ) - 1_2 ):
lowercase =1
for j in range(1_3 ):
product *= int(n[i + j] )
if product > largest_product:
lowercase =product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 72 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __magic_name__ ( unittest.TestCase ):
def __init__( self , snake_case_ , snake_case_=7 , snake_case_=3 , snake_case_=18 , snake_case_=30 , snake_case_=4_00 , snake_case_=True , snake_case_=None , snake_case_=True , ):
lowercase =size if size is not None else {'''height''': 18, '''width''': 18}
lowercase =parent
lowercase =batch_size
lowercase =num_channels
lowercase =image_size
lowercase =min_resolution
lowercase =max_resolution
lowercase =do_resize
lowercase =size
lowercase =apply_ocr
def _A( self ):
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def _A( self ):
lowercase =LayoutLMvaImageProcessingTester(self )
@property
def _A( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A( self ):
lowercase =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case_ , '''do_resize''' ) )
self.assertTrue(hasattr(snake_case_ , '''size''' ) )
self.assertTrue(hasattr(snake_case_ , '''apply_ocr''' ) )
def _A( self ):
lowercase =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
lowercase =self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def _A( self ):
pass
def _A( self ):
# Initialize image_processing
lowercase =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase =prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , Image.Image )
# Test not batched input
lowercase =image_processing(image_inputs[0] , return_tensors='''pt''' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
self.assertIsInstance(encoding.words , snake_case_ )
self.assertIsInstance(encoding.boxes , snake_case_ )
# Test batched
lowercase =image_processing(snake_case_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _A( self ):
# Initialize image_processing
lowercase =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase =prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ , numpify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , np.ndarray )
# Test not batched input
lowercase =image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
lowercase =image_processing(snake_case_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _A( self ):
# Initialize image_processing
lowercase =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase =prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ , torchify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , torch.Tensor )
# Test not batched input
lowercase =image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
lowercase =image_processing(snake_case_ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _A( self ):
# with apply_OCR = True
lowercase =LayoutLMvaImageProcessor()
from datasets import load_dataset
lowercase =load_dataset('''hf-internal-testing/fixtures_docvqa''' , split='''test''' )
lowercase =Image.open(ds[0]['''file'''] ).convert('''RGB''' )
lowercase =image_processing(snake_case_ , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
lowercase =[['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']] # noqa: E231
lowercase =[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , snake_case_ )
self.assertListEqual(encoding.boxes , snake_case_ )
# with apply_OCR = False
lowercase =LayoutLMvaImageProcessor(apply_ocr=snake_case_ )
lowercase =image_processing(snake_case_ , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_24, 2_24) )
| 72 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *snake_case_ , **snake_case_ ):
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 72 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , snake_case_ , snake_case_ ):
super().__init__()
# make sure scheduler can always be converted to DDIM
lowercase =DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=snake_case_ , scheduler=snake_case_ )
@torch.no_grad()
def __call__( self , snake_case_ = 1 , snake_case_ = None , snake_case_ = 0.0 , snake_case_ = 50 , snake_case_ = None , snake_case_ = "pil" , snake_case_ = True , ):
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size , snake_case_ ):
lowercase =(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
lowercase =(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(snake_case_ , snake_case_ ) and len(snake_case_ ) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(snake_case_ )}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
lowercase =randn_tensor(snake_case_ , generator=snake_case_ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(snake_case_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase =self.unet(snake_case_ , snake_case_ ).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
lowercase =self.scheduler.step(
snake_case_ , snake_case_ , snake_case_ , eta=snake_case_ , use_clipped_model_output=snake_case_ , generator=snake_case_ ).prev_sample
lowercase =(image / 2 + 0.5).clamp(0 , 1 )
lowercase =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowercase =self.numpy_to_pil(snake_case_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case_ )
| 72 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
'''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'encodec'
def __init__( self , snake_case_=[1.5, 3.0, 6.0, 12.0, 24.0] , snake_case_=2_40_00 , snake_case_=1 , snake_case_=False , snake_case_=None , snake_case_=None , snake_case_=1_28 , snake_case_=32 , snake_case_=1 , snake_case_=[8, 5, 4, 2] , snake_case_="weight_norm" , snake_case_=7 , snake_case_=7 , snake_case_=3 , snake_case_=2 , snake_case_=True , snake_case_="reflect" , snake_case_=2 , snake_case_=2 , snake_case_=1.0 , snake_case_=10_24 , snake_case_=None , snake_case_=True , **snake_case_ , ):
lowercase =target_bandwidths
lowercase =sampling_rate
lowercase =audio_channels
lowercase =normalize
lowercase =chunk_length_s
lowercase =overlap
lowercase =hidden_size
lowercase =num_filters
lowercase =num_residual_layers
lowercase =upsampling_ratios
lowercase =norm_type
lowercase =kernel_size
lowercase =last_kernel_size
lowercase =residual_kernel_size
lowercase =dilation_growth_rate
lowercase =use_causal_conv
lowercase =pad_mode
lowercase =compress
lowercase =num_lstm_layers
lowercase =trim_right_ratio
lowercase =codebook_size
lowercase =codebook_dim if codebook_dim is not None else hidden_size
lowercase =use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' )
super().__init__(**snake_case_ )
@property
def _A( self ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A( self ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A( self ):
lowercase =np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A( self ):
return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 72 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_UpperCAmelCase : List[str] = {
'''cola''': 2,
'''mnli''': 3,
'''mrpc''': 2,
'''sst-2''': 2,
'''sts-b''': 1,
'''qqp''': 2,
'''qnli''': 2,
'''rte''': 2,
'''wnli''': 2,
}
logging.set_verbosity_info()
def UpperCamelCase ( lowercase_ : Optional[int] , lowercase_ : Dict , lowercase_ : Dict , lowercase_ : Dict=None ) -> Dict:
'''simple docstring'''
lowercase =XLNetConfig.from_json_file(lowercase_ )
lowercase =finetuning_task.lower() if finetuning_task is not None else ''''''
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(f'Building PyTorch XLNetForSequenceClassification model from configuration: {config}' )
lowercase =finetuning_task
lowercase =GLUE_TASKS_NUM_LABELS[finetuning_task]
lowercase =XLNetForSequenceClassification(lowercase_ )
elif "squad" in finetuning_task:
lowercase =finetuning_task
lowercase =XLNetForQuestionAnswering(lowercase_ )
else:
lowercase =XLNetLMHeadModel(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
lowercase =os.path.join(lowercase_ , lowercase_ )
lowercase =os.path.join(lowercase_ , lowercase_ )
print(f'Save PyTorch model to {os.path.abspath(lowercase_ )}' )
torch.save(model.state_dict() , lowercase_ )
print(f'Save configuration file to {os.path.abspath(lowercase_ )}' )
with open(lowercase_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_UpperCAmelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--xlnet_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained XLNet model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--finetuning_task''',
default=None,
type=str,
help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
)
_UpperCAmelCase : Any = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 72 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : int = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : int = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Dict = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_UpperCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'umt5'
UpperCamelCase__ = ['past_key_values']
def __init__( self , snake_case_=25_01_12 , snake_case_=5_12 , snake_case_=64 , snake_case_=10_24 , snake_case_=8 , snake_case_=None , snake_case_=6 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gated-gelu" , snake_case_=True , snake_case_=True , snake_case_="T5Tokenizer" , snake_case_=True , snake_case_=0 , snake_case_=1 , snake_case_=0 , **snake_case_ , ):
super().__init__(
is_encoder_decoder=snake_case_ , tokenizer_class=snake_case_ , tie_word_embeddings=snake_case_ , pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , **snake_case_ , )
lowercase =vocab_size
lowercase =d_model
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =(
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =feed_forward_proj
lowercase =use_cache
lowercase =self.feed_forward_proj.split('''-''' )
lowercase =act_info[-1]
lowercase =act_info[0] == '''gated'''
if len(snake_case_ ) > 1 and act_info[0] != "gated" or len(snake_case_ ) > 2:
raise ValueError(
f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
if feed_forward_proj == "gated-gelu":
lowercase ='''gelu_new'''
@property
def _A( self ):
return self.d_model
@property
def _A( self ):
return self.num_heads
@property
def _A( self ):
return self.num_layers
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def _A( self ):
lowercase ={
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
lowercase ='''past_encoder_sequence + sequence'''
lowercase ={0: '''batch'''}
lowercase ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
lowercase ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='''inputs''' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def _A( self ):
return 13
@property
def _A( self ):
return 5E-4
| 72 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance cannot be 0 or negative''' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
# General docstring
_UpperCAmelCase : Any = '''PoolFormerConfig'''
# Base docstring
_UpperCAmelCase : List[Any] = '''sail/poolformer_s12'''
_UpperCAmelCase : str = [1, 5_12, 7, 7]
# Image classification docstring
_UpperCAmelCase : Any = '''sail/poolformer_s12'''
_UpperCAmelCase : Union[str, Any] = '''tabby, tabby cat'''
_UpperCAmelCase : str = [
'''sail/poolformer_s12''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def UpperCamelCase ( lowercase_ : Optional[Any] , lowercase_ : float = 0.0 , lowercase_ : bool = False ) -> Optional[Any]:
'''simple docstring'''
if drop_prob == 0.0 or not training:
return input
lowercase =1 - drop_prob
lowercase =(input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
lowercase =keep_prob + torch.rand(lowercase_ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
lowercase =input.div(lowercase_ ) * random_tensor
return output
class __magic_name__ ( nn.Module ):
def __init__( self , snake_case_ = None ):
super().__init__()
lowercase =drop_prob
def _A( self , snake_case_ ):
return drop_path(snake_case_ , self.drop_prob , self.training )
def _A( self ):
return "p={}".format(self.drop_prob )
class __magic_name__ ( nn.Module ):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_=None ):
super().__init__()
lowercase =patch_size if isinstance(snake_case_ , collections.abc.Iterable ) else (patch_size, patch_size)
lowercase =stride if isinstance(snake_case_ , collections.abc.Iterable ) else (stride, stride)
lowercase =padding if isinstance(snake_case_ , collections.abc.Iterable ) else (padding, padding)
lowercase =nn.Convad(snake_case_ , snake_case_ , kernel_size=snake_case_ , stride=snake_case_ , padding=snake_case_ )
lowercase =norm_layer(snake_case_ ) if norm_layer else nn.Identity()
def _A( self , snake_case_ ):
lowercase =self.projection(snake_case_ )
lowercase =self.norm(snake_case_ )
return embeddings
class __magic_name__ ( nn.GroupNorm ):
def __init__( self , snake_case_ , **snake_case_ ):
super().__init__(1 , snake_case_ , **snake_case_ )
class __magic_name__ ( nn.Module ):
def __init__( self , snake_case_ ):
super().__init__()
lowercase =nn.AvgPoolad(snake_case_ , stride=1 , padding=pool_size // 2 , count_include_pad=snake_case_ )
def _A( self , snake_case_ ):
return self.pool(snake_case_ ) - hidden_states
class __magic_name__ ( nn.Module ):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
super().__init__()
lowercase =nn.Convad(snake_case_ , snake_case_ , 1 )
lowercase =nn.Convad(snake_case_ , snake_case_ , 1 )
lowercase =PoolFormerDropPath(snake_case_ )
if isinstance(config.hidden_act , snake_case_ ):
lowercase =ACTaFN[config.hidden_act]
else:
lowercase =config.hidden_act
def _A( self , snake_case_ ):
lowercase =self.conva(snake_case_ )
lowercase =self.act_fn(snake_case_ )
lowercase =self.drop(snake_case_ )
lowercase =self.conva(snake_case_ )
lowercase =self.drop(snake_case_ )
return hidden_states
class __magic_name__ ( nn.Module ):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
super().__init__()
lowercase =PoolFormerPooling(snake_case_ )
lowercase =PoolFormerOutput(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
lowercase =PoolFormerGroupNorm(snake_case_ )
lowercase =PoolFormerGroupNorm(snake_case_ )
# Useful for training neural nets
lowercase =PoolFormerDropPath(snake_case_ ) if drop_path > 0.0 else nn.Identity()
lowercase =config.use_layer_scale
if config.use_layer_scale:
lowercase =nn.Parameter(
config.layer_scale_init_value * torch.ones((snake_case_) ) , requires_grad=snake_case_ )
lowercase =nn.Parameter(
config.layer_scale_init_value * torch.ones((snake_case_) ) , requires_grad=snake_case_ )
def _A( self , snake_case_ ):
if self.use_layer_scale:
lowercase =self.pooling(self.before_norm(snake_case_ ) )
lowercase =self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
lowercase =hidden_states + self.drop_path(snake_case_ )
lowercase =()
lowercase =self.output(self.after_norm(snake_case_ ) )
lowercase =self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
lowercase =hidden_states + self.drop_path(snake_case_ )
lowercase =(output,) + outputs
return outputs
else:
lowercase =self.drop_path(self.pooling(self.before_norm(snake_case_ ) ) )
# First residual connection
lowercase =pooling_output + hidden_states
lowercase =()
# Second residual connection inside the PoolFormerOutput block
lowercase =self.drop_path(self.output(self.after_norm(snake_case_ ) ) )
lowercase =hidden_states + layer_output
lowercase =(output,) + outputs
return outputs
class __magic_name__ ( nn.Module ):
def __init__( self , snake_case_ ):
super().__init__()
lowercase =config
# stochastic depth decay rule
lowercase =[x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
lowercase =[]
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
lowercase =nn.ModuleList(snake_case_ )
# Transformer blocks
lowercase =[]
lowercase =0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
lowercase =[]
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
snake_case_ , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(snake_case_ ) )
lowercase =nn.ModuleList(snake_case_ )
def _A( self , snake_case_ , snake_case_=False , snake_case_=True ):
lowercase =() if output_hidden_states else None
lowercase =pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
lowercase , lowercase =layers
# Get patch embeddings from hidden_states
lowercase =embedding_layer(snake_case_ )
# Send the embeddings through the blocks
for _, blk in enumerate(snake_case_ ):
lowercase =blk(snake_case_ )
lowercase =layer_outputs[0]
if output_hidden_states:
lowercase =all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=snake_case_ , hidden_states=snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = PoolFormerConfig
UpperCamelCase__ = 'poolformer'
UpperCamelCase__ = 'pixel_values'
UpperCamelCase__ = True
def _A( self , snake_case_ ):
if isinstance(snake_case_ , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(snake_case_ , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def _A( self , snake_case_ , snake_case_=False ):
if isinstance(snake_case_ , snake_case_ ):
lowercase =value
_UpperCAmelCase : List[Any] = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
_UpperCAmelCase : Any = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
'''
@add_start_docstrings(
'The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.' , __SCREAMING_SNAKE_CASE , )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , snake_case_ ):
super().__init__(snake_case_ )
lowercase =config
lowercase =PoolFormerEncoder(snake_case_ )
# Initialize weights and apply final processing
self.post_init()
def _A( self ):
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(snake_case_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=snake_case_ , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _A( self , snake_case_ = None , snake_case_ = None , snake_case_ = None , ):
lowercase =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase =return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
lowercase =self.encoder(
snake_case_ , output_hidden_states=snake_case_ , return_dict=snake_case_ , )
lowercase =encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=snake_case_ , hidden_states=encoder_outputs.hidden_states , )
class __magic_name__ ( nn.Module ):
def __init__( self , snake_case_ ):
super().__init__()
lowercase =nn.Linear(config.hidden_size , config.hidden_size )
def _A( self , snake_case_ ):
lowercase =self.dense(snake_case_ )
return output
@add_start_docstrings(
'\n PoolFormer Model transformer with an image classification head on top\n ' , __SCREAMING_SNAKE_CASE , )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self , snake_case_ ):
super().__init__(snake_case_ )
lowercase =config.num_labels
lowercase =PoolFormerModel(snake_case_ )
# Final norm
lowercase =PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
lowercase =(
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=snake_case_ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _A( self , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , ):
lowercase =return_dict if return_dict is not None else self.config.use_return_dict
lowercase =self.poolformer(
snake_case_ , output_hidden_states=snake_case_ , return_dict=snake_case_ , )
lowercase =outputs[0]
lowercase =self.classifier(self.norm(snake_case_ ).mean([-2, -1] ) )
lowercase =None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase ='''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase ='''single_label_classification'''
else:
lowercase ='''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase =MSELoss()
if self.num_labels == 1:
lowercase =loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase =loss_fct(snake_case_ , snake_case_ )
elif self.config.problem_type == "single_label_classification":
lowercase =CrossEntropyLoss()
lowercase =loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase =BCEWithLogitsLoss()
lowercase =loss_fct(snake_case_ , snake_case_ )
if not return_dict:
lowercase =(logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=snake_case_ , logits=snake_case_ , hidden_states=outputs.hidden_states )
| 72 |
'''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 __magic_name__ :
def __init__( self , snake_case_ , snake_case_=13 , snake_case_=7 , snake_case_=6 , snake_case_=17 , snake_case_=23 , snake_case_=11 , snake_case_=True , ):
lowercase =parent
lowercase =batch_size
lowercase =seq_length
lowercase =act_dim
lowercase =state_dim
lowercase =hidden_size
lowercase =max_length
lowercase =is_training
def _A( self ):
lowercase =floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase =ids_tensor((self.batch_size, self.seq_length) , vocab_size=10_00 )
lowercase =random_attention_mask((self.batch_size, self.seq_length) )
lowercase =self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _A( self ):
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 , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
lowercase =DecisionTransformerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowercase =model(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , 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 ):
lowercase =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) =config_and_inputs
lowercase ={
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = (DecisionTransformerModel,) if is_torch_available() else ()
UpperCamelCase__ = ()
UpperCamelCase__ = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
UpperCamelCase__ = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def _A( self ):
lowercase =DecisionTransformerModelTester(self )
lowercase =ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def _A( self ):
self.config_tester.run_common_tests()
def _A( self ):
lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
@slow
def _A( self ):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase =DecisionTransformerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _A( self ):
lowercase , lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase =model_class(snake_case_ )
lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase =[*signature.parameters.keys()]
lowercase =[
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def _A( self ):
lowercase =2 # number of steps of autoregressive prediction we will perform
lowercase =10 # defined by the RL environment, may be normalized
lowercase =DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
lowercase =model.to(snake_case_ )
lowercase =model.config
torch.manual_seed(0 )
lowercase =torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ) # env.reset()
lowercase =torch.tensor(
[[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]] , device=snake_case_ )
lowercase =torch.tensor(snake_case_ , device=snake_case_ , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowercase =state
lowercase =torch.zeros(1 , 0 , config.act_dim , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.zeros(1 , 0 , device=snake_case_ , dtype=torch.floataa )
lowercase =torch.tensor(0 , device=snake_case_ , dtype=torch.long ).reshape(1 , 1 )
for step in range(snake_case_ ):
lowercase =torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=snake_case_ )] , dim=1 )
lowercase =torch.cat([rewards, torch.zeros(1 , 1 , device=snake_case_ )] , dim=1 )
lowercase =torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowercase , lowercase , lowercase =model(
states=snake_case_ , actions=snake_case_ , rewards=snake_case_ , returns_to_go=snake_case_ , timesteps=snake_case_ , attention_mask=snake_case_ , return_dict=snake_case_ , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
lowercase , lowercase , lowercase , lowercase =( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=snake_case_ , dtype=torch.floataa ),
1.0,
False,
{},
)
lowercase =action_pred[0, -1]
lowercase =torch.cat([states, state] , dim=1 )
lowercase =returns_to_go[0, -1] - reward
lowercase =torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowercase =torch.cat(
[timesteps, torch.ones((1, 1) , device=snake_case_ , dtype=torch.long ) * (step + 1)] , dim=1 )
| 72 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = KandinskyInpaintPipeline
UpperCamelCase__ = ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
UpperCamelCase__ = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
UpperCamelCase__ = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
UpperCamelCase__ = False
@property
def _A( self ):
return 32
@property
def _A( self ):
return 32
@property
def _A( self ):
return self.time_input_dim
@property
def _A( self ):
return self.time_input_dim * 4
@property
def _A( self ):
return 1_00
@property
def _A( self ):
lowercase =XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def _A( self ):
torch.manual_seed(0 )
lowercase =MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
lowercase =MultilingualCLIP(snake_case_ )
lowercase =text_encoder.eval()
return text_encoder
@property
def _A( self ):
torch.manual_seed(0 )
lowercase ={
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase =UNetaDConditionModel(**snake_case_ )
return model
@property
def _A( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _A( self ):
torch.manual_seed(0 )
lowercase =VQModel(**self.dummy_movq_kwargs )
return model
def _A( self ):
lowercase =self.dummy_text_encoder
lowercase =self.dummy_tokenizer
lowercase =self.dummy_unet
lowercase =self.dummy_movq
lowercase =DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='''linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=snake_case_ , )
lowercase ={
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _A( self , snake_case_ , snake_case_=0 ):
lowercase =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
lowercase =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(snake_case_ )
# create init_image
lowercase =floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
lowercase =image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase =Image.fromarray(np.uinta(snake_case_ ) ).convert('''RGB''' ).resize((2_56, 2_56) )
# create mask
lowercase =np.ones((64, 64) , dtype=np.floataa )
lowercase =0
if str(snake_case_ ).startswith('''mps''' ):
lowercase =torch.manual_seed(snake_case_ )
else:
lowercase =torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
lowercase ={
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def _A( self ):
lowercase ='''cpu'''
lowercase =self.get_dummy_components()
lowercase =self.pipeline_class(**snake_case_ )
lowercase =pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
lowercase =pipe(**self.get_dummy_inputs(snake_case_ ) )
lowercase =output.images
lowercase =pipe(
**self.get_dummy_inputs(snake_case_ ) , return_dict=snake_case_ , )[0]
lowercase =image[0, -3:, -3:, -1]
lowercase =image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}' )
assert image.shape == (1, 64, 64, 3)
lowercase =np.array(
[0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
def _A( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase ):
def _A( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A( self ):
lowercase =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
lowercase =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase =np.ones((7_68, 7_68) , dtype=np.floataa )
lowercase =0
lowercase ='''a hat'''
lowercase =KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case_ )
lowercase =KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
lowercase =pipeline.to(snake_case_ )
pipeline.set_progress_bar_config(disable=snake_case_ )
lowercase =torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase , lowercase =pipe_prior(
snake_case_ , generator=snake_case_ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
lowercase =pipeline(
snake_case_ , image=snake_case_ , mask_image=snake_case_ , image_embeds=snake_case_ , negative_image_embeds=snake_case_ , generator=snake_case_ , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='''np''' , )
lowercase =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(snake_case_ , snake_case_ )
| 72 |
'''simple docstring'''
from math import pi, sqrt, tan
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or breadth < 0 or height < 0:
raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
return 4 * pi * radius**2
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
return 3 * pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cone() only accepts non-negative values''' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'''surface_area_conical_frustum() only accepts non-negative values''' )
lowercase =(height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
return 2 * pi * radius * (height + radius)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if torus_radius < 0 or tube_radius < 0:
raise ValueError('''surface_area_torus() only accepts non-negative values''' )
if torus_radius < tube_radius:
raise ValueError(
'''surface_area_torus() does not support spindle or self intersecting tori''' )
return 4 * pow(lowercase_ , 2 ) * torus_radius * tube_radius
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or width < 0:
raise ValueError('''area_rectangle() only accepts non-negative values''' )
return length * width
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''area_square() only accepts non-negative values''' )
return side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_triangle() only accepts non-negative values''' )
return (base * height) / 2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('''Given three sides do not form a triangle''' )
lowercase =(sidea + sidea + sidea) / 2
lowercase =sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_parallelogram() only accepts non-negative values''' )
return base * height
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if basea < 0 or basea < 0 or height < 0:
raise ValueError('''area_trapezium() only accepts non-negative values''' )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''area_circle() only accepts non-negative values''' )
return pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_x < 0 or radius_y < 0:
raise ValueError('''area_ellipse() only accepts non-negative values''' )
return pi * radius_x * radius_y
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('''area_rhombus() only accepts non-negative values''' )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase ( lowercase_ : int , lowercase_ : float ) -> float:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or sides < 3:
raise ValueError(
'''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
elif length < 0:
raise ValueError(
'''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('''[DEMO] Areas of various geometric shapes: \n''')
print(F"""Rectangle: {area_rectangle(10, 20) = }""")
print(F"""Square: {area_square(10) = }""")
print(F"""Triangle: {area_triangle(10, 10) = }""")
print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
print(F"""Rhombus: {area_rhombus(10, 20) = }""")
print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
print(F"""Circle: {area_circle(20) = }""")
print(F"""Ellipse: {area_ellipse(10, 20) = }""")
print('''\nSurface Areas of various geometric shapes: \n''')
print(F"""Cube: {surface_area_cube(20) = }""")
print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
print(F"""Sphere: {surface_area_sphere(20) = }""")
print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
print(F"""Cone: {surface_area_cone(10, 20) = }""")
print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
print(F"""Torus: {surface_area_torus(20, 10) = }""")
print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
print(F"""Square: {area_reg_polygon(4, 10) = }""")
print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
| 72 | 1 |
'''simple docstring'''
from math import pi, sqrt, tan
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or breadth < 0 or height < 0:
raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
return 4 * pi * radius**2
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
return 3 * pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cone() only accepts non-negative values''' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'''surface_area_conical_frustum() only accepts non-negative values''' )
lowercase =(height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0 or height < 0:
raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
return 2 * pi * radius * (height + radius)
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if torus_radius < 0 or tube_radius < 0:
raise ValueError('''surface_area_torus() only accepts non-negative values''' )
if torus_radius < tube_radius:
raise ValueError(
'''surface_area_torus() does not support spindle or self intersecting tori''' )
return 4 * pow(lowercase_ , 2 ) * torus_radius * tube_radius
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if length < 0 or width < 0:
raise ValueError('''area_rectangle() only accepts non-negative values''' )
return length * width
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('''area_square() only accepts non-negative values''' )
return side_length**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_triangle() only accepts non-negative values''' )
return (base * height) / 2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('''Given three sides do not form a triangle''' )
lowercase =(sidea + sidea + sidea) / 2
lowercase =sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if base < 0 or height < 0:
raise ValueError('''area_parallelogram() only accepts non-negative values''' )
return base * height
def UpperCamelCase ( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if basea < 0 or basea < 0 or height < 0:
raise ValueError('''area_trapezium() only accepts non-negative values''' )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase ( lowercase_ : float ) -> float:
'''simple docstring'''
if radius < 0:
raise ValueError('''area_circle() only accepts non-negative values''' )
return pi * radius**2
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if radius_x < 0 or radius_y < 0:
raise ValueError('''area_ellipse() only accepts non-negative values''' )
return pi * radius_x * radius_y
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> float:
'''simple docstring'''
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('''area_rhombus() only accepts non-negative values''' )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase ( lowercase_ : int , lowercase_ : float ) -> float:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) or sides < 3:
raise ValueError(
'''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
elif length < 0:
raise ValueError(
'''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('''[DEMO] Areas of various geometric shapes: \n''')
print(F"""Rectangle: {area_rectangle(10, 20) = }""")
print(F"""Square: {area_square(10) = }""")
print(F"""Triangle: {area_triangle(10, 10) = }""")
print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
print(F"""Rhombus: {area_rhombus(10, 20) = }""")
print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
print(F"""Circle: {area_circle(20) = }""")
print(F"""Ellipse: {area_ellipse(10, 20) = }""")
print('''\nSurface Areas of various geometric shapes: \n''')
print(F"""Cube: {surface_area_cube(20) = }""")
print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
print(F"""Sphere: {surface_area_sphere(20) = }""")
print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
print(F"""Cone: {surface_area_cone(10, 20) = }""")
print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
print(F"""Torus: {surface_area_torus(20, 10) = }""")
print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
print(F"""Square: {area_reg_polygon(4, 10) = }""")
print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
| 72 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = BarthezTokenizer
UpperCamelCase__ = BarthezTokenizerFast
UpperCamelCase__ = True
UpperCamelCase__ = True
def _A( self ):
super().setUp()
lowercase =BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case_ )
lowercase =tokenizer
def _A( self ):
lowercase ='''<pad>'''
lowercase =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def _A( self ):
lowercase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(snake_case_ ) , 10_11_22 )
def _A( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def _A( self ):
lowercase =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowercase =[0, 57, 30_18, 7_03_07, 91, 2]
lowercase =self.tokenizer(
snake_case_ , max_length=len(snake_case_ ) , padding=snake_case_ , truncation=snake_case_ , return_tensors='''pt''' )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
lowercase =batch.input_ids.tolist()[0]
self.assertListEqual(snake_case_ , snake_case_ )
def _A( self ):
if not self.test_rust_tokenizer:
return
lowercase =self.get_tokenizer()
lowercase =self.get_rust_tokenizer()
lowercase ='''I was born in 92000, and this is falsé.'''
lowercase =tokenizer.tokenize(snake_case_ )
lowercase =rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
lowercase =self.get_rust_tokenizer()
lowercase =tokenizer.encode(snake_case_ )
lowercase =rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
@slow
def _A( self ):
# fmt: off
lowercase ={'''input_ids''': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowercase =[
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=snake_case_ , )
| 72 | 1 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCamelCase ( lowercase_ : List[str] ) -> int:
'''simple docstring'''
lowercase =args.pruning_method
lowercase =args.threshold
lowercase =args.model_name_or_path.rstrip('''/''' )
lowercase =args.target_model_path
print(f'Load fine-pruned model from {model_name_or_path}' )
lowercase =torch.load(os.path.join(lowercase_ , '''pytorch_model.bin''' ) )
lowercase ={}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
lowercase =tensor
print(f'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
lowercase =tensor
print(f'Copied layer {name}' )
elif "bias" in name:
lowercase =tensor
print(f'Copied layer {name}' )
else:
if pruning_method == "magnitude":
lowercase =MagnitudeBinarizer.apply(inputs=lowercase_ , threshold=lowercase_ )
lowercase =tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
lowercase =name[:-6]
lowercase =model[f'{prefix_}mask_scores']
lowercase =TopKBinarizer.apply(lowercase_ , lowercase_ )
lowercase =tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
lowercase =name[:-6]
lowercase =model[f'{prefix_}mask_scores']
lowercase =ThresholdBinarizer.apply(lowercase_ , lowercase_ , lowercase_ )
lowercase =tensor * mask
print(f'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
lowercase =name[:-6]
lowercase =model[f'{prefix_}mask_scores']
lowercase , lowercase =-0.1, 1.1
lowercase =torch.sigmoid(lowercase_ )
lowercase =s * (r - l) + l
lowercase =s_bar.clamp(min=0.0 , max=1.0 )
lowercase =tensor * mask
print(f'Pruned layer {name}' )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
lowercase =os.path.join(
os.path.dirname(lowercase_ ) , f'bertarized_{os.path.basename(lowercase_ )}' )
if not os.path.isdir(lowercase_ ):
shutil.copytree(lowercase_ , lowercase_ )
print(f'\nCreated folder {target_model_path}' )
torch.save(lowercase_ , os.path.join(lowercase_ , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
_UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'''--pruning_method''',
choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
type=str,
required=True,
help=(
'''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
''' sigmoied_threshold = Soft movement pruning)'''
),
)
parser.add_argument(
'''--threshold''',
type=float,
required=False,
help=(
'''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
'''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
'''Not needed for `l0`'''
),
)
parser.add_argument(
'''--model_name_or_path''',
type=str,
required=True,
help='''Folder containing the model that was previously fine-pruned''',
)
parser.add_argument(
'''--target_model_path''',
default=None,
type=str,
required=False,
help='''Folder containing the model that was previously fine-pruned''',
)
_UpperCAmelCase : List[Any] = parser.parse_args()
main(args)
| 72 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_text_model'
UpperCamelCase__ = ['past_key_values']
UpperCamelCase__ = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , snake_case_=5_02_44 , snake_case_=7_68 , snake_case_=64 , snake_case_=20_48 , snake_case_=12 , snake_case_=12 , snake_case_=32 , snake_case_=1_28 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="gelu_new" , snake_case_=0 , snake_case_=False , snake_case_=0 , snake_case_=1 , snake_case_=False , snake_case_=True , **snake_case_ , ):
lowercase =vocab_size
lowercase =hidden_size
lowercase =d_kv
lowercase =d_ff
lowercase =num_layers
lowercase =num_heads
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =dropout_rate
lowercase =layer_norm_epsilon
lowercase =initializer_factor
lowercase =use_cache
lowercase =eos_token_id
lowercase =decoder_start_token_id
# for backwards compatibility
lowercase =dense_act_fn
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , tie_word_embeddings=snake_case_ , is_decoder=snake_case_ , **snake_case_ , )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct_vision_model'
def __init__( self , snake_case_=7_68 , snake_case_=7_68 , snake_case_=20_48 , snake_case_=64 , snake_case_=12 , snake_case_=12 , snake_case_="gelu_new" , snake_case_=1E-6 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=1E-10 , snake_case_=1.0 , snake_case_=40_96 , snake_case_=32 , snake_case_=1_28 , **snake_case_ , ):
super().__init__(**snake_case_ )
lowercase =hidden_size
lowercase =patch_embed_hidden_size
lowercase =d_ff
lowercase =dropout_rate
lowercase =num_hidden_layers
lowercase =num_attention_heads
lowercase =initializer_range
lowercase =initializer_factor
lowercase =attention_dropout
lowercase =layer_norm_eps
lowercase =dense_act_fn
lowercase =seq_len
lowercase =relative_attention_num_buckets
lowercase =relative_attention_max_distance
lowercase =d_kv
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
cls._set_token_in_kwargs(snake_case_ )
lowercase , lowercase =cls.get_config_dict(snake_case_ , **snake_case_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('''model_type''' ) == "pix2struct":
lowercase =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case_ , **snake_case_ )
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'pix2struct'
UpperCamelCase__ = True
def __init__( self , snake_case_=None , snake_case_=None , snake_case_=1.0 , snake_case_=0.02 , snake_case_=False , snake_case_=False , snake_case_=True , **snake_case_ , ):
super().__init__(tie_word_embeddings=snake_case_ , is_encoder_decoder=snake_case_ , **snake_case_ )
if text_config is None:
lowercase ={}
logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' )
if vision_config is None:
lowercase ={}
logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' )
lowercase =PixaStructTextConfig(**snake_case_ )
lowercase =PixaStructVisionConfig(**snake_case_ )
lowercase =self.text_config.decoder_start_token_id
lowercase =self.text_config.pad_token_id
lowercase =self.text_config.eos_token_id
lowercase =initializer_factor
lowercase =initializer_range
lowercase =self.initializer_range
lowercase =self.initializer_range
lowercase =is_vqa
@classmethod
def _A( cls , snake_case_ , snake_case_ , **snake_case_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case_ )
def _A( self ):
lowercase =copy.deepcopy(self.__dict__ )
lowercase =self.text_config.to_dict()
lowercase =self.vision_config.to_dict()
lowercase =self.__class__.model_type
return output
| 72 | 1 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_UpperCAmelCase : Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_UpperCAmelCase : str = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"""{len(upper_files)} files contain uppercase characters:""")
print('''\n'''.join(upper_files) + '''\n''')
_UpperCAmelCase : int = [file for file in filepaths if ''' ''' in file]
if space_files:
print(F"""{len(space_files)} files contain space characters:""")
print('''\n'''.join(space_files) + '''\n''')
_UpperCAmelCase : List[Any] = [file for file in filepaths if '''-''' in file]
if hyphen_files:
print(F"""{len(hyphen_files)} files contain hyphen characters:""")
print('''\n'''.join(hyphen_files) + '''\n''')
_UpperCAmelCase : Optional[Any] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"""{len(nodir_files)} files are not in a directory:""")
print('''\n'''.join(nodir_files) + '''\n''')
_UpperCAmelCase : Any = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 72 |
'''simple docstring'''
def UpperCamelCase ( ) -> int:
'''simple docstring'''
return 1
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(lowercase_ )
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(lowercase_ )
def UpperCamelCase ( lowercase_ : int = 2_0_0 ) -> int:
'''simple docstring'''
return two_pound(lowercase_ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 72 | 1 |
'''simple docstring'''
from math import loga
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(lowercase_ , lowercase_ ):
raise TypeError('''Input value must be a \'int\' type''' )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''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 __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = ['image_processor', 'tokenizer']
UpperCamelCase__ = 'BlipImageProcessor'
UpperCamelCase__ = 'AutoTokenizer'
def __init__( self , snake_case_ , snake_case_ , snake_case_ ):
super().__init__(snake_case_ , snake_case_ )
# add QFormer tokenizer
lowercase =qformer_tokenizer
def __call__( self , snake_case_ = None , snake_case_ = None , snake_case_ = True , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = True , snake_case_ = None , **snake_case_ , ):
if images is None and text is None:
raise ValueError('''You have to specify at least images or text.''' )
lowercase =BatchFeature()
if text is not None:
lowercase =self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
encoding.update(snake_case_ )
lowercase =self.qformer_tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
lowercase =qformer_text_encoding.pop('''input_ids''' )
lowercase =qformer_text_encoding.pop('''attention_mask''' )
if images is not None:
lowercase =self.image_processor(snake_case_ , return_tensors=snake_case_ )
encoding.update(snake_case_ )
return encoding
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def _A( self , *snake_case_ , **snake_case_ ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _A( self ):
lowercase =self.tokenizer.model_input_names
lowercase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _A( self , snake_case_ , **snake_case_ ):
if os.path.isfile(snake_case_ ):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
lowercase =os.path.join(snake_case_ , '''qformer_tokenizer''' )
self.qformer_tokenizer.save_pretrained(snake_case_ )
return super().save_pretrained(snake_case_ , **snake_case_ )
@classmethod
def _A( cls , snake_case_ , **snake_case_ ):
lowercase =AutoTokenizer.from_pretrained(snake_case_ , subfolder='''qformer_tokenizer''' )
lowercase =cls._get_arguments_from_pretrained(snake_case_ , **snake_case_ )
args.append(snake_case_ )
return cls(*snake_case_ )
| 72 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 'encoder-decoder'
UpperCamelCase__ = True
def __init__( self , **snake_case_ ):
super().__init__(**snake_case_ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
lowercase =kwargs.pop('''encoder''' )
lowercase =encoder_config.pop('''model_type''' )
lowercase =kwargs.pop('''decoder''' )
lowercase =decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
lowercase =AutoConfig.for_model(snake_case_ , **snake_case_ )
lowercase =AutoConfig.for_model(snake_case_ , **snake_case_ )
lowercase =True
@classmethod
def _A( cls , snake_case_ , snake_case_ , **snake_case_ ):
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
lowercase =True
lowercase =True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **snake_case_ )
def _A( self ):
lowercase =copy.deepcopy(self.__dict__ )
lowercase =self.encoder.to_dict()
lowercase =self.decoder.to_dict()
lowercase =self.__class__.model_type
return output
| 72 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_UpperCAmelCase : Dict = '''\
@inproceedings{lin-2004-rouge,
title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
author = "Lin, Chin-Yew",
booktitle = "Text Summarization Branches Out",
month = jul,
year = "2004",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W04-1013",
pages = "74--81",
}
'''
_UpperCAmelCase : Union[str, Any] = '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''
_UpperCAmelCase : Dict = '''
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
`"rougeL"`: Longest common subsequence based scoring.
`"rougeLSum"`: rougeLsum splits text using `"\n"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric(\'rouge\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
[\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
>>> print(results["rouge1"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results["rouge1"].mid.fmeasure)
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def _A( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
'''https://github.com/google-research/google-research/tree/master/rouge''',
] , )
def _A( self , snake_case_ , snake_case_ , snake_case_=None , snake_case_=True , snake_case_=False ):
if rouge_types is None:
lowercase =['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']
lowercase =rouge_scorer.RougeScorer(rouge_types=snake_case_ , use_stemmer=snake_case_ )
if use_aggregator:
lowercase =scoring.BootstrapAggregator()
else:
lowercase =[]
for ref, pred in zip(snake_case_ , snake_case_ ):
lowercase =scorer.score(snake_case_ , snake_case_ )
if use_aggregator:
aggregator.add_scores(snake_case_ )
else:
scores.append(snake_case_ )
if use_aggregator:
lowercase =aggregator.aggregate()
else:
lowercase ={}
for key in scores[0]:
lowercase =[score[key] for score in scores]
return result
| 72 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def UpperCamelCase ( lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : Union[str, Any] ) -> str:
'''simple docstring'''
lowercase =f'{file}_{class_name}_{test_name}'
done_test[_id] += 1
with open(lowercase_ , '''r''' ) as f:
lowercase =f.readlines()
lowercase =f'class {class_name}('
lowercase =f'{4 * " "}def {test_name}('
lowercase =f'{8 * " "}{correct_line.split()[0]}'
lowercase =f'{1_6 * " "}{correct_line.split()[0]}'
lowercase =False
lowercase =False
lowercase =False
lowercase =False
lowercase =0
lowercase =0
lowercase =[]
for line in lines:
if line.startswith(lowercase_ ):
lowercase =True
elif in_class and line.startswith(lowercase_ ):
lowercase =True
elif in_class and in_func and (line.startswith(lowercase_ ) or line.startswith(lowercase_ )):
lowercase =len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
lowercase =True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
lowercase =True
if in_class and in_func and in_line and insert_line:
new_lines.append(f'{spaces * " "}{correct_line}' )
lowercase =lowercase =lowercase =lowercase =False
else:
new_lines.append(lowercase_ )
with open(lowercase_ , '''w''' ) as f:
for line in new_lines:
f.write(lowercase_ )
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : str=None ) -> str:
'''simple docstring'''
if fail is not None:
with open(lowercase_ , '''r''' ) as f:
lowercase ={l.strip() for l in f.readlines()}
else:
lowercase =None
with open(lowercase_ , '''r''' ) as f:
lowercase =f.readlines()
lowercase =defaultdict(lowercase_ )
for line in correct_lines:
lowercase , lowercase , lowercase , lowercase =line.split(''';''' )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if __name__ == "__main__":
_UpperCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
_UpperCAmelCase : int = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 72 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = '''▁'''
_UpperCAmelCase : Union[str, Any] = {'''vocab_file''': '''spiece.model'''}
_UpperCAmelCase : Union[str, Any] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
_UpperCAmelCase : List[Any] = {
'''google/pegasus-xsum''': 5_12,
}
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self , snake_case_ , snake_case_="<pad>" , snake_case_="</s>" , snake_case_="<unk>" , snake_case_="<mask_2>" , snake_case_="<mask_1>" , snake_case_=None , snake_case_=1_03 , snake_case_ = None , **snake_case_ , ):
lowercase =offset
if additional_special_tokens is not None:
if not isinstance(snake_case_ , snake_case_ ):
raise TypeError(
f'additional_special_tokens should be of type {type(snake_case_ )}, but is'
f' {type(snake_case_ )}' )
lowercase =(
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(snake_case_ ) , self.offset - 1 )
]
if len(set(snake_case_ ) ) != len(snake_case_ ):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
lowercase =additional_special_tokens_extended
else:
lowercase =[mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=snake_case_ , unk_token=snake_case_ , mask_token=snake_case_ , pad_token=snake_case_ , mask_token_sent=snake_case_ , offset=snake_case_ , additional_special_tokens=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
lowercase =mask_token_sent
lowercase =vocab_file
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case_ )
# add special tokens to encoder dict
lowercase ={
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
lowercase ={v: k for k, v in self.encoder.items()}
@property
def _A( self ):
return len(self.sp_model ) + self.offset
def _A( self ):
lowercase ={self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
lowercase =self.__dict__.copy()
lowercase =None
return state
def __setstate__( self , snake_case_ ):
lowercase =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase ={}
lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A( self , snake_case_ ):
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def _A( self , snake_case_ ):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowercase =self.sp_model.piece_to_id(snake_case_ )
return sp_id + self.offset
def _A( self , snake_case_ ):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowercase =self.sp_model.IdToPiece(index - self.offset )
return token
def _A( self , snake_case_ ):
lowercase =[]
lowercase =''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(snake_case_ ) + token
lowercase =[]
else:
current_sub_tokens.append(snake_case_ )
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def _A( self , snake_case_=False ):
return 1
def _A( self , snake_case_ ):
lowercase =set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def _A( self , snake_case_ , snake_case_ = None , snake_case_ = False ):
if already_has_special_tokens:
return self._special_token_mask(snake_case_ )
elif token_ids_a is None:
return self._special_token_mask(snake_case_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A( self , snake_case_ , snake_case_=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A( self , snake_case_ , snake_case_ = None ):
if not os.path.isdir(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase =os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , '''wb''' ) as fi:
lowercase =self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
| 72 | 1 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : Optional[int]="shi-labs/oneformer_demo" ) -> str:
'''simple docstring'''
with open(hf_hub_download(lowercase_ , lowercase_ , repo_type='''dataset''' ) , '''r''' ) as f:
lowercase =json.load(lowercase_ )
lowercase ={}
lowercase =[]
lowercase =[]
for key, info in class_info.items():
lowercase =info['''name''']
class_names.append(info['''name'''] )
if info["isthing"]:
thing_ids.append(int(lowercase_ ) )
lowercase =thing_ids
lowercase =class_names
return metadata
class __magic_name__ ( unittest.TestCase ):
def __init__( self , snake_case_ , snake_case_=7 , snake_case_=3 , snake_case_=30 , snake_case_=4_00 , snake_case_=None , snake_case_=True , snake_case_=True , snake_case_=[0.5, 0.5, 0.5] , snake_case_=[0.5, 0.5, 0.5] , snake_case_=10 , snake_case_=False , snake_case_=2_55 , snake_case_="shi-labs/oneformer_demo" , snake_case_="ade20k_panoptic.json" , snake_case_=10 , ):
lowercase =parent
lowercase =batch_size
lowercase =num_channels
lowercase =min_resolution
lowercase =max_resolution
lowercase =do_resize
lowercase ={'''shortest_edge''': 32, '''longest_edge''': 13_33} if size is None else size
lowercase =do_normalize
lowercase =image_mean
lowercase =image_std
lowercase =class_info_file
lowercase =prepare_metadata(snake_case_ , snake_case_ )
lowercase =num_text
lowercase =repo_path
# for the post_process_functions
lowercase =2
lowercase =10
lowercase =10
lowercase =3
lowercase =4
lowercase =num_labels
lowercase =do_reduce_labels
lowercase =ignore_index
def _A( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def _A( self , snake_case_ , snake_case_=False ):
if not batched:
lowercase =image_inputs[0]
if isinstance(snake_case_ , Image.Image ):
lowercase , lowercase =image.size
else:
lowercase , lowercase =image.shape[1], image.shape[2]
if w < h:
lowercase =int(self.size['''shortest_edge'''] * h / w )
lowercase =self.size['''shortest_edge''']
elif w > h:
lowercase =self.size['''shortest_edge''']
lowercase =int(self.size['''shortest_edge'''] * w / h )
else:
lowercase =self.size['''shortest_edge''']
lowercase =self.size['''shortest_edge''']
else:
lowercase =[]
for image in image_inputs:
lowercase , lowercase =self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase =max(snake_case_ , key=lambda snake_case_ : item[0] )[0]
lowercase =max(snake_case_ , key=lambda snake_case_ : item[1] )[1]
return expected_height, expected_width
def _A( self ):
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
UpperCamelCase__ = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
UpperCamelCase__ = image_processing_class
def _A( self ):
lowercase =OneFormerImageProcessorTester(self )
@property
def _A( self ):
return self.image_processing_tester.prepare_image_processor_dict()
def _A( self ):
lowercase =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case_ , '''image_mean''' ) )
self.assertTrue(hasattr(snake_case_ , '''image_std''' ) )
self.assertTrue(hasattr(snake_case_ , '''do_normalize''' ) )
self.assertTrue(hasattr(snake_case_ , '''do_resize''' ) )
self.assertTrue(hasattr(snake_case_ , '''size''' ) )
self.assertTrue(hasattr(snake_case_ , '''ignore_index''' ) )
self.assertTrue(hasattr(snake_case_ , '''class_info_file''' ) )
self.assertTrue(hasattr(snake_case_ , '''num_text''' ) )
self.assertTrue(hasattr(snake_case_ , '''repo_path''' ) )
self.assertTrue(hasattr(snake_case_ , '''metadata''' ) )
self.assertTrue(hasattr(snake_case_ , '''do_reduce_labels''' ) )
def _A( self ):
pass
def _A( self ):
# Initialize image_processor
lowercase =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase =prepare_image_inputs(self.image_processing_tester , equal_resolution=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , Image.Image )
# Test not batched input
lowercase =image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase =self.image_processing_tester.get_expected_values(snake_case_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase , lowercase =self.image_processing_tester.get_expected_values(snake_case_ , batched=snake_case_ )
lowercase =image_processor(
snake_case_ , ['''semantic'''] * len(snake_case_ ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def _A( self ):
# Initialize image_processor
lowercase =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase =prepare_image_inputs(self.image_processing_tester , equal_resolution=snake_case_ , numpify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , np.ndarray )
# Test not batched input
lowercase =image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase =self.image_processing_tester.get_expected_values(snake_case_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase , lowercase =self.image_processing_tester.get_expected_values(snake_case_ , batched=snake_case_ )
lowercase =image_processor(
snake_case_ , ['''semantic'''] * len(snake_case_ ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def _A( self ):
# Initialize image_processor
lowercase =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase =prepare_image_inputs(self.image_processing_tester , equal_resolution=snake_case_ , torchify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , torch.Tensor )
# Test not batched input
lowercase =image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
lowercase , lowercase =self.image_processing_tester.get_expected_values(snake_case_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase , lowercase =self.image_processing_tester.get_expected_values(snake_case_ , batched=snake_case_ )
lowercase =image_processor(
snake_case_ , ['''semantic'''] * len(snake_case_ ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def _A( self , snake_case_=False , snake_case_=False , snake_case_="np" ):
lowercase =self.image_processing_class(**self.image_processor_dict )
# prepare image and target
lowercase =self.image_processing_tester.num_labels
lowercase =None
lowercase =None
lowercase =prepare_image_inputs(self.image_processing_tester , equal_resolution=snake_case_ )
if with_segmentation_maps:
lowercase =num_labels
if is_instance_map:
lowercase =list(range(snake_case_ ) ) * 2
lowercase =dict(enumerate(snake_case_ ) )
lowercase =[
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
lowercase =[Image.fromarray(snake_case_ ) for annotation in annotations]
lowercase =image_processor(
snake_case_ , ['''semantic'''] * len(snake_case_ ) , snake_case_ , return_tensors='''pt''' , instance_id_to_semantic_id=snake_case_ , pad_and_return_pixel_mask=snake_case_ , )
return inputs
def _A( self ):
pass
def _A( self ):
def common(snake_case_=False , snake_case_=None ):
lowercase =self.comm_get_image_processor_inputs(
with_segmentation_maps=snake_case_ , is_instance_map=snake_case_ , segmentation_type=snake_case_ )
lowercase =inputs['''mask_labels''']
lowercase =inputs['''class_labels''']
lowercase =inputs['''pixel_values''']
lowercase =inputs['''text_inputs''']
# check the batch_size
for mask_label, class_label, text_input in zip(snake_case_ , snake_case_ , snake_case_ ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(snake_case_ ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=snake_case_ )
common(is_instance_map=snake_case_ , segmentation_type='''pil''' )
common(is_instance_map=snake_case_ , segmentation_type='''pil''' )
def _A( self ):
lowercase =np.zeros((20, 50) )
lowercase =1
lowercase =1
lowercase =1
lowercase =binary_mask_to_rle(snake_case_ )
self.assertEqual(len(snake_case_ ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def _A( self ):
lowercase =self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
lowercase =self.image_processing_tester.get_fake_oneformer_outputs()
lowercase =fature_extractor.post_process_semantic_segmentation(snake_case_ )
self.assertEqual(len(snake_case_ ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
lowercase =[(1, 4) for i in range(self.image_processing_tester.batch_size )]
lowercase =fature_extractor.post_process_semantic_segmentation(snake_case_ , target_sizes=snake_case_ )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def _A( self ):
lowercase =self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
lowercase =self.image_processing_tester.get_fake_oneformer_outputs()
lowercase =image_processor.post_process_instance_segmentation(snake_case_ , threshold=0 )
self.assertTrue(len(snake_case_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('''segmentation''' in el )
self.assertTrue('''segments_info''' in el )
self.assertEqual(type(el['''segments_info'''] ) , snake_case_ )
self.assertEqual(
el['''segmentation'''].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def _A( self ):
lowercase =self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
lowercase =self.image_processing_tester.get_fake_oneformer_outputs()
lowercase =image_processor.post_process_panoptic_segmentation(snake_case_ , threshold=0 )
self.assertTrue(len(snake_case_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('''segmentation''' in el )
self.assertTrue('''segments_info''' in el )
self.assertEqual(type(el['''segments_info'''] ) , snake_case_ )
self.assertEqual(
el['''segmentation'''].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 72 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 72 | 1 |
'''simple docstring'''
import math
def UpperCamelCase ( lowercase_ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowercase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCamelCase ( lowercase_ : float = 0.1 ) -> int:
'''simple docstring'''
lowercase =3
lowercase =3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(lowercase_ )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Tuple ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase_ , lowercase_ ):
lowercase =np.full((len(lowercase_ ), sequence_length, 2) , lowercase_ )
else:
lowercase =np.full((len(lowercase_ ), sequence_length) , lowercase_ )
for i, tensor in enumerate(lowercase_ ):
if padding_side == "right":
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
else:
if isinstance(lowercase_ , lowercase_ ):
lowercase =tensor[:sequence_length]
else:
lowercase =tensor[:sequence_length]
return out_tensor.tolist()
def UpperCamelCase ( lowercase_ : Optional[Any] ) -> str:
'''simple docstring'''
lowercase =ord(lowercase_ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
lowercase =unicodedata.category(lowercase_ )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class __magic_name__ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = 42
UpperCamelCase__ = True
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = -1_00
UpperCamelCase__ = "pt"
def _A( self , snake_case_ ):
import torch
lowercase ='''label''' if '''label''' in features[0].keys() else '''labels'''
lowercase =[feature[label_name] for feature in features] if label_name in features[0].keys() else None
lowercase =self.tokenizer.pad(
snake_case_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
lowercase =torch.tensor(batch['''entity_ids'''] ).shape[1]
lowercase =self.tokenizer.padding_side
if padding_side == "right":
lowercase =[
list(snake_case_ ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_ )) for label in labels
]
else:
lowercase =[
[self.label_pad_token_id] * (sequence_length - len(snake_case_ )) + list(snake_case_ ) for label in labels
]
lowercase =[feature['''ner_tags'''] for feature in features]
lowercase =padding_tensor(snake_case_ , -1 , snake_case_ , snake_case_ )
lowercase =[feature['''original_entity_spans'''] for feature in features]
lowercase =padding_tensor(snake_case_ , (-1, -1) , snake_case_ , snake_case_ )
lowercase ={k: torch.tensor(snake_case_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 72 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : List[str] = {
'''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:
_UpperCAmelCase : List[Any] = [
'''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
_UpperCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 72 |
'''simple docstring'''
_UpperCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCamelCase ( lowercase_ : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
lowercase =f'a bytes-like object is required, not \'{data.__class__.__name__}\''
raise TypeError(lowercase_ )
lowercase =''''''.join(bin(lowercase_ )[2:].zfill(8 ) for byte in data )
lowercase =len(lowercase_ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase =b'''=''' * ((6 - len(lowercase_ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(lowercase_ ) % 6)
else:
lowercase =b''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(lowercase_ ) , 6 ) ).encode()
+ padding
)
def UpperCamelCase ( lowercase_ : str ) -> bytes:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
lowercase =(
'''argument should be a bytes-like object or ASCII string, '''
f'not \'{encoded_data.__class__.__name__}\''
)
raise TypeError(lowercase_ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(lowercase_ , lowercase_ ):
try:
lowercase =encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
lowercase =encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(lowercase_ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase =encoded_data[:-padding]
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase =''''''.join(
bin(B64_CHARSET.index(lowercase_ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase =[
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(lowercase_ ) , 8 )
]
return bytes(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 1 |
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