code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
_SCREAMING_SNAKE_CASE = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
),
},
}
_SCREAMING_SNAKE_CASE = {
"""moussaKam/mbarthez""": 1_0_2_4,
"""moussaKam/barthez""": 1_0_2_4,
"""moussaKam/barthez-orangesum-title""": 1_0_2_4,
}
_SCREAMING_SNAKE_CASE = """▁"""
class SCREAMING_SNAKE_CASE_ ( UpperCAmelCase__ ):
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = ["input_ids", "attention_mask"]
__lowerCAmelCase = BarthezTokenizer
def __init__( self : Optional[Any] , lowerCamelCase_ : Dict=None , lowerCamelCase_ : str=None , lowerCamelCase_ : List[Any]="<s>" , lowerCamelCase_ : Optional[int]="</s>" , lowerCamelCase_ : List[str]="</s>" , lowerCamelCase_ : Tuple="<s>" , lowerCamelCase_ : str="<unk>" , lowerCamelCase_ : Any="<pad>" , lowerCamelCase_ : Union[str, Any]="<mask>" , **lowerCamelCase_ : Union[str, Any] , ):
"""simple docstring"""
UpperCamelCase = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else mask_token
super().__init__(
lowerCamelCase_ , tokenizer_file=lowerCamelCase_ , bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , **lowerCamelCase_ , )
UpperCamelCase = vocab_file
UpperCamelCase = False if not self.vocab_file else True
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
UpperCamelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : List[int] , lowerCamelCase_ : Optional[List[int]] = None ):
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : str , lowerCamelCase_ : Optional[str] = None ):
"""simple docstring"""
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(lowerCamelCase_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCamelCase = os.path.join(
lowerCamelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ):
copyfile(self.vocab_file , lowerCamelCase_ )
return (out_vocab_file,)
| 343 | '''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 67 | 0 |
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__ ( UpperCAmelCase__ ):
def __init__( self , __snake_case , __snake_case ) -> List[Any]:
'''simple docstring'''
super().__init__()
# make sure scheduler can always be converted to DDIM
__a =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 , ) -> List[Any]:
'''simple docstring'''
if isinstance(self.unet.config.sample_size , __snake_case ):
__a =(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
__a =(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.' )
__a =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
__a =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
__a =self.scheduler.step(
__snake_case , __snake_case , __snake_case , eta=__snake_case , use_clipped_model_output=__snake_case , generator=__snake_case ).prev_sample
__a =(image / 2 + 0.5).clamp(0 , 1 )
__a =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__a =self.numpy_to_pil(__snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__snake_case )
| 218 | '''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __lowerCAmelCase ( UpperCamelCase__ ) -> list[list[float]]:
__lowerCamelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(UpperCamelCase__ ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
__lowerCamelCase = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creates a copy of the matrix with swapped positions of the elements
__lowerCamelCase = [[0.0, 0.0], [0.0, 0.0]]
__lowerCamelCase , __lowerCamelCase = matrix[1][1], matrix[0][0]
__lowerCamelCase , __lowerCamelCase = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(UpperCamelCase__ ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(UpperCamelCase__ ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
__lowerCamelCase = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creating cofactor matrix
__lowerCamelCase = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
__lowerCamelCase = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
__lowerCamelCase = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
__lowerCamelCase = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
__lowerCamelCase = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
__lowerCamelCase = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
__lowerCamelCase = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
__lowerCamelCase = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
__lowerCamelCase = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
__lowerCamelCase = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
__lowerCamelCase = array(UpperCamelCase__ )
for i in range(3 ):
for j in range(3 ):
__lowerCamelCase = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
__lowerCamelCase = array(UpperCamelCase__ )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(UpperCamelCase__ )
# Calculate the inverse of the matrix
return [[float(d(UpperCamelCase__ ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''' )
| 67 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase : Any = 16
_lowerCamelCase : Optional[Any] = 32
def __lowerCamelCase ( A__ , A__ = 16 ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = AutoTokenizer.from_pretrained('bert-base-cased' )
UpperCamelCase = load_dataset('glue' , 'mrpc' )
def tokenize_function(A__ ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(A__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase = 16
elif accelerator.mixed_precision != "no":
UpperCamelCase = 8
else:
UpperCamelCase = None
return tokenizer.pad(
UpperCamelCase__ , padding='longest' , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors='pt' , )
# Instantiate dataloaders.
UpperCamelCase = DataLoader(
tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
UpperCamelCase = DataLoader(
tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCamelCase : Tuple = mocked_dataloaders # noqa: F811
def __lowerCamelCase ( A__ , A__ ) -> Any:
"""simple docstring"""
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , UpperCamelCase__ ) == "1":
UpperCamelCase = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
UpperCamelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='all' , project_dir=args.project_dir )
else:
UpperCamelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase = config['lr']
UpperCamelCase = int(config['num_epochs'] )
UpperCamelCase = int(config['seed'] )
UpperCamelCase = int(config['batch_size'] )
set_seed(UpperCamelCase__ )
UpperCamelCase , UpperCamelCase = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
UpperCamelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
UpperCamelCase = batch_size // MAX_GPU_BATCH_SIZE
UpperCamelCase = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
UpperCamelCase = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
UpperCamelCase = os.path.split(UpperCamelCase__ )[-1].split('.' )[0]
accelerator.init_trackers(UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
UpperCamelCase = 0
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
UpperCamelCase = model(**UpperCamelCase__ )
UpperCamelCase = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
UpperCamelCase = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase = model(**UpperCamelCase__ )
UpperCamelCase = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
UpperCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , UpperCamelCase__ )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
'accuracy': eval_metric['accuracy'],
'f1': eval_metric['f1'],
'train_loss': total_loss.item() / len(UpperCamelCase__ ),
'epoch': epoch,
} , step=UpperCamelCase__ , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def __lowerCamelCase ( ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
parser.add_argument(
'--with_tracking' , action='store_true' , help='Whether to load in all available experiment trackers from the environment and use them for logging.' , )
parser.add_argument(
'--project_dir' , type=UpperCamelCase__ , default='logs' , help='Location on where to store experiment tracking logs` and relevent project information' , )
UpperCamelCase = parser.parse_args()
UpperCamelCase = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 28 | '''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 =logging.get_logger(__name__)
def __lowerCAmelCase ( UpperCamelCase__=None , UpperCamelCase__=None ) -> int:
return field(default_factory=lambda: default , metadata=UpperCamelCase__ )
@dataclass
class a__ :
lowerCamelCase : List[str] =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"
)
} , )
lowerCamelCase : List[int] =list_field(
default=[8] , metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
lowerCamelCase : List[int] =list_field(
default=[8, 3_2, 1_2_8, 5_1_2] , metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} , )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} , )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} , )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Use FP16 to accelerate inference."} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Benchmark training of model"} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Verbose memory tracing"} )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} , )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} , )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Trace memory line by line"} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Save result to a CSV file"} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Save all print statements in a log file"} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Whether to print environment information"} )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , 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."
)
} , )
lowerCamelCase : str =field(
default=F'''inference_time_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving time results to csv."} , )
lowerCamelCase : str =field(
default=F'''inference_memory_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving memory results to csv."} , )
lowerCamelCase : str =field(
default=F'''train_time_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving time results to csv for training."} , )
lowerCamelCase : str =field(
default=F'''train_memory_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving memory results to csv for training."} , )
lowerCamelCase : str =field(
default=F'''env_info_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving environment information."} , )
lowerCamelCase : str =field(
default=F'''log_{round(time() )}.csv''' , metadata={"help": "Log filename used if print statements are saved in log."} , )
lowerCamelCase : int =field(default=3 , metadata={"help": "Times an experiment will be run."} )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} , )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
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.''' , a , )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
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
| 67 | 0 |
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
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'spm_char.model'}
a_ = {
'vocab_file': {
'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
}
}
a_ = {
'microsoft/speecht5_asr': 1_024,
'microsoft/speecht5_tts': 1_024,
'microsoft/speecht5_vc': 1_024,
}
class _lowercase ( UpperCAmelCase__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ["input_ids", "attention_mask"]
def __init__( self : List[Any] , snake_case : Dict , snake_case : Tuple="<s>" , snake_case : List[Any]="</s>" , snake_case : Union[str, Any]="<unk>" , snake_case : List[Any]="<pad>" , snake_case : Optional[Dict[str, Any]] = None , **snake_case : Dict , ) -> Dict:
"""simple docstring"""
UpperCamelCase_ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , pad_token=snake_case , sp_model_kwargs=self.sp_model_kwargs , **snake_case , )
UpperCamelCase_ : Dict = vocab_file
UpperCamelCase_ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
"""simple docstring"""
return self.sp_model.get_piece_size()
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : List[Any] = {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 : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : int = self.__dict__.copy()
UpperCamelCase_ : Dict = None
return state
def __setstate__( self : Tuple , snake_case : Dict ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ : Dict = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCamelCase_ : List[Any] = {}
UpperCamelCase_ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , snake_case : str ) -> str:
"""simple docstring"""
return self.sp_model.encode(snake_case , out_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : List[str] ) -> Dict:
"""simple docstring"""
return self.sp_model.piece_to_id(snake_case )
def SCREAMING_SNAKE_CASE__ ( self : str , snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : List[str] = self.sp_model.IdToPiece(snake_case )
return token
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case : Dict ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = []
UpperCamelCase_ : Optional[int] = ''
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
UpperCamelCase_ : Any = []
else:
current_sub_tokens.append(snake_case )
out_string += self.sp_model.decode(snake_case )
return out_string.strip()
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : str , snake_case : List[str]=None ) -> Optional[int]:
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : List[int] , snake_case : Optional[List[int]] = None , snake_case : bool = False ) -> int:
"""simple docstring"""
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 )
UpperCamelCase_ : int = [1]
if token_ids_a is None:
return ([0] * len(snake_case )) + suffix_ones
return ([0] * len(snake_case )) + ([0] * len(snake_case )) + suffix_ones
def SCREAMING_SNAKE_CASE__ ( self : Any , snake_case : str , snake_case : Optional[str] = None ) -> Optional[int]:
"""simple docstring"""
if not os.path.isdir(snake_case ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCamelCase_ : Dict = 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:
UpperCamelCase_ : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(snake_case )
return (out_vocab_file,)
| 175 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
__UpperCAmelCase =None
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase ={
"vocab_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
),
},
}
__UpperCAmelCase ={
"moussaKam/mbarthez": 1_0_2_4,
"moussaKam/barthez": 1_0_2_4,
"moussaKam/barthez-orangesum-title": 1_0_2_4,
}
__UpperCAmelCase ="▁"
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Optional[int] =VOCAB_FILES_NAMES
lowerCamelCase : Optional[Any] =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : int =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Optional[Any] =["input_ids", "attention_mask"]
lowerCamelCase : Union[str, Any] =BarthezTokenizer
def __init__( self : Optional[Any] , a : Dict=None , a : str=None , a : List[Any]="<s>" , a : Optional[int]="</s>" , a : List[str]="</s>" , a : Tuple="<s>" , a : str="<unk>" , a : Any="<pad>" , a : Union[str, Any]="<mask>" , **a : Union[str, Any] , ):
"""simple docstring"""
__lowerCamelCase = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else mask_token
super().__init__(
a , tokenizer_file=a , bos_token=a , eos_token=a , unk_token=a , sep_token=a , cls_token=a , pad_token=a , mask_token=a , **a , )
__lowerCamelCase = vocab_file
__lowerCamelCase = False if not self.vocab_file else True
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , a : List[int] , a : Optional[List[int]] = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowerCamelCase = [self.cls_token_id]
__lowerCamelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self : Tuple , a : List[int] , a : Optional[List[int]] = None ):
"""simple docstring"""
__lowerCamelCase = [self.sep_token_id]
__lowerCamelCase = [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 SCREAMING_SNAKE_CASE__ ( self : List[Any] , a : str , a : Optional[str] = None ):
"""simple docstring"""
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(a ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCamelCase = os.path.join(
a , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a ):
copyfile(self.vocab_file , a )
return (out_vocab_file,)
| 67 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowerCAmelCase_ ( __a , __a ) -> List[Any]:
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
lowerCamelCase__: str =flax_key_tuple[:-1] + ("weight",)
lowerCamelCase__: List[str] =torch.permute(UpperCamelCase__ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase__ ):
# linear layer
lowerCamelCase__: Tuple =flax_key_tuple[:-1] + ("weight",)
lowerCamelCase__: int =flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowerCamelCase__: Optional[int] =flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def lowerCAmelCase_ ( __a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
if "metadata" in layer:
lowerCamelCase__: Optional[Any] =layer.split("metadata" )
lowerCamelCase__: List[str] ="".join(split_layer[0] )[:-1]
lowerCamelCase__: List[Any] =[tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
lowerCamelCase__: Optional[int] =layer.split("kvstore" )
lowerCamelCase__: str ="".join(split_layer[0] )[:-1]
lowerCamelCase__: str =[tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
lowerCamelCase__: Dict =layer.split("/" )
lowerCamelCase__: Any ="/".join(split_layer[:-1] )
lowerCamelCase__: Union[str, Any] =(split_layer[-1],)
if "kvstore/path" in layer:
lowerCamelCase__: Tuple =F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
elif "kvstore/driver" in layer:
lowerCamelCase__: int ="file"
else:
lowerCamelCase__: Union[str, Any] =checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowerCAmelCase_ ( __a , __a ) -> Dict:
"""simple docstring"""
lowerCamelCase__: List[str] =rename_keys(UpperCamelCase__ )
lowerCamelCase__: str ={}
for k, v in current_block.items():
lowerCamelCase__: Union[str, Any] =v
lowerCamelCase__: Optional[Any] =new_current_block
torch.save(UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase_ ( __a , __a , __a , __a , __a = WEIGHTS_NAME ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: List[str] =convert_file_size_to_int(UpperCamelCase__ )
lowerCamelCase__: Dict =[]
lowerCamelCase__: Dict ={}
lowerCamelCase__: str =0
lowerCamelCase__: Tuple =0
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
lowerCamelCase__: int =serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
lowerCamelCase__: Union[str, Any] =flatten_dict(UpperCamelCase__ , sep="/" )
lowerCamelCase__: List[Any] ={}
for layer in checkpoint_info.keys():
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: int =get_key_and_tensorstore_dict(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if curr_real_layer_name in all_layers:
lowerCamelCase__: int =content
else:
lowerCamelCase__: int ={split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
lowerCamelCase__: Dict =ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
lowerCamelCase__: Any =torch.tensor(UpperCamelCase__ )
lowerCamelCase__: int =raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
lowerCamelCase__ , lowerCamelCase__: Any =rename_base_flax_keys(tuple(key.split("/" ) ) , UpperCamelCase__ )
lowerCamelCase__: Tuple ="/".join(UpperCamelCase__ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
lowerCamelCase__: str =os.path.join(
UpperCamelCase__ , weights_name.replace(".bin" , F"""-{len(UpperCamelCase__ )+1:05d}-of-???.bin""" ) )
rename_and_save_block(UpperCamelCase__ , UpperCamelCase__ )
sharded_state_dicts.append(current_block.keys() )
del current_block
lowerCamelCase__: str ={}
lowerCamelCase__: Optional[Any] =0
lowerCamelCase__: int =raw_weights.to(getattr(UpperCamelCase__ , UpperCamelCase__ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
lowerCamelCase__: Optional[Any] =os.path.join(UpperCamelCase__ , weights_name.replace(".bin" , F"""-{len(UpperCamelCase__ )+1:05d}-of-???.bin""" ) )
rename_and_save_block(UpperCamelCase__ , UpperCamelCase__ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(UpperCamelCase__ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
lowerCamelCase__: List[str] ={}
lowerCamelCase__: List[str] ={}
for idx, shard in enumerate(UpperCamelCase__ ):
lowerCamelCase__: int =weights_name.replace(
".bin" , F"""-{idx+1:05d}-of-{len(UpperCamelCase__ ):05d}.bin""" ) # len(sharded_state_dicts):05d}
lowerCamelCase__: Union[str, Any] =os.path.join(UpperCamelCase__ , weights_name.replace(".bin" , F"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(UpperCamelCase__ , os.path.join(UpperCamelCase__ , UpperCamelCase__ ) )
lowerCamelCase__: List[Any] =shard
for key in shard:
lowerCamelCase__: Any =shard_file
# Add the metadata
lowerCamelCase__: Tuple ={"total_size": total_size}
lowerCamelCase__: Union[str, Any] ={"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(UpperCamelCase__ , UpperCamelCase__ ) , "w" , encoding="utf-8" ) as f:
lowerCamelCase__: Any =json.dumps(UpperCamelCase__ , indent=2 , sort_keys=UpperCamelCase__ ) + "\n"
f.write(UpperCamelCase__ )
return metadata, index
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
type=str,
required=False,
help="Path to the output pytorch model.",
)
__A = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
lowerCamelCase__: int =SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
lowerCamelCase__: str =SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
lowerCamelCase__: str =TaTokenizer.from_pretrained("t5-small" )
lowerCamelCase__: int ="A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
lowerCamelCase__: List[str] =tokenizer(UpperCamelCase__ , return_tensors="pt" ).input_ids
lowerCamelCase__: Tuple =model.generate(UpperCamelCase__ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 10 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ = 1_00_00_00 ) -> int:
__lowerCamelCase = set(range(3 , UpperCamelCase__ , 2 ) )
primes.add(2 )
for p in range(3 , UpperCamelCase__ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , UpperCamelCase__ , UpperCamelCase__ ) ) )
__lowerCamelCase = [float(UpperCamelCase__ ) for n in range(limit + 1 )]
for p in primes:
for n in range(UpperCamelCase__ , limit + 1 , UpperCamelCase__ ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 67 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
_a = {
'''facebook/maskformer-swin-base-ade''': (
'''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
_a = logging.get_logger(__name__)
class A_ ( UpperCAmelCase__ ):
_lowercase : Union[str, Any] = "maskformer"
_lowercase : Dict = {"hidden_size": "mask_feature_size"}
_lowercase : Optional[Any] = ["resnet", "swin"]
_lowercase : Optional[int] = ["detr"]
def __init__( self : List[Any] , UpperCAmelCase : int = 2_5_6 , UpperCAmelCase : int = 2_5_6 , UpperCAmelCase : float = 0.1 , UpperCAmelCase : bool = False , UpperCAmelCase : Optional[Dict] = None , UpperCAmelCase : Optional[Dict] = None , UpperCAmelCase : float = 0.02 , UpperCAmelCase : float = 1.0 , UpperCAmelCase : float = 1.0 , UpperCAmelCase : float = 1.0 , UpperCAmelCase : float = 20.0 , UpperCAmelCase : Optional[bool] = None , **UpperCAmelCase : List[Any] , ) -> Any:
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
__lowerCAmelCase: Any = SwinConfig(
image_size=3_8_4 , in_channels=3 , patch_size=4 , embed_dim=1_2_8 , depths=[2, 2, 1_8, 2] , num_heads=[4, 8, 1_6, 3_2] , window_size=1_2 , drop_path_rate=0.3 , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , )
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: str = backbone_config.pop('model_type' )
__lowerCAmelCase: List[Any] = CONFIG_MAPPING[backbone_model_type]
__lowerCAmelCase: int = config_class.from_dict(UpperCAmelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
F'''Supported model types: {','.join(self.backbones_supported )}''' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
__lowerCAmelCase: str = DetrConfig()
else:
# verify that the decoder is supported
__lowerCAmelCase: str = (
decoder_config.pop('model_type' ) if isinstance(UpperCAmelCase , UpperCAmelCase ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
F'''Transformer Decoder {decoder_type} not supported, please use one of'''
F''' {','.join(self.decoders_supported )}''' )
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: Any = CONFIG_MAPPING[decoder_type]
__lowerCAmelCase: List[Any] = config_class.from_dict(UpperCAmelCase )
__lowerCAmelCase: List[str] = backbone_config
__lowerCAmelCase: Dict = decoder_config
# main feature dimension for the model
__lowerCAmelCase: Optional[int] = fpn_feature_size
__lowerCAmelCase: int = mask_feature_size
# initializer
__lowerCAmelCase: Optional[Any] = init_std
__lowerCAmelCase: Tuple = init_xavier_std
# Hungarian matcher && loss
__lowerCAmelCase: Any = cross_entropy_weight
__lowerCAmelCase: List[str] = dice_weight
__lowerCAmelCase: Optional[int] = mask_weight
__lowerCAmelCase: Optional[Any] = use_auxiliary_loss
__lowerCAmelCase: int = no_object_weight
__lowerCAmelCase: Dict = output_auxiliary_logits
__lowerCAmelCase: Tuple = self.decoder_config.encoder_attention_heads
__lowerCAmelCase: Dict = self.decoder_config.num_hidden_layers
super().__init__(**UpperCAmelCase )
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : PretrainedConfig , UpperCAmelCase : PretrainedConfig , **UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
return cls(
backbone_config=UpperCAmelCase , decoder_config=UpperCAmelCase , **UpperCAmelCase , )
def UpperCAmelCase ( self : Tuple ) -> List[str]:
__lowerCAmelCase: int = copy.deepcopy(self.__dict__ )
__lowerCAmelCase: Optional[int] = self.backbone_config.to_dict()
__lowerCAmelCase: Optional[int] = self.decoder_config.to_dict()
__lowerCAmelCase: List[Any] = self.__class__.model_type
return output
| 322 | '''simple docstring'''
import unittest
from transformers import 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class a__ :
def __init__( self : Union[str, Any] , a : Union[str, Any] , a : Tuple=13 , a : Optional[Any]=7 , a : List[Any]=True , a : Optional[Any]=True , a : Any=True , a : Union[str, Any]=99 , a : Any=32 , a : int=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Optional[Any]="gelu" , a : Union[str, Any]=0.1 , a : Any=0.1 , a : Optional[int]=5_12 , a : int=16 , a : Optional[Any]=2 , a : Union[str, Any]=0.02 , a : Any=3 , a : Dict=4 , a : Any=None , ):
"""simple docstring"""
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
__lowerCamelCase = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_token_type_ids:
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
__lowerCamelCase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , a : Dict , a : List[str] , a : Tuple , a : List[Any] , *a : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase = OpenAIGPTModel(config=a )
model.to(a )
model.eval()
__lowerCamelCase = model(a , token_type_ids=a , head_mask=a )
__lowerCamelCase = model(a , token_type_ids=a )
__lowerCamelCase = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , a : Union[str, Any] , a : Dict , a : Union[str, Any] , a : Tuple , *a : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase = OpenAIGPTLMHeadModel(a )
model.to(a )
model.eval()
__lowerCamelCase = model(a , token_type_ids=a , labels=a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , a : Tuple , a : Optional[int] , a : Union[str, Any] , a : Optional[Any] , *a : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = OpenAIGPTDoubleHeadsModel(a )
model.to(a )
model.eval()
__lowerCamelCase = model(a , token_type_ids=a , labels=a )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , a : int , a : Dict , a : Optional[Any] , a : str , *a : int ):
"""simple docstring"""
__lowerCamelCase = self.num_labels
__lowerCamelCase = OpenAIGPTForSequenceClassification(a )
model.to(a )
model.eval()
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = model(a , token_type_ids=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) = config_and_inputs
__lowerCamelCase = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class a__ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
lowerCamelCase : List[str] =(
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
lowerCamelCase : str =(
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
lowerCamelCase : Optional[int] =(
{
"feature-extraction": OpenAIGPTModel,
"text-classification": OpenAIGPTForSequenceClassification,
"text-generation": OpenAIGPTLMHeadModel,
"zero-shot": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , a : Tuple , a : Optional[int] , a : int , a : str , a : Any ):
"""simple docstring"""
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , a : int , a : Optional[int] , a : str=False ):
"""simple docstring"""
__lowerCamelCase = super()._prepare_for_class(a , a , return_labels=a )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
__lowerCamelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=a , )
__lowerCamelCase = inputs_dict['''labels''']
__lowerCamelCase = inputs_dict['''labels''']
__lowerCamelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=a , )
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = OpenAIGPTModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=a , n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*a )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*a )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*a )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*a )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = OpenAIGPTModel.from_pretrained(a )
self.assertIsNotNone(a )
@require_torch
class a__ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
__lowerCamelCase = OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
model.to(a )
__lowerCamelCase = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=a ) # the president is
__lowerCamelCase = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
__lowerCamelCase = model.generate(a , do_sample=a )
self.assertListEqual(output_ids[0].tolist() , a )
| 67 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class lowerCamelCase_ ( unittest.TestCase ):
def __init__( self : Union[str, Any] , _A : Any , _A : List[Any]=7 , _A : Tuple=3 , _A : Tuple=18 , _A : Optional[int]=30 , _A : Tuple=400 , _A : int=True , _A : Dict=None , _A : List[str]=True , _A : str=None , _A : Union[str, Any]=True , _A : List[Any]=[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] , _A : List[str]=[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] , _A : str=True , ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = size if size is not None else {'''height''': 224, '''width''': 224}
UpperCAmelCase__ : Any = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
UpperCAmelCase__ : int = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Dict = num_channels
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Optional[int] = min_resolution
UpperCAmelCase__ : Any = max_resolution
UpperCAmelCase__ : int = do_resize
UpperCAmelCase__ : Tuple = size
UpperCAmelCase__ : Optional[int] = do_center_crop
UpperCAmelCase__ : int = crop_size
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : List[str] = image_mean
UpperCAmelCase__ : str = image_std
UpperCAmelCase__ : Tuple = do_convert_rgb
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def lowercase_ ( self : List[Any] , _A : str=False , _A : int=False , _A : Any=False ):
'''simple docstring'''
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
UpperCAmelCase__ : Optional[int] = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
UpperCAmelCase__ : Dict = []
for i in range(self.batch_size ):
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
UpperCAmelCase__ : Optional[Any] = [Image.fromarray(np.moveaxis(_A , 0 , -1 ) ) for x in image_inputs]
if torchify:
UpperCAmelCase__ : Union[str, Any] = [torch.from_numpy(_A ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class lowerCamelCase_ ( UpperCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = ChineseCLIPImageProcessor if is_vision_available() else None
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = ChineseCLIPImageProcessingTester(self , do_center_crop=_A )
@property
def lowercase_ ( self : str ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , '''do_resize''' ) )
self.assertTrue(hasattr(_A , '''size''' ) )
self.assertTrue(hasattr(_A , '''do_center_crop''' ) )
self.assertTrue(hasattr(_A , '''center_crop''' ) )
self.assertTrue(hasattr(_A , '''do_normalize''' ) )
self.assertTrue(hasattr(_A , '''image_mean''' ) )
self.assertTrue(hasattr(_A , '''image_std''' ) )
self.assertTrue(hasattr(_A , '''do_convert_rgb''' ) )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 224, '''width''': 224} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
UpperCAmelCase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def lowercase_ ( self : str ):
'''simple docstring'''
pass
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : List[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A , Image.Image )
# Test not batched input
UpperCAmelCase__ : List[str] = 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.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase__ : Any = image_processing(_A , 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.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : Any = self.image_processor_tester.prepare_inputs(equal_resolution=_A , numpify=_A )
for image in image_inputs:
self.assertIsInstance(_A , np.ndarray )
# Test not batched input
UpperCAmelCase__ : Optional[int] = 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.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase__ : Dict = image_processing(_A , 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.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : Any = self.image_processor_tester.prepare_inputs(equal_resolution=_A , torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
# Test not batched input
UpperCAmelCase__ : Tuple = 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.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase__ : List[Any] = image_processing(_A , 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.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
@require_torch
@require_vision
class lowerCamelCase_ ( UpperCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = ChineseCLIPImageProcessor if is_vision_available() else None
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Dict = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=_A )
UpperCAmelCase__ : Union[str, Any] = 3
@property
def lowercase_ ( self : int ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , '''do_resize''' ) )
self.assertTrue(hasattr(_A , '''size''' ) )
self.assertTrue(hasattr(_A , '''do_center_crop''' ) )
self.assertTrue(hasattr(_A , '''center_crop''' ) )
self.assertTrue(hasattr(_A , '''do_normalize''' ) )
self.assertTrue(hasattr(_A , '''image_mean''' ) )
self.assertTrue(hasattr(_A , '''image_std''' ) )
self.assertTrue(hasattr(_A , '''do_convert_rgb''' ) )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
pass
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : List[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A , Image.Image )
# Test not batched input
UpperCAmelCase__ : int = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase__ : Optional[int] = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 181 | '''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Optional[int] =["image_processor", "tokenizer"]
lowerCamelCase : Union[str, Any] ="LayoutLMv2ImageProcessor"
lowerCamelCase : int =("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
def __init__( self : Optional[int] , a : Any=None , a : Any=None , **a : Union[str, Any] ):
"""simple docstring"""
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , a , )
__lowerCamelCase = kwargs.pop('''feature_extractor''' )
__lowerCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(a , a )
def __call__( self : Tuple , a : Optional[int] , a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , a : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , a : Union[List[List[int]], List[List[List[int]]]] = None , a : Optional[Union[List[int], List[List[int]]]] = None , a : bool = True , a : Union[bool, str, PaddingStrategy] = False , a : Union[bool, str, TruncationStrategy] = None , a : Optional[int] = None , a : int = 0 , a : Optional[int] = None , a : Optional[bool] = None , a : Optional[bool] = None , a : bool = False , a : bool = False , a : bool = False , a : bool = False , a : bool = True , a : Optional[Union[str, TensorType]] = None , **a : Tuple , ):
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'''You cannot provide bounding boxes '''
'''if you initialized the image processor with apply_ocr set to True.''' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''' )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.''' )
# first, apply the image processor
__lowerCamelCase = self.image_processor(images=a , return_tensors=a )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(a , a ):
__lowerCamelCase = [text] # add batch dimension (as the image processor always adds a batch dimension)
__lowerCamelCase = features['''words''']
__lowerCamelCase = self.tokenizer(
text=text if text is not None else features['''words'''] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['''boxes'''] , word_labels=a , add_special_tokens=a , padding=a , truncation=a , max_length=a , stride=a , pad_to_multiple_of=a , return_token_type_ids=a , return_attention_mask=a , return_overflowing_tokens=a , return_special_tokens_mask=a , return_offsets_mapping=a , return_length=a , verbose=a , return_tensors=a , **a , )
# add pixel values
__lowerCamelCase = features.pop('''pixel_values''' )
if return_overflowing_tokens is True:
__lowerCamelCase = self.get_overflowing_images(a , encoded_inputs['''overflow_to_sample_mapping'''] )
__lowerCamelCase = images
return encoded_inputs
def SCREAMING_SNAKE_CASE__ ( self : Tuple , a : Optional[Any] , a : str ):
"""simple docstring"""
__lowerCamelCase = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(a ) != len(a ):
raise ValueError(
'''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
f""" {len(a )} and {len(a )}""" )
return images_with_overflow
def SCREAMING_SNAKE_CASE__ ( self : List[str] , *a : Optional[Any] , **a : Union[str, Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*a , **a )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , *a : Union[str, Any] , **a : Tuple ):
"""simple docstring"""
return self.tokenizer.decode(*a , **a )
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , a , )
return self.image_processor_class
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , a , )
return self.image_processor
| 67 | 0 |
'''simple docstring'''
class __UpperCamelCase :
def __init__( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =set_counts
lowerCamelCase_ =max(lowerCAmelCase )
lowerCamelCase_ =len(lowerCAmelCase )
lowerCamelCase_ =[1] * num_sets
lowerCamelCase_ =list(range(lowerCAmelCase ) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.get_parent(lowerCAmelCase )
lowerCamelCase_ =self.get_parent(lowerCAmelCase )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowerCamelCase_ =0
lowerCamelCase_ =dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowerCamelCase_ =self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowerCamelCase_ =0
lowerCamelCase_ =src_parent
lowerCamelCase_ =self.set_counts[src_parent]
lowerCamelCase_ =max(self.max_set, lowerCAmelCase )
return True
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if self.parents[disj_set] == disj_set:
return disj_set
lowerCamelCase_ =self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 75 | '''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
class a__ ( UpperCAmelCase__ ):
def __init__( self : Optional[Any] , a : Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
"""simple docstring"""
super().__init__()
__lowerCamelCase = nn.ModuleList(a )
def SCREAMING_SNAKE_CASE__ ( self : Any , a : torch.FloatTensor , a : Union[torch.Tensor, float, int] , a : torch.Tensor , a : List[torch.tensor] , a : List[float] , a : Optional[torch.Tensor] = None , a : Optional[torch.Tensor] = None , a : Optional[torch.Tensor] = None , a : Optional[Dict[str, Any]] = None , a : bool = False , a : bool = True , ):
"""simple docstring"""
for i, (image, scale, controlnet) in enumerate(zip(a , a , self.nets ) ):
__lowerCamelCase , __lowerCamelCase = controlnet(
a , a , a , a , a , a , a , a , a , a , a , )
# merge samples
if i == 0:
__lowerCamelCase , __lowerCamelCase = down_samples, mid_sample
else:
__lowerCamelCase = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(a , a )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def SCREAMING_SNAKE_CASE__ ( self : Any , a : Union[str, os.PathLike] , a : bool = True , a : Callable = None , a : bool = False , a : Optional[str] = None , ):
"""simple docstring"""
__lowerCamelCase = 0
__lowerCamelCase = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
a , is_main_process=a , save_function=a , safe_serialization=a , variant=a , )
idx += 1
__lowerCamelCase = model_path_to_save + f"""_{idx}"""
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : List[str] , a : Optional[Union[str, os.PathLike]] , **a : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = 0
__lowerCamelCase = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
__lowerCamelCase = pretrained_model_path
while os.path.isdir(a ):
__lowerCamelCase = ControlNetModel.from_pretrained(a , **a )
controlnets.append(a )
idx += 1
__lowerCamelCase = pretrained_model_path + f"""_{idx}"""
logger.info(f"""{len(a )} controlnets loaded from {pretrained_model_path}.""" )
if len(a ) == 0:
raise ValueError(
f"""No ControlNets found under {os.path.dirname(a )}. Expected at least {pretrained_model_path + '_0'}.""" )
return cls(a )
| 67 | 0 |
def A ( _lowercase = 50 ):
SCREAMING_SNAKE_CASE : List[str] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 182 | '''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
__UpperCAmelCase =logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__ )
class a__ ( UpperCAmelCase__ ):
def __init__( self : List[str] , *a : Union[str, Any] , **a : Optional[Any] ):
"""simple docstring"""
super().__init__(*a , **a )
requires_backends(self , '''vision''' )
self.check_model_type(a )
def __call__( self : Any , a : Union[str, List[str], "Image.Image", List["Image.Image"]] , **a : Optional[int] ):
"""simple docstring"""
return super().__call__(a , **a )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , **a : Any ):
"""simple docstring"""
return {}, {}, {}
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , a : List[str] ):
"""simple docstring"""
__lowerCamelCase = load_image(a )
__lowerCamelCase = image.size
__lowerCamelCase = self.image_processor(images=a , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE__ ( self : int , a : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = self.model(**a )
return model_outputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , a : Any ):
"""simple docstring"""
__lowerCamelCase = model_outputs.predicted_depth
__lowerCamelCase = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=a )
__lowerCamelCase = prediction.squeeze().cpu().numpy()
__lowerCamelCase = (output * 2_55 / np.max(a )).astype('''uint8''' )
__lowerCamelCase = Image.fromarray(a )
__lowerCamelCase = {}
__lowerCamelCase = predicted_depth
__lowerCamelCase = depth
return output_dict
| 67 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
a = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
a = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
]
)
def lowercase (snake_case__ : List[Any] , snake_case__ : List[Any] , snake_case__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase = state_dict.pop(UpperCamelCase__ )
lowerCAmelCase = val
def lowercase (snake_case__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowerCAmelCase = key.replace("""backbone.0.body""" , """backbone.conv_encoder.model""" )
lowerCAmelCase = value
else:
lowerCAmelCase = value
return new_state_dict
def lowercase (snake_case__ : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase = """"""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowerCAmelCase = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowerCAmelCase = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase = in_proj_weight[:256, :]
lowerCAmelCase = in_proj_bias[:256]
lowerCAmelCase = in_proj_weight[256:512, :]
lowerCAmelCase = in_proj_bias[256:512]
lowerCAmelCase = in_proj_weight[-256:, :]
lowerCAmelCase = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowerCAmelCase = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
lowerCAmelCase = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase = in_proj_weight[:256, :]
lowerCAmelCase = in_proj_bias[:256]
lowerCAmelCase = in_proj_weight[256:512, :]
lowerCAmelCase = in_proj_bias[256:512]
lowerCAmelCase = in_proj_weight[-256:, :]
lowerCAmelCase = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowerCAmelCase = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
lowerCAmelCase = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowerCAmelCase = in_proj_weight_cross_attn[:256, :]
lowerCAmelCase = in_proj_bias_cross_attn[:256]
lowerCAmelCase = in_proj_weight_cross_attn[256:512, :]
lowerCAmelCase = in_proj_bias_cross_attn[256:512]
lowerCAmelCase = in_proj_weight_cross_attn[-256:, :]
lowerCAmelCase = in_proj_bias_cross_attn[-256:]
def lowercase (snake_case__ : List[str] , snake_case__ : List[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase , lowerCAmelCase = image.size
lowerCAmelCase = max(UpperCamelCase__ , UpperCamelCase__ )
lowerCAmelCase = 800 if """detection""" in checkpoint_url else 1_000
lowerCAmelCase = target_max_size / current_max_size
lowerCAmelCase = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def lowercase (snake_case__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase = F.to_tensor(UpperCamelCase__ )
lowerCAmelCase = F.normalize(UpperCamelCase__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def lowercase (snake_case__ : List[Any] , snake_case__ : Any , snake_case__ : Optional[int] ) -> Dict:
'''simple docstring'''
logger.info("""Converting model...""" )
# load original state dict
lowerCAmelCase = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location="""cpu""" )
# rename keys
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCAmelCase = rename_backbone_keys(UpperCamelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCamelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowerCAmelCase = """model."""
for key in state_dict.copy().keys():
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
lowerCAmelCase = state_dict.pop(UpperCamelCase__ )
lowerCAmelCase = val
# create HuggingFace model and load state dict
lowerCAmelCase = TableTransformerConfig(
backbone="""resnet18""" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowerCAmelCase = 15
lowerCAmelCase = 2
lowerCAmelCase = {0: """table""", 1: """table rotated"""}
lowerCAmelCase = idalabel
lowerCAmelCase = {v: k for k, v in idalabel.items()}
else:
lowerCAmelCase = 125
lowerCAmelCase = 6
lowerCAmelCase = {
0: """table""",
1: """table column""",
2: """table row""",
3: """table column header""",
4: """table projected row header""",
5: """table spanning cell""",
}
lowerCAmelCase = idalabel
lowerCAmelCase = {v: k for k, v in idalabel.items()}
lowerCAmelCase = DetrImageProcessor(
format="""coco_detection""" , max_size=800 if """detection""" in checkpoint_url else 1_000 )
lowerCAmelCase = TableTransformerForObjectDetection(UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
# verify our conversion
lowerCAmelCase = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
lowerCAmelCase = hf_hub_download(repo_id="""nielsr/example-pdf""" , repo_type="""dataset""" , filename=UpperCamelCase__ )
lowerCAmelCase = Image.open(UpperCamelCase__ ).convert("""RGB""" )
lowerCAmelCase = normalize(resize(UpperCamelCase__ , UpperCamelCase__ ) ).unsqueeze(0 )
lowerCAmelCase = model(UpperCamelCase__ )
if "detection" in checkpoint_url:
lowerCAmelCase = (1, 15, 3)
lowerCAmelCase = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]] )
lowerCAmelCase = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]] )
else:
lowerCAmelCase = (1, 125, 7)
lowerCAmelCase = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]] )
lowerCAmelCase = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , UpperCamelCase__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , UpperCamelCase__ , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
image_processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
# Push model to HF hub
logger.info("""Pushing model to the hub...""" )
lowerCAmelCase = (
"""microsoft/table-transformer-detection"""
if """detection""" in checkpoint_url
else """microsoft/table-transformer-structure-recognition"""
)
model.push_to_hub(UpperCamelCase__ )
image_processor.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
type=str,
choices=[
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
],
help='URL of the Table Transformer checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
a = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 155 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase ={
"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 =[
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapModel",
"ClapPreTrainedModel",
"ClapTextModel",
"ClapTextModelWithProjection",
"ClapAudioModel",
"ClapAudioModelWithProjection",
]
__UpperCAmelCase =["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 =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 67 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class a :
"""simple docstring"""
def __init__( self : List[str] , __lowercase : Optional[int] , __lowercase : Any=13 , __lowercase : Optional[int]=7 , __lowercase : Optional[Any]=False , __lowercase : Tuple=True , __lowercase : int=False , __lowercase : str=False , __lowercase : List[Any]=19 , __lowercase : Union[str, Any]=32 , __lowercase : Optional[int]=5 , __lowercase : Tuple=4 , __lowercase : Optional[int]=37 , __lowercase : int="gelu" , __lowercase : Optional[int]=0.1 , __lowercase : Union[str, Any]=0.1 , __lowercase : List[Any]=512 , __lowercase : Dict=16 , __lowercase : List[str]=2 , __lowercase : Optional[Any]=0.02 , __lowercase : int=3 , __lowercase : Optional[Any]=4 , __lowercase : int=None , ) -> str:
__UpperCAmelCase : List[str] = parent
__UpperCAmelCase : Union[str, Any] = batch_size
__UpperCAmelCase : Dict = seq_length
__UpperCAmelCase : Any = is_training
__UpperCAmelCase : str = use_input_mask
__UpperCAmelCase : List[str] = use_token_type_ids
__UpperCAmelCase : List[str] = use_labels
__UpperCAmelCase : List[Any] = vocab_size
__UpperCAmelCase : Union[str, Any] = hidden_size
__UpperCAmelCase : Optional[Any] = num_hidden_layers
__UpperCAmelCase : List[str] = num_attention_heads
__UpperCAmelCase : int = intermediate_size
__UpperCAmelCase : str = hidden_act
__UpperCAmelCase : int = hidden_dropout_prob
__UpperCAmelCase : List[str] = attention_probs_dropout_prob
__UpperCAmelCase : List[str] = max_position_embeddings
__UpperCAmelCase : Optional[int] = type_vocab_size
__UpperCAmelCase : Union[str, Any] = type_sequence_label_size
__UpperCAmelCase : Optional[int] = initializer_range
__UpperCAmelCase : int = num_labels
__UpperCAmelCase : int = num_choices
__UpperCAmelCase : Union[str, Any] = scope
def UpperCAmelCase ( self : Any ) -> List[str]:
__UpperCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase : List[Any] = None
if self.use_input_mask:
__UpperCAmelCase : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCAmelCase : Any = None
__UpperCAmelCase : Optional[Any] = None
__UpperCAmelCase : Optional[Any] = None
if self.use_labels:
__UpperCAmelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCAmelCase : int = ids_tensor([self.batch_size] , self.num_choices )
__UpperCAmelCase : Optional[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
__UpperCAmelCase : str = EsmConfig(
vocab_size=33 , hidden_size=self.hidden_size , pad_token_id=1 , 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 , is_folding_model=__lowercase , esmfold_config={"""trunk""": {"""num_blocks""": 2}, """fp16_esm""": False} , )
return config
def UpperCAmelCase ( self : Any , __lowercase : Any , __lowercase : Dict , __lowercase : str , __lowercase : List[Any] , __lowercase : Union[str, Any] , __lowercase : int ) -> List[str]:
__UpperCAmelCase : int = EsmForProteinFolding(config=__lowercase ).float()
model.to(__lowercase )
model.eval()
__UpperCAmelCase : Optional[Any] = model(__lowercase , attention_mask=__lowercase )
__UpperCAmelCase : int = model(__lowercase )
__UpperCAmelCase : Tuple = model(__lowercase )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def UpperCAmelCase ( self : Optional[Any] ) -> int:
__UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : Optional[int] = config_and_inputs
__UpperCAmelCase : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
a : List[Any] = False
a : List[Any] = (EsmForProteinFolding,) if is_torch_available() else ()
a : Tuple = ()
a : Any = {} if is_torch_available() else {}
a : Any = False
def UpperCAmelCase ( self : List[str] ) -> str:
__UpperCAmelCase : Dict = EsmFoldModelTester(self )
__UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : str ) -> List[Any]:
__UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
@unittest.skip("""Does not support attention outputs""" )
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
pass
@unittest.skip
def UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]:
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def UpperCAmelCase ( self : Optional[int] ) -> Tuple:
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def UpperCAmelCase ( self : Dict ) -> Dict:
pass
@unittest.skip("""ESMFold does not support passing input embeds!""" )
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def UpperCAmelCase ( self : Union[str, Any] ) -> str:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def UpperCAmelCase ( self : Dict ) -> Optional[int]:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def UpperCAmelCase ( self : List[str] ) -> List[str]:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def UpperCAmelCase ( self : Optional[int] ) -> Tuple:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def UpperCAmelCase ( self : int ) -> str:
pass
@unittest.skip("""ESMFold does not output hidden states in the normal way.""" )
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
pass
@unittest.skip("""ESMfold does not output hidden states in the normal way.""" )
def UpperCAmelCase ( self : Optional[Any] ) -> Dict:
pass
@unittest.skip("""ESMFold only has one output format.""" )
def UpperCAmelCase ( self : Dict ) -> Dict:
pass
@unittest.skip("""This test doesn\'t work for ESMFold and doesn\'t test core functionality""" )
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[int]:
pass
@unittest.skip("""ESMFold does not support input chunking.""" )
def UpperCAmelCase ( self : List[Any] ) -> Dict:
pass
@unittest.skip("""ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.""" )
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
pass
@unittest.skip("""ESMFold doesn\'t support torchscript compilation.""" )
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
pass
@unittest.skip("""ESMFold doesn\'t support torchscript compilation.""" )
def UpperCAmelCase ( self : int ) -> Union[str, Any]:
pass
@unittest.skip("""ESMFold doesn\'t support torchscript compilation.""" )
def UpperCAmelCase ( self : str ) -> Dict:
pass
@unittest.skip("""ESMFold doesn\'t support data parallel.""" )
def UpperCAmelCase ( self : str ) -> Optional[int]:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
pass
@require_torch
class a ( UpperCAmelCase__ ):
"""simple docstring"""
@slow
def UpperCAmelCase ( self : str ) -> int:
__UpperCAmelCase : Tuple = EsmForProteinFolding.from_pretrained("""facebook/esmfold_v1""" ).float()
model.eval()
__UpperCAmelCase : Tuple = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
__UpperCAmelCase : Any = model(__lowercase )["""positions"""]
__UpperCAmelCase : int = torch.tensor([2.5_828, 0.7_993, -10.9_334] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , __lowercase , atol=1e-4 ) )
| 114 | '''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class a__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase = '''ylacombe/bark-small'''
__lowerCamelCase = tempfile.mkdtemp()
__lowerCamelCase = '''en_speaker_1'''
__lowerCamelCase = '''This is a test string'''
__lowerCamelCase = '''speaker_embeddings_path.json'''
__lowerCamelCase = '''speaker_embeddings'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , **a : Dict ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint , **a )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = BarkProcessor(tokenizer=a )
processor.save_pretrained(self.tmpdirname )
__lowerCamelCase = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
__lowerCamelCase = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
__lowerCamelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__lowerCamelCase = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='''(BOS)''' , eos_token='''(EOS)''' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
__lowerCamelCase = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
__lowerCamelCase = 35
__lowerCamelCase = 2
__lowerCamelCase = 8
__lowerCamelCase = {
'''semantic_prompt''': np.ones(a ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
__lowerCamelCase = processor(text=self.input_string , voice_preset=a )
__lowerCamelCase = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(a , np.array([] ) ).tolist() )
# test loading voice preset from npz file
__lowerCamelCase = os.path.join(self.tmpdirname , '''file.npz''' )
np.savez(a , **a )
__lowerCamelCase = processor(text=self.input_string , voice_preset=a )
__lowerCamelCase = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(a , np.array([] ) ).tolist() )
# test loading voice preset from the hub
__lowerCamelCase = processor(text=self.input_string , voice_preset=self.voice_preset )
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
__lowerCamelCase = self.get_tokenizer()
__lowerCamelCase = BarkProcessor(tokenizer=a )
__lowerCamelCase = processor(text=self.input_string )
__lowerCamelCase = tokenizer(
self.input_string , padding='''max_length''' , max_length=2_56 , add_special_tokens=a , return_attention_mask=a , return_token_type_ids=a , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 67 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class SCREAMING_SNAKE_CASE_ :
def __init__( self : List[Any] , lowerCamelCase_ : Any ):
"""simple docstring"""
UpperCamelCase = data
UpperCamelCase = None
class SCREAMING_SNAKE_CASE_ :
def __init__( self : Any ):
"""simple docstring"""
UpperCamelCase = None
UpperCamelCase = None
def __iter__( self : Any ):
"""simple docstring"""
UpperCamelCase = self.head
while self.head:
yield node.data
UpperCamelCase = node.next
if node == self.head:
break
def __len__( self : Any ):
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self : Union[str, Any] ):
"""simple docstring"""
return "->".join(str(lowerCamelCase_ ) for item in iter(self ) )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Any ):
"""simple docstring"""
self.insert_nth(len(self ) , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Any ):
"""simple docstring"""
self.insert_nth(0 , lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int , lowerCamelCase_ : Any ):
"""simple docstring"""
if index < 0 or index > len(self ):
raise IndexError("""list index out of range.""" )
UpperCamelCase = Node(lowerCamelCase_ )
if self.head is None:
UpperCamelCase = new_node # first node points itself
UpperCamelCase = UpperCamelCase = new_node
elif index == 0: # insert at head
UpperCamelCase = self.head
UpperCamelCase = UpperCamelCase = new_node
else:
UpperCamelCase = self.head
for _ in range(index - 1 ):
UpperCamelCase = temp.next
UpperCamelCase = temp.next
UpperCamelCase = new_node
if index == len(self ) - 1: # insert at tail
UpperCamelCase = new_node
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
return self.delete_nth(0 )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : int = 0 ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise IndexError("""list index out of range.""" )
UpperCamelCase = self.head
if self.head == self.tail: # just one node
UpperCamelCase = UpperCamelCase = None
elif index == 0: # delete head node
UpperCamelCase = self.tail.next.next
UpperCamelCase = self.head.next
else:
UpperCamelCase = self.head
for _ in range(index - 1 ):
UpperCamelCase = temp.next
UpperCamelCase = temp.next
UpperCamelCase = temp.next.next
if index == len(self ) - 1: # delete at tail
UpperCamelCase = temp
return delete_node.data
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
return len(self ) == 0
def lowercase( ) -> None:
'''simple docstring'''
UpperCamelCase = CircularLinkedList()
assert len(UpperCamelCase__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(UpperCamelCase__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(UpperCamelCase__ ) == i
circular_linked_list.insert_nth(UpperCamelCase__ , i + 1 )
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 343 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase ={"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase =[
"VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTMSNModel",
"ViTMSNForImageClassification",
"ViTMSNPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
__UpperCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 67 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
def UpperCamelCase_( _snake_case : Dict=None , _snake_case : Dict=None ):
"""simple docstring"""
return field(default_factory=lambda: default , metadata=UpperCamelCase__ )
@dataclass
class __magic_name__ :
SCREAMING_SNAKE_CASE = 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'
)
} , )
SCREAMING_SNAKE_CASE = list_field(
default=[8] , metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'} )
SCREAMING_SNAKE_CASE = list_field(
default=[8, 3_2, 1_2_8, 5_1_2] , metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'} , )
SCREAMING_SNAKE_CASE = field(
default=UpperCAmelCase__ , metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'} , )
SCREAMING_SNAKE_CASE = field(
default=UpperCAmelCase__ , metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'} , )
SCREAMING_SNAKE_CASE = field(
default=UpperCAmelCase__ , metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'} )
SCREAMING_SNAKE_CASE = field(default=UpperCAmelCase__ , metadata={'help': 'Use FP16 to accelerate inference.'} )
SCREAMING_SNAKE_CASE = field(default=UpperCAmelCase__ , metadata={'help': 'Benchmark training of model'} )
SCREAMING_SNAKE_CASE = field(default=UpperCAmelCase__ , metadata={'help': 'Verbose memory tracing'} )
SCREAMING_SNAKE_CASE = field(
default=UpperCAmelCase__ , metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'} , )
SCREAMING_SNAKE_CASE = field(
default=UpperCAmelCase__ , metadata={
'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
} , )
SCREAMING_SNAKE_CASE = field(default=UpperCAmelCase__ , metadata={'help': 'Trace memory line by line'} )
SCREAMING_SNAKE_CASE = field(default=UpperCAmelCase__ , metadata={'help': 'Save result to a CSV file'} )
SCREAMING_SNAKE_CASE = field(default=UpperCAmelCase__ , metadata={'help': 'Save all print statements in a log file'} )
SCREAMING_SNAKE_CASE = field(default=UpperCAmelCase__ , metadata={'help': 'Whether to print environment information'} )
SCREAMING_SNAKE_CASE = field(
default=UpperCAmelCase__ , 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.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=f'''inference_time_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving time results to csv.'} , )
SCREAMING_SNAKE_CASE = field(
default=f'''inference_memory_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving memory results to csv.'} , )
SCREAMING_SNAKE_CASE = field(
default=f'''train_time_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving time results to csv for training.'} , )
SCREAMING_SNAKE_CASE = field(
default=f'''train_memory_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving memory results to csv for training.'} , )
SCREAMING_SNAKE_CASE = field(
default=f'''env_info_{round(time() )}.csv''' , metadata={'help': 'CSV filename used if saving environment information.'} , )
SCREAMING_SNAKE_CASE = field(
default=f'''log_{round(time() )}.csv''' , metadata={'help': 'Log filename used if print statements are saved in log.'} , )
SCREAMING_SNAKE_CASE = field(default=3 , metadata={'help': 'Times an experiment will be run.'} )
SCREAMING_SNAKE_CASE = field(
default=UpperCAmelCase__ , metadata={
'help': (
'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
' model weights.'
)
} , )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
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 __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def __magic_name__ ( self ) -> Dict:
'''simple docstring'''
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 __magic_name__ ( self ) -> Any:
'''simple docstring'''
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
| 218 | '''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
__UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
re.sub('''<n>''' , '''''' , UpperCamelCase__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase__ ) )
| 67 | 0 |
'''simple docstring'''
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowerCamelCase ( A__ , A__ ) -> Tuple:
"""simple docstring"""
UpperCamelCase = args.log_outputs
UpperCamelCase = '_'.join(args.dataset.split('/' ) + [args.config, args.split] )
# load metric
UpperCamelCase = load_metric('wer' )
UpperCamelCase = load_metric('cer' )
# compute metrics
UpperCamelCase = wer.compute(references=result['target'] , predictions=result['prediction'] )
UpperCamelCase = cer.compute(references=result['target'] , predictions=result['prediction'] )
# print & log results
UpperCamelCase = F"""WER: {wer_result}\nCER: {cer_result}"""
print(UpperCamelCase__ )
with open(F"""{dataset_id}_eval_results.txt""" , 'w' ) as f:
f.write(UpperCamelCase__ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase = F"""log_{dataset_id}_predictions.txt"""
UpperCamelCase = F"""log_{dataset_id}_targets.txt"""
with open(UpperCamelCase__ , 'w' ) as p, open(UpperCamelCase__ , 'w' ) as t:
# mapping function to write output
def write_to_file(A__ , A__ ):
p.write(F"""{i}""" + '\n' )
p.write(batch['prediction'] + '\n' )
t.write(F"""{i}""" + '\n' )
t.write(batch['target'] + '\n' )
result.map(UpperCamelCase__ , with_indices=UpperCamelCase__ )
def __lowerCamelCase ( A__ ) -> str:
"""simple docstring"""
UpperCamelCase = '[,?.!\-\;\:"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase = re.sub(UpperCamelCase__ , '' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase = ['\n\n', '\n', ' ', ' ']
for t in token_sequences_to_ignore:
UpperCamelCase = ' '.join(text.split(UpperCamelCase__ ) )
return text
def __lowerCamelCase ( A__ ) -> List[Any]:
"""simple docstring"""
# load dataset
UpperCamelCase = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=UpperCamelCase__ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase = feature_extractor.sampling_rate
# resample audio
UpperCamelCase = dataset.cast_column('audio' , Audio(sampling_rate=UpperCamelCase__ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase = 0 if torch.cuda.is_available() else -1
UpperCamelCase = pipeline('automatic-speech-recognition' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(A__ ):
UpperCamelCase = asr(
batch['audio']['array'] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
UpperCamelCase = prediction['text']
UpperCamelCase = normalize_text(batch['sentence'] )
return batch
# run inference on all examples
UpperCamelCase = dataset.map(UpperCamelCase__ , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
_lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
)
parser.add_argument(
"--dataset",
type=str,
required=True,
help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
)
parser.add_argument(
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
)
parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
parser.add_argument(
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
)
parser.add_argument(
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
)
parser.add_argument(
"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
)
parser.add_argument(
"--device",
type=int,
default=None,
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
)
_lowerCamelCase : int = parser.parse_args()
main(args)
| 28 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Optional[int] ="gpt_neox_japanese"
def __init__( self : List[Any] , a : Tuple=3_20_00 , a : Dict=25_60 , a : Union[str, Any]=32 , a : Dict=32 , a : Dict=4 , a : Optional[Any]="gelu" , a : Any=1.00 , a : str=1_00_00 , a : List[str]=20_48 , a : str=0.02 , a : Union[str, Any]=1e-5 , a : Optional[Any]=True , a : str=3_19_96 , a : List[str]=3_19_99 , a : str=0.1 , a : Union[str, Any]=0.0 , **a : Optional[Any] , ):
"""simple docstring"""
super().__init__(bos_token_id=a , eos_token_id=a , **a )
__lowerCamelCase = vocab_size
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_multiple_size
__lowerCamelCase = hidden_act
__lowerCamelCase = rotary_pct
__lowerCamelCase = rotary_emb_base
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = use_cache
__lowerCamelCase = attention_dropout
__lowerCamelCase = hidden_dropout
| 67 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}
class _lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase = "convnextv2"
def __init__( self : int , snake_case : Dict=3 , snake_case : List[str]=4 , snake_case : str=4 , snake_case : Dict=None , snake_case : List[str]=None , snake_case : Tuple="gelu" , snake_case : Tuple=0.02 , snake_case : Dict=1e-12 , snake_case : Any=0.0 , snake_case : List[str]=2_2_4 , snake_case : Optional[Any]=None , snake_case : int=None , **snake_case : Any , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**snake_case )
UpperCamelCase_ : str = num_channels
UpperCamelCase_ : Optional[Any] = patch_size
UpperCamelCase_ : Optional[Any] = num_stages
UpperCamelCase_ : str = [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
UpperCamelCase_ : Any = [3, 3, 9, 3] if depths is None else depths
UpperCamelCase_ : Optional[Any] = hidden_act
UpperCamelCase_ : str = initializer_range
UpperCamelCase_ : Union[str, Any] = layer_norm_eps
UpperCamelCase_ : Optional[int] = drop_path_rate
UpperCamelCase_ : Union[str, Any] = image_size
UpperCamelCase_ : Optional[int] = ['stem'] + [f"stage{idx}" for idx in range(1 , len(self.depths ) + 1 )]
UpperCamelCase_, UpperCamelCase_ : str = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
| 175 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
while second != 0:
__lowerCamelCase = first & second
first ^= second
__lowerCamelCase = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase =int(input("Enter the first number: ").strip())
__UpperCAmelCase =int(input("Enter the second number: ").strip())
print(f'{add(first, second) = }')
| 67 | 0 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowercase_ = RoCBertTokenizer
lowercase_ = None
lowercase_ = False
lowercase_ = True
lowercase_ = filter_non_english
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Tuple:
'''simple docstring'''
super().setUp()
lowerCamelCase__: Optional[int] =["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
lowerCamelCase__: int ={}
lowerCamelCase__: int ={}
for i, value in enumerate(UpperCAmelCase_):
lowerCamelCase__: Union[str, Any] =i
lowerCamelCase__: List[Any] =i
lowerCamelCase__: Dict =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"])
lowerCamelCase__: List[str] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_shape_file"])
lowerCamelCase__: str =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_pronunciation_file"])
with open(self.vocab_file , "w" , encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
with open(self.word_shape_file , "w" , encoding="utf-8") as word_shape_writer:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ , ensure_ascii=UpperCAmelCase_)
with open(self.word_pronunciation_file , "w" , encoding="utf-8") as word_pronunciation_writer:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ , ensure_ascii=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->str:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file)
lowerCamelCase__: Optional[int] =tokenizer.tokenize("你好[SEP]你是谁")
self.assertListEqual(UpperCAmelCase_ , ["你", "好", "[SEP]", "你", "是", "谁"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_) , [5, 6, 2, 5, 7, 8])
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(UpperCAmelCase_) , [5, 6, 2, 5, 7, 8])
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(UpperCAmelCase_) , [5, 6, 2, 5, 7, 8])
def SCREAMING_SNAKE_CASE_ (self : int) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz") , ["ah", "\u535A", "\u63A8", "zz"])
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: Dict =RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? ") , ["hello", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->int:
'''simple docstring'''
lowerCamelCase__: str =RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , strip_accents=UpperCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hällo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["h\u00E9llo"])
def SCREAMING_SNAKE_CASE_ (self : str) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: List[str] =RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , strip_accents=UpperCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hallo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["hallo", "!", "how", "are", "you", "?"])
self.assertListEqual(tokenizer.tokenize("H\u00E9llo") , ["hello"])
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->str:
'''simple docstring'''
lowerCamelCase__: List[str] =RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? ") , ["HeLLo", "!", "how", "Are", "yoU", "?"])
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->List[str]:
'''simple docstring'''
lowerCamelCase__: Optional[int] =RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , strip_accents=UpperCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["HäLLo", "!", "how", "Are", "yoU", "?"])
def SCREAMING_SNAKE_CASE_ (self : Any) ->int:
'''simple docstring'''
lowerCamelCase__: Optional[int] =RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , strip_accents=UpperCAmelCase_)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? ") , ["HaLLo", "!", "how", "Are", "yoU", "?"])
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: List[Any] =RoCBertBasicTokenizer(do_lower_case=UpperCAmelCase_ , never_split=["[UNK]"])
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]") , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"])
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Any:
'''simple docstring'''
lowerCamelCase__: Tuple =["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
lowerCamelCase__: Tuple ={}
for i, token in enumerate(UpperCAmelCase_):
lowerCamelCase__: List[Any] =i
lowerCamelCase__: Optional[Any] =RoCBertWordpieceTokenizer(vocab=UpperCAmelCase_ , unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize("") , [])
self.assertListEqual(tokenizer.tokenize("unwanted running") , ["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize("unwantedX running") , ["[UNK]", "runn", "##ing"])
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Tuple:
'''simple docstring'''
self.assertTrue(_is_whitespace(" "))
self.assertTrue(_is_whitespace("\t"))
self.assertTrue(_is_whitespace("\r"))
self.assertTrue(_is_whitespace("\n"))
self.assertTrue(_is_whitespace("\u00A0"))
self.assertFalse(_is_whitespace("A"))
self.assertFalse(_is_whitespace("-"))
def SCREAMING_SNAKE_CASE_ (self : Any) ->int:
'''simple docstring'''
self.assertTrue(_is_control("\u0005"))
self.assertFalse(_is_control("A"))
self.assertFalse(_is_control(" "))
self.assertFalse(_is_control("\t"))
self.assertFalse(_is_control("\r"))
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Dict:
'''simple docstring'''
self.assertTrue(_is_punctuation("-"))
self.assertTrue(_is_punctuation("$"))
self.assertTrue(_is_punctuation("`"))
self.assertTrue(_is_punctuation("."))
self.assertFalse(_is_punctuation("A"))
self.assertFalse(_is_punctuation(" "))
def SCREAMING_SNAKE_CASE_ (self : str) ->Any:
'''simple docstring'''
lowerCamelCase__: List[str] =self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(UpperCAmelCase_) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]])
if self.test_rust_tokenizer:
lowerCamelCase__: Optional[int] =self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(UpperCAmelCase_) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]])
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Union[str, Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
lowerCamelCase__: str =self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: Optional[int] =F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
lowerCamelCase__: Optional[Any] =tokenizer_r.encode_plus(
UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , return_token_type_ids=UpperCAmelCase_ , return_offsets_mapping=UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ , )
lowerCamelCase__: int =tokenizer_r.do_lower_case if hasattr(UpperCAmelCase_ , "do_lower_case") else False
lowerCamelCase__: Tuple =(
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]))
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"])
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->List[str]:
'''simple docstring'''
lowerCamelCase__: Optional[Any] =["的", "人", "有"]
lowerCamelCase__: int ="".join(UpperCAmelCase_)
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
lowerCamelCase__: int =True
lowerCamelCase__: str =self.tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: List[str] =self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: Optional[int] =tokenizer_p.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
lowerCamelCase__: Dict =tokenizer_r.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
lowerCamelCase__: int =tokenizer_r.convert_ids_to_tokens(UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =tokenizer_p.convert_ids_to_tokens(UpperCAmelCase_)
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =False
lowerCamelCase__: Union[str, Any] =self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =self.tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: Optional[int] =tokenizer_r.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
lowerCamelCase__: List[str] =tokenizer_p.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
lowerCamelCase__: int =tokenizer_r.convert_ids_to_tokens(UpperCAmelCase_)
lowerCamelCase__: Any =tokenizer_p.convert_ids_to_tokens(UpperCAmelCase_)
# it is expected that only the first Chinese character is not preceded by "##".
lowerCamelCase__: Tuple =[
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(UpperCAmelCase_)
]
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_)
@slow
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Dict =self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file)
lowerCamelCase__: List[Any] =tokenizer.encode("你好" , add_special_tokens=UpperCAmelCase_)
lowerCamelCase__: Dict =tokenizer.encode("你是谁" , add_special_tokens=UpperCAmelCase_)
lowerCamelCase__: str =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_)
lowerCamelCase__: Optional[int] =tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ , UpperCAmelCase_)
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def SCREAMING_SNAKE_CASE_ (self : str) ->int:
'''simple docstring'''
lowerCamelCase__: Dict =self.get_tokenizers(do_lower_case=UpperCAmelCase_)
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}"""):
lowerCamelCase__: Union[str, Any] ="你好，你是谁"
lowerCamelCase__: Union[str, Any] =tokenizer.tokenize(UpperCAmelCase_)
lowerCamelCase__: Dict =tokenizer.convert_tokens_to_ids(UpperCAmelCase_)
lowerCamelCase__: Optional[int] =tokenizer.convert_tokens_to_shape_ids(UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =tokenizer.convert_tokens_to_pronunciation_ids(UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =tokenizer.prepare_for_model(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
lowerCamelCase__: Dict =tokenizer.encode_plus(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_)
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_)
| 10 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase ={
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase =[
"TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TimeSeriesTransformerForPrediction",
"TimeSeriesTransformerModel",
"TimeSeriesTransformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 67 | 0 |
from datetime import datetime
import requests
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> bytes:
"""simple docstring"""
__lowerCAmelCase: Optional[Any] = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__lowerCAmelCase: Optional[Any] = requests.get(base_url + url ).json()[0]['urls'][0]['src']
return requests.get(UpperCamelCase__ ).content
if __name__ == "__main__":
_a = input('''Enter Video/IGTV url: ''').strip()
_a = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, '''wb''') as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| 322 | '''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> None:
__lowerCamelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ ), f"""{len(UpperCamelCase__ )} != {len(UpperCamelCase__ )}"""
dest_layers.load_state_dict(layers_to_copy.state_dict() )
__UpperCAmelCase ={
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
1_2: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 1_1],
4: [0, 4, 8, 1_1],
6: [0, 2, 4, 7, 9, 1_1],
9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
1_2: list(range(1_2)),
},
1_6: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 1_5],
3: [0, 8, 1_5],
4: [0, 5, 1_0, 1_5],
6: [0, 3, 6, 9, 1_2, 1_5],
8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
1_6: list(range(1_6)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCAmelCase ={
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
try:
__lowerCamelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
f"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
f""" {n_student}""" )
return list(range(UpperCamelCase__ ) )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> List[int]:
if n_student > n_teacher:
raise ValueError(f"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}""" )
elif n_teacher == n_student:
return list(range(UpperCamelCase__ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ = "student" , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__=False , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ , ) -> Tuple[PreTrainedModel, List[int], List[int]]:
__lowerCamelCase = '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
assert (e is not None) or (d is not None), _msg
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
AutoTokenizer.from_pretrained(UpperCamelCase__ ).save_pretrained(UpperCamelCase__ ) # purely for convenience
__lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__ ).eval()
else:
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ), f"""teacher must be a model or string got type {type(UpperCamelCase__ )}"""
__lowerCamelCase = teacher.config.to_diff_dict()
try:
__lowerCamelCase , __lowerCamelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
__lowerCamelCase = teacher_e
if d is None:
__lowerCamelCase = teacher_d
init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d} )
except AttributeError: # T5
if hasattr(teacher.config , '''num_encoder_layers''' ):
__lowerCamelCase , __lowerCamelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
__lowerCamelCase , __lowerCamelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
__lowerCamelCase = teacher_e
if d is None:
__lowerCamelCase = teacher_d
if hasattr(teacher.config , '''num_encoder_layers''' ):
init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d} )
else:
init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(UpperCamelCase__ )
# Copy weights
__lowerCamelCase = teacher.config_class(**UpperCamelCase__ )
__lowerCamelCase = AutoModelForSeqaSeqLM.from_config(UpperCamelCase__ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
__lowerCamelCase = student.load_state_dict(teacher.state_dict() , strict=UpperCamelCase__ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
__lowerCamelCase , __lowerCamelCase = list(range(UpperCamelCase__ ) ), list(range(UpperCamelCase__ ) )
logger.info(
f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
f""" {save_path}""" )
student.save_pretrained(UpperCamelCase__ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
__lowerCamelCase = pick_layers_to_copy(UpperCamelCase__ , UpperCamelCase__ )
if d_layers_to_copy is None:
__lowerCamelCase = pick_layers_to_copy(UpperCamelCase__ , UpperCamelCase__ )
try:
if hasattr(
UpperCamelCase__ , '''prophetnet''' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , UpperCamelCase__ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , UpperCamelCase__ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , UpperCamelCase__ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , UpperCamelCase__ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , UpperCamelCase__ )
copy_layers(teacher.decoder.block , student.decoder.block , UpperCamelCase__ )
logger.info(
f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}""" )
__lowerCamelCase = {
'''teacher_type''': teacher.config.model_type,
'''copied_encoder_layers''': e_layers_to_copy,
'''copied_decoder_layers''': d_layers_to_copy,
}
student.save_pretrained(UpperCamelCase__ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 67 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a__ ( lowerCAmelCase__ , lowerCAmelCase__=0.9_9_9 , lowerCAmelCase__="cosine" , ) -> Optional[int]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowerCAmelCase__ ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowerCAmelCase__ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
UpperCAmelCase__ : Tuple = []
for i in range(UpperCamelCase__ ):
UpperCAmelCase__ : List[str] = i / num_diffusion_timesteps
UpperCAmelCase__ : Optional[int] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(UpperCamelCase__ ) / alpha_bar_fn(UpperCamelCase__ ) , UpperCamelCase__ ) )
return torch.tensor(UpperCamelCase__ , dtype=torch.floataa )
class lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
lowerCAmelCase__ = [e.name for e in KarrasDiffusionSchedulers]
lowerCAmelCase__ = 2
@register_to_config
def __init__( self : Optional[Any] , _A : int = 1_000 , _A : float = 0.0_0_0_8_5 , _A : float = 0.0_1_2 , _A : str = "linear" , _A : Optional[Union[np.ndarray, List[float]]] = None , _A : str = "epsilon" , _A : Optional[bool] = False , _A : Optional[bool] = False , _A : float = 1.0 , _A : str = "linspace" , _A : int = 0 , ):
'''simple docstring'''
if trained_betas is not None:
UpperCAmelCase__ : Tuple = torch.tensor(_A , dtype=torch.floataa )
elif beta_schedule == "linear":
UpperCAmelCase__ : List[str] = torch.linspace(_A , _A , _A , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
UpperCAmelCase__ : Optional[Any] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _A , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
UpperCAmelCase__ : Tuple = betas_for_alpha_bar(_A , alpha_transform_type='''cosine''' )
elif beta_schedule == "exp":
UpperCAmelCase__ : Dict = betas_for_alpha_bar(_A , alpha_transform_type='''exp''' )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
UpperCAmelCase__ : List[str] = 1.0 - self.betas
UpperCAmelCase__ : Tuple = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(_A , _A , _A )
UpperCAmelCase__ : Optional[int] = use_karras_sigmas
def lowercase_ ( self : Dict , _A : Optional[Any] , _A : List[Any]=None ):
'''simple docstring'''
if schedule_timesteps is None:
UpperCAmelCase__ : Union[str, Any] = self.timesteps
UpperCAmelCase__ : int = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
UpperCAmelCase__ : Dict = 1 if len(_A ) > 1 else 0
else:
UpperCAmelCase__ : Optional[Any] = timestep.cpu().item() if torch.is_tensor(_A ) else timestep
UpperCAmelCase__ : Optional[int] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowercase_ ( self : List[Any] , _A : torch.FloatTensor , _A : Union[float, torch.FloatTensor] , ):
'''simple docstring'''
UpperCAmelCase__ : int = self.index_for_timestep(_A )
UpperCAmelCase__ : Optional[int] = self.sigmas[step_index]
UpperCAmelCase__ : Tuple = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowercase_ ( self : Any , _A : int , _A : Union[str, torch.device] = None , _A : Optional[int] = None , ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = num_inference_steps
UpperCAmelCase__ : Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
UpperCAmelCase__ : str = np.linspace(0 , num_train_timesteps - 1 , _A , dtype=_A )[::-1].copy()
elif self.config.timestep_spacing == "leading":
UpperCAmelCase__ : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ : Dict = (np.arange(0 , _A ) * step_ratio).round()[::-1].copy().astype(_A )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
UpperCAmelCase__ : List[str] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
UpperCAmelCase__ : Union[str, Any] = (np.arange(_A , 0 , -step_ratio )).round().copy().astype(_A )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
UpperCAmelCase__ : int = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
UpperCAmelCase__ : Tuple = np.log(_A )
UpperCAmelCase__ : Optional[int] = np.interp(_A , np.arange(0 , len(_A ) ) , _A )
if self.config.use_karras_sigmas:
UpperCAmelCase__ : Union[str, Any] = self._convert_to_karras(in_sigmas=_A , num_inference_steps=self.num_inference_steps )
UpperCAmelCase__ : Any = np.array([self._sigma_to_t(_A , _A ) for sigma in sigmas] )
UpperCAmelCase__ : Optional[int] = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
UpperCAmelCase__ : Dict = torch.from_numpy(_A ).to(device=_A )
UpperCAmelCase__ : str = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
UpperCAmelCase__ : Tuple = torch.from_numpy(_A )
UpperCAmelCase__ : int = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(_A ).startswith('''mps''' ):
# mps does not support float64
UpperCAmelCase__ : Optional[int] = timesteps.to(_A , dtype=torch.floataa )
else:
UpperCAmelCase__ : Union[str, Any] = timesteps.to(device=_A )
# empty dt and derivative
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : Optional[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
UpperCAmelCase__ : List[str] = defaultdict(_A )
def lowercase_ ( self : int , _A : Any , _A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = np.log(_A )
# get distribution
UpperCAmelCase__ : List[str] = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
UpperCAmelCase__ : List[str] = np.cumsum((dists >= 0) , axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
UpperCAmelCase__ : List[str] = low_idx + 1
UpperCAmelCase__ : Any = log_sigmas[low_idx]
UpperCAmelCase__ : str = log_sigmas[high_idx]
# interpolate sigmas
UpperCAmelCase__ : List[Any] = (low - log_sigma) / (low - high)
UpperCAmelCase__ : Any = np.clip(_A , 0 , 1 )
# transform interpolation to time range
UpperCAmelCase__ : Any = (1 - w) * low_idx + w * high_idx
UpperCAmelCase__ : int = t.reshape(sigma.shape )
return t
def lowercase_ ( self : Optional[Any] , _A : torch.FloatTensor , _A : Dict ):
'''simple docstring'''
UpperCAmelCase__ : int = in_sigmas[-1].item()
UpperCAmelCase__ : Tuple = in_sigmas[0].item()
UpperCAmelCase__ : Tuple = 7.0 # 7.0 is the value used in the paper
UpperCAmelCase__ : Optional[Any] = np.linspace(0 , 1 , _A )
UpperCAmelCase__ : List[str] = sigma_min ** (1 / rho)
UpperCAmelCase__ : List[str] = sigma_max ** (1 / rho)
UpperCAmelCase__ : Dict = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def lowercase_ ( self : Tuple ):
'''simple docstring'''
return self.dt is None
def lowercase_ ( self : Optional[Any] , _A : Union[torch.FloatTensor, np.ndarray] , _A : Union[float, torch.FloatTensor] , _A : Union[torch.FloatTensor, np.ndarray] , _A : bool = True , ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.index_for_timestep(_A )
# advance index counter by 1
UpperCAmelCase__ : Any = timestep.cpu().item() if torch.is_tensor(_A ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
UpperCAmelCase__ : Optional[Any] = self.sigmas[step_index]
UpperCAmelCase__ : Union[str, Any] = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
UpperCAmelCase__ : Any = self.sigmas[step_index - 1]
UpperCAmelCase__ : List[Any] = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
UpperCAmelCase__ : Tuple = 0
UpperCAmelCase__ : str = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
UpperCAmelCase__ : str = sigma_hat if self.state_in_first_order else sigma_next
UpperCAmelCase__ : Tuple = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase__ : Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_next
UpperCAmelCase__ : List[Any] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
UpperCAmelCase__ : List[Any] = model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.config.clip_sample:
UpperCAmelCase__ : Optional[Any] = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
UpperCAmelCase__ : Dict = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
UpperCAmelCase__ : Union[str, Any] = sigma_next - sigma_hat
# store for 2nd order step
UpperCAmelCase__ : Any = derivative
UpperCAmelCase__ : List[str] = dt
UpperCAmelCase__ : Optional[Any] = sample
else:
# 2. 2nd order / Heun's method
UpperCAmelCase__ : List[str] = (sample - pred_original_sample) / sigma_next
UpperCAmelCase__ : Union[str, Any] = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
UpperCAmelCase__ : List[str] = self.dt
UpperCAmelCase__ : int = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
UpperCAmelCase__ : List[Any] = None
UpperCAmelCase__ : Optional[int] = None
UpperCAmelCase__ : int = None
UpperCAmelCase__ : Dict = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_A )
def lowercase_ ( self : Tuple , _A : torch.FloatTensor , _A : torch.FloatTensor , _A : torch.FloatTensor , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(_A ):
# mps does not support float64
UpperCAmelCase__ : Dict = self.timesteps.to(original_samples.device , dtype=torch.floataa )
UpperCAmelCase__ : Any = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
UpperCAmelCase__ : Union[str, Any] = self.timesteps.to(original_samples.device )
UpperCAmelCase__ : Union[str, Any] = timesteps.to(original_samples.device )
UpperCAmelCase__ : List[Any] = [self.index_for_timestep(_A , _A ) for t in timesteps]
UpperCAmelCase__ : Union[str, Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
UpperCAmelCase__ : Optional[int] = sigma.unsqueeze(-1 )
UpperCAmelCase__ : Any = original_samples + noise * sigma
return noisy_samples
def __len__( self : Tuple ):
'''simple docstring'''
return self.config.num_train_timesteps
| 181 | '''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
__UpperCAmelCase =["gpt2"]
__UpperCAmelCase ="gpt2"
if is_tf_available():
class a__ ( tf.Module ):
def __init__( self : str , a : Union[str, Any] ):
"""simple docstring"""
super().__init__()
__lowerCamelCase = tokenizer
__lowerCamelCase = AutoConfig.from_pretrained(a )
__lowerCamelCase = TFGPTaLMHeadModel.from_config(a )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name='''text''' ),) )
def SCREAMING_SNAKE_CASE__ ( self : str , a : Tuple ):
"""simple docstring"""
__lowerCamelCase = self.tokenizer(a )
__lowerCamelCase = tokenized['''input_ids'''].to_tensor()
__lowerCamelCase = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
__lowerCamelCase = self.model(input_ids=a , attention_mask=a )['''logits''']
return outputs
@require_tf
@require_keras_nlp
class a__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
super().setUp()
__lowerCamelCase = [GPTaTokenizer.from_pretrained(a ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
__lowerCamelCase = [TFGPTaTokenizer.from_pretrained(a ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
__lowerCamelCase = [
'''This is a straightforward English test sentence.''',
'''This one has some weird characters\rto\nsee\r\nif those\u00E9break things.''',
'''Now we\'re going to add some Chinese: 一 二 三 一二三''',
'''And some much more rare Chinese: 齉 堃 齉堃''',
'''Je vais aussi écrire en français pour tester les accents''',
'''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
]
__lowerCamelCase = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
__lowerCamelCase = tokenizer([test_inputs] , return_tensors='''tf''' )
__lowerCamelCase = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
__lowerCamelCase = python_outputs[key].numpy()
__lowerCamelCase = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(a , tf.intaa ) == tf_outputs_values ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase = tf.function(a )
for test_inputs in self.test_sentences:
__lowerCamelCase = tf.constant(a )
__lowerCamelCase = compiled_tokenizer(a )
__lowerCamelCase = tf_tokenizer(a )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase = ModelToSave(tokenizer=a )
__lowerCamelCase = tf.convert_to_tensor([self.test_sentences[0]] )
__lowerCamelCase = model.serving(a ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
__lowerCamelCase = Path(a ) / '''saved.model'''
tf.saved_model.save(a , a , signatures={'''serving_default''': model.serving} )
__lowerCamelCase = tf.saved_model.load(a )
__lowerCamelCase = loaded_model.signatures['''serving_default'''](a )['''output_0''']
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase = tf.convert_to_tensor([self.test_sentences[0]] )
__lowerCamelCase = tf_tokenizer(a ) # Build model with some sample inputs
__lowerCamelCase = tf_tokenizer.get_config()
__lowerCamelCase = TFGPTaTokenizer.from_config(a )
__lowerCamelCase = model_from_config(a )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
__lowerCamelCase = 12_31_23
for max_length in [3, 5, 10_24]:
__lowerCamelCase = tf.convert_to_tensor([self.test_sentences[0]] )
__lowerCamelCase = tf_tokenizer(a , max_length=a )
__lowerCamelCase = out['''input_ids'''].numpy().shape[1]
assert out_length == max_length
| 67 | 0 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def a_ ( __snake_case : Union[str, Any] ) -> int:
"""simple docstring"""
lowerCamelCase_ =torch.exp(UpperCamelCase__ )
lowerCamelCase_ =torch.sum(UpperCamelCase__ , dim=1 ) # sum of exp(x_i)
lowerCamelCase_ =torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(UpperCamelCase__ ) - B / A
class __UpperCamelCase ( nn.Module ):
def __init__( self, lowerCAmelCase ):
"""simple docstring"""
super().__init__()
lowerCamelCase_ =config.output_attentions
lowerCamelCase_ =config.output_hidden_states
lowerCamelCase_ =nn.ModuleList([BertLayer(lowerCAmelCase ) for _ in range(config.num_hidden_layers )] )
lowerCamelCase_ =nn.ModuleList([BertHighway(lowerCAmelCase ) for _ in range(config.num_hidden_layers )] )
lowerCamelCase_ =[-1 for _ in range(config.num_hidden_layers )]
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if (type(lowerCAmelCase ) is float) or (type(lowerCAmelCase ) is int):
for i in range(len(self.early_exit_entropy ) ):
lowerCamelCase_ =x
else:
lowerCamelCase_ =x
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, ):
"""simple docstring"""
lowerCamelCase_ =()
lowerCamelCase_ =()
lowerCamelCase_ =()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
lowerCamelCase_ =all_hidden_states + (hidden_states,)
lowerCamelCase_ =layer_module(
lowerCAmelCase, lowerCAmelCase, head_mask[i], lowerCAmelCase, lowerCAmelCase )
lowerCamelCase_ =layer_outputs[0]
if self.output_attentions:
lowerCamelCase_ =all_attentions + (layer_outputs[1],)
lowerCamelCase_ =(hidden_states,)
if self.output_hidden_states:
lowerCamelCase_ =current_outputs + (all_hidden_states,)
if self.output_attentions:
lowerCamelCase_ =current_outputs + (all_attentions,)
lowerCamelCase_ =self.highway[i](lowerCAmelCase )
# logits, pooled_output
if not self.training:
lowerCamelCase_ =highway_exit[0]
lowerCamelCase_ =entropy(lowerCAmelCase )
lowerCamelCase_ =highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
lowerCamelCase_ =all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
lowerCamelCase_ =(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(lowerCAmelCase, i + 1 )
else:
lowerCamelCase_ =all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
lowerCamelCase_ =all_hidden_states + (hidden_states,)
lowerCamelCase_ =(hidden_states,)
if self.output_hidden_states:
lowerCamelCase_ =outputs + (all_hidden_states,)
if self.output_attentions:
lowerCamelCase_ =outputs + (all_attentions,)
lowerCamelCase_ =outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
'The Bert Model transformer with early exiting (DeeBERT). ' , UpperCAmelCase__ , )
class __UpperCamelCase ( UpperCAmelCase__ ):
def __init__( self, lowerCAmelCase ):
"""simple docstring"""
super().__init__(lowerCAmelCase )
lowerCamelCase_ =config
lowerCamelCase_ =BertEmbeddings(lowerCAmelCase )
lowerCamelCase_ =DeeBertEncoder(lowerCAmelCase )
lowerCamelCase_ =BertPooler(lowerCAmelCase )
self.init_weights()
def lowercase__ ( self ):
"""simple docstring"""
self.encoder.init_highway_pooler(self.pooler )
def lowercase__ ( self ):
"""simple docstring"""
return self.embeddings.word_embeddings
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =value
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase )
@add_start_docstrings_to_model_forward(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
elif input_ids is not None:
lowerCamelCase_ =input_ids.size()
elif inputs_embeds is not None:
lowerCamelCase_ =inputs_embeds.size()[:-1]
else:
raise ValueError('''You have to specify either input_ids or inputs_embeds''' )
lowerCamelCase_ =input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowerCamelCase_ =torch.ones(lowerCAmelCase, device=lowerCAmelCase )
if encoder_attention_mask is None:
lowerCamelCase_ =torch.ones(lowerCAmelCase, device=lowerCAmelCase )
if token_type_ids is None:
lowerCamelCase_ =torch.zeros(lowerCAmelCase, dtype=torch.long, device=lowerCAmelCase )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowerCamelCase_ =self.get_extended_attention_mask(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
lowerCamelCase_ =encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
lowerCamelCase_ =encoder_attention_mask[:, None, None, :]
lowerCamelCase_ =encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
lowerCamelCase_ =(1.0 - encoder_extended_attention_mask) * -10_000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowerCamelCase_ =self.get_head_mask(lowerCAmelCase, self.config.num_hidden_layers )
lowerCamelCase_ =self.embeddings(
input_ids=lowerCAmelCase, position_ids=lowerCAmelCase, token_type_ids=lowerCAmelCase, inputs_embeds=lowerCAmelCase )
lowerCamelCase_ =self.encoder(
lowerCAmelCase, attention_mask=lowerCAmelCase, head_mask=lowerCAmelCase, encoder_hidden_states=lowerCAmelCase, encoder_attention_mask=lowerCAmelCase, )
lowerCamelCase_ =encoder_outputs[0]
lowerCamelCase_ =self.pooler(lowerCAmelCase )
lowerCamelCase_ =(
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class __UpperCamelCase ( UpperCAmelCase__ ):
def __init__( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =message
lowerCamelCase_ =exit_layer # start from 1!
class __UpperCamelCase ( nn.Module ):
def __init__( self, lowerCAmelCase ):
"""simple docstring"""
super().__init__()
lowerCamelCase_ =BertPooler(lowerCAmelCase )
lowerCamelCase_ =nn.Dropout(config.hidden_dropout_prob )
lowerCamelCase_ =nn.Linear(config.hidden_size, config.num_labels )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =encoder_outputs[0]
lowerCamelCase_ =self.pooler(lowerCAmelCase )
# "return" pooler_output
# BertModel
lowerCamelCase_ =(pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
lowerCamelCase_ =bmodel_output[1]
lowerCamelCase_ =self.dropout(lowerCAmelCase )
lowerCamelCase_ =self.classifier(lowerCAmelCase )
return logits, pooled_output
@add_start_docstrings(
'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n also takes care of multi-layer training. ' , UpperCAmelCase__ , )
class __UpperCamelCase ( UpperCAmelCase__ ):
def __init__( self, lowerCAmelCase ):
"""simple docstring"""
super().__init__(lowerCAmelCase )
lowerCamelCase_ =config.num_labels
lowerCamelCase_ =config.num_hidden_layers
lowerCamelCase_ =DeeBertModel(lowerCAmelCase )
lowerCamelCase_ =nn.Dropout(config.hidden_dropout_prob )
lowerCamelCase_ =nn.Linear(config.hidden_size, self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=-1, lowerCAmelCase=False, ):
"""simple docstring"""
lowerCamelCase_ =self.num_layers
try:
lowerCamelCase_ =self.bert(
lowerCAmelCase, attention_mask=lowerCAmelCase, token_type_ids=lowerCAmelCase, position_ids=lowerCAmelCase, head_mask=lowerCAmelCase, inputs_embeds=lowerCAmelCase, )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
lowerCamelCase_ =outputs[1]
lowerCamelCase_ =self.dropout(lowerCAmelCase )
lowerCamelCase_ =self.classifier(lowerCAmelCase )
lowerCamelCase_ =(logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
lowerCamelCase_ =e.message
lowerCamelCase_ =e.exit_layer
lowerCamelCase_ =outputs[0]
if not self.training:
lowerCamelCase_ =entropy(lowerCAmelCase )
lowerCamelCase_ =[]
lowerCamelCase_ =[]
if labels is not None:
if self.num_labels == 1:
# We are doing regression
lowerCamelCase_ =MSELoss()
lowerCamelCase_ =loss_fct(logits.view(-1 ), labels.view(-1 ) )
else:
lowerCamelCase_ =CrossEntropyLoss()
lowerCamelCase_ =loss_fct(logits.view(-1, self.num_labels ), labels.view(-1 ) )
# work with highway exits
lowerCamelCase_ =[]
for highway_exit in outputs[-1]:
lowerCamelCase_ =highway_exit[0]
if not self.training:
highway_logits_all.append(lowerCAmelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
lowerCamelCase_ =MSELoss()
lowerCamelCase_ =loss_fct(highway_logits.view(-1 ), labels.view(-1 ) )
else:
lowerCamelCase_ =CrossEntropyLoss()
lowerCamelCase_ =loss_fct(highway_logits.view(-1, self.num_labels ), labels.view(-1 ) )
highway_losses.append(lowerCAmelCase )
if train_highway:
lowerCamelCase_ =(sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
lowerCamelCase_ =(loss,) + outputs
if not self.training:
lowerCamelCase_ =outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
lowerCamelCase_ =(
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 75 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCamelCase__ , int(b / 2 ) ) * actual_power(UpperCamelCase__ , int(b / 2 ) )
else:
return a * actual_power(UpperCamelCase__ , int(b / 2 ) ) * actual_power(UpperCamelCase__ , int(b / 2 ) )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
if b < 0:
return 1 / actual_power(UpperCamelCase__ , UpperCamelCase__ )
return actual_power(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
print(power(-2, -3))
| 67 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class lowercase__ ( UpperCAmelCase__):
UpperCamelCase_ = "camembert"
def __init__( self : int , UpperCamelCase__ : Dict=3_0522 , UpperCamelCase__ : List[Any]=768 , UpperCamelCase__ : int=12 , UpperCamelCase__ : Union[str, Any]=12 , UpperCamelCase__ : int=3072 , UpperCamelCase__ : Any="gelu" , UpperCamelCase__ : Optional[Any]=0.1 , UpperCamelCase__ : Any=0.1 , UpperCamelCase__ : List[Any]=512 , UpperCamelCase__ : List[str]=2 , UpperCamelCase__ : Union[str, Any]=0.02 , UpperCamelCase__ : List[Any]=1E-12 , UpperCamelCase__ : Any=1 , UpperCamelCase__ : List[str]=0 , UpperCamelCase__ : Optional[int]=2 , UpperCamelCase__ : Union[str, Any]="absolute" , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : List[str]=None , **UpperCamelCase__ : Any , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Dict = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : int = position_embedding_type
SCREAMING_SNAKE_CASE : Dict = use_cache
SCREAMING_SNAKE_CASE : Any = classifier_dropout
class lowercase__ ( UpperCAmelCase__):
@property
def __A ( self : Optional[Any] ):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[int] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
SCREAMING_SNAKE_CASE : str = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 182 | '''simple docstring'''
import logging
import os
from .state import PartialState
class a__ ( logging.LoggerAdapter ):
@staticmethod
def SCREAMING_SNAKE_CASE__ ( a : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def SCREAMING_SNAKE_CASE__ ( self : int , a : Optional[int] , a : str , *a : Optional[int] , **a : List[Any] ):
"""simple docstring"""
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
__lowerCamelCase = kwargs.pop('''main_process_only''' , a )
__lowerCamelCase = kwargs.pop('''in_order''' , a )
if self.isEnabledFor(a ):
if self._should_log(a ):
__lowerCamelCase , __lowerCamelCase = self.process(a , a )
self.logger.log(a , a , *a , **a )
elif in_order:
__lowerCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
__lowerCamelCase , __lowerCamelCase = self.process(a , a )
self.logger.log(a , a , *a , **a )
state.wait_for_everyone()
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ = None ) -> Optional[int]:
if log_level is None:
__lowerCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , UpperCamelCase__ )
__lowerCamelCase = logging.getLogger(UpperCamelCase__ )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(UpperCamelCase__ , {} )
| 67 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCAmelCase__ ( ) -> str:
'''simple docstring'''
A__ = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
A__ = parser.parse_args_into_dataclasses()[0]
A__ = TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_ )
try:
A__ = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
A__ = "Arg --no_{0} is no longer used, please use --no-{0} instead."
A__ = " ".join(str(SCREAMING_SNAKE_CASE_ ).split(" " )[:-1] )
A__ = ""
A__ = eval(str(SCREAMING_SNAKE_CASE_ ).split(" " )[-1] )
A__ = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
A__ = full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_ )
raise ValueError(SCREAMING_SNAKE_CASE_ )
benchmark.run()
if __name__ == "__main__":
main()
| 68 |
def lowerCAmelCase__ ( ) -> Any:
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simple docstring'''
A__ = 1
A__ = 2
while i * i <= n:
A__ = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def lowerCAmelCase__ ( ) -> Dict:
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(SCREAMING_SNAKE_CASE_ ) > 5_0_0 )
if __name__ == "__main__":
print(solution())
| 68 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowerCAmelCase__ = False
class a__ ( unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
A__ = "A painting of a squirrel eating a burger "
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=lowercase , generator=lowercase , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowercase )
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
A__ = generator.manual_seed(0 )
A__ = pipe(
prompt=lowercase , generator=lowercase , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = VersatileDiffusionTextToImagePipeline.from_pretrained(
"shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
A__ = "A painting of a squirrel eating a burger "
A__ = torch.manual_seed(0 )
A__ = pipe(
prompt=lowercase , generator=lowercase , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" ).images
A__ = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
A__ = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 68 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCase__ = get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
lowerCAmelCase__ = json.load(f)
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return FSMTTokenizer.from_pretrained(lowercase )
def UpperCamelCase ( self , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = FSMTForConditionalGeneration.from_pretrained(lowercase ).to(lowercase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = F'facebook/wmt19-{pair}'
A__ = self.get_tokenizer(lowercase )
A__ = self.get_model(lowercase )
A__ = bleu_data[pair]["src"]
A__ = bleu_data[pair]["tgt"]
A__ = tokenizer(lowercase , return_tensors="pt" , truncation=lowercase , padding="longest" ).to(lowercase )
A__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
A__ = tokenizer.batch_decode(
lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase )
A__ = calculate_bleu(lowercase , lowercase )
print(lowercase )
self.assertGreaterEqual(scores["bleu"] , lowercase )
| 68 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'switch_transformers'
__lowerCamelCase = ['past_key_values']
__lowerCamelCase = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self , lowercase=32128 , lowercase=768 , lowercase=64 , lowercase=2048 , lowercase=64 , lowercase=12 , lowercase=3 , lowercase=12 , lowercase=3 , lowercase=12 , lowercase=8 , lowercase=False , lowercase=0.01 , lowercase="float32" , lowercase=False , lowercase=32 , lowercase=128 , lowercase=0.1 , lowercase=1e-6 , lowercase=0.001 , lowercase=0.001 , lowercase=1.0 , lowercase="relu" , lowercase=True , lowercase=False , lowercase=True , lowercase=0 , lowercase=1 , **lowercase , ) -> Tuple:
'''simple docstring'''
A__ = vocab_size
A__ = d_model
A__ = d_kv
A__ = d_ff
A__ = num_sparse_encoder_layers
A__ = num_layers
A__ = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
A__ = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
A__ = self.num_layers // self.num_sparse_encoder_layers
else:
A__ = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
A__ = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
A__ = self.num_decoder_layers # HACK: this will create 0 sparse layers
A__ = num_heads
A__ = num_experts
A__ = expert_capacity
A__ = router_bias
A__ = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
A__ = router_dtype
A__ = router_ignore_padding_tokens
A__ = relative_attention_num_buckets
A__ = relative_attention_max_distance
A__ = dropout_rate
A__ = layer_norm_epsilon
A__ = initializer_factor
A__ = feed_forward_proj
A__ = use_cache
A__ = add_router_probs
A__ = router_z_loss_coef
A__ = router_aux_loss_coef
A__ = self.feed_forward_proj.split("-" )
A__ = act_info[-1]
A__ = act_info[0] == "gated"
if len(lowercase ) > 1 and act_info[0] != "gated" or len(lowercase ) > 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'" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
A__ = "gelu_new"
super().__init__(
pad_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , **lowercase , )
| 68 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> list:
'''simple docstring'''
A__ = int(SCREAMING_SNAKE_CASE_ )
if n_element < 1:
A__ = ValueError("a should be a positive number" )
raise my_error
A__ = [1]
A__ , A__ , A__ = (0, 0, 0)
A__ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
lowerCAmelCase__ = input("""Enter the last number (nth term) of the Hamming Number Series: """)
print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
lowerCAmelCase__ = hamming(int(n))
print("""-----------------------------------------------------""")
print(f"""The list with nth numbers is: {hamming_numbers}""")
print("""-----------------------------------------------------""")
| 68 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
"""GroupViTTextConfig""",
"""GroupViTVisionConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GroupViTModel""",
"""GroupViTPreTrainedModel""",
"""GroupViTTextModel""",
"""GroupViTVisionModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFGroupViTModel""",
"""TFGroupViTPreTrainedModel""",
"""TFGroupViTTextModel""",
"""TFGroupViTVisionModel""",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 |
import copy
import random
from transformers import CLIPTokenizer
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowercase , **lowercase )
A__ = {}
def UpperCamelCase ( self , lowercase , *lowercase , **lowercase ) -> str:
'''simple docstring'''
A__ = super().add_tokens(lowercase , *lowercase , **lowercase )
if num_added_tokens == 0:
raise ValueError(
F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
" `placeholder_token` that is not already in the tokenizer." )
def UpperCamelCase ( self , lowercase , *lowercase , lowercase=1 , **lowercase ) -> Any:
'''simple docstring'''
A__ = []
if num_vec_per_token == 1:
self.try_adding_tokens(lowercase , *lowercase , **lowercase )
output.append(lowercase )
else:
A__ = []
for i in range(lowercase ):
A__ = placeholder_token + F'_{i}'
self.try_adding_tokens(lowercase , *lowercase , **lowercase )
output.append(lowercase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F'The tokenizer already has placeholder token {token} that can get confused with'
F' {placeholder_token}keep placeholder tokens independent' )
A__ = output
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=1.0 ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = []
for i in range(len(lowercase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=lowercase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
A__ = self.token_map[placeholder_token]
A__ = tokens[: 1 + int(len(lowercase ) * prop_tokens_to_load )]
if vector_shuffle:
A__ = copy.copy(lowercase )
random.shuffle(lowercase )
A__ = text.replace(lowercase , " ".join(lowercase ) )
return text
def __call__( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase ) -> str:
'''simple docstring'''
return super().__call__(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase ) , *lowercase , **lowercase , )
def UpperCamelCase ( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase ) -> List[str]:
'''simple docstring'''
return super().encode(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase ) , *lowercase , **lowercase , )
| 68 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = inspect.getfile(accelerate.test_utils )
A__ = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
A__ = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = F'\n {self.test_dir}/xla_spawn.py\n --num_cores 8\n {self.test_file_path}\n '.split()
A__ = [sys.executable] + distributed_args
execute_subprocess_async(lowercase , env=os.environ.copy() )
| 68 |
from collections import deque
from math import floor
from random import random
from time import time
class a__ :
"""simple docstring"""
def __init__( self ) -> Dict:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase , lowercase=1 ) -> Tuple:
'''simple docstring'''
if self.graph.get(lowercase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
A__ = [[w, v]]
if not self.graph.get(lowercase ):
A__ = []
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return list(self.graph )
def UpperCamelCase ( self , lowercase , lowercase ) -> int:
'''simple docstring'''
if self.graph.get(lowercase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Any:
'''simple docstring'''
if s == d:
return []
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return visited
def UpperCamelCase ( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
if c == -1:
A__ = floor(random() * 10000 ) + 10
for i in range(lowercase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A__ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase , lowercase , 1 )
def UpperCamelCase ( self , lowercase=-2 ) -> Any:
'''simple docstring'''
A__ = deque()
A__ = []
if s == -2:
A__ = list(self.graph )[0]
d.append(lowercase )
visited.append(lowercase )
while d:
A__ = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return len(self.graph[u] )
def UpperCamelCase ( self , lowercase=-2 ) -> str:
'''simple docstring'''
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
A__ = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return sorted_nodes
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return list(lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return False
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Any:
'''simple docstring'''
A__ = time()
self.dfs(lowercase , lowercase )
A__ = time()
return end - begin
def UpperCamelCase ( self , lowercase=-2 ) -> int:
'''simple docstring'''
A__ = time()
self.bfs(lowercase )
A__ = time()
return end - begin
class a__ :
"""simple docstring"""
def __init__( self ) -> int:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase , lowercase=1 ) -> Union[str, Any]:
'''simple docstring'''
if self.graph.get(lowercase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
A__ = [[w, v]]
# add the other way
if self.graph.get(lowercase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
A__ = [[w, u]]
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
if self.graph.get(lowercase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase )
# the other way round
if self.graph.get(lowercase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowercase )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> List[str]:
'''simple docstring'''
if s == d:
return []
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return visited
def UpperCamelCase ( self , lowercase=-1 ) -> str:
'''simple docstring'''
if c == -1:
A__ = floor(random() * 10000 ) + 10
for i in range(lowercase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A__ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase , lowercase , 1 )
def UpperCamelCase ( self , lowercase=-2 ) -> Dict:
'''simple docstring'''
A__ = deque()
A__ = []
if s == -2:
A__ = list(self.graph )[0]
d.append(lowercase )
visited.append(lowercase )
while d:
A__ = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
return len(self.graph[u] )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return list(lowercase )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return False
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
return list(self.graph )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = time()
self.dfs(lowercase , lowercase )
A__ = time()
return end - begin
def UpperCamelCase ( self , lowercase=-2 ) -> List[Any]:
'''simple docstring'''
A__ = time()
self.bfs(lowercase )
A__ = time()
return end - begin
| 68 | 1 |
import random
class a__ :
"""simple docstring"""
@staticmethod
def UpperCamelCase ( lowercase ) -> tuple[list[int], list[int]]:
'''simple docstring'''
A__ = [ord(lowercase ) for i in text]
A__ = []
A__ = []
for i in plain:
A__ = random.randint(1 , 300 )
A__ = (i + k) * k
cipher.append(lowercase )
key.append(lowercase )
return cipher, key
@staticmethod
def UpperCamelCase ( lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = []
for i in range(len(lowercase ) ):
A__ = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowercase ) )
return "".join(lowercase )
if __name__ == "__main__":
lowerCAmelCase__ , lowerCAmelCase__ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 68 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
"""
lowerCAmelCase__ = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""
lowerCAmelCase__ = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the CUAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'exact_match': Exact match (the normalized answer exactly match the gold answer)
'f1': The F-score of predicted tokens versus the gold answer
'aupr': Area Under the Precision-Recall curve
'prec_at_80_recall': Precision at 80% recall
'prec_at_90_recall': Precision at 90% recall
Examples:
>>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> cuad_metric = datasets.load_metric(\"cuad\")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def UpperCamelCase ( self , lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
A__ = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
A__ = evaluate(dataset=lowercase , predictions=lowercase )
return score
| 68 | 1 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class a__ :
"""simple docstring"""
def __init__( self , lowercase , ) -> List[Any]:
'''simple docstring'''
A__ = parent
A__ = 13
A__ = 7
A__ = True
A__ = True
A__ = False
A__ = True
A__ = 99
A__ = 32
A__ = 2
A__ = 4
A__ = 37
A__ = "gelu"
A__ = 0.1
A__ = 0.1
A__ = 512
A__ = 16
A__ = 2
A__ = 0.02
A__ = 3
A__ = 4
A__ = None
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = None
if self.use_input_mask:
A__ = random_attention_mask([self.batch_size, self.seq_length] )
A__ = None
A__ = None
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A__ = ids_tensor([self.batch_size] , self.num_choices )
A__ = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
A__ = TFDistilBertModel(config=lowercase )
A__ = {"input_ids": input_ids, "attention_mask": input_mask}
A__ = model(lowercase )
A__ = [input_ids, input_mask]
A__ = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = TFDistilBertForMaskedLM(config=lowercase )
A__ = {"input_ids": input_ids, "attention_mask": input_mask}
A__ = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = TFDistilBertForQuestionAnswering(config=lowercase )
A__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
}
A__ = model(lowercase )
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 UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[str]:
'''simple docstring'''
A__ = self.num_labels
A__ = TFDistilBertForSequenceClassification(lowercase )
A__ = {"input_ids": input_ids, "attention_mask": input_mask}
A__ = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
A__ = self.num_choices
A__ = TFDistilBertForMultipleChoice(lowercase )
A__ = tf.tile(tf.expand_dims(lowercase , 1 ) , (1, self.num_choices, 1) )
A__ = tf.tile(tf.expand_dims(lowercase , 1 ) , (1, self.num_choices, 1) )
A__ = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
}
A__ = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
A__ = self.num_labels
A__ = TFDistilBertForTokenClassification(lowercase )
A__ = {"input_ids": input_ids, "attention_mask": input_mask}
A__ = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
((A__) , (A__) , (A__) , (A__) , (A__) , (A__)) = config_and_inputs
A__ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
__lowerCamelCase = (
{
'feature-extraction': TFDistilBertModel,
'fill-mask': TFDistilBertForMaskedLM,
'question-answering': TFDistilBertForQuestionAnswering,
'text-classification': TFDistilBertForSequenceClassification,
'token-classification': TFDistilBertForTokenClassification,
'zero-shot': TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = TFDistilBertModelTester(self )
A__ = ConfigTester(self , config_class=lowercase , dim=37 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*lowercase )
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase )
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase )
@slow
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
A__ = TFDistilBertModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@require_tf
class a__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = TFDistilBertModel.from_pretrained("distilbert-base-uncased" )
A__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
A__ = model(lowercase )[0]
A__ = [1, 6, 768]
self.assertEqual(output.shape , lowercase )
A__ = tf.constant(
[
[
[0.1926_1885, -0.1373_2955, 0.411_9799],
[0.2215_0156, -0.0742_2661, 0.3903_7204],
[0.2275_6018, -0.089_6414, 0.370_1467],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , lowercase , atol=1e-4 )
| 68 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
A__ = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
A__ = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
return torch.mm(SCREAMING_SNAKE_CASE_ , normalized_text_embeds.t() )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = CLIPConfig
__lowerCamelCase = ['CLIPEncoderLayer']
def __init__( self , lowercase ) -> Optional[int]:
'''simple docstring'''
super().__init__(lowercase )
A__ = CLIPVisionModel(config.vision_config )
A__ = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=lowercase )
A__ = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(17 ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(3 ) , requires_grad=lowercase )
@torch.no_grad()
def UpperCamelCase ( self , lowercase , lowercase ) -> Any:
'''simple docstring'''
A__ = self.vision_model(lowercase )[1] # pooled_output
A__ = self.visual_projection(lowercase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
A__ = cosine_distance(lowercase , self.special_care_embeds ).cpu().float().numpy()
A__ = cosine_distance(lowercase , self.concept_embeds ).cpu().float().numpy()
A__ = []
A__ = image_embeds.shape[0]
for i in range(lowercase ):
A__ = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
A__ = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
A__ = special_cos_dist[i][concept_idx]
A__ = self.special_care_embeds_weights[concept_idx].item()
A__ = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]} )
A__ = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
A__ = cos_dist[i][concept_idx]
A__ = self.concept_embeds_weights[concept_idx].item()
A__ = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(lowercase )
result.append(lowercase )
A__ = [len(res["bad_concepts"] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCamelCase ( self , lowercase , lowercase ) -> Any:
'''simple docstring'''
A__ = self.vision_model(lowercase )[1] # pooled_output
A__ = self.visual_projection(lowercase )
A__ = cosine_distance(lowercase , self.special_care_embeds )
A__ = cosine_distance(lowercase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
A__ = 0.0
A__ = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
A__ = torch.any(special_scores > 0 , dim=1 )
A__ = special_care * 0.01
A__ = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
A__ = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
A__ = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 68 | 1 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowerCAmelCase__ = get_logger(__name__)
class a__ :
"""simple docstring"""
__lowerCamelCase = 'dummy_data'
__lowerCamelCase = 'datasets'
__lowerCamelCase = False
def __init__( self , lowercase , lowercase , lowercase , lowercase = None , lowercase = False , lowercase = True , lowercase = None , ) -> Dict:
'''simple docstring'''
A__ = 0
A__ = dataset_name
A__ = cache_dir
A__ = use_local_dummy_data
A__ = config
# download_callbacks take a single url as input
A__ = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
A__ = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
A__ = str(lowercase )
# to be downloaded
A__ = None
A__ = None
@property
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
if self._dummy_file is None:
A__ = self.download_dummy_data()
return self._dummy_file
@property
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" , self.version_name )
@property
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
return os.path.join(self.dummy_data_folder , "dummy_data.zip" )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
A__ = cached_path(
lowercase , cache_dir=self.cache_dir , extract_compressed_file=lowercase , force_extract=lowercase )
return os.path.join(lowercase , self.dummy_file_name )
@property
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
if self._bucket_url is None:
A__ = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , "/" ) )
return self._bucket_url
@property
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , "/" ).split("/" )[:-1] )
def UpperCamelCase ( self , lowercase , *lowercase ) -> Any:
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
A__ = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
A__ = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowercase , lowercase ):
return self.create_dummy_data_dict(lowercase , lowercase )
elif isinstance(lowercase , (list, tuple) ):
return self.create_dummy_data_list(lowercase , lowercase )
else:
return self.create_dummy_data_single(lowercase , lowercase )
def UpperCamelCase ( self , lowercase , *lowercase ) -> Dict:
'''simple docstring'''
return self.download_and_extract(lowercase )
def UpperCamelCase ( self , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
return self.download_and_extract(lowercase )
def UpperCamelCase ( self , lowercase , *lowercase , **lowercase ) -> Optional[int]:
'''simple docstring'''
return path
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
return {}
def UpperCamelCase ( self , lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowercase , lowercase ):
for single_url in single_urls:
download_callback(lowercase )
else:
A__ = single_urls
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowercase , lowercase ):
A__ = [os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) ) for x in single_urls]
else:
A__ = single_urls
A__ = os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) )
A__ = value
# make sure that values are unique
if all(isinstance(lowercase , lowercase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
A__ = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCamelCase ( self , lowercase , lowercase ) -> Dict:
'''simple docstring'''
A__ = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
A__ = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" , lowercase ) ) for url in data_url )
A__ = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
A__ = [data_url[0]] * len(lowercase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
A__ = os.path.join(lowercase , urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(lowercase )
return dummy_data_list
def UpperCamelCase ( self , lowercase , lowercase ) -> List[str]:
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
A__ = os.path.join(lowercase , urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(lowercase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def UpperCamelCase ( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
def _iter_archive_members(lowercase ):
# this preserves the order of the members inside the ZIP archive
A__ = Path(self.dummy_file ).parent
A__ = path.relative_to(lowercase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
A__ = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(lowercase )
A__ = Path(lowercase )
A__ = _iter_archive_members(lowercase ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(lowercase ).as_posix(), file_path.open("rb" )
def UpperCamelCase ( self , lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
A__ = [paths]
for path in paths:
if os.path.isfile(lowercase ):
if os.path.basename(lowercase ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowercase ):
if os.path.basename(lowercase ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(lowercase ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(lowercase , lowercase )
| 68 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 68 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = field(default='automatic-speech-recognition' , metadata={'include_in_asdict_even_if_is_default': True} )
__lowerCamelCase = Features({'audio': Audio()} )
__lowerCamelCase = Features({'transcription': Value('string' )} )
__lowerCamelCase = "audio"
__lowerCamelCase = "transcription"
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
if self.audio_column not in features:
raise ValueError(F'Column {self.audio_column} is not present in features.' )
if not isinstance(features[self.audio_column] , lowercase ):
raise ValueError(F'Column {self.audio_column} is not an Audio type.' )
A__ = copy.deepcopy(self )
A__ = self.input_schema.copy()
A__ = features[self.audio_column]
A__ = input_schema
return task_template
@property
def UpperCamelCase ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 68 |
import string
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> None:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
A__ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
A__ = string.ascii_uppercase.find(SCREAMING_SNAKE_CASE_ )
A__ = num - key
if num < 0:
A__ = num + len(string.ascii_uppercase )
A__ = translated + string.ascii_uppercase[num]
else:
A__ = translated + symbol
print(F'Decryption using Key #{key}: {translated}' )
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
A__ = input("Encrypted message: " )
A__ = message.upper()
decrypt(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 68 | 1 |
lowerCAmelCase__ = tuple[float, float, float]
lowerCAmelCase__ = tuple[float, float, float]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Pointad , SCREAMING_SNAKE_CASE_: Pointad ) -> Vectorad:
'''simple docstring'''
A__ = end_pointa[0] - end_pointa[0]
A__ = end_pointa[1] - end_pointa[1]
A__ = end_pointa[2] - end_pointa[2]
return (x, y, z)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Vectorad , SCREAMING_SNAKE_CASE_: Vectorad ) -> Vectorad:
'''simple docstring'''
A__ = ab[1] * ac[2] - ab[2] * ac[1] # *i
A__ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
A__ = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Vectorad , SCREAMING_SNAKE_CASE_: int ) -> bool:
'''simple docstring'''
return tuple(round(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for x in vector ) == (0, 0, 0)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Pointad , SCREAMING_SNAKE_CASE_: Pointad , SCREAMING_SNAKE_CASE_: Pointad , SCREAMING_SNAKE_CASE_: int = 1_0 ) -> bool:
'''simple docstring'''
A__ = create_vector(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A__ = create_vector(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return is_zero_vector(get_ad_vectors_cross(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
| 68 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = SpeechTaTokenizer
__lowerCamelCase = False
__lowerCamelCase = True
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
A__ = SpeechTaTokenizer(lowercase )
A__ = AddedToken("<mask>" , lstrip=lowercase , rstrip=lowercase )
A__ = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = "this is a test"
A__ = "this is a test"
return input_text, output_text
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=20 , lowercase=5 ) -> Optional[Any]:
'''simple docstring'''
A__ , A__ = self.get_input_output_texts(lowercase )
A__ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A__ = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
return text, ids
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = "<pad>"
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-4] , "œ" )
self.assertEqual(vocab_keys[-2] , "<mask>" )
self.assertEqual(vocab_keys[-1] , "<ctc_blank>" )
self.assertEqual(len(lowercase ) , 81 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.get_tokenizers(do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
A__ = tokenizer.add_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size + len(lowercase ) )
A__ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
A__ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
A__ = tokenizer.add_special_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size_a + len(lowercase ) )
A__ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.get_tokenizer()
A__ = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(lowercase , [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
A__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
A__ = tokenizer.convert_tokens_to_ids(lowercase )
# fmt: off
self.assertListEqual(lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
A__ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
A__ = {
"input_ids": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name="microsoft/speecht5_asr" , revision="c5ef64c71905caeccde0e4462ef3f9077224c524" , sequences=lowercase , )
| 68 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
lowerCAmelCase__ = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
lowerCAmelCase__ = {
"""google/realm-cc-news-pretrained-embedder""": 5_1_2,
"""google/realm-cc-news-pretrained-encoder""": 5_1_2,
"""google/realm-cc-news-pretrained-scorer""": 5_1_2,
"""google/realm-cc-news-pretrained-openqa""": 5_1_2,
"""google/realm-orqa-nq-openqa""": 5_1_2,
"""google/realm-orqa-nq-reader""": 5_1_2,
"""google/realm-orqa-wq-openqa""": 5_1_2,
"""google/realm-orqa-wq-reader""": 5_1_2,
}
lowerCAmelCase__ = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = RealmTokenizer
def __init__( self , lowercase=None , lowercase=None , lowercase=True , lowercase="[UNK]" , lowercase="[SEP]" , lowercase="[PAD]" , lowercase="[CLS]" , lowercase="[MASK]" , lowercase=True , lowercase=None , **lowercase , ) -> List[str]:
'''simple docstring'''
super().__init__(
lowercase , tokenizer_file=lowercase , do_lower_case=lowercase , unk_token=lowercase , sep_token=lowercase , pad_token=lowercase , cls_token=lowercase , mask_token=lowercase , tokenize_chinese_chars=lowercase , strip_accents=lowercase , **lowercase , )
A__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , lowercase ) != do_lower_case
or normalizer_state.get("strip_accents" , lowercase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , lowercase ) != tokenize_chinese_chars
):
A__ = getattr(lowercase , normalizer_state.pop("type" ) )
A__ = do_lower_case
A__ = strip_accents
A__ = tokenize_chinese_chars
A__ = normalizer_class(**lowercase )
A__ = do_lower_case
def UpperCamelCase ( self , lowercase , **lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = PaddingStrategy.MAX_LENGTH
A__ = text
A__ = kwargs.pop("text_pair" , lowercase )
A__ = kwargs.pop("return_tensors" , lowercase )
A__ = {
"input_ids": [],
"attention_mask": [],
"token_type_ids": [],
}
for idx, candidate_text in enumerate(lowercase ):
if batch_text_pair is not None:
A__ = batch_text_pair[idx]
else:
A__ = None
A__ = super().__call__(lowercase , lowercase , return_tensors=lowercase , **lowercase )
A__ = encoded_candidates.get("input_ids" )
A__ = encoded_candidates.get("attention_mask" )
A__ = encoded_candidates.get("token_type_ids" )
if encoded_input_ids is not None:
output_data["input_ids"].append(lowercase )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(lowercase )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(lowercase )
A__ = {key: item for key, item in output_data.items() if len(lowercase ) != 0}
return BatchEncoding(lowercase , tensor_type=lowercase )
def UpperCamelCase ( self , lowercase , lowercase=None ) -> str:
'''simple docstring'''
A__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase ( self , lowercase , lowercase = None ) -> List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [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 ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase ( self , lowercase , lowercase = None ) -> Tuple[str]:
'''simple docstring'''
A__ = self._tokenizer.model.save(lowercase , name=lowercase )
return tuple(lowercase )
| 68 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> List[str]:
'''simple docstring'''
A__ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
A__ = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
A__ = F'{src_lang}-{tgt_lang}'
A__ = F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
A__ = os.path.join(SCREAMING_SNAKE_CASE_ , "README.md" )
print(F'Generating {path}' )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
# make sure we are under the root of the project
lowerCAmelCase__ = Path(__file__).resolve().parent.parent.parent
lowerCAmelCase__ = repo_dir / """model_cards"""
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = model_name.split("""-""")
lowerCAmelCase__ = model_cards_dir / """facebook""" / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 68 | 1 |
from PIL import Image
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Image , SCREAMING_SNAKE_CASE_: int ) -> Image:
'''simple docstring'''
A__ = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(SCREAMING_SNAKE_CASE_: int ) -> int:
return int(1_2_8 + factor * (c - 1_2_8) )
return img.point(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
# Load image
with Image.open("""image_data/lena.jpg""") as img:
# Change contrast to 170
lowerCAmelCase__ = change_contrast(img, 1_7_0)
cont_img.save("""image_data/lena_high_contrast.png""", format="""png""")
| 68 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = feature_size
A__ = sampling_rate
A__ = padding_value
A__ = kwargs.pop("padding_side" , "right" )
A__ = kwargs.pop("return_attention_mask" , lowercase )
super().__init__(**lowercase )
def UpperCamelCase ( self , lowercase , lowercase = True , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , ) -> BatchFeature:
'''simple docstring'''
if isinstance(lowercase , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
A__ = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
F' to this method that includes {self.model_input_names[0]}, but you provided'
F' {list(processed_features.keys() )}' )
A__ = processed_features[self.model_input_names[0]]
A__ = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(lowercase ) == 0:
if return_attention_mask:
A__ = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
A__ = required_input[0]
if isinstance(lowercase , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
A__ = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(lowercase ):
A__ = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(lowercase ):
A__ = "tf"
elif is_torch_tensor(lowercase ):
A__ = "pt"
elif isinstance(lowercase , (int, float, list, tuple, np.ndarray) ):
A__ = "np"
else:
raise ValueError(
F'type of {first_element} unknown: {type(lowercase )}. '
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
A__ = to_numpy(lowercase )
else:
A__ = [to_numpy(lowercase ) for v in value]
# Convert padding_strategy in PaddingStrategy
A__ = self._get_padding_strategies(padding=lowercase , max_length=lowercase )
A__ = processed_features[self.model_input_names[0]]
A__ = len(lowercase )
if not all(len(lowercase ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
A__ = []
for i in range(lowercase ):
A__ = {k: v[i] for k, v in processed_features.items()}
# truncation
A__ = self._truncate(
lowercase , max_length=lowercase , pad_to_multiple_of=lowercase , truncation=lowercase , )
truncated_inputs.append(lowercase )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
A__ = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
A__ = PaddingStrategy.MAX_LENGTH
A__ = {}
for i in range(lowercase ):
# padding
A__ = self._pad(
truncated_inputs[i] , max_length=lowercase , padding_strategy=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , )
for key, value in outputs.items():
if key not in batch_outputs:
A__ = []
if value.dtype is np.dtype(np.floataa ):
A__ = value.astype(np.floataa )
batch_outputs[key].append(lowercase )
return BatchFeature(lowercase , tensor_type=lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = PaddingStrategy.DO_NOT_PAD , lowercase = None , lowercase = None , ) -> dict:
'''simple docstring'''
A__ = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
A__ = len(lowercase )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
A__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
A__ = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
A__ = np.ones(len(lowercase ) , dtype=np.intaa )
if needs_to_be_padded:
A__ = max_length - len(lowercase )
if self.padding_side == "right":
if return_attention_mask:
A__ = np.pad(
processed_features["attention_mask"] , (0, difference) )
A__ = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
A__ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
A__ = np.pad(
processed_features["attention_mask"] , (difference, 0) )
A__ = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
A__ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , ) -> Union[str, Any]:
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
A__ = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
A__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
A__ = len(lowercase ) > max_length
if needs_to_be_truncated:
A__ = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
A__ = processed_features["attention_mask"][:max_length]
return processed_features
def UpperCamelCase ( self , lowercase=False , lowercase=None ) -> Any:
'''simple docstring'''
if padding is not False:
if padding is True:
A__ = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(lowercase , lowercase ):
A__ = PaddingStrategy(lowercase )
elif isinstance(lowercase , lowercase ):
A__ = padding
else:
A__ = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 68 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase__ = False
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase=32 ) -> Union[str, Any]:
'''simple docstring'''
set_seed(0 )
A__ = UNetaDModel(sample_size=lowercase , in_channels=3 , out_channels=3 )
A__ = torch.optim.SGD(model.parameters() , lr=0.0001 )
return model, optimizer
@slow
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = "cpu" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
A__ = DDPMScheduler(
num_train_timesteps=1000 , beta_start=0.0001 , beta_end=0.02 , beta_schedule="linear" , clip_sample=lowercase , )
A__ = DDIMScheduler(
num_train_timesteps=1000 , beta_start=0.0001 , beta_end=0.02 , beta_schedule="linear" , clip_sample=lowercase , )
assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
# shared batches for DDPM and DDIM
set_seed(0 )
A__ = [torch.randn((4, 3, 32, 32) ).clip(-1 , 1 ).to(lowercase ) for _ in range(4 )]
A__ = [torch.randn((4, 3, 32, 32) ).to(lowercase ) for _ in range(4 )]
A__ = [torch.randint(0 , 1000 , (4,) ).long().to(lowercase ) for _ in range(4 )]
# train with a DDPM scheduler
A__ , A__ = self.get_model_optimizer(resolution=32 )
model.train().to(lowercase )
for i in range(4 ):
optimizer.zero_grad()
A__ = ddpm_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
A__ = model(lowercase , timesteps[i] ).sample
A__ = torch.nn.functional.mse_loss(lowercase , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
# recreate the model and optimizer, and retry with DDIM
A__ , A__ = self.get_model_optimizer(resolution=32 )
model.train().to(lowercase )
for i in range(4 ):
optimizer.zero_grad()
A__ = ddim_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
A__ = model(lowercase , timesteps[i] ).sample
A__ = torch.nn.functional.mse_loss(lowercase , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
self.assertTrue(torch.allclose(lowercase , lowercase , atol=1e-5 ) )
self.assertTrue(torch.allclose(lowercase , lowercase , atol=1e-5 ) )
| 68 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
"""GroupViTTextConfig""",
"""GroupViTVisionConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GroupViTModel""",
"""GroupViTPreTrainedModel""",
"""GroupViTTextModel""",
"""GroupViTVisionModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFGroupViTModel""",
"""TFGroupViTPreTrainedModel""",
"""TFGroupViTTextModel""",
"""TFGroupViTVisionModel""",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
lowerCAmelCase__ = float("""nan""")
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = sys.stdout
A__ = open(lowercase , "a" )
def __getattr__( self , lowercase ) -> List[Any]:
'''simple docstring'''
return getattr(self.stdout , lowercase )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
self.stdout.write(lowercase )
# strip tqdm codes
self.file.write(re.sub(R"^.*\r" , "" , lowercase , 0 , re.M ) )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int]=8_0 , SCREAMING_SNAKE_CASE_: List[Any]=False ) -> Union[str, Any]:
'''simple docstring'''
A__ = []
# deal with critical env vars
A__ = ["CUDA_VISIBLE_DEVICES"]
for key in env_keys:
A__ = os.environ.get(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if val is not None:
cmd.append(F'{key}={val}' )
# python executable (not always needed if the script is executable)
A__ = sys.executable if full_python_path else sys.executable.split("/" )[-1]
cmd.append(SCREAMING_SNAKE_CASE_ )
# now the normal args
cmd += list(map(shlex.quote , sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
A__ = []
A__ = ""
while len(SCREAMING_SNAKE_CASE_ ) > 0:
current_line += F'{cmd.pop(0 )} '
if len(SCREAMING_SNAKE_CASE_ ) == 0 or len(SCREAMING_SNAKE_CASE_ ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(SCREAMING_SNAKE_CASE_ )
A__ = ""
return "\\\n".join(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: int ) -> Union[str, Any]:
'''simple docstring'''
A__ = re.sub(R"[\\\n]+" , " " , args.base_cmd )
# remove --output_dir if any and set our own
A__ = re.sub("--output_dir\s+[^\s]+" , "" , args.base_cmd )
args.base_cmd += F' --output_dir {output_dir}'
# ensure we have --overwrite_output_dir
A__ = re.sub("--overwrite_output_dir\s+" , "" , args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: str ) -> int:
'''simple docstring'''
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0 , 1_0_0 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222] )} , )
A__ = subprocess.run(SCREAMING_SNAKE_CASE_ , capture_output=SCREAMING_SNAKE_CASE_ , text=SCREAMING_SNAKE_CASE_ )
if verbose:
print("STDOUT" , result.stdout )
print("STDERR" , result.stderr )
# save the streams
A__ = variation.replace(" " , "-" )
with open(Path(SCREAMING_SNAKE_CASE_ ) / F'log.{prefix}.stdout.txt' , "w" ) as f:
f.write(result.stdout )
with open(Path(SCREAMING_SNAKE_CASE_ ) / F'log.{prefix}.stderr.txt' , "w" ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print("failed" )
return {target_metric_key: nan}
with io.open(F'{output_dir}/all_results.json' , "r" , encoding="utf-8" ) as f:
A__ = json.load(SCREAMING_SNAKE_CASE_ )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: str , ) -> List[Any]:
'''simple docstring'''
A__ = []
A__ = []
A__ = F'{id}: {variation:<{longest_variation_len}}'
A__ = F'{preamble}: '
A__ = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(SCREAMING_SNAKE_CASE_ ) , desc=SCREAMING_SNAKE_CASE_ , leave=SCREAMING_SNAKE_CASE_ ):
A__ = process_run_single(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A__ = single_run_metrics[target_metric_key]
if not math.isnan(SCREAMING_SNAKE_CASE_ ):
metrics.append(SCREAMING_SNAKE_CASE_ )
results.append(SCREAMING_SNAKE_CASE_ )
outcome += "✓"
else:
outcome += "✘"
A__ = F'\33[2K\r{outcome}'
if len(SCREAMING_SNAKE_CASE_ ) > 0:
A__ = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
A__ = round(mean_metrics[target_metric_key] , 2 )
A__ = F'{outcome} {mean_target}'
if len(SCREAMING_SNAKE_CASE_ ) > 1:
results_str += F' {tuple(round(SCREAMING_SNAKE_CASE_ , 2 ) for x in results )}'
print(SCREAMING_SNAKE_CASE_ )
A__ = variation
return mean_metrics
else:
print(SCREAMING_SNAKE_CASE_ )
return {variation_key: variation, target_metric_key: nan}
def lowerCAmelCase__ ( ) -> Tuple:
'''simple docstring'''
A__ = torch.cuda.get_device_properties(torch.device("cuda" ) )
return F'\nDatetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch : {torch.__version__}\ncuda : {torch.version.cuda}\npython : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**3_0:0.2f}GB\n'
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
A__ = pd.DataFrame(SCREAMING_SNAKE_CASE_ )
A__ = "variation"
A__ = "diff_%"
A__ = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
A__ = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(SCREAMING_SNAKE_CASE_ ):
# as a fallback, use the minimal value as the sentinel
A__ = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(SCREAMING_SNAKE_CASE_ ):
A__ = df.apply(
lambda SCREAMING_SNAKE_CASE_ : round(1_0_0 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0 , axis="columns" , )
# re-order columns
A__ = [variation_key, target_metric_key, diff_key, *report_metric_keys]
A__ = df.reindex(SCREAMING_SNAKE_CASE_ , axis="columns" ) # reorder cols
# capitalize
A__ = df.rename(str.capitalize , axis="columns" )
# make the cols as narrow as possible
A__ = df.rename(lambda SCREAMING_SNAKE_CASE_ : c.replace("_" , "<br>" ) , axis="columns" )
A__ = df.rename(lambda SCREAMING_SNAKE_CASE_ : c.replace("_" , "\n" ) , axis="columns" )
A__ = ["", "Copy between the cut-here-lines and paste as is to github or a forum"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=SCREAMING_SNAKE_CASE_ , floatfmt=".2f" )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=SCREAMING_SNAKE_CASE_ , floatfmt=".2f" )]
print("\n\n".join(SCREAMING_SNAKE_CASE_ ) )
def lowerCAmelCase__ ( ) -> Optional[int]:
'''simple docstring'''
A__ = argparse.ArgumentParser()
parser.add_argument(
"--base-cmd" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Base cmd" , )
parser.add_argument(
"--variations" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , nargs="+" , required=SCREAMING_SNAKE_CASE_ , help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'" , )
parser.add_argument(
"--base-variation" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Baseline variation to compare to. if None the minimal target value will be used to compare against" , )
parser.add_argument(
"--target-metric-key" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second" , )
parser.add_argument(
"--report-metric-keys" , default="" , type=SCREAMING_SNAKE_CASE_ , help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples" , )
parser.add_argument(
"--repeat-times" , default=1 , type=SCREAMING_SNAKE_CASE_ , help="How many times to re-run each variation - an average will be reported" , )
parser.add_argument(
"--output_dir" , default="output_benchmark" , type=SCREAMING_SNAKE_CASE_ , help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked" , )
parser.add_argument(
"--verbose" , default=SCREAMING_SNAKE_CASE_ , action="store_true" , help="Whether to show the outputs of each run or just the benchmark progress" , )
A__ = parser.parse_args()
A__ = args.output_dir
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
A__ = get_base_command(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# split each dimension into its --foo variations
A__ = [list(map(str.strip , re.split(R"\|" , SCREAMING_SNAKE_CASE_ ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
A__ = list(map(str.strip , map(" ".join , itertools.product(*SCREAMING_SNAKE_CASE_ ) ) ) )
A__ = max(len(SCREAMING_SNAKE_CASE_ ) for x in variations )
# split wanted keys
A__ = args.report_metric_keys.split()
# capture prints into a log file for convenience
A__ = F'benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S" )}.txt'
print(F'\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt' )
print(F'and this script\'s output is also piped into {report_fn}' )
A__ = Tee(SCREAMING_SNAKE_CASE_ )
print(F'\n*** Running {len(SCREAMING_SNAKE_CASE_ )} benchmarks:' )
print(F'Base command: {" ".join(SCREAMING_SNAKE_CASE_ )}' )
A__ = "variation"
A__ = []
for id, variation in enumerate(tqdm(SCREAMING_SNAKE_CASE_ , desc="Total completion: " , leave=SCREAMING_SNAKE_CASE_ ) ):
A__ = base_cmd + variation.split()
results.append(
process_run(
id + 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , args.target_metric_key , SCREAMING_SNAKE_CASE_ , args.repeat_times , SCREAMING_SNAKE_CASE_ , args.verbose , ) )
process_results(SCREAMING_SNAKE_CASE_ , args.target_metric_key , SCREAMING_SNAKE_CASE_ , args.base_variation , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'gpt_neox_japanese'
def __init__( self , lowercase=32000 , lowercase=2560 , lowercase=32 , lowercase=32 , lowercase=4 , lowercase="gelu" , lowercase=1.00 , lowercase=10000 , lowercase=2048 , lowercase=0.02 , lowercase=1e-5 , lowercase=True , lowercase=31996 , lowercase=31999 , lowercase=0.1 , lowercase=0.0 , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_multiple_size
A__ = hidden_act
A__ = rotary_pct
A__ = rotary_emb_base
A__ = initializer_range
A__ = layer_norm_eps
A__ = use_cache
A__ = attention_dropout
A__ = hidden_dropout
| 68 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
lowerCAmelCase__ = {"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class a__ ( unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__lowerCamelCase = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
__lowerCamelCase = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
__lowerCamelCase = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = pipeline(
task="text-classification" , model="hf-internal-testing/tiny-random-distilbert" , framework="pt" )
A__ = text_classifier("This is great !" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "LABEL_0", "score": 0.504}] )
A__ = text_classifier("This is great !" , top_k=2 )
self.assertEqual(
nested_simplify(lowercase ) , [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}] )
A__ = text_classifier(["This is great !", "This is bad"] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase ) , [
[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
] , )
A__ = text_classifier("This is great !" , top_k=1 )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "LABEL_0", "score": 0.504}] )
# Legacy behavior
A__ = text_classifier("This is great !" , return_all_scores=lowercase )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "LABEL_0", "score": 0.504}] )
A__ = text_classifier("This is great !" , return_all_scores=lowercase )
self.assertEqual(
nested_simplify(lowercase ) , [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]] )
A__ = text_classifier(["This is great !", "Something else"] , return_all_scores=lowercase )
self.assertEqual(
nested_simplify(lowercase ) , [
[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
] , )
A__ = text_classifier(["This is great !", "Something else"] , return_all_scores=lowercase )
self.assertEqual(
nested_simplify(lowercase ) , [
{"label": "LABEL_0", "score": 0.504},
{"label": "LABEL_0", "score": 0.504},
] , )
@require_torch
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
import torch
A__ = pipeline(
task="text-classification" , model="hf-internal-testing/tiny-random-distilbert" , framework="pt" , device=torch.device("cpu" ) , )
A__ = text_classifier("This is great !" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "LABEL_0", "score": 0.504}] )
@require_tf
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = pipeline(
task="text-classification" , model="hf-internal-testing/tiny-random-distilbert" , framework="tf" )
A__ = text_classifier("This is great !" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "LABEL_0", "score": 0.504}] )
@slow
@require_torch
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = pipeline("text-classification" )
A__ = text_classifier("This is great !" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "POSITIVE", "score": 1.0}] )
A__ = text_classifier("This is bad !" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "NEGATIVE", "score": 1.0}] )
A__ = text_classifier("Birds are a type of animal" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "POSITIVE", "score": 0.988}] )
@slow
@require_tf
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = pipeline("text-classification" , framework="tf" )
A__ = text_classifier("This is great !" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "POSITIVE", "score": 1.0}] )
A__ = text_classifier("This is bad !" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "NEGATIVE", "score": 1.0}] )
A__ = text_classifier("Birds are a type of animal" )
self.assertEqual(nested_simplify(lowercase ) , [{"label": "POSITIVE", "score": 0.988}] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> List[str]:
'''simple docstring'''
A__ = TextClassificationPipeline(model=lowercase , tokenizer=lowercase )
return text_classifier, ["HuggingFace is in", "This is another test"]
def UpperCamelCase ( self , lowercase , lowercase ) -> List[str]:
'''simple docstring'''
A__ = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
A__ = "HuggingFace is in"
A__ = text_classifier(lowercase )
self.assertEqual(nested_simplify(lowercase ) , [{"label": ANY(lowercase ), "score": ANY(lowercase )}] )
self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
A__ = ["HuggingFace is in ", "Paris is in France"]
A__ = text_classifier(lowercase )
self.assertEqual(
nested_simplify(lowercase ) , [{"label": ANY(lowercase ), "score": ANY(lowercase )}, {"label": ANY(lowercase ), "score": ANY(lowercase )}] , )
self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
self.assertTrue(outputs[1]["label"] in model.config.idalabel.values() )
# Forcing to get all results with `top_k=None`
# This is NOT the legacy format
A__ = text_classifier(lowercase , top_k=lowercase )
A__ = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(lowercase ) , [[{"label": ANY(lowercase ), "score": ANY(lowercase )}] * N, [{"label": ANY(lowercase ), "score": ANY(lowercase )}] * N] , )
A__ = {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
A__ = text_classifier(lowercase )
self.assertEqual(
nested_simplify(lowercase ) , {"label": ANY(lowercase ), "score": ANY(lowercase )} , )
self.assertTrue(outputs["label"] in model.config.idalabel.values() )
# This might be used a text pair, but tokenizer + pipe interaction
# makes it hard to understand that it's not using the pair properly
# https://github.com/huggingface/transformers/issues/17305
# We disabled this usage instead as it was outputting wrong outputs.
A__ = [["HuggingFace is in ", "Paris is in France"]]
with self.assertRaises(lowercase ):
text_classifier(lowercase )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
A__ = text_classifier([[["HuggingFace is in ", "Paris is in France"]]] )
self.assertEqual(
nested_simplify(lowercase ) , [{"label": ANY(lowercase ), "score": ANY(lowercase )}] , )
self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
| 68 |
import warnings
from functools import wraps
from typing import Callable
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Callable ) -> Callable:
'''simple docstring'''
@wraps(SCREAMING_SNAKE_CASE_ )
def _inner_fn(*SCREAMING_SNAKE_CASE_: int , **SCREAMING_SNAKE_CASE_: Union[str, Any] ):
warnings.warn(
(F'\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.') , SCREAMING_SNAKE_CASE_ , )
return fn(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
return _inner_fn
| 68 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: Optional[Any]=False ) -> Union[str, Any]:
'''simple docstring'''
A__ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
("pre_logits.fc.weight", "pooler.dense.weight"),
("pre_logits.fc.bias", "pooler.dense.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
A__ = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: Dict=False ) -> Tuple:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
A__ = ""
else:
A__ = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A__ = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
A__ = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
A__ = in_proj_weight[
: config.hidden_size, :
]
A__ = in_proj_bias[: config.hidden_size]
A__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A__ = in_proj_weight[
-config.hidden_size :, :
]
A__ = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> Union[str, Any]:
'''simple docstring'''
A__ = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: List[str] ) -> Any:
'''simple docstring'''
A__ = dct.pop(SCREAMING_SNAKE_CASE_ )
A__ = val
def lowerCAmelCase__ ( ) -> List[Any]:
'''simple docstring'''
A__ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A__ = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: str ) -> str:
'''simple docstring'''
A__ = ViTConfig()
A__ = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
A__ = True
A__ = int(vit_name[-1_2:-1_0] )
A__ = int(vit_name[-9:-6] )
else:
A__ = 1_0_0_0
A__ = "huggingface/label-files"
A__ = "imagenet-1k-id2label.json"
A__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="dataset" ) , "r" ) )
A__ = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
A__ = int(vit_name[-6:-4] )
A__ = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("tiny" ):
A__ = 1_9_2
A__ = 7_6_8
A__ = 1_2
A__ = 3
elif vit_name[9:].startswith("small" ):
A__ = 3_8_4
A__ = 1_5_3_6
A__ = 1_2
A__ = 6
else:
pass
else:
if vit_name[4:].startswith("small" ):
A__ = 7_6_8
A__ = 2_3_0_4
A__ = 8
A__ = 8
elif vit_name[4:].startswith("base" ):
pass
elif vit_name[4:].startswith("large" ):
A__ = 1_0_2_4
A__ = 4_0_9_6
A__ = 2_4
A__ = 1_6
elif vit_name[4:].startswith("huge" ):
A__ = 1_2_8_0
A__ = 5_1_2_0
A__ = 3_2
A__ = 1_6
# load original model from timm
A__ = timm.create_model(SCREAMING_SNAKE_CASE_ , pretrained=SCREAMING_SNAKE_CASE_ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
A__ = timm_model.state_dict()
if base_model:
remove_classification_head_(SCREAMING_SNAKE_CASE_ )
A__ = create_rename_keys(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
read_in_q_k_v(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# load HuggingFace model
if vit_name[-5:] == "in21k":
A__ = ViTModel(SCREAMING_SNAKE_CASE_ ).eval()
else:
A__ = ViTForImageClassification(SCREAMING_SNAKE_CASE_ ).eval()
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
A__ = DeiTImageProcessor(size=config.image_size )
else:
A__ = ViTImageProcessor(size=config.image_size )
A__ = image_processor(images=prepare_img() , return_tensors="pt" )
A__ = encoding["pixel_values"]
A__ = model(SCREAMING_SNAKE_CASE_ )
if base_model:
A__ = timm_model.forward_features(SCREAMING_SNAKE_CASE_ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(SCREAMING_SNAKE_CASE_ , outputs.pooler_output , atol=1e-3 )
else:
A__ = timm_model(SCREAMING_SNAKE_CASE_ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(SCREAMING_SNAKE_CASE_ , outputs.logits , atol=1e-3 )
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--vit_name""",
default="""vit_base_patch16_224""",
type=str,
help="""Name of the ViT timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
lowerCAmelCase__ = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 68 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
lowerCAmelCase__ = """\
Text data.
Second line of data."""
lowerCAmelCase__ = """file"""
@pytest.fixture(scope="session" )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> Optional[int]:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / (FILE_PATH + ".zstd")
A__ = bytes(SCREAMING_SNAKE_CASE_ , "utf-8" )
with zstd.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any ) -> List[str]:
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , SCREAMING_SNAKE_CASE_ ) , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return FILE_PATH
@pytest.mark.parametrize("compression_format" , ["gzip", "xz", "zstd"] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: int ) -> Any:
'''simple docstring'''
A__ = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
A__ = input_paths[compression_format]
A__ = tmp_path / "cache"
A__ = DownloadConfig(cache_dir=SCREAMING_SNAKE_CASE_ , extract_compressed_file=SCREAMING_SNAKE_CASE_ )
A__ = cached_path(SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("default_extracted" , [True, False] )
@pytest.mark.parametrize("default_cache_dir" , [True, False] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: str ) -> Dict:
'''simple docstring'''
A__ = "custom_cache"
A__ = "custom_extracted_dir"
A__ = tmp_path / "custom_extracted_path"
if default_extracted:
A__ = ("downloads" if default_cache_dir else custom_cache_dir, "extracted")
else:
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR" , SCREAMING_SNAKE_CASE_ )
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH" , str(SCREAMING_SNAKE_CASE_ ) )
A__ = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
A__ = xz_file
A__ = (
DownloadConfig(extract_compressed_file=SCREAMING_SNAKE_CASE_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=SCREAMING_SNAKE_CASE_ )
)
A__ = cached_path(SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
assert Path(SCREAMING_SNAKE_CASE_ ).parent.parts[-2:] == expected
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> Optional[int]:
'''simple docstring'''
A__ = str(Path(SCREAMING_SNAKE_CASE_ ).resolve() )
assert cached_path(SCREAMING_SNAKE_CASE_ ) == text_file
# relative path
A__ = str(Path(SCREAMING_SNAKE_CASE_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(SCREAMING_SNAKE_CASE_ ) == text_file
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[str]:
'''simple docstring'''
A__ = str(tmp_path.resolve() / "__missing_file__.txt" )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path(SCREAMING_SNAKE_CASE_ )
# relative path
A__ = "./__missing_file__.txt"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> Union[str, Any]:
'''simple docstring'''
A__ = get_from_cache(F'tmp://{tmpfs_file}' )
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
assert output_file_content == FILE_CONTENT
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( ) -> List[Any]:
'''simple docstring'''
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> int:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_get("https://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[Any]:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
ftp_get("ftp://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
ftp_head("ftp://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> str:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
fsspec_get("s3://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
fsspec_head("s3://huggingface.co" )
| 68 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=snake_case )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = field(default='image-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
__lowerCamelCase = Features({'image': Image()} )
__lowerCamelCase = Features({'labels': ClassLabel} )
__lowerCamelCase = "image"
__lowerCamelCase = "labels"
def UpperCamelCase ( self , lowercase ) -> List[Any]:
'''simple docstring'''
if self.label_column not in features:
raise ValueError(F'Column {self.label_column} is not present in features.' )
if not isinstance(features[self.label_column] , lowercase ):
raise ValueError(F'Column {self.label_column} is not a ClassLabel.' )
A__ = copy.deepcopy(self )
A__ = self.label_schema.copy()
A__ = features[self.label_column]
A__ = label_schema
return task_template
@property
def UpperCamelCase ( self ) -> Dict[str, str]:
'''simple docstring'''
return {
self.image_column: "image",
self.label_column: "labels",
}
| 68 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class a__ :
"""simple docstring"""
__lowerCamelCase = BlenderbotSmallConfig
__lowerCamelCase = {}
__lowerCamelCase = 'gelu'
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=32 , lowercase=2 , lowercase=4 , lowercase=37 , lowercase=0.1 , lowercase=0.1 , lowercase=20 , lowercase=2 , lowercase=1 , lowercase=0 , ) -> Any:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = eos_token_id
A__ = pad_token_id
A__ = bos_token_id
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A__ = tf.concat([input_ids, eos_tensor] , axis=1 )
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
A__ = prepare_blenderbot_small_inputs_dict(lowercase , lowercase , lowercase )
return config, inputs_dict
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = TFBlenderbotSmallModel(config=lowercase ).get_decoder()
A__ = inputs_dict["input_ids"]
A__ = input_ids[:1, :]
A__ = inputs_dict["attention_mask"][:1, :]
A__ = inputs_dict["head_mask"]
A__ = 1
# first forward pass
A__ = model(lowercase , attention_mask=lowercase , head_mask=lowercase , use_cache=lowercase )
A__ , A__ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
A__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
A__ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
A__ = tf.concat([input_ids, next_tokens] , axis=-1 )
A__ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
A__ = model(lowercase , attention_mask=lowercase )[0]
A__ = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
A__ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
A__ = output_from_no_past[:, -3:, random_slice_idx]
A__ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase , lowercase , rtol=1e-3 )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Optional[Any]=None , SCREAMING_SNAKE_CASE_: Optional[int]=None , SCREAMING_SNAKE_CASE_: Optional[int]=None , SCREAMING_SNAKE_CASE_: Dict=None , SCREAMING_SNAKE_CASE_: List[str]=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
A__ = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A__ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
A__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
A__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
__lowerCamelCase = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase = (
{
'conversational': TFBlenderbotSmallForConditionalGeneration,
'feature-extraction': TFBlenderbotSmallModel,
'summarization': TFBlenderbotSmallForConditionalGeneration,
'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
'translation': TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = TFBlenderbotSmallModelTester(self )
A__ = ConfigTester(self , config_class=lowercase )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase )
@require_tokenizers
@require_tf
class a__ ( unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = [
'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
' i\'m going to throw up.\nand why is that?'
]
__lowerCamelCase = 'facebook/blenderbot_small-90M'
@cached_property
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
@cached_property
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.tokenizer(self.src_text , return_tensors="tf" )
A__ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=lowercase , )
A__ = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowercase )[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 68 | 1 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer
from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = WavaVecaPhonemeCTCTokenizer
__lowerCamelCase = False
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
A__ = (
"<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
"əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" " )
A__ = dict(zip(lowercase , range(len(lowercase ) ) ) )
A__ = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
A__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowercase ) + "\n" )
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=20 , lowercase=5 ) -> Tuple[str, list]:
'''simple docstring'''
A__ = [(i, tokenizer.decode([i] , clean_up_tokenization_spaces=lowercase )) for i in range(len(lowercase ) )]
A__ = list(filter(lambda lowercase : [t[0]] == tokenizer.encode(t[1] , do_phonemize=lowercase ) , lowercase ) )
if max_length is not None and len(lowercase ) > max_length:
A__ = toks[:max_length]
if min_length is not None and len(lowercase ) < min_length and len(lowercase ) > 0:
while len(lowercase ) < min_length:
A__ = toks + toks
# toks_str = [t[1] for t in toks]
A__ = [t[0] for t in toks]
# Ensure consistency
A__ = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
if " " not in output_txt and len(lowercase ) > 1:
A__ = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=lowercase )
+ " "
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=lowercase )
)
if with_prefix_space:
A__ = " " + output_txt
A__ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
return output_txt, output_ids
def UpperCamelCase ( self , **lowercase ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname , **lowercase )
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
# check adding a single token
tokenizer.add_tokens("xxx" )
A__ = tokenizer("m xxx ɪ" , do_phonemize=lowercase ).input_ids
self.assertEqual(lowercase , [13, 392, 17] ) # xxx should be last token
tokenizer.add_tokens(["aaa", "bbb", "ccc"] )
A__ = tokenizer("m aaa ɪ ccc" , do_phonemize=lowercase ).input_ids
self.assertEqual(lowercase , [13, 393, 17, 395] ) # aaa and ccc should be after xxx and 2 after aaa
A__ = tokenizer("maɪ c" , do_phonemize=lowercase ).input_ids
self.assertEqual(lowercase , [3, 200] ) # mai should be <unk> (=3)
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
A__ = "Hello how are you"
A__ = tokenizer.phonemize(lowercase , phonemizer_lang="en-us" )
self.assertEqual(lowercase , "h ə l oʊ h aʊ ɑːɹ j uː" )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
A__ = "Hello how are you"
A__ = tokenizer.phonemize(lowercase , phonemizer_lang="en-us" )
self.assertEqual(tokenizer(lowercase ).input_ids , tokenizer(lowercase , do_phonemize=lowercase ).input_ids )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
A__ = "Hello how are you"
A__ = tokenizer.phonemize(lowercase , phonemizer_lang="en-us" )
A__ = tokenizer.decode(tokenizer(lowercase ).input_ids )
self.assertEqual(lowercase , lowercase )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
A__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, 24, 22, 5, 77],
]
A__ = tokenizer.decode(sample_ids[0] )
A__ = tokenizer.batch_decode(lowercase )
self.assertEqual(lowercase , batch_tokens[0] )
self.assertEqual(lowercase , ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"] )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
A__ = "Hello how are you"
A__ = tokenizer.phonemize(lowercase , phonemizer_lang="en-us" )
self.assertEqual(lowercase , "h ə l oʊ | h aʊ | ɑːɹ | j uː |" )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
A__ = "Hello how are you"
A__ = tokenizer.phonemize(lowercase , phonemizer_lang="en-us" )
self.assertEqual(tokenizer(lowercase ).input_ids , tokenizer(lowercase , do_phonemize=lowercase ).input_ids )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
# fmt: off
A__ = [
[11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
[tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
]
# fmt: on
# decode with word_del_token filter
A__ = tokenizer.decode(sample_ids[0] )
A__ = tokenizer.batch_decode(lowercase )
self.assertEqual(lowercase , batch_tokens[0] )
self.assertEqual(lowercase , ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"] )
# decode with no word_del_token filter
A__ = tokenizer.decode(sample_ids[0] , filter_word_delimiter_token=lowercase )
A__ = tokenizer.batch_decode(lowercase , filter_word_delimiter_token=lowercase )
self.assertEqual(lowercase , batch_tokens[0] )
self.assertEqual(lowercase , ["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"] )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
A__ = "Hello how are you"
A__ = tokenizer.phonemize(lowercase , phonemizer_lang="en-us" )
A__ = tokenizer.decode(tokenizer(lowercase ).input_ids , filter_word_delimiter_token=lowercase )
self.assertEqual(lowercase , lowercase )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token="|" )
tokenizer.add_tokens("|" )
A__ = "Hello how are you"
A__ = tokenizer.phonemize(lowercase , phonemizer_lang="en-us" )
A__ = tokenizer.decode(tokenizer(lowercase ).input_ids , filter_word_delimiter_token=lowercase )
self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |" )] ).strip() , lowercase )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained(
"facebook/wav2vec2-lv-60-espeak-cv-ft" , word_delimiter_token=lowercase )
A__ = "Hello how are you"
A__ = tokenizer(lowercase , phonemizer_lang="en-us" ).input_ids
A__ = tokenizer(lowercase , phonemizer_lang="fr-fr" ).input_ids
self.assertNotEqual(lowercase , lowercase )
A__ = tokenizer.decode(lowercase )
A__ = tokenizer.decode(lowercase )
self.assertEqual(lowercase , "h ə l oʊ h aʊ ɑːɹ j uː" )
self.assertEqual(lowercase , "ɛ l o h aʊ a ʁ j u" )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
A__ = "Hello how Are you"
A__ = "hello how are you"
A__ = tokenizer(lowercase ).input_ids
A__ = tokenizer(lowercase ).input_ids
self.assertEqual(lowercase , lowercase )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
tokenizer.add_tokens(["!", "?"] )
tokenizer.add_special_tokens({"cls_token": "$$$"} )
# fmt: off
A__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
[24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
]
# fmt: on
A__ = tokenizer.batch_decode(lowercase )
self.assertEqual(lowercase , ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"] )
@staticmethod
def UpperCamelCase ( lowercase , lowercase ) -> List[str]:
'''simple docstring'''
A__ = [d[key] for d in offsets]
return retrieved_list
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.get_tokenizer(word_delimiter_token="|" )
tokenizer.add_tokens("|" )
# fmt: off
# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
A__ = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
# fmt: on
A__ = tokenizer.decode(lowercase , output_char_offsets=lowercase , filter_word_delimiter_token=lowercase )
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs.keys() ) , 2 )
self.assertTrue("text" in outputs )
self.assertTrue("char_offsets" in outputs )
self.assertTrue(isinstance(lowercase , lowercase ) )
# check that order of chars is correct and identical for both outputs
self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"] , "char" ) ) , outputs.text )
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "char" ) , ["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"] )
# check that offsets are actually correct for char
# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "start_offset" ) , [0, 1, 4, 7, 9, 11, 12, 15, 16] )
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"] , "end_offset" ) , [1, 4, 6, 9, 10, 12, 15, 16, 17] )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = self.get_tokenizer(word_delimiter_token="|" )
def check_list_tuples_equal(lowercase , lowercase ):
self.assertTrue(isinstance(lowercase , lowercase ) )
self.assertTrue(isinstance(outputs_list[0] , lowercase ) )
# transform list to ModelOutput
A__ = WavaVecaPhonemeCTCTokenizerOutput(
{k: [d[k] for d in outputs_list] for k in outputs_list[0]} )
self.assertListEqual(outputs_batch["text"] , outputs_batch_a["text"] )
def recursive_check(lowercase , lowercase ):
if isinstance(lowercase , lowercase ):
[recursive_check(lowercase , lowercase ) for la, la in zip(lowercase , lowercase )]
self.assertEqual(lowercase , lowercase )
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch["char_offsets"] , outputs_batch_a["char_offsets"] )
# fmt: off
A__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
A__ = tokenizer.batch_decode(lowercase , output_char_offsets=lowercase )
A__ = [tokenizer.decode(lowercase , output_char_offsets=lowercase ) for ids in sample_ids]
check_list_tuples_equal(lowercase , lowercase )
@unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes" )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes" )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency" )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing" )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.get_tokenizers(do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
A__ = tokenizer.add_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size + len(lowercase ) )
A__ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
A__ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
A__ = tokenizer.add_special_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size_a + len(lowercase ) )
A__ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode." )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode." )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = self.get_tokenizers(fast=lowercase , do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A__ = ["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
A__ = tokenizer.convert_tokens_to_string(lowercase )
self.assertIsInstance(output["text"] , lowercase )
| 68 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ['pixel_values']
def __init__( self , lowercase = True , lowercase = None , lowercase = PILImageResampling.BICUBIC , lowercase = True , lowercase = 1 / 255 , lowercase = True , lowercase = None , lowercase = None , lowercase = True , **lowercase , ) -> None:
'''simple docstring'''
super().__init__(**lowercase )
A__ = size if size is not None else {"height": 384, "width": 384}
A__ = get_size_dict(lowercase , default_to_square=lowercase )
A__ = do_resize
A__ = size
A__ = resample
A__ = do_rescale
A__ = rescale_factor
A__ = do_normalize
A__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A__ = image_std if image_std is not None else OPENAI_CLIP_STD
A__ = do_convert_rgb
def UpperCamelCase ( self , lowercase , lowercase , lowercase = PILImageResampling.BICUBIC , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
A__ = get_size_dict(lowercase , default_to_square=lowercase )
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()}' )
A__ = (size["height"], size["width"])
return resize(lowercase , size=lowercase , resample=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase = None , **lowercase , ) -> Optional[Any]:
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ) -> PIL.Image.Image:
'''simple docstring'''
A__ = do_resize if do_resize is not None else self.do_resize
A__ = resample if resample is not None else self.resample
A__ = do_rescale if do_rescale is not None else self.do_rescale
A__ = rescale_factor if rescale_factor is not None else self.rescale_factor
A__ = do_normalize if do_normalize is not None else self.do_normalize
A__ = image_mean if image_mean is not None else self.image_mean
A__ = image_std if image_std is not None else self.image_std
A__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A__ = size if size is not None else self.size
A__ = get_size_dict(lowercase , default_to_square=lowercase )
A__ = make_list_of_images(lowercase )
if not valid_images(lowercase ):
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 or resample is None:
raise ValueError("Size and resample 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_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A__ = [convert_to_rgb(lowercase ) for image in images]
# All transformations expect numpy arrays.
A__ = [to_numpy_array(lowercase ) for image in images]
if do_resize:
A__ = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_rescale:
A__ = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
A__ = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
A__ = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
A__ = BatchFeature(data={"pixel_values": images} , tensor_type=lowercase )
return encoded_outputs
| 68 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = 42
__lowerCamelCase = None
__lowerCamelCase = None
def lowerCAmelCase__ ( ) -> Node | None:
'''simple docstring'''
A__ = Node(1 )
A__ = Node(2 )
A__ = Node(3 )
A__ = Node(4 )
A__ = Node(5 )
return tree
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> list[int]:
'''simple docstring'''
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> list[int]:
'''simple docstring'''
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> list[int]:
'''simple docstring'''
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> int:
'''simple docstring'''
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> Sequence[Node | None]:
'''simple docstring'''
A__ = []
if root is None:
return output
A__ = deque([root] )
while process_queue:
A__ = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None , SCREAMING_SNAKE_CASE_: int ) -> Sequence[Node | None]:
'''simple docstring'''
A__ = []
def populate_output(SCREAMING_SNAKE_CASE_: Node | None , SCREAMING_SNAKE_CASE_: int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return output
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None , SCREAMING_SNAKE_CASE_: int ) -> Sequence[Node | None]:
'''simple docstring'''
A__ = []
def populate_output(SCREAMING_SNAKE_CASE_: Node | None , SCREAMING_SNAKE_CASE_: int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return output
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> Sequence[Node | None] | list[Any]:
'''simple docstring'''
if root is None:
return []
A__ = []
A__ = 0
A__ = height(SCREAMING_SNAKE_CASE_ )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
A__ = 1
else:
output.append(get_nodes_from_right_to_left(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
A__ = 0
return output
def lowerCAmelCase__ ( ) -> None: # Main function for testing.
'''simple docstring'''
A__ = make_tree()
print(F'In-order Traversal: {inorder(SCREAMING_SNAKE_CASE_ )}' )
print(F'Pre-order Traversal: {preorder(SCREAMING_SNAKE_CASE_ )}' )
print(F'Post-order Traversal: {postorder(SCREAMING_SNAKE_CASE_ )}' , "\n" )
print(F'Height of Tree: {height(SCREAMING_SNAKE_CASE_ )}' , "\n" )
print("Complete Level Order Traversal: " )
print(level_order(SCREAMING_SNAKE_CASE_ ) , "\n" )
print("Level-wise order Traversal: " )
for level in range(1 , height(SCREAMING_SNAKE_CASE_ ) + 1 ):
print(F'Level {level}:' , get_nodes_from_left_to_right(SCREAMING_SNAKE_CASE_ , level=SCREAMING_SNAKE_CASE_ ) )
print("\nZigZag order Traversal: " )
print(zigzag(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 68 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
lowerCAmelCase__ = """hf-internal-testing/tiny-random-bert"""
lowerCAmelCase__ = os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
lowerCAmelCase__ = """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = cached_file(lowercase , lowercase )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(lowercase ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(lowercase , lowercase ) ) )
with open(os.path.join(lowercase , "refs" , "main" ) ) as f:
A__ = f.read()
self.assertEqual(lowercase , os.path.join(lowercase , "snapshots" , lowercase , lowercase ) )
self.assertTrue(os.path.isfile(lowercase ) )
# File is cached at the same place the second time.
A__ = cached_file(lowercase , lowercase )
self.assertEqual(lowercase , lowercase )
# Using a specific revision to test the full commit hash.
A__ = cached_file(lowercase , lowercase , revision="9b8c223" )
self.assertEqual(lowercase , os.path.join(lowercase , "snapshots" , lowercase , lowercase ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaisesRegex(lowercase , "is not a valid model identifier" ):
A__ = cached_file("tiny-random-bert" , lowercase )
with self.assertRaisesRegex(lowercase , "is not a valid git identifier" ):
A__ = cached_file(lowercase , lowercase , revision="aaaa" )
with self.assertRaisesRegex(lowercase , "does not appear to have a file named" ):
A__ = cached_file(lowercase , "conf" )
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(lowercase , "does not appear to have a file named" ):
A__ = cached_file(lowercase , "conf" )
with open(os.path.join(lowercase , "refs" , "main" ) ) as f:
A__ = f.read()
self.assertTrue(os.path.isfile(os.path.join(lowercase , ".no_exist" , lowercase , "conf" ) ) )
A__ = cached_file(lowercase , "conf" , _raise_exceptions_for_missing_entries=lowercase )
self.assertIsNone(lowercase )
A__ = cached_file(lowercase , "conf" , local_files_only=lowercase , _raise_exceptions_for_missing_entries=lowercase )
self.assertIsNone(lowercase )
A__ = mock.Mock()
A__ = 500
A__ = {}
A__ = HTTPError
A__ = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" , return_value=lowercase ) as mock_head:
A__ = cached_file(lowercase , "conf" , _raise_exceptions_for_connection_errors=lowercase )
self.assertIsNone(lowercase )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
self.assertIsNone(get_file_from_repo("bert-base-cased" , "ahah.txt" ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(lowercase , "is not a valid model identifier" ):
get_file_from_repo("bert-base-case" , lowercase )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(lowercase , "is not a valid git identifier" ):
get_file_from_repo("bert-base-cased" , lowercase , revision="ahaha" )
A__ = get_file_from_repo("bert-base-cased" , lowercase )
# The name is the cached name which is not very easy to test, so instead we load the content.
A__ = json.loads(open(lowercase , "r" ).read() )
self.assertEqual(config["hidden_size"] , 768 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
A__ = Path(lowercase ) / "a.txt"
filename.touch()
self.assertEqual(get_file_from_repo(lowercase , "a.txt" ) , str(lowercase ) )
self.assertIsNone(get_file_from_repo(lowercase , "b.txt" ) )
| 68 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger()
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = 42
__lowerCamelCase = field(default_factory=snake_case )
__lowerCamelCase = field(default_factory=snake_case )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
A__ = len(list(m.modules() ) ) == 1 or isinstance(lowercase , nn.Convad ) or isinstance(lowercase , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowercase )
def __call__( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowercase )
[x.remove() for x in self.handles]
return self
@property
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
return list(filter(lambda lowercase : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 0
__lowerCamelCase = field(default_factory=snake_case )
__lowerCamelCase = field(default_factory=snake_case )
def __call__( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = Tracker(self.dest )(lowercase ).parametrized
A__ = Tracker(self.src )(lowercase ).parametrized
A__ = list(filter(lambda lowercase : type(lowercase ) not in self.src_skip , lowercase ) )
A__ = list(filter(lambda lowercase : type(lowercase ) not in self.dest_skip , lowercase ) )
if len(lowercase ) != len(lowercase ):
raise Exception(
F'Numbers of operations are different. Source module has {len(lowercase )} operations while'
F' destination module has {len(lowercase )}.' )
for dest_m, src_m in zip(lowercase , lowercase ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'Transfered from={src_m} to={dest_m}' )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: ResNetConfig , SCREAMING_SNAKE_CASE_: Path , SCREAMING_SNAKE_CASE_: bool = True ) -> Optional[int]:
'''simple docstring'''
print(F'Converting {name}...' )
with torch.no_grad():
A__ = timm.create_model(SCREAMING_SNAKE_CASE_ , pretrained=SCREAMING_SNAKE_CASE_ ).eval()
A__ = ResNetForImageClassification(SCREAMING_SNAKE_CASE_ ).eval()
A__ = ModuleTransfer(src=SCREAMING_SNAKE_CASE_ , dest=SCREAMING_SNAKE_CASE_ )
A__ = torch.randn((1, 3, 2_2_4, 2_2_4) )
module_transfer(SCREAMING_SNAKE_CASE_ )
assert torch.allclose(from_model(SCREAMING_SNAKE_CASE_ ) , our_model(SCREAMING_SNAKE_CASE_ ).logits ), "The model logits don't match the original one."
A__ = F'resnet{"-".join(name.split("resnet" ) )}'
print(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add model" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
# we can use the convnext one
A__ = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add image processor" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
print(F'Pushed {checkpoint_name}' )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Path , SCREAMING_SNAKE_CASE_: str = None , SCREAMING_SNAKE_CASE_: bool = True ) -> str:
'''simple docstring'''
A__ = "imagenet-1k-id2label.json"
A__ = 1_0_0_0
A__ = (1, num_labels)
A__ = "huggingface/label-files"
A__ = num_labels
A__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="dataset" ) , "r" ) )
A__ = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
A__ = partial(SCREAMING_SNAKE_CASE_ , num_labels=SCREAMING_SNAKE_CASE_ , idalabel=SCREAMING_SNAKE_CASE_ , labelaid=SCREAMING_SNAKE_CASE_ )
A__ = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(SCREAMING_SNAKE_CASE_ , names_to_config[model_name] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return config, expected_shape
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
""" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
lowerCAmelCase__ = parser.parse_args()
lowerCAmelCase__ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 68 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = AutoencoderKL
__lowerCamelCase = 'sample'
__lowerCamelCase = 1e-2
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = 4
A__ = 3
A__ = (32, 32)
A__ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase )
return {"sample": image}
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
A__ = self.dummy_input
return init_dict, inputs_dict
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skipIf(torch_device == "mps" , "Gradient checkpointing skipped on MPS" )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ , A__ = self.prepare_init_args_and_inputs_for_common()
A__ = self.model_class(**lowercase )
model.to(lowercase )
assert not model.is_gradient_checkpointing and model.training
A__ = model(**lowercase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
A__ = torch.randn_like(lowercase )
A__ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
A__ = self.model_class(**lowercase )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
A__ = model_a(**lowercase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
A__ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
A__ = dict(model.named_parameters() )
A__ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ , A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(lowercase )
A__ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" )
A__ = model.to(lowercase )
model.eval()
if torch_device == "mps":
A__ = torch.manual_seed(0 )
else:
A__ = torch.Generator(device=lowercase ).manual_seed(0 )
A__ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
A__ = image.to(lowercase )
with torch.no_grad():
A__ = model(lowercase , sample_posterior=lowercase , generator=lowercase ).sample
A__ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
A__ = torch.tensor(
[
-4.00_78e-01,
-3.83_23e-04,
-1.26_81e-01,
-1.14_62e-01,
2.00_95e-01,
1.08_93e-01,
-8.82_47e-02,
-3.03_61e-01,
-9.86_44e-03,
] )
elif torch_device == "cpu":
A__ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
A__ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase , lowercase , rtol=1e-2 ) )
@slow
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
return F'gaussian_noise_s={seed}_shape={"_".join([str(lowercase ) for s in shape] )}.npy'
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self , lowercase=0 , lowercase=(4, 3, 512, 512) , lowercase=False ) -> Optional[int]:
'''simple docstring'''
A__ = torch.floataa if fpaa else torch.floataa
A__ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase , lowercase ) ) ).to(lowercase ).to(lowercase )
return image
def UpperCamelCase ( self , lowercase="CompVis/stable-diffusion-v1-4" , lowercase=False ) -> Any:
'''simple docstring'''
A__ = "fp16" if fpaa else None
A__ = torch.floataa if fpaa else torch.floataa
A__ = AutoencoderKL.from_pretrained(
lowercase , subfolder="vae" , torch_dtype=lowercase , revision=lowercase , )
model.to(lowercase ).eval()
return model
def UpperCamelCase ( self , lowercase=0 ) -> List[str]:
'''simple docstring'''
if torch_device == "mps":
return torch.manual_seed(lowercase )
return torch.Generator(device=lowercase ).manual_seed(lowercase )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowercase , lowercase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , fpaa=lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Dict:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
with torch.no_grad():
A__ = model(lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowercase , lowercase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
A__ = sample[-1, -2:, :2, -2:].flatten().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def UpperCamelCase ( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase , lowercase , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model.encode(lowercase ).latent_dist
A__ = dist.sample(generator=lowercase )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
A__ = sample[0, -1, -3:, -3:].flatten().cpu()
A__ = torch.tensor(lowercase )
A__ = 3e-3 if torch_device != "mps" else 1e-2
assert torch_all_close(lowercase , lowercase , atol=lowercase )
| 68 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCAmelCase__ = get_logger(__name__)
class a__ ( enum.Enum ):
"""simple docstring"""
__lowerCamelCase = 'all_checks'
__lowerCamelCase = 'basic_checks'
__lowerCamelCase = 'no_checks'
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[dict] , SCREAMING_SNAKE_CASE_: dict , SCREAMING_SNAKE_CASE_: Tuple=None ) -> Union[str, Any]:
'''simple docstring'''
if expected_checksums is None:
logger.info("Unable to verify checksums." )
return
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
A__ = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
A__ = " for " + verification_name if verification_name is not None else ""
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise NonMatchingChecksumError(
F'Checksums didn\'t match{for_verification_name}:\n'
F'{bad_urls}\n'
"Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
logger.info("All the checksums matched successfully" + for_verification_name )
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[dict] , SCREAMING_SNAKE_CASE_: dict ) -> List[str]:
'''simple docstring'''
if expected_splits is None:
logger.info("Unable to verify splits sizes." )
return
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
A__ = [
{"expected": expected_splits[name], "recorded": recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE_ ) )
logger.info("All the splits matched successfully." )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: bool = True ) -> dict:
'''simple docstring'''
if record_checksum:
A__ = shaaaa()
with open(SCREAMING_SNAKE_CASE_ , "rb" ) as f:
for chunk in iter(lambda: f.read(1 << 2_0 ) , b"" ):
m.update(SCREAMING_SNAKE_CASE_ )
A__ = m.hexdigest()
else:
A__ = None
return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE_ ), "checksum": checksum}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] ) -> str:
'''simple docstring'''
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 68 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase__ = logging.getLogger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = label_idx
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
A__ = []
A__ = []
for line in f:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
A__ = []
A__ = []
else:
A__ = line.split(" " )
words.append(splits[0] )
if len(lowercase ) > 1:
labels.append(splits[self.label_idx].replace("\n" , "" ) )
else:
# Examples could have no label for mode = "test"
labels.append("O" )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
writer.write(lowercase )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
A__ = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(lowercase )
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0] )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(label_idx=-2 )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class a__ ( snake_case ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
for sentence in parse_incr(lowercase ):
A__ = []
A__ = []
for token in sentence:
words.append(token["form"] )
labels.append(token["upos"] )
assert len(lowercase ) == len(lowercase )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = 0
for sentence in parse_incr(lowercase ):
A__ = preds_list[example_id]
A__ = ""
for token in sentence:
out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '
out += "\n"
writer.write(lowercase )
example_id += 1
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 68 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() , encoding="utf-8" , check=lowercase , )
assert hasattr(self , "env" )
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
# distributed data settings
A__ = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=lowercase , instance_count=lowercase , instance_type=self.instance_type , debugger_hook_config=lowercase , hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=lowercase , py_version="py36" , )
def UpperCamelCase ( self , lowercase ) -> str:
'''simple docstring'''
TrainingJobAnalytics(lowercase ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv' )
@parameterized.expand([(2,)] )
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.create_estimator(lowercase )
# run training
estimator.fit()
# result dataframe
A__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
A__ = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"] )
A__ = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
A__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get("TrainingTimeInSeconds" , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy )
assert all(t <= self.results["eval_loss"] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F'{estimator.latest_training_job.name}.json' , "w" ) as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} , lowercase )
| 68 |
import random
class a__ :
"""simple docstring"""
@staticmethod
def UpperCamelCase ( lowercase ) -> tuple[list[int], list[int]]:
'''simple docstring'''
A__ = [ord(lowercase ) for i in text]
A__ = []
A__ = []
for i in plain:
A__ = random.randint(1 , 300 )
A__ = (i + k) * k
cipher.append(lowercase )
key.append(lowercase )
return cipher, key
@staticmethod
def UpperCamelCase ( lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = []
for i in range(len(lowercase ) ):
A__ = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowercase ) )
return "".join(lowercase )
if __name__ == "__main__":
lowerCAmelCase__ , lowerCAmelCase__ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 68 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase = 101 ) -> Any:
'''simple docstring'''
A__ = length
def __len__( self ) -> str:
'''simple docstring'''
return self.length
def __getitem__( self , lowercase ) -> int:
'''simple docstring'''
return i
class a__ :
"""simple docstring"""
def __call__( self , lowercase ) -> Any:
'''simple docstring'''
return {"input_ids": torch.tensor(lowercase ), "labels": torch.tensor(lowercase )}
class a__ ( nn.Module ):
"""simple docstring"""
def __init__( self ) -> Tuple:
'''simple docstring'''
super().__init__()
# Add some (unused) params otherwise DDP will complain.
A__ = nn.Linear(120 , 80 )
def UpperCamelCase ( self , lowercase , lowercase=None ) -> List[str]:
'''simple docstring'''
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class a__ ( snake_case ):
"""simple docstring"""
@require_torch_neuroncore
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = F'--nproc_per_node=2\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n '.split()
A__ = self.get_auto_remove_tmp_dir()
A__ = F'--output_dir {output_dir}'.split()
A__ = ["torchrun"] + distributed_args + args
execute_subprocess_async(lowercase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class a__ ( snake_case ):
"""simple docstring"""
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = F'--nproc_per_node={torch.cuda.device_count()}\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n '.split()
A__ = self.get_auto_remove_tmp_dir()
A__ = F'--output_dir {output_dir}'.split()
A__ = ["torchrun"] + distributed_args + args
execute_subprocess_async(lowercase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
lowerCAmelCase__ = HfArgumentParser((TrainingArguments,))
lowerCAmelCase__ = parser.parse_args_into_dataclasses()[0]
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [1_0_1, 4_0, 7]:
lowerCAmelCase__ = DummyDataset(dataset_length)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: EvalPrediction ) -> Dict:
'''simple docstring'''
A__ = list(range(len(SCREAMING_SNAKE_CASE_ ) ) )
A__ = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
F'{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}' )
return {"success": success}
lowerCAmelCase__ = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
lowerCAmelCase__ = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
lowerCAmelCase__ = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
lowerCAmelCase__ = 2
lowerCAmelCase__ = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
lowerCAmelCase__ = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
lowerCAmelCase__ = None
| 68 |
def lowerCAmelCase__ ( ) -> Any:
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simple docstring'''
A__ = 1
A__ = 2
while i * i <= n:
A__ = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def lowerCAmelCase__ ( ) -> Dict:
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(SCREAMING_SNAKE_CASE_ ) > 5_0_0 )
if __name__ == "__main__":
print(solution())
| 68 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class a__ :
"""simple docstring"""
def __init__( self , lowercase , ) -> Any:
'''simple docstring'''
A__ = parent
A__ = 13
A__ = 7
A__ = 30
A__ = self.seq_length + self.mem_len
A__ = 15
A__ = True
A__ = True
A__ = 99
A__ = [10, 50, 80]
A__ = 32
A__ = 32
A__ = 4
A__ = 8
A__ = 128
A__ = 2
A__ = 2
A__ = None
A__ = 1
A__ = 0
A__ = 3
A__ = self.vocab_size - 1
A__ = 0.01
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
random.seed(self.seed )
tf.random.set_seed(self.seed )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = TFTransfoXLModel(lowercase )
A__ , A__ = model(lowercase ).to_tuple()
A__ = {"input_ids": input_ids_a, "mems": mems_a}
A__ , A__ = model(lowercase ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase ) -> List[str]:
'''simple docstring'''
A__ = TFTransfoXLLMHeadModel(lowercase )
A__ , A__ = model(lowercase ).to_tuple()
A__ = {"input_ids": input_ids_a, "labels": lm_labels}
A__ , A__ = model(lowercase ).to_tuple()
A__ , A__ = model([input_ids_a, mems_a] ).to_tuple()
A__ = {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}
A__ , A__ = model(lowercase ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase ) -> Dict:
'''simple docstring'''
A__ = TFTransfoXLForSequenceClassification(lowercase )
A__ = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
((A__) , (A__) , (A__) , (A__)) = config_and_inputs
A__ = {"input_ids": input_ids_a}
return config, inputs_dict
@require_tf
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
__lowerCamelCase = () if is_tf_available() else ()
__lowerCamelCase = (
{
'feature-extraction': TFTransfoXLModel,
'text-classification': TFTransfoXLForSequenceClassification,
'text-generation': TFTransfoXLLMHeadModel,
'zero-shot': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = TFTransfoXLModelTester(self )
A__ = ConfigTester(self , config_class=lowercase , d_embed=37 )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
self.model_tester.set_seed()
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*lowercase )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
self.model_tester.set_seed()
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*lowercase )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowercase )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
A__ = model_class(lowercase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
A__ = model.get_output_embeddings()
assert isinstance(lowercase , tf.keras.layers.Layer )
A__ = model.get_bias()
assert name is None
else:
A__ = model.get_output_embeddings()
assert x is None
A__ = model.get_bias()
assert name is None
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@slow
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = TFTransfoXLModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss." )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@require_tf
class a__ ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip("Skip test until #12651 is resolved." )
@slow
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103" )
# fmt: off
A__ = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
A__ = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
A__ = model.generate(lowercase , max_length=200 , do_sample=lowercase )
self.assertListEqual(output_ids[0].numpy().tolist() , lowercase )
| 68 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCase__ = get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
lowerCAmelCase__ = json.load(f)
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return FSMTTokenizer.from_pretrained(lowercase )
def UpperCamelCase ( self , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = FSMTForConditionalGeneration.from_pretrained(lowercase ).to(lowercase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = F'facebook/wmt19-{pair}'
A__ = self.get_tokenizer(lowercase )
A__ = self.get_model(lowercase )
A__ = bleu_data[pair]["src"]
A__ = bleu_data[pair]["tgt"]
A__ = tokenizer(lowercase , return_tensors="pt" , truncation=lowercase , padding="longest" ).to(lowercase )
A__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
A__ = tokenizer.batch_decode(
lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase )
A__ = calculate_bleu(lowercase , lowercase )
print(lowercase )
self.assertGreaterEqual(scores["bleu"] , lowercase )
| 68 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")
# TF training parameters
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> List[str]:
'''simple docstring'''
return TrainCommand(SCREAMING_SNAKE_CASE_ )
class a__ ( snake_case ):
"""simple docstring"""
@staticmethod
def UpperCamelCase ( lowercase ) -> Any:
'''simple docstring'''
A__ = parser.add_parser("train" , help="CLI tool to train a model on a task." )
train_parser.add_argument(
"--train_data" , type=lowercase , required=lowercase , help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences." , )
train_parser.add_argument(
"--column_label" , type=lowercase , default=0 , help="Column of the dataset csv file with example labels." )
train_parser.add_argument(
"--column_text" , type=lowercase , default=1 , help="Column of the dataset csv file with example texts." )
train_parser.add_argument(
"--column_id" , type=lowercase , default=2 , help="Column of the dataset csv file with example ids." )
train_parser.add_argument(
"--skip_first_row" , action="store_true" , help="Skip the first row of the csv file (headers)." )
train_parser.add_argument("--validation_data" , type=lowercase , default="" , help="path to validation dataset." )
train_parser.add_argument(
"--validation_split" , type=lowercase , default=0.1 , help="if validation dataset is not provided, fraction of train dataset to use as validation dataset." , )
train_parser.add_argument("--output" , type=lowercase , default="./" , help="path to saved the trained model." )
train_parser.add_argument(
"--task" , type=lowercase , default="text_classification" , help="Task to train the model on." )
train_parser.add_argument(
"--model" , type=lowercase , default="bert-base-uncased" , help="Model's name or path to stored model." )
train_parser.add_argument("--train_batch_size" , type=lowercase , default=32 , help="Batch size for training." )
train_parser.add_argument("--valid_batch_size" , type=lowercase , default=64 , help="Batch size for validation." )
train_parser.add_argument("--learning_rate" , type=lowercase , default=3e-5 , help="Learning rate." )
train_parser.add_argument("--adam_epsilon" , type=lowercase , default=1e-08 , help="Epsilon for Adam optimizer." )
train_parser.set_defaults(func=lowercase )
def __init__( self , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = logging.get_logger("transformers-cli/training" )
A__ = "tf" if is_tf_available() else "torch"
os.makedirs(args.output , exist_ok=lowercase )
A__ = args.output
A__ = args.column_label
A__ = args.column_text
A__ = args.column_id
self.logger.info(F'Loading {args.task} pipeline for {args.model}' )
if args.task == "text_classification":
A__ = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'Loading dataset from {args.train_data}' )
A__ = Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
A__ = None
if args.validation_data:
self.logger.info(F'Loading validation dataset from {args.validation_data}' )
A__ = Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
A__ = args.validation_split
A__ = args.train_batch_size
A__ = args.valid_batch_size
A__ = args.learning_rate
A__ = args.adam_epsilon
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
raise NotImplementedError
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 68 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> list:
'''simple docstring'''
A__ = int(SCREAMING_SNAKE_CASE_ )
if n_element < 1:
A__ = ValueError("a should be a positive number" )
raise my_error
A__ = [1]
A__ , A__ , A__ = (0, 0, 0)
A__ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
lowerCAmelCase__ = input("""Enter the last number (nth term) of the Hamming Number Series: """)
print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
lowerCAmelCase__ = hamming(int(n))
print("""-----------------------------------------------------""")
print(f"""The list with nth numbers is: {hamming_numbers}""")
print("""-----------------------------------------------------""")
| 68 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase__ = 1_6
lowerCAmelCase__ = 3_2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Accelerator , SCREAMING_SNAKE_CASE_: DatasetDict , SCREAMING_SNAKE_CASE_: List[int] , SCREAMING_SNAKE_CASE_: List[int] , SCREAMING_SNAKE_CASE_: int = 1_6 ) -> Union[str, Any]:
'''simple docstring'''
A__ = AutoTokenizer.from_pretrained("bert-base-cased" )
A__ = DatasetDict(
{
"train": dataset["train"].select(SCREAMING_SNAKE_CASE_ ),
"validation": dataset["train"].select(SCREAMING_SNAKE_CASE_ ),
"test": dataset["validation"],
} )
def tokenize_function(SCREAMING_SNAKE_CASE_: str ):
# max_length=None => use the model max length (it's actually the default)
A__ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A__ = datasets.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A__ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(SCREAMING_SNAKE_CASE_: List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A__ = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A__ = 1_6
elif accelerator.mixed_precision != "no":
A__ = 8
else:
A__ = None
return tokenizer.pad(
SCREAMING_SNAKE_CASE_ , padding="longest" , max_length=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_tensors="pt" , )
# Instantiate dataloaders.
A__ = DataLoader(
tokenized_datasets["train"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
A__ = DataLoader(
tokenized_datasets["validation"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
A__ = DataLoader(
tokenized_datasets["test"] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
return train_dataloader, eval_dataloader, test_dataloader
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: int ) -> Dict:
'''simple docstring'''
A__ = []
# Download the dataset
A__ = load_dataset("glue" , "mrpc" )
# Create our splits
A__ = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
A__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A__ = config["lr"]
A__ = int(config["num_epochs"] )
A__ = int(config["seed"] )
A__ = int(config["batch_size"] )
A__ = evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
A__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A__ = batch_size // MAX_GPU_BATCH_SIZE
A__ = MAX_GPU_BATCH_SIZE
set_seed(SCREAMING_SNAKE_CASE_ )
# New Code #
# Create our folds:
A__ = kfold.split(np.zeros(datasets["train"].num_rows ) , datasets["train"]["label"] )
A__ = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(SCREAMING_SNAKE_CASE_ ):
A__ , A__ , A__ = get_fold_dataloaders(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A__ = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=SCREAMING_SNAKE_CASE_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A__ = model.to(accelerator.device )
# Instantiate optimizer
A__ = AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE_ )
# Instantiate scheduler
A__ = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE_ , num_warmup_steps=1_0_0 , num_training_steps=(len(SCREAMING_SNAKE_CASE_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A__ , A__ , A__ , A__ , A__ = accelerator.prepare(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE_ ):
model.train()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A__ = model(**SCREAMING_SNAKE_CASE_ )
A__ = outputs.loss
A__ = loss / gradient_accumulation_steps
accelerator.backward(SCREAMING_SNAKE_CASE_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ = model(**SCREAMING_SNAKE_CASE_ )
A__ = outputs.logits.argmax(dim=-1 )
A__ , A__ = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ , )
A__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , SCREAMING_SNAKE_CASE_ )
# New Code #
# We also run predictions on the test set at the very end
A__ = []
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ = model(**SCREAMING_SNAKE_CASE_ )
A__ = outputs.logits
A__ , A__ = accelerator.gather_for_metrics((predictions, batch["labels"]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(SCREAMING_SNAKE_CASE_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
A__ = torch.cat(SCREAMING_SNAKE_CASE_ , dim=0 )
A__ = torch.stack(SCREAMING_SNAKE_CASE_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
A__ = metric.compute(predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ )
accelerator.print("Average test metrics from all folds:" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( ) -> Tuple:
'''simple docstring'''
A__ = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
# New Code #
parser.add_argument("--num_folds" , type=SCREAMING_SNAKE_CASE_ , default=3 , help="The number of splits to perform across the dataset" )
A__ = parser.parse_args()
A__ = {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
training_function(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 68 |
import copy
import random
from transformers import CLIPTokenizer
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowercase , **lowercase )
A__ = {}
def UpperCamelCase ( self , lowercase , *lowercase , **lowercase ) -> str:
'''simple docstring'''
A__ = super().add_tokens(lowercase , *lowercase , **lowercase )
if num_added_tokens == 0:
raise ValueError(
F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
" `placeholder_token` that is not already in the tokenizer." )
def UpperCamelCase ( self , lowercase , *lowercase , lowercase=1 , **lowercase ) -> Any:
'''simple docstring'''
A__ = []
if num_vec_per_token == 1:
self.try_adding_tokens(lowercase , *lowercase , **lowercase )
output.append(lowercase )
else:
A__ = []
for i in range(lowercase ):
A__ = placeholder_token + F'_{i}'
self.try_adding_tokens(lowercase , *lowercase , **lowercase )
output.append(lowercase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F'The tokenizer already has placeholder token {token} that can get confused with'
F' {placeholder_token}keep placeholder tokens independent' )
A__ = output
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=1.0 ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = []
for i in range(len(lowercase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=lowercase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
A__ = self.token_map[placeholder_token]
A__ = tokens[: 1 + int(len(lowercase ) * prop_tokens_to_load )]
if vector_shuffle:
A__ = copy.copy(lowercase )
random.shuffle(lowercase )
A__ = text.replace(lowercase , " ".join(lowercase ) )
return text
def __call__( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase ) -> str:
'''simple docstring'''
return super().__call__(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase ) , *lowercase , **lowercase , )
def UpperCamelCase ( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase ) -> List[str]:
'''simple docstring'''
return super().encode(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase ) , *lowercase , **lowercase , )
| 68 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class a__ :
"""simple docstring"""
__lowerCamelCase = BlenderbotSmallConfig
__lowerCamelCase = {}
__lowerCamelCase = 'gelu'
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=32 , lowercase=2 , lowercase=4 , lowercase=37 , lowercase=0.1 , lowercase=0.1 , lowercase=20 , lowercase=2 , lowercase=1 , lowercase=0 , ) -> Any:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = eos_token_id
A__ = pad_token_id
A__ = bos_token_id
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A__ = tf.concat([input_ids, eos_tensor] , axis=1 )
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
A__ = prepare_blenderbot_small_inputs_dict(lowercase , lowercase , lowercase )
return config, inputs_dict
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = TFBlenderbotSmallModel(config=lowercase ).get_decoder()
A__ = inputs_dict["input_ids"]
A__ = input_ids[:1, :]
A__ = inputs_dict["attention_mask"][:1, :]
A__ = inputs_dict["head_mask"]
A__ = 1
# first forward pass
A__ = model(lowercase , attention_mask=lowercase , head_mask=lowercase , use_cache=lowercase )
A__ , A__ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
A__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
A__ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
A__ = tf.concat([input_ids, next_tokens] , axis=-1 )
A__ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
A__ = model(lowercase , attention_mask=lowercase )[0]
A__ = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
A__ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
A__ = output_from_no_past[:, -3:, random_slice_idx]
A__ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase , lowercase , rtol=1e-3 )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Optional[Any]=None , SCREAMING_SNAKE_CASE_: Optional[int]=None , SCREAMING_SNAKE_CASE_: Optional[int]=None , SCREAMING_SNAKE_CASE_: Dict=None , SCREAMING_SNAKE_CASE_: List[str]=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
A__ = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A__ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
A__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
A__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
__lowerCamelCase = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase = (
{
'conversational': TFBlenderbotSmallForConditionalGeneration,
'feature-extraction': TFBlenderbotSmallModel,
'summarization': TFBlenderbotSmallForConditionalGeneration,
'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
'translation': TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = TFBlenderbotSmallModelTester(self )
A__ = ConfigTester(self , config_class=lowercase )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase )
@require_tokenizers
@require_tf
class a__ ( unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = [
'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
' i\'m going to throw up.\nand why is that?'
]
__lowerCamelCase = 'facebook/blenderbot_small-90M'
@cached_property
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
@cached_property
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.tokenizer(self.src_text , return_tensors="tf" )
A__ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=lowercase , )
A__ = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowercase )[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 68 |
from collections import deque
from math import floor
from random import random
from time import time
class a__ :
"""simple docstring"""
def __init__( self ) -> Dict:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase , lowercase=1 ) -> Tuple:
'''simple docstring'''
if self.graph.get(lowercase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
A__ = [[w, v]]
if not self.graph.get(lowercase ):
A__ = []
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return list(self.graph )
def UpperCamelCase ( self , lowercase , lowercase ) -> int:
'''simple docstring'''
if self.graph.get(lowercase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Any:
'''simple docstring'''
if s == d:
return []
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return visited
def UpperCamelCase ( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
if c == -1:
A__ = floor(random() * 10000 ) + 10
for i in range(lowercase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A__ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase , lowercase , 1 )
def UpperCamelCase ( self , lowercase=-2 ) -> Any:
'''simple docstring'''
A__ = deque()
A__ = []
if s == -2:
A__ = list(self.graph )[0]
d.append(lowercase )
visited.append(lowercase )
while d:
A__ = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return len(self.graph[u] )
def UpperCamelCase ( self , lowercase=-2 ) -> str:
'''simple docstring'''
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
A__ = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return sorted_nodes
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return list(lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return False
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Any:
'''simple docstring'''
A__ = time()
self.dfs(lowercase , lowercase )
A__ = time()
return end - begin
def UpperCamelCase ( self , lowercase=-2 ) -> int:
'''simple docstring'''
A__ = time()
self.bfs(lowercase )
A__ = time()
return end - begin
class a__ :
"""simple docstring"""
def __init__( self ) -> int:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase , lowercase=1 ) -> Union[str, Any]:
'''simple docstring'''
if self.graph.get(lowercase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
A__ = [[w, v]]
# add the other way
if self.graph.get(lowercase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
A__ = [[w, u]]
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
if self.graph.get(lowercase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase )
# the other way round
if self.graph.get(lowercase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowercase )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> List[str]:
'''simple docstring'''
if s == d:
return []
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return visited
def UpperCamelCase ( self , lowercase=-1 ) -> str:
'''simple docstring'''
if c == -1:
A__ = floor(random() * 10000 ) + 10
for i in range(lowercase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A__ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase , lowercase , 1 )
def UpperCamelCase ( self , lowercase=-2 ) -> Dict:
'''simple docstring'''
A__ = deque()
A__ = []
if s == -2:
A__ = list(self.graph )[0]
d.append(lowercase )
visited.append(lowercase )
while d:
A__ = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
return len(self.graph[u] )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return list(lowercase )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return False
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
return list(self.graph )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = time()
self.dfs(lowercase , lowercase )
A__ = time()
return end - begin
def UpperCamelCase ( self , lowercase=-2 ) -> List[Any]:
'''simple docstring'''
A__ = time()
self.bfs(lowercase )
A__ = time()
return end - begin
| 68 | 1 |
from __future__ import annotations
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> None:
'''simple docstring'''
A__ = data
A__ = None
A__ = None
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowerCAmelCase__ ( ) -> None: # Main function for testing.
'''simple docstring'''
A__ = Node(1 )
A__ = Node(2 )
A__ = Node(3 )
A__ = Node(4 )
A__ = Node(5 )
A__ = Node(6 )
A__ = Node(7 )
A__ = Node(8 )
A__ = Node(9 )
print(is_full_binary_tree(SCREAMING_SNAKE_CASE_ ) )
print(depth_of_tree(SCREAMING_SNAKE_CASE_ ) )
print("Tree is: " )
display(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 68 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
"""
lowerCAmelCase__ = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""
lowerCAmelCase__ = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the CUAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'exact_match': Exact match (the normalized answer exactly match the gold answer)
'f1': The F-score of predicted tokens versus the gold answer
'aupr': Area Under the Precision-Recall curve
'prec_at_80_recall': Precision at 80% recall
'prec_at_90_recall': Precision at 90% recall
Examples:
>>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> cuad_metric = datasets.load_metric(\"cuad\")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def UpperCamelCase ( self , lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
A__ = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
A__ = evaluate(dataset=lowercase , predictions=lowercase )
return score
| 68 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
lowerCAmelCase__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ['pixel_values']
def __init__( self , lowercase = True , lowercase = None , lowercase = PILImageResampling.BICUBIC , lowercase = True , lowercase = None , lowercase = True , lowercase = 1 / 255 , lowercase = True , lowercase = None , lowercase = None , lowercase = True , **lowercase , ) -> None:
'''simple docstring'''
super().__init__(**lowercase )
A__ = size if size is not None else {"shortest_edge": 224}
A__ = get_size_dict(lowercase , default_to_square=lowercase )
A__ = crop_size if crop_size is not None else {"height": 224, "width": 224}
A__ = get_size_dict(lowercase , default_to_square=lowercase , param_name="crop_size" )
A__ = do_resize
A__ = size
A__ = resample
A__ = do_center_crop
A__ = crop_size
A__ = do_rescale
A__ = rescale_factor
A__ = do_normalize
A__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A__ = image_std if image_std is not None else OPENAI_CLIP_STD
A__ = do_convert_rgb
def UpperCamelCase ( self , lowercase , lowercase , lowercase = PILImageResampling.BICUBIC , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
A__ = get_size_dict(lowercase , default_to_square=lowercase )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A__ = get_resize_output_image_size(lowercase , size=size["shortest_edge"] , default_to_square=lowercase )
return resize(lowercase , size=lowercase , resample=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
A__ = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(lowercase , size=(size["height"], size["width"]) , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase = None , **lowercase , ) -> int:
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ) -> PIL.Image.Image:
'''simple docstring'''
A__ = do_resize if do_resize is not None else self.do_resize
A__ = size if size is not None else self.size
A__ = get_size_dict(lowercase , param_name="size" , default_to_square=lowercase )
A__ = resample if resample is not None else self.resample
A__ = do_center_crop if do_center_crop is not None else self.do_center_crop
A__ = crop_size if crop_size is not None else self.crop_size
A__ = get_size_dict(lowercase , param_name="crop_size" , default_to_square=lowercase )
A__ = do_rescale if do_rescale is not None else self.do_rescale
A__ = rescale_factor if rescale_factor is not None else self.rescale_factor
A__ = do_normalize if do_normalize is not None else self.do_normalize
A__ = image_mean if image_mean is not None else self.image_mean
A__ = image_std if image_std is not None else self.image_std
A__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A__ = make_list_of_images(lowercase )
if not valid_images(lowercase ):
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_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A__ = [convert_to_rgb(lowercase ) for image in images]
# All transformations expect numpy arrays.
A__ = [to_numpy_array(lowercase ) for image in images]
if do_resize:
A__ = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_center_crop:
A__ = [self.center_crop(image=lowercase , size=lowercase ) for image in images]
if do_rescale:
A__ = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
A__ = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
A__ = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
A__ = {"pixel_values": images}
return BatchFeature(data=lowercase , tensor_type=lowercase )
| 68 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
A__ = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
A__ = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
return torch.mm(SCREAMING_SNAKE_CASE_ , normalized_text_embeds.t() )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = CLIPConfig
__lowerCamelCase = ['CLIPEncoderLayer']
def __init__( self , lowercase ) -> Optional[int]:
'''simple docstring'''
super().__init__(lowercase )
A__ = CLIPVisionModel(config.vision_config )
A__ = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=lowercase )
A__ = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(17 ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(3 ) , requires_grad=lowercase )
@torch.no_grad()
def UpperCamelCase ( self , lowercase , lowercase ) -> Any:
'''simple docstring'''
A__ = self.vision_model(lowercase )[1] # pooled_output
A__ = self.visual_projection(lowercase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
A__ = cosine_distance(lowercase , self.special_care_embeds ).cpu().float().numpy()
A__ = cosine_distance(lowercase , self.concept_embeds ).cpu().float().numpy()
A__ = []
A__ = image_embeds.shape[0]
for i in range(lowercase ):
A__ = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
A__ = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
A__ = special_cos_dist[i][concept_idx]
A__ = self.special_care_embeds_weights[concept_idx].item()
A__ = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]} )
A__ = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
A__ = cos_dist[i][concept_idx]
A__ = self.concept_embeds_weights[concept_idx].item()
A__ = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(lowercase )
result.append(lowercase )
A__ = [len(res["bad_concepts"] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCamelCase ( self , lowercase , lowercase ) -> Any:
'''simple docstring'''
A__ = self.vision_model(lowercase )[1] # pooled_output
A__ = self.visual_projection(lowercase )
A__ = cosine_distance(lowercase , self.special_care_embeds )
A__ = cosine_distance(lowercase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
A__ = 0.0
A__ = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
A__ = torch.any(special_scores > 0 , dim=1 )
A__ = special_care * 0.01
A__ = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
A__ = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
A__ = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 68 | 1 |
from math import ceil
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> Tuple:
'''simple docstring'''
A__ = list(range(0 , SCREAMING_SNAKE_CASE_ ) )
A__ = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
A__ = []
for i in device_map_blocks:
if device_map_blocks.count(SCREAMING_SNAKE_CASE_ ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(SCREAMING_SNAKE_CASE_ )
# Missing blocks
A__ = [i for i in blocks if i not in device_map_blocks]
A__ = [i for i in device_map_blocks if i not in blocks]
if len(SCREAMING_SNAKE_CASE_ ) != 0:
raise ValueError(
"Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
" These attention blocks were specified more than once: " + str(SCREAMING_SNAKE_CASE_ ) )
if len(SCREAMING_SNAKE_CASE_ ) != 0:
raise ValueError(
"There are attention blocks for this model that are not specified in the device_map. Add these attention "
"blocks to a device on the device_map: " + str(SCREAMING_SNAKE_CASE_ ) )
if len(SCREAMING_SNAKE_CASE_ ) != 0:
raise ValueError(
"The device_map contains more attention blocks than this model has. Remove these from the device_map:"
+ str(SCREAMING_SNAKE_CASE_ ) )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: List[Any] ) -> Any:
'''simple docstring'''
A__ = list(range(SCREAMING_SNAKE_CASE_ ) )
A__ = int(ceil(n_layers / len(SCREAMING_SNAKE_CASE_ ) ) )
A__ = [layers[i : i + n_blocks] for i in range(0 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )]
return dict(zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
| 68 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 68 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LongformerConfig""",
"""LongformerOnnxConfig""",
],
"""tokenization_longformer""": ["""LongformerTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ["""LongformerTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LongformerForMaskedLM""",
"""LongformerForMultipleChoice""",
"""LongformerForQuestionAnswering""",
"""LongformerForSequenceClassification""",
"""LongformerForTokenClassification""",
"""LongformerModel""",
"""LongformerPreTrainedModel""",
"""LongformerSelfAttention""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLongformerForMaskedLM""",
"""TFLongformerForMultipleChoice""",
"""TFLongformerForQuestionAnswering""",
"""TFLongformerForSequenceClassification""",
"""TFLongformerForTokenClassification""",
"""TFLongformerModel""",
"""TFLongformerPreTrainedModel""",
"""TFLongformerSelfAttention""",
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 |
import string
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> None:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
A__ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
A__ = string.ascii_uppercase.find(SCREAMING_SNAKE_CASE_ )
A__ = num - key
if num < 0:
A__ = num + len(string.ascii_uppercase )
A__ = translated + string.ascii_uppercase[num]
else:
A__ = translated + symbol
print(F'Decryption using Key #{key}: {translated}' )
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
A__ = input("Encrypted message: " )
A__ = message.upper()
decrypt(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 68 | 1 |
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> str:
'''simple docstring'''
A__ = data
A__ = None
def __repr__( self ) -> str:
'''simple docstring'''
return F'Node({self.data})'
class a__ :
"""simple docstring"""
def __init__( self ) -> str:
'''simple docstring'''
A__ = None
def __iter__( self ) -> Any:
'''simple docstring'''
A__ = self.head
while node:
yield node.data
A__ = node.next
def __len__( self ) -> int:
'''simple docstring'''
return sum(1 for _ in self )
def __repr__( self ) -> str:
'''simple docstring'''
return "->".join([str(lowercase ) for item in self] )
def __getitem__( self , lowercase ) -> Any:
'''simple docstring'''
if not 0 <= index < len(self ):
raise ValueError("list index out of range." )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , lowercase , lowercase ) -> None:
'''simple docstring'''
if not 0 <= index < len(self ):
raise ValueError("list index out of range." )
A__ = self.head
for _ in range(lowercase ):
A__ = current.next
A__ = data
def UpperCamelCase ( self , lowercase ) -> None:
'''simple docstring'''
self.insert_nth(len(self ) , lowercase )
def UpperCamelCase ( self , lowercase ) -> None:
'''simple docstring'''
self.insert_nth(0 , lowercase )
def UpperCamelCase ( self , lowercase , lowercase ) -> None:
'''simple docstring'''
if not 0 <= index <= len(self ):
raise IndexError("list index out of range" )
A__ = Node(lowercase )
if self.head is None:
A__ = new_node
elif index == 0:
A__ = self.head # link new_node to head
A__ = new_node
else:
A__ = self.head
for _ in range(index - 1 ):
A__ = temp.next
A__ = temp.next
A__ = new_node
def UpperCamelCase ( self ) -> None: # print every node data
'''simple docstring'''
print(self )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
return self.delete_nth(0 )
def UpperCamelCase ( self ) -> Any: # delete from tail
'''simple docstring'''
return self.delete_nth(len(self ) - 1 )
def UpperCamelCase ( self , lowercase = 0 ) -> Any:
'''simple docstring'''
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("List index out of range." )
A__ = self.head # default first node
if index == 0:
A__ = self.head.next
else:
A__ = self.head
for _ in range(index - 1 ):
A__ = temp.next
A__ = temp.next
A__ = temp.next.next
return delete_node.data
def UpperCamelCase ( self ) -> bool:
'''simple docstring'''
return self.head is None
def UpperCamelCase ( self ) -> None:
'''simple docstring'''
A__ = None
A__ = self.head
while current:
# Store the current node's next node.
A__ = current.next
# Make the current node's next point backwards
A__ = prev
# Make the previous node be the current node
A__ = current
# Make the current node the next node (to progress iteration)
A__ = next_node
# Return prev in order to put the head at the end
A__ = prev
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
A__ = LinkedList()
assert linked_list.is_empty() is True
assert str(SCREAMING_SNAKE_CASE_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(SCREAMING_SNAKE_CASE_ ) == i
linked_list.insert_nth(SCREAMING_SNAKE_CASE_ , i + 1 )
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(1 , 1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(0 , 1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(SCREAMING_SNAKE_CASE_ ) == 9
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(1 , 1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
A__ = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(SCREAMING_SNAKE_CASE_ ) == "->".join(str(SCREAMING_SNAKE_CASE_ ) for i in range(-8 , 1 ) )
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
A__ = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"dlrow olleH",
7,
5_5_5_5,
0,
-192.55555,
"Hello, world!",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
A__ = LinkedList()
for i in test_input:
linked_list.insert_tail(SCREAMING_SNAKE_CASE_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(SCREAMING_SNAKE_CASE_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
A__ = linked_list.delete_head()
assert result == -9
assert (
str(SCREAMING_SNAKE_CASE_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
A__ = linked_list.delete_tail()
assert result == 12.2
assert (
str(SCREAMING_SNAKE_CASE_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
A__ = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(SCREAMING_SNAKE_CASE_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("Hello again, world!" ) )
assert (
str(SCREAMING_SNAKE_CASE_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(SCREAMING_SNAKE_CASE_ )
assert (
str(SCREAMING_SNAKE_CASE_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(SCREAMING_SNAKE_CASE_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def lowerCAmelCase__ ( ) -> Tuple:
'''simple docstring'''
from doctest import testmod
testmod()
A__ = LinkedList()
linked_list.insert_head(input("Inserting 1st at head " ).strip() )
linked_list.insert_head(input("Inserting 2nd at head " ).strip() )
print("\nPrint list:" )
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail " ).strip() )
linked_list.insert_tail(input("Inserting 2nd at tail " ).strip() )
print("\nPrint list:" )
linked_list.print_list()
print("\nDelete head" )
linked_list.delete_head()
print("Delete tail" )
linked_list.delete_tail()
print("\nPrint list:" )
linked_list.print_list()
print("\nReverse linked list" )
linked_list.reverse()
print("\nPrint list:" )
linked_list.print_list()
print("\nString representation of linked list:" )
print(SCREAMING_SNAKE_CASE_ )
print("\nReading/changing Node data using indexing:" )
print(F'Element at Position 1: {linked_list[1]}' )
A__ = input("Enter New Value: " ).strip()
print("New list:" )
print(SCREAMING_SNAKE_CASE_ )
print(F'length of linked_list is : {len(SCREAMING_SNAKE_CASE_ )}' )
if __name__ == "__main__":
main()
| 68 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = SpeechTaTokenizer
__lowerCamelCase = False
__lowerCamelCase = True
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
A__ = SpeechTaTokenizer(lowercase )
A__ = AddedToken("<mask>" , lstrip=lowercase , rstrip=lowercase )
A__ = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = "this is a test"
A__ = "this is a test"
return input_text, output_text
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=20 , lowercase=5 ) -> Optional[Any]:
'''simple docstring'''
A__ , A__ = self.get_input_output_texts(lowercase )
A__ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A__ = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
return text, ids
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = "<pad>"
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-4] , "œ" )
self.assertEqual(vocab_keys[-2] , "<mask>" )
self.assertEqual(vocab_keys[-1] , "<ctc_blank>" )
self.assertEqual(len(lowercase ) , 81 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.get_tokenizers(do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
A__ = tokenizer.add_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size + len(lowercase ) )
A__ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
A__ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
A__ = tokenizer.add_special_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size_a + len(lowercase ) )
A__ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.get_tokenizer()
A__ = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(lowercase , [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
A__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
A__ = tokenizer.convert_tokens_to_ids(lowercase )
# fmt: off
self.assertListEqual(lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
A__ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
A__ = {
"input_ids": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
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],
"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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name="microsoft/speecht5_asr" , revision="c5ef64c71905caeccde0e4462ef3f9077224c524" , sequences=lowercase , )
| 68 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[list[int]] , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: set ) -> int:
'''simple docstring'''
A__ , A__ = len(SCREAMING_SNAKE_CASE_ ), len(grid[0] )
if (
min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
A__ = 0
count += depth_first_search(SCREAMING_SNAKE_CASE_ , row + 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += depth_first_search(SCREAMING_SNAKE_CASE_ , row - 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
count += depth_first_search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , col + 1 , SCREAMING_SNAKE_CASE_ )
count += depth_first_search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , col - 1 , SCREAMING_SNAKE_CASE_ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 68 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> List[str]:
'''simple docstring'''
A__ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
A__ = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
A__ = F'{src_lang}-{tgt_lang}'
A__ = F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
A__ = os.path.join(SCREAMING_SNAKE_CASE_ , "README.md" )
print(F'Generating {path}' )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
# make sure we are under the root of the project
lowerCAmelCase__ = Path(__file__).resolve().parent.parent.parent
lowerCAmelCase__ = repo_dir / """model_cards"""
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = model_name.split("""-""")
lowerCAmelCase__ = model_cards_dir / """facebook""" / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 68 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: List[Any]=False , SCREAMING_SNAKE_CASE_: Union[str, Any]=False ) -> int:
'''simple docstring'''
A__ = "backbone." if is_semantic else ""
A__ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'{prefix}blocks.{i}.norm1.weight', F'beit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'beit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(F'{prefix}blocks.{i}.attn.proj.weight', F'beit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append(
(F'{prefix}blocks.{i}.attn.proj.bias', F'beit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'{prefix}blocks.{i}.norm2.weight', F'beit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'beit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.weight', F'beit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.bias', F'beit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'beit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'beit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
(F'{prefix}cls_token', "beit.embeddings.cls_token"),
(F'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
(F'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
(F'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("mask_token", "beit.embeddings.mask_token"),
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("fc_norm.weight", "beit.pooler.layernorm.weight"),
("fc_norm.bias", "beit.pooler.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: List[str]=False , SCREAMING_SNAKE_CASE_: int=False ) -> int:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
A__ = "backbone." if is_semantic else ""
# queries, keys and values
A__ = state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight' )
A__ = state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias' )
A__ = state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias' )
A__ = in_proj_weight[
: config.hidden_size, :
]
A__ = q_bias
A__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A__ = in_proj_weight[
-config.hidden_size :, :
]
A__ = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
A__ = state_dict.pop(F'{prefix}blocks.{i}.gamma_1' )
A__ = state_dict.pop(F'{prefix}blocks.{i}.gamma_2' )
A__ = gamma_a
A__ = gamma_a
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[Any] ) -> int:
'''simple docstring'''
A__ = dct.pop(SCREAMING_SNAKE_CASE_ )
A__ = val
def lowerCAmelCase__ ( ) -> Any:
'''simple docstring'''
A__ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A__ = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: str=False ) -> List[Any]:
'''simple docstring'''
A__ = False if "rvlcdip" in checkpoint_url else True
A__ = BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_ , use_mask_token=SCREAMING_SNAKE_CASE_ )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
A__ = 1_0_2_4
A__ = 4_0_9_6
A__ = 2_4
A__ = 1_6
# labels
if "rvlcdip" in checkpoint_url:
A__ = 1_6
A__ = "huggingface/label-files"
A__ = "rvlcdip-id2label.json"
A__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="dataset" ) , "r" ) )
A__ = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
A__ = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ , map_location="cpu" )["model"]
A__ = create_rename_keys(SCREAMING_SNAKE_CASE_ , has_lm_head=SCREAMING_SNAKE_CASE_ )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
read_in_q_k_v(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , has_lm_head=SCREAMING_SNAKE_CASE_ )
# load HuggingFace model
A__ = BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_ ) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_ )
model.eval()
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# Check outputs on an image
A__ = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=SCREAMING_SNAKE_CASE_ )
A__ = prepare_img()
A__ = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="pt" )
A__ = encoding["pixel_values"]
A__ = model(SCREAMING_SNAKE_CASE_ )
A__ = outputs.logits
# verify logits
A__ = [1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_ ), "Shape of logits not as expected"
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
if has_lm_head:
A__ = "dit-base" if "base" in checkpoint_url else "dit-large"
else:
A__ = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
image_processor.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
model.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
lowerCAmelCase__ = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 68 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = feature_size
A__ = sampling_rate
A__ = padding_value
A__ = kwargs.pop("padding_side" , "right" )
A__ = kwargs.pop("return_attention_mask" , lowercase )
super().__init__(**lowercase )
def UpperCamelCase ( self , lowercase , lowercase = True , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , ) -> BatchFeature:
'''simple docstring'''
if isinstance(lowercase , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
A__ = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
F' to this method that includes {self.model_input_names[0]}, but you provided'
F' {list(processed_features.keys() )}' )
A__ = processed_features[self.model_input_names[0]]
A__ = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(lowercase ) == 0:
if return_attention_mask:
A__ = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
A__ = required_input[0]
if isinstance(lowercase , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
A__ = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(lowercase ):
A__ = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(lowercase ):
A__ = "tf"
elif is_torch_tensor(lowercase ):
A__ = "pt"
elif isinstance(lowercase , (int, float, list, tuple, np.ndarray) ):
A__ = "np"
else:
raise ValueError(
F'type of {first_element} unknown: {type(lowercase )}. '
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
A__ = to_numpy(lowercase )
else:
A__ = [to_numpy(lowercase ) for v in value]
# Convert padding_strategy in PaddingStrategy
A__ = self._get_padding_strategies(padding=lowercase , max_length=lowercase )
A__ = processed_features[self.model_input_names[0]]
A__ = len(lowercase )
if not all(len(lowercase ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
A__ = []
for i in range(lowercase ):
A__ = {k: v[i] for k, v in processed_features.items()}
# truncation
A__ = self._truncate(
lowercase , max_length=lowercase , pad_to_multiple_of=lowercase , truncation=lowercase , )
truncated_inputs.append(lowercase )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
A__ = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
A__ = PaddingStrategy.MAX_LENGTH
A__ = {}
for i in range(lowercase ):
# padding
A__ = self._pad(
truncated_inputs[i] , max_length=lowercase , padding_strategy=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , )
for key, value in outputs.items():
if key not in batch_outputs:
A__ = []
if value.dtype is np.dtype(np.floataa ):
A__ = value.astype(np.floataa )
batch_outputs[key].append(lowercase )
return BatchFeature(lowercase , tensor_type=lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = PaddingStrategy.DO_NOT_PAD , lowercase = None , lowercase = None , ) -> dict:
'''simple docstring'''
A__ = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
A__ = len(lowercase )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
A__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
A__ = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
A__ = np.ones(len(lowercase ) , dtype=np.intaa )
if needs_to_be_padded:
A__ = max_length - len(lowercase )
if self.padding_side == "right":
if return_attention_mask:
A__ = np.pad(
processed_features["attention_mask"] , (0, difference) )
A__ = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
A__ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
A__ = np.pad(
processed_features["attention_mask"] , (difference, 0) )
A__ = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
A__ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , ) -> Union[str, Any]:
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
A__ = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
A__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
A__ = len(lowercase ) > max_length
if needs_to_be_truncated:
A__ = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
A__ = processed_features["attention_mask"][:max_length]
return processed_features
def UpperCamelCase ( self , lowercase=False , lowercase=None ) -> Any:
'''simple docstring'''
if padding is not False:
if padding is True:
A__ = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(lowercase , lowercase ):
A__ = PaddingStrategy(lowercase )
elif isinstance(lowercase , lowercase ):
A__ = padding
else:
A__ = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 68 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> list:
'''simple docstring'''
A__ = int(SCREAMING_SNAKE_CASE_ )
if n_element < 1:
A__ = ValueError("a should be a positive number" )
raise my_error
A__ = [1]
A__ , A__ , A__ = (0, 0, 0)
A__ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
lowerCAmelCase__ = input("""Enter the last number (nth term) of the Hamming Number Series: """)
print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
lowerCAmelCase__ = hamming(int(n))
print("""-----------------------------------------------------""")
print(f"""The list with nth numbers is: {hamming_numbers}""")
print("""-----------------------------------------------------""")
| 68 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
"""GroupViTTextConfig""",
"""GroupViTVisionConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GroupViTModel""",
"""GroupViTPreTrainedModel""",
"""GroupViTTextModel""",
"""GroupViTVisionModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFGroupViTModel""",
"""TFGroupViTPreTrainedModel""",
"""TFGroupViTTextModel""",
"""TFGroupViTVisionModel""",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> int:
'''simple docstring'''
return {key.lstrip("-" ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def lowerCAmelCase__ ( ) -> Optional[int]:
'''simple docstring'''
A__ = ArgumentParser(
"HuggingFace Datasets CLI tool" , usage="datasets-cli <command> [<args>]" , allow_abbrev=SCREAMING_SNAKE_CASE_ )
A__ = parser.add_subparsers(help="datasets-cli command helpers" )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
TestCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
RunBeamCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
DummyDataCommand.register_subcommand(SCREAMING_SNAKE_CASE_ )
# Parse args
A__ , A__ = parser.parse_known_args()
if not hasattr(SCREAMING_SNAKE_CASE_ , "func" ):
parser.print_help()
exit(1 )
A__ = parse_unknown_args(SCREAMING_SNAKE_CASE_ )
# Run
A__ = args.func(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
service.run()
if __name__ == "__main__":
main()
| 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'gpt_neox_japanese'
def __init__( self , lowercase=32000 , lowercase=2560 , lowercase=32 , lowercase=32 , lowercase=4 , lowercase="gelu" , lowercase=1.00 , lowercase=10000 , lowercase=2048 , lowercase=0.02 , lowercase=1e-5 , lowercase=True , lowercase=31996 , lowercase=31999 , lowercase=0.1 , lowercase=0.0 , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_multiple_size
A__ = hidden_act
A__ = rotary_pct
A__ = rotary_emb_base
A__ = initializer_range
A__ = layer_norm_eps
A__ = use_cache
A__ = attention_dropout
A__ = hidden_dropout
| 68 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'gpt_neox_japanese'
def __init__( self , lowercase=32000 , lowercase=2560 , lowercase=32 , lowercase=32 , lowercase=4 , lowercase="gelu" , lowercase=1.00 , lowercase=10000 , lowercase=2048 , lowercase=0.02 , lowercase=1e-5 , lowercase=True , lowercase=31996 , lowercase=31999 , lowercase=0.1 , lowercase=0.0 , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_multiple_size
A__ = hidden_act
A__ = rotary_pct
A__ = rotary_emb_base
A__ = initializer_range
A__ = layer_norm_eps
A__ = use_cache
A__ = attention_dropout
A__ = hidden_dropout
| 68 |
import warnings
from functools import wraps
from typing import Callable
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Callable ) -> Callable:
'''simple docstring'''
@wraps(SCREAMING_SNAKE_CASE_ )
def _inner_fn(*SCREAMING_SNAKE_CASE_: int , **SCREAMING_SNAKE_CASE_: Union[str, Any] ):
warnings.warn(
(F'\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.') , SCREAMING_SNAKE_CASE_ , )
return fn(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
return _inner_fn
| 68 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> None:
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
A__ , A__ = array[indexa], array[indexa]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> None:
'''simple docstring'''
if length > 1:
A__ = int(length / 2 )
for i in range(SCREAMING_SNAKE_CASE_ , low + middle ):
comp_and_swap(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , i + middle , SCREAMING_SNAKE_CASE_ )
bitonic_merge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
bitonic_merge(SCREAMING_SNAKE_CASE_ , low + middle , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> None:
'''simple docstring'''
if length > 1:
A__ = int(length / 2 )
bitonic_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 1 )
bitonic_sort(SCREAMING_SNAKE_CASE_ , low + middle , SCREAMING_SNAKE_CASE_ , 0 )
bitonic_merge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
lowerCAmelCase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowerCAmelCase__ = [int(item.strip()) for item in user_input.split(""",""")]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print("""\nSorted array in ascending order is: """, end="""""")
print(*unsorted, sep=""", """)
bitonic_merge(unsorted, 0, len(unsorted), 0)
print("""Sorted array in descending order is: """, end="""""")
print(*unsorted, sep=""", """)
| 68 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
lowerCAmelCase__ = """\
Text data.
Second line of data."""
lowerCAmelCase__ = """file"""
@pytest.fixture(scope="session" )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> Optional[int]:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / (FILE_PATH + ".zstd")
A__ = bytes(SCREAMING_SNAKE_CASE_ , "utf-8" )
with zstd.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any ) -> List[str]:
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , SCREAMING_SNAKE_CASE_ ) , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return FILE_PATH
@pytest.mark.parametrize("compression_format" , ["gzip", "xz", "zstd"] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: int ) -> Any:
'''simple docstring'''
A__ = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
A__ = input_paths[compression_format]
A__ = tmp_path / "cache"
A__ = DownloadConfig(cache_dir=SCREAMING_SNAKE_CASE_ , extract_compressed_file=SCREAMING_SNAKE_CASE_ )
A__ = cached_path(SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("default_extracted" , [True, False] )
@pytest.mark.parametrize("default_cache_dir" , [True, False] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: str ) -> Dict:
'''simple docstring'''
A__ = "custom_cache"
A__ = "custom_extracted_dir"
A__ = tmp_path / "custom_extracted_path"
if default_extracted:
A__ = ("downloads" if default_cache_dir else custom_cache_dir, "extracted")
else:
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR" , SCREAMING_SNAKE_CASE_ )
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH" , str(SCREAMING_SNAKE_CASE_ ) )
A__ = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
A__ = xz_file
A__ = (
DownloadConfig(extract_compressed_file=SCREAMING_SNAKE_CASE_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=SCREAMING_SNAKE_CASE_ )
)
A__ = cached_path(SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
assert Path(SCREAMING_SNAKE_CASE_ ).parent.parts[-2:] == expected
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> Optional[int]:
'''simple docstring'''
A__ = str(Path(SCREAMING_SNAKE_CASE_ ).resolve() )
assert cached_path(SCREAMING_SNAKE_CASE_ ) == text_file
# relative path
A__ = str(Path(SCREAMING_SNAKE_CASE_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(SCREAMING_SNAKE_CASE_ ) == text_file
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[str]:
'''simple docstring'''
A__ = str(tmp_path.resolve() / "__missing_file__.txt" )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path(SCREAMING_SNAKE_CASE_ )
# relative path
A__ = "./__missing_file__.txt"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> Union[str, Any]:
'''simple docstring'''
A__ = get_from_cache(F'tmp://{tmpfs_file}' )
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
assert output_file_content == FILE_CONTENT
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( ) -> List[Any]:
'''simple docstring'''
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> int:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_get("https://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[Any]:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
ftp_get("ftp://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
ftp_head("ftp://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> str:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
fsspec_get("s3://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
fsspec_head("s3://huggingface.co" )
| 68 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> list:
'''simple docstring'''
A__ = [0] * len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
# use last results for better performance - dynamic programming
A__ = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
A__ = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
A__ = j
return prefix_result
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> int:
'''simple docstring'''
return max(prefix_function(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 68 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class a__ :
"""simple docstring"""
__lowerCamelCase = BlenderbotSmallConfig
__lowerCamelCase = {}
__lowerCamelCase = 'gelu'
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=32 , lowercase=2 , lowercase=4 , lowercase=37 , lowercase=0.1 , lowercase=0.1 , lowercase=20 , lowercase=2 , lowercase=1 , lowercase=0 , ) -> Any:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = eos_token_id
A__ = pad_token_id
A__ = bos_token_id
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A__ = tf.concat([input_ids, eos_tensor] , axis=1 )
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
A__ = prepare_blenderbot_small_inputs_dict(lowercase , lowercase , lowercase )
return config, inputs_dict
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = TFBlenderbotSmallModel(config=lowercase ).get_decoder()
A__ = inputs_dict["input_ids"]
A__ = input_ids[:1, :]
A__ = inputs_dict["attention_mask"][:1, :]
A__ = inputs_dict["head_mask"]
A__ = 1
# first forward pass
A__ = model(lowercase , attention_mask=lowercase , head_mask=lowercase , use_cache=lowercase )
A__ , A__ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
A__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
A__ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
A__ = tf.concat([input_ids, next_tokens] , axis=-1 )
A__ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
A__ = model(lowercase , attention_mask=lowercase )[0]
A__ = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
A__ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
A__ = output_from_no_past[:, -3:, random_slice_idx]
A__ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase , lowercase , rtol=1e-3 )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Optional[Any]=None , SCREAMING_SNAKE_CASE_: Optional[int]=None , SCREAMING_SNAKE_CASE_: Optional[int]=None , SCREAMING_SNAKE_CASE_: Dict=None , SCREAMING_SNAKE_CASE_: List[str]=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
A__ = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A__ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
A__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
A__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
__lowerCamelCase = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase = (
{
'conversational': TFBlenderbotSmallForConditionalGeneration,
'feature-extraction': TFBlenderbotSmallModel,
'summarization': TFBlenderbotSmallForConditionalGeneration,
'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
'translation': TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = TFBlenderbotSmallModelTester(self )
A__ = ConfigTester(self , config_class=lowercase )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase )
@require_tokenizers
@require_tf
class a__ ( unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = [
'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
' i\'m going to throw up.\nand why is that?'
]
__lowerCamelCase = 'facebook/blenderbot_small-90M'
@cached_property
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
@cached_property
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.tokenizer(self.src_text , return_tensors="tf" )
A__ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=lowercase , )
A__ = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowercase )[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 68 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: List[str] ) -> Tuple:
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: List[str]=0 ) -> Union[str, Any]:
'''simple docstring'''
return sorted(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : x[column] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: Dict=float("inf" ) ) -> List[Any]:
'''simple docstring'''
for i in range(points_counts - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE_ ):
A__ = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
A__ = current_dis
return min_dis
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: List[str]=float("inf" ) ) -> int:
'''simple docstring'''
for i in range(min(6 , points_counts - 1 ) , SCREAMING_SNAKE_CASE_ ):
for j in range(max(0 , i - 6 ) , SCREAMING_SNAKE_CASE_ ):
A__ = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
A__ = current_dis
return min_dis
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Dict:
'''simple docstring'''
if points_counts <= 3:
return dis_between_closest_pair(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# recursion
A__ = points_counts // 2
A__ = closest_pair_of_points_sqr(
SCREAMING_SNAKE_CASE_ , points_sorted_on_y[:mid] , SCREAMING_SNAKE_CASE_ )
A__ = closest_pair_of_points_sqr(
SCREAMING_SNAKE_CASE_ , points_sorted_on_y[mid:] , points_counts - mid )
A__ = min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A__ = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(SCREAMING_SNAKE_CASE_ )
A__ = dis_between_closest_in_strip(
SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
return min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> Optional[int]:
'''simple docstring'''
A__ = column_based_sort(SCREAMING_SNAKE_CASE_ , column=0 )
A__ = column_based_sort(SCREAMING_SNAKE_CASE_ , column=1 )
return (
closest_pair_of_points_sqr(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
) ** 0.5
if __name__ == "__main__":
lowerCAmelCase__ = [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points)))
| 68 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ['pixel_values']
def __init__( self , lowercase = True , lowercase = None , lowercase = PILImageResampling.BICUBIC , lowercase = True , lowercase = 1 / 255 , lowercase = True , lowercase = None , lowercase = None , lowercase = True , **lowercase , ) -> None:
'''simple docstring'''
super().__init__(**lowercase )
A__ = size if size is not None else {"height": 384, "width": 384}
A__ = get_size_dict(lowercase , default_to_square=lowercase )
A__ = do_resize
A__ = size
A__ = resample
A__ = do_rescale
A__ = rescale_factor
A__ = do_normalize
A__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A__ = image_std if image_std is not None else OPENAI_CLIP_STD
A__ = do_convert_rgb
def UpperCamelCase ( self , lowercase , lowercase , lowercase = PILImageResampling.BICUBIC , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
A__ = get_size_dict(lowercase , default_to_square=lowercase )
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()}' )
A__ = (size["height"], size["width"])
return resize(lowercase , size=lowercase , resample=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase = None , **lowercase , ) -> Optional[Any]:
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ) -> PIL.Image.Image:
'''simple docstring'''
A__ = do_resize if do_resize is not None else self.do_resize
A__ = resample if resample is not None else self.resample
A__ = do_rescale if do_rescale is not None else self.do_rescale
A__ = rescale_factor if rescale_factor is not None else self.rescale_factor
A__ = do_normalize if do_normalize is not None else self.do_normalize
A__ = image_mean if image_mean is not None else self.image_mean
A__ = image_std if image_std is not None else self.image_std
A__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A__ = size if size is not None else self.size
A__ = get_size_dict(lowercase , default_to_square=lowercase )
A__ = make_list_of_images(lowercase )
if not valid_images(lowercase ):
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 or resample is None:
raise ValueError("Size and resample 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_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A__ = [convert_to_rgb(lowercase ) for image in images]
# All transformations expect numpy arrays.
A__ = [to_numpy_array(lowercase ) for image in images]
if do_resize:
A__ = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_rescale:
A__ = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
A__ = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
A__ = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
A__ = BatchFeature(data={"pixel_values": images} , tensor_type=lowercase )
return encoded_outputs
| 68 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0_0 ) -> int:
'''simple docstring'''
A__ = 2**power
A__ = str(SCREAMING_SNAKE_CASE_ )
A__ = list(SCREAMING_SNAKE_CASE_ )
A__ = 0
for i in list_num:
sum_of_num += int(SCREAMING_SNAKE_CASE_ )
return sum_of_num
if __name__ == "__main__":
lowerCAmelCase__ = int(input("""Enter the power of 2: """).strip())
print("""2 ^ """, power, """ = """, 2**power)
lowerCAmelCase__ = solution(power)
print("""Sum of the digits is: """, result)
| 68 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
lowerCAmelCase__ = """hf-internal-testing/tiny-random-bert"""
lowerCAmelCase__ = os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
lowerCAmelCase__ = """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = cached_file(lowercase , lowercase )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(lowercase ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(lowercase , lowercase ) ) )
with open(os.path.join(lowercase , "refs" , "main" ) ) as f:
A__ = f.read()
self.assertEqual(lowercase , os.path.join(lowercase , "snapshots" , lowercase , lowercase ) )
self.assertTrue(os.path.isfile(lowercase ) )
# File is cached at the same place the second time.
A__ = cached_file(lowercase , lowercase )
self.assertEqual(lowercase , lowercase )
# Using a specific revision to test the full commit hash.
A__ = cached_file(lowercase , lowercase , revision="9b8c223" )
self.assertEqual(lowercase , os.path.join(lowercase , "snapshots" , lowercase , lowercase ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaisesRegex(lowercase , "is not a valid model identifier" ):
A__ = cached_file("tiny-random-bert" , lowercase )
with self.assertRaisesRegex(lowercase , "is not a valid git identifier" ):
A__ = cached_file(lowercase , lowercase , revision="aaaa" )
with self.assertRaisesRegex(lowercase , "does not appear to have a file named" ):
A__ = cached_file(lowercase , "conf" )
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(lowercase , "does not appear to have a file named" ):
A__ = cached_file(lowercase , "conf" )
with open(os.path.join(lowercase , "refs" , "main" ) ) as f:
A__ = f.read()
self.assertTrue(os.path.isfile(os.path.join(lowercase , ".no_exist" , lowercase , "conf" ) ) )
A__ = cached_file(lowercase , "conf" , _raise_exceptions_for_missing_entries=lowercase )
self.assertIsNone(lowercase )
A__ = cached_file(lowercase , "conf" , local_files_only=lowercase , _raise_exceptions_for_missing_entries=lowercase )
self.assertIsNone(lowercase )
A__ = mock.Mock()
A__ = 500
A__ = {}
A__ = HTTPError
A__ = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" , return_value=lowercase ) as mock_head:
A__ = cached_file(lowercase , "conf" , _raise_exceptions_for_connection_errors=lowercase )
self.assertIsNone(lowercase )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
self.assertIsNone(get_file_from_repo("bert-base-cased" , "ahah.txt" ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(lowercase , "is not a valid model identifier" ):
get_file_from_repo("bert-base-case" , lowercase )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(lowercase , "is not a valid git identifier" ):
get_file_from_repo("bert-base-cased" , lowercase , revision="ahaha" )
A__ = get_file_from_repo("bert-base-cased" , lowercase )
# The name is the cached name which is not very easy to test, so instead we load the content.
A__ = json.loads(open(lowercase , "r" ).read() )
self.assertEqual(config["hidden_size"] , 768 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
A__ = Path(lowercase ) / "a.txt"
filename.touch()
self.assertEqual(get_file_from_repo(lowercase , "a.txt" ) , str(lowercase ) )
self.assertIsNone(get_file_from_repo(lowercase , "b.txt" ) )
| 68 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_0_0 * 2**2_0, 9_0_0 * 2**2_0] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Dict ) -> List[str]:
'''simple docstring'''
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , "IN_MEMORY_MAX_SIZE" , SCREAMING_SNAKE_CASE_ )
A__ = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
A__ = dataset_size < in_memory_max_size
else:
A__ = False
A__ = is_small_dataset(SCREAMING_SNAKE_CASE_ )
assert result == expected
| 68 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = AutoencoderKL
__lowerCamelCase = 'sample'
__lowerCamelCase = 1e-2
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = 4
A__ = 3
A__ = (32, 32)
A__ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase )
return {"sample": image}
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
A__ = self.dummy_input
return init_dict, inputs_dict
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skipIf(torch_device == "mps" , "Gradient checkpointing skipped on MPS" )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ , A__ = self.prepare_init_args_and_inputs_for_common()
A__ = self.model_class(**lowercase )
model.to(lowercase )
assert not model.is_gradient_checkpointing and model.training
A__ = model(**lowercase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
A__ = torch.randn_like(lowercase )
A__ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
A__ = self.model_class(**lowercase )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
A__ = model_a(**lowercase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
A__ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
A__ = dict(model.named_parameters() )
A__ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ , A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(lowercase )
A__ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" )
A__ = model.to(lowercase )
model.eval()
if torch_device == "mps":
A__ = torch.manual_seed(0 )
else:
A__ = torch.Generator(device=lowercase ).manual_seed(0 )
A__ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
A__ = image.to(lowercase )
with torch.no_grad():
A__ = model(lowercase , sample_posterior=lowercase , generator=lowercase ).sample
A__ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
A__ = torch.tensor(
[
-4.00_78e-01,
-3.83_23e-04,
-1.26_81e-01,
-1.14_62e-01,
2.00_95e-01,
1.08_93e-01,
-8.82_47e-02,
-3.03_61e-01,
-9.86_44e-03,
] )
elif torch_device == "cpu":
A__ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
A__ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase , lowercase , rtol=1e-2 ) )
@slow
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
return F'gaussian_noise_s={seed}_shape={"_".join([str(lowercase ) for s in shape] )}.npy'
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self , lowercase=0 , lowercase=(4, 3, 512, 512) , lowercase=False ) -> Optional[int]:
'''simple docstring'''
A__ = torch.floataa if fpaa else torch.floataa
A__ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase , lowercase ) ) ).to(lowercase ).to(lowercase )
return image
def UpperCamelCase ( self , lowercase="CompVis/stable-diffusion-v1-4" , lowercase=False ) -> Any:
'''simple docstring'''
A__ = "fp16" if fpaa else None
A__ = torch.floataa if fpaa else torch.floataa
A__ = AutoencoderKL.from_pretrained(
lowercase , subfolder="vae" , torch_dtype=lowercase , revision=lowercase , )
model.to(lowercase ).eval()
return model
def UpperCamelCase ( self , lowercase=0 ) -> List[str]:
'''simple docstring'''
if torch_device == "mps":
return torch.manual_seed(lowercase )
return torch.Generator(device=lowercase ).manual_seed(lowercase )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowercase , lowercase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , fpaa=lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Dict:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
with torch.no_grad():
A__ = model(lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowercase , lowercase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
A__ = sample[-1, -2:, :2, -2:].flatten().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def UpperCamelCase ( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase , lowercase , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model.encode(lowercase ).latent_dist
A__ = dist.sample(generator=lowercase )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
A__ = sample[0, -1, -3:, -3:].flatten().cpu()
A__ = torch.tensor(lowercase )
A__ = 3e-3 if torch_device != "mps" else 1e-2
assert torch_all_close(lowercase , lowercase , atol=lowercase )
| 68 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[dict, list, tuple, torch.Tensor] ) -> List[Tuple[int, ...]]:
'''simple docstring'''
A__ = []
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
for v in tree.values():
shapes.extend(_fetch_dims(SCREAMING_SNAKE_CASE_ ) )
elif isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(SCREAMING_SNAKE_CASE_ ) )
elif isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError("Not supported" )
return shapes
@torch.jit.ignore
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Tuple[int, ...] ) -> Tuple[int, ...]:
'''simple docstring'''
A__ = []
for d in reversed(SCREAMING_SNAKE_CASE_ ):
idx.append(flat_idx % d )
A__ = flat_idx // d
return tuple(reversed(SCREAMING_SNAKE_CASE_ ) )
@torch.jit.ignore
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Sequence[int] , SCREAMING_SNAKE_CASE_: Sequence[int] , SCREAMING_SNAKE_CASE_: Sequence[int] , SCREAMING_SNAKE_CASE_: Optional[Sequence[bool]] = None , SCREAMING_SNAKE_CASE_: Optional[Sequence[bool]] = None , ) -> List[Tuple[slice, ...]]:
'''simple docstring'''
def reduce_edge_list(SCREAMING_SNAKE_CASE_: List[bool] ) -> None:
A__ = True
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
A__ = -1 * (i + 1)
l[reversed_idx] &= tally
A__ = l[reversed_idx]
if start_edges is None:
A__ = [s == 0 for s in start]
reduce_edge_list(SCREAMING_SNAKE_CASE_ )
if end_edges is None:
A__ = [e == (d - 1) for e, d in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )]
reduce_edge_list(SCREAMING_SNAKE_CASE_ )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return [()]
elif len(SCREAMING_SNAKE_CASE_ ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
A__ = []
A__ = []
# Dimensions common to start and end can be selected directly
for s, e in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if s == e:
path_list.append(slice(SCREAMING_SNAKE_CASE_ , s + 1 ) )
else:
break
A__ = tuple(SCREAMING_SNAKE_CASE_ )
A__ = len(SCREAMING_SNAKE_CASE_ )
# start == end, and we're done
if divergence_idx == len(SCREAMING_SNAKE_CASE_ ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
A__ = start[divergence_idx]
return tuple(
path + (slice(SCREAMING_SNAKE_CASE_ , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
A__ = end[divergence_idx]
return tuple(
path + (slice(SCREAMING_SNAKE_CASE_ , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
A__ = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: torch.Tensor , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> torch.Tensor:
'''simple docstring'''
A__ = t.shape[:no_batch_dims]
A__ = list(_flat_idx_to_idx(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# _get_minimal_slice_set is inclusive
A__ = list(_flat_idx_to_idx(flat_end - 1 , SCREAMING_SNAKE_CASE_ ) )
# Get an ordered list of slices to perform
A__ = _get_minimal_slice_set(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
A__ = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Callable , SCREAMING_SNAKE_CASE_: Dict[str, Any] , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: bool = False , SCREAMING_SNAKE_CASE_: Any = None , SCREAMING_SNAKE_CASE_: bool = False , ) -> Any:
'''simple docstring'''
if not (len(SCREAMING_SNAKE_CASE_ ) > 0):
raise ValueError("Must provide at least one input" )
A__ = [shape[:no_batch_dims] for shape in _fetch_dims(SCREAMING_SNAKE_CASE_ )]
A__ = tuple([max(SCREAMING_SNAKE_CASE_ ) for s in zip(*SCREAMING_SNAKE_CASE_ )] )
def _prep_inputs(SCREAMING_SNAKE_CASE_: torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
A__ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
A__ = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
A__ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
A__ = tensor_tree_map(_prep_inputs , SCREAMING_SNAKE_CASE_ )
A__ = None
if _out is not None:
A__ = tensor_tree_map(lambda SCREAMING_SNAKE_CASE_ : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
A__ = 1
for d in orig_batch_dims:
flat_batch_dim *= d
A__ = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(SCREAMING_SNAKE_CASE_: torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
A__ = 0
A__ = prepped_outputs
for _ in range(SCREAMING_SNAKE_CASE_ ):
# Chunk the input
if not low_mem:
A__ = _select_chunk
else:
A__ = partial(
_chunk_slice , flat_start=SCREAMING_SNAKE_CASE_ , flat_end=min(SCREAMING_SNAKE_CASE_ , i + chunk_size ) , no_batch_dims=len(SCREAMING_SNAKE_CASE_ ) , )
A__ = tensor_tree_map(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Run the layer on the chunk
A__ = layer(**SCREAMING_SNAKE_CASE_ )
# Allocate space for the output
if out is None:
A__ = tensor_tree_map(lambda SCREAMING_SNAKE_CASE_ : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , SCREAMING_SNAKE_CASE_ )
# Put the chunk in its pre-allocated space
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
def assign(SCREAMING_SNAKE_CASE_: dict , SCREAMING_SNAKE_CASE_: dict ) -> None:
for k, v in da.items():
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
assign(SCREAMING_SNAKE_CASE_ , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
A__ = da[k]
assign(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
for xa, xa in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
A__ = xa
elif isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
A__ = output_chunk
else:
raise ValueError("Not supported" )
i += chunk_size
A__ = tensor_tree_map(lambda SCREAMING_SNAKE_CASE_ : t.view(orig_batch_dims + t.shape[1:] ) , SCREAMING_SNAKE_CASE_ )
return out
class a__ :
"""simple docstring"""
def __init__( self , lowercase = 512 , ) -> Any:
'''simple docstring'''
A__ = max_chunk_size
A__ = None
A__ = None
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
logging.info("Tuning chunk size..." )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
A__ = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
A__ = [c for c in candidates if c > min_chunk_size]
A__ = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(lowercase ) -> bool:
try:
with torch.no_grad():
fn(*lowercase , chunk_size=lowercase )
return True
except RuntimeError:
return False
A__ = 0
A__ = len(lowercase ) - 1
while i > min_viable_chunk_size_index:
A__ = test_chunk_size(candidates[i] )
if not viable:
A__ = (min_viable_chunk_size_index + i) // 2
else:
A__ = i
A__ = (i + len(lowercase ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def UpperCamelCase ( self , lowercase , lowercase ) -> bool:
'''simple docstring'''
A__ = True
for aa, aa in zip(lowercase , lowercase ):
assert type(lowercase ) == type(lowercase )
if isinstance(lowercase , (list, tuple) ):
consistent &= self._compare_arg_caches(lowercase , lowercase )
elif isinstance(lowercase , lowercase ):
A__ = [v for _, v in sorted(aa.items() , key=lambda lowercase : x[0] )]
A__ = [v for _, v in sorted(aa.items() , key=lambda lowercase : x[0] )]
consistent &= self._compare_arg_caches(lowercase , lowercase )
else:
consistent &= aa == aa
return consistent
def UpperCamelCase ( self , lowercase , lowercase , lowercase , ) -> int:
'''simple docstring'''
A__ = True
A__ = tree_map(lambda lowercase : a.shape if isinstance(lowercase , torch.Tensor ) else a , lowercase , lowercase )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(lowercase )
A__ = self._compare_arg_caches(self.cached_arg_data , lowercase )
else:
# Otherwise, we can reuse the precomputed value
A__ = False
if not consistent:
A__ = self._determine_favorable_chunk_size(
lowercase , lowercase , lowercase , )
A__ = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 68 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase__ = logging.getLogger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = label_idx
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
A__ = []
A__ = []
for line in f:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
A__ = []
A__ = []
else:
A__ = line.split(" " )
words.append(splits[0] )
if len(lowercase ) > 1:
labels.append(splits[self.label_idx].replace("\n" , "" ) )
else:
# Examples could have no label for mode = "test"
labels.append("O" )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
writer.write(lowercase )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
A__ = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(lowercase )
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0] )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(label_idx=-2 )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class a__ ( snake_case ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
for sentence in parse_incr(lowercase ):
A__ = []
A__ = []
for token in sentence:
words.append(token["form"] )
labels.append(token["upos"] )
assert len(lowercase ) == len(lowercase )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = 0
for sentence in parse_incr(lowercase ):
A__ = preds_list[example_id]
A__ = ""
for token in sentence:
out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '
out += "\n"
writer.write(lowercase )
example_id += 1
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 68 | 1 |
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
lowerCAmelCase__ = """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 lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Optional[Any]=None ) -> Tuple:
'''simple docstring'''
require_version(deps[pkg] , SCREAMING_SNAKE_CASE_ )
| 68 |
import random
class a__ :
"""simple docstring"""
@staticmethod
def UpperCamelCase ( lowercase ) -> tuple[list[int], list[int]]:
'''simple docstring'''
A__ = [ord(lowercase ) for i in text]
A__ = []
A__ = []
for i in plain:
A__ = random.randint(1 , 300 )
A__ = (i + k) * k
cipher.append(lowercase )
key.append(lowercase )
return cipher, key
@staticmethod
def UpperCamelCase ( lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = []
for i in range(len(lowercase ) ):
A__ = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowercase ) )
return "".join(lowercase )
if __name__ == "__main__":
lowerCAmelCase__ , lowerCAmelCase__ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 68 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
lowerCAmelCase__ = random.Random()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: Optional[int]=1.0 , SCREAMING_SNAKE_CASE_: Tuple=None , SCREAMING_SNAKE_CASE_: Dict=None ) -> List[Any]:
'''simple docstring'''
if rng is None:
A__ = global_rng
A__ = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowercase , lowercase=7 , lowercase=400 , lowercase=2000 , lowercase=1 , lowercase=0.0 , lowercase=16000 , lowercase=True , lowercase=80 , lowercase=16 , lowercase=64 , lowercase="hann_window" , lowercase=80 , lowercase=7600 , lowercase=1e-10 , lowercase=True , ) -> Union[str, Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = min_seq_length
A__ = max_seq_length
A__ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
A__ = feature_size
A__ = padding_value
A__ = sampling_rate
A__ = do_normalize
A__ = num_mel_bins
A__ = hop_length
A__ = win_length
A__ = win_function
A__ = fmin
A__ = fmax
A__ = mel_floor
A__ = return_attention_mask
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def UpperCamelCase ( self , lowercase=False , lowercase=False ) -> Union[str, Any]:
'''simple docstring'''
def _flatten(lowercase ):
return list(itertools.chain(*lowercase ) )
if equal_length:
A__ = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
A__ = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
A__ = [np.asarray(lowercase ) for x in speech_inputs]
return speech_inputs
def UpperCamelCase ( self , lowercase=False , lowercase=False ) -> Union[str, Any]:
'''simple docstring'''
if equal_length:
A__ = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
A__ = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
A__ = [np.asarray(lowercase ) for x in speech_inputs]
return speech_inputs
@require_torch
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = SpeechTaFeatureExtractor
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = SpeechTaFeatureExtractionTester(self )
def UpperCamelCase ( self , lowercase ) -> Dict:
'''simple docstring'''
self.assertTrue(np.all(np.mean(lowercase , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowercase , axis=0 ) - 1 ) < 1e-3 ) )
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = [np.asarray(lowercase ) for speech_input in speech_inputs]
# Test not batched input
A__ = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
A__ = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
# Test batched
A__ = feat_extract(lowercase , return_tensors="np" ).input_values
A__ = feat_extract(lowercase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase ):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = ["longest", "max_length", "do_not_pad"]
A__ = [None, 1600, None]
for max_length, padding in zip(lowercase , lowercase ):
A__ = feat_extract(lowercase , padding=lowercase , max_length=lowercase , return_tensors="np" )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = range(800 , 1400 , 200 )
A__ = [floats_list((1, x) )[0] for x in lengths]
A__ = ["longest", "max_length", "do_not_pad"]
A__ = [None, 1600, None]
for max_length, padding in zip(lowercase , lowercase ):
A__ = feat_extract(lowercase , max_length=lowercase , padding=lowercase )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = feat_extract(
lowercase , truncation=lowercase , max_length=1000 , padding="max_length" , return_tensors="np" )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = feat_extract(
lowercase , truncation=lowercase , max_length=1000 , padding="longest" , return_tensors="np" )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = feat_extract(
lowercase , truncation=lowercase , max_length=2000 , padding="longest" , return_tensors="np" )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = np.random.rand(100 ).astype(np.floataa )
A__ = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
A__ = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
A__ = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = [np.asarray(lowercase ) for speech_input in speech_inputs]
# Test feature size
A__ = feature_extractor(audio_target=lowercase , padding=lowercase , return_tensors="np" ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
A__ = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_values
A__ = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
# Test batched
A__ = feature_extractor(lowercase , return_tensors="np" ).input_values
A__ = feature_extractor(lowercase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase ):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
A__ = [floats_list((1, x) )[0] for x in (800, 800, 800)]
A__ = np.asarray(lowercase )
A__ = feature_extractor(lowercase , return_tensors="np" ).input_values
A__ = feature_extractor(lowercase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase ):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.feat_extract_tester.prepare_inputs_for_target()
A__ = self.feature_extraction_class(**self.feat_extract_dict )
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(lowercase ) == len(lowercase ) for x, y in zip(lowercase , processed_features[input_name] ) ) )
A__ = self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase )
A__ = BatchFeature({input_name: speech_inputs} , tensor_type="np" )
A__ = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
A__ = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase )
A__ = self.feature_extraction_class(**self.feat_extract_dict )
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} , tensor_type="pt" )
A__ = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
A__ = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_dict )
A__ = self.feat_extract_tester.prepare_inputs_for_target()
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} )
A__ = feat_extract.num_mel_bins # hack!
A__ = feat_extract.pad(lowercase , padding="longest" , return_tensors="np" )[input_name]
A__ = feat_extract.pad(lowercase , padding="longest" , return_tensors="pt" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.feat_extract_dict
A__ = True
A__ = self.feature_extraction_class(**lowercase )
A__ = self.feat_extract_tester.prepare_inputs_for_target()
A__ = [len(lowercase ) for x in speech_inputs]
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} )
A__ = feat_extract.num_mel_bins # hack!
A__ = feat_extract.pad(lowercase , padding="longest" , return_tensors="np" )
self.assertIn("attention_mask" , lowercase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , lowercase )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.feat_extract_dict
A__ = True
A__ = self.feature_extraction_class(**lowercase )
A__ = self.feat_extract_tester.prepare_inputs_for_target()
A__ = [len(lowercase ) for x in speech_inputs]
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} )
A__ = min(lowercase )
A__ = feat_extract.num_mel_bins # hack!
A__ = feat_extract.pad(
lowercase , padding="max_length" , max_length=lowercase , truncation=lowercase , return_tensors="np" )
self.assertIn("attention_mask" , lowercase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
from datasets import load_dataset
A__ = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
A__ = ds.sort("id" ).select(range(lowercase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = torch.tensor(
[2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03] )
# fmt: on
A__ = self._load_datasamples(1 )
A__ = SpeechTaFeatureExtractor()
A__ = feature_extractor(lowercase , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 93680) )
self.assertTrue(torch.allclose(input_values[0, :30] , lowercase , atol=1e-6 ) )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
A__ = self._load_datasamples(1 )
A__ = SpeechTaFeatureExtractor()
A__ = feature_extractor(audio_target=lowercase , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , lowercase , atol=1e-4 ) )
| 68 |
def lowerCAmelCase__ ( ) -> Any:
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simple docstring'''
A__ = 1
A__ = 2
while i * i <= n:
A__ = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def lowerCAmelCase__ ( ) -> Dict:
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(SCREAMING_SNAKE_CASE_ ) > 5_0_0 )
if __name__ == "__main__":
print(solution())
| 68 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, 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_mobilenet_va import MobileNetVaConfig
lowerCAmelCase__ = logging.get_logger(__name__)
# General docstring
lowerCAmelCase__ = """MobileNetV1Config"""
# Base docstring
lowerCAmelCase__ = """google/mobilenet_v1_1.0_224"""
lowerCAmelCase__ = [1, 1_0_2_4, 7, 7]
# Image classification docstring
lowerCAmelCase__ = """google/mobilenet_v1_1.0_224"""
lowerCAmelCase__ = """tabby, tabby cat"""
lowerCAmelCase__ = [
"""google/mobilenet_v1_1.0_224""",
"""google/mobilenet_v1_0.75_192""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: Optional[Any]=None ) -> Dict:
'''simple docstring'''
A__ = {}
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__ = model.mobilenet_va
else:
A__ = model
A__ = "MobilenetV1/Conv2d_0/"
A__ = backbone.conv_stem.convolution.weight
A__ = backbone.conv_stem.normalization.bias
A__ = backbone.conv_stem.normalization.weight
A__ = backbone.conv_stem.normalization.running_mean
A__ = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
A__ = i + 1
A__ = i * 2
A__ = backbone.layer[pt_index]
A__ = F'MobilenetV1/Conv2d_{tf_index}_depthwise/'
A__ = pointer.convolution.weight
A__ = pointer.normalization.bias
A__ = pointer.normalization.weight
A__ = pointer.normalization.running_mean
A__ = pointer.normalization.running_var
A__ = backbone.layer[pt_index + 1]
A__ = F'MobilenetV1/Conv2d_{tf_index}_pointwise/'
A__ = pointer.convolution.weight
A__ = pointer.normalization.bias
A__ = pointer.normalization.weight
A__ = pointer.normalization.running_mean
A__ = pointer.normalization.running_var
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__ = "MobilenetV1/Logits/Conv2d_1c_1x1/"
A__ = model.classifier.weight
A__ = model.classifier.bias
return tf_to_pt_map
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: str ) -> Union[str, Any]:
'''simple docstring'''
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions." )
raise
# Load weights from TF model
A__ = tf.train.list_variables(SCREAMING_SNAKE_CASE_ )
A__ = {}
for name, shape in init_vars:
logger.info(F'Loading TF weight {name} with shape {shape}' )
A__ = tf.train.load_variable(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A__ = array
# Build TF to PyTorch weights loading map
A__ = _build_tf_to_pytorch_map(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for name, pointer in tf_to_pt_map.items():
logger.info(F'Importing {name}' )
if name not in tf_weights:
logger.info(F'{name} not in tf pre-trained weights, skipping' )
continue
A__ = tf_weights[name]
if "depthwise_weights" in name:
logger.info("Transposing depthwise" )
A__ = np.transpose(SCREAMING_SNAKE_CASE_ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info("Transposing" )
if len(pointer.shape ) == 2: # copying into linear layer
A__ = array.squeeze().transpose()
else:
A__ = np.transpose(SCREAMING_SNAKE_CASE_ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'Pointer shape {pointer.shape} and array shape {array.shape} mismatched' )
logger.info(F'Initialize PyTorch weight {name} {array.shape}' )
A__ = torch.from_numpy(SCREAMING_SNAKE_CASE_ )
tf_weights.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
tf_weights.pop(name + "/RMSProp" , SCREAMING_SNAKE_CASE_ )
tf_weights.pop(name + "/RMSProp_1" , SCREAMING_SNAKE_CASE_ )
tf_weights.pop(name + "/ExponentialMovingAverage" , SCREAMING_SNAKE_CASE_ )
logger.info(F'Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}' )
return model
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: torch.Tensor , SCREAMING_SNAKE_CASE_: nn.Convad ) -> torch.Tensor:
'''simple docstring'''
A__ , A__ = features.shape[-2:]
A__ , A__ = conv_layer.stride
A__ , A__ = conv_layer.kernel_size
if in_height % stride_height == 0:
A__ = max(kernel_height - stride_height , 0 )
else:
A__ = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
A__ = max(kernel_width - stride_width , 0 )
else:
A__ = max(kernel_width - (in_width % stride_width) , 0 )
A__ = pad_along_width // 2
A__ = pad_along_width - pad_left
A__ = pad_along_height // 2
A__ = pad_along_height - pad_top
A__ = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , "constant" , 0.0 )
class a__ ( nn.Module ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase = 1 , lowercase = 1 , lowercase = False , lowercase = True , lowercase = True , ) -> None:
'''simple docstring'''
super().__init__()
A__ = config
if in_channels % groups != 0:
raise ValueError(F'Input channels ({in_channels}) are not divisible by {groups} groups.' )
if out_channels % groups != 0:
raise ValueError(F'Output channels ({out_channels}) are not divisible by {groups} groups.' )
A__ = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
A__ = nn.Convad(
in_channels=lowercase , out_channels=lowercase , kernel_size=lowercase , stride=lowercase , padding=lowercase , groups=lowercase , bias=lowercase , padding_mode="zeros" , )
if use_normalization:
A__ = nn.BatchNormad(
num_features=lowercase , eps=config.layer_norm_eps , momentum=0.9997 , affine=lowercase , track_running_stats=lowercase , )
else:
A__ = None
if use_activation:
if isinstance(lowercase , lowercase ):
A__ = ACTaFN[use_activation]
elif isinstance(config.hidden_act , lowercase ):
A__ = ACTaFN[config.hidden_act]
else:
A__ = config.hidden_act
else:
A__ = None
def UpperCamelCase ( self , lowercase ) -> torch.Tensor:
'''simple docstring'''
if self.config.tf_padding:
A__ = apply_tf_padding(lowercase , self.convolution )
A__ = self.convolution(lowercase )
if self.normalization is not None:
A__ = self.normalization(lowercase )
if self.activation is not None:
A__ = self.activation(lowercase )
return features
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = MobileNetVaConfig
__lowerCamelCase = load_tf_weights_in_mobilenet_va
__lowerCamelCase = 'mobilenet_v1'
__lowerCamelCase = 'pixel_values'
__lowerCamelCase = False
def UpperCamelCase ( self , lowercase ) -> None:
'''simple docstring'''
if isinstance(lowercase , (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(lowercase , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
lowerCAmelCase__ = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): 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.
"""
lowerCAmelCase__ = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.' , snake_case , )
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase , lowercase = True ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(lowercase )
A__ = config
A__ = 32
A__ = max(int(depth * config.depth_multiplier ) , config.min_depth )
A__ = MobileNetVaConvLayer(
lowercase , in_channels=config.num_channels , out_channels=lowercase , kernel_size=3 , stride=2 , )
A__ = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
A__ = nn.ModuleList()
for i in range(13 ):
A__ = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
A__ = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
lowercase , in_channels=lowercase , out_channels=lowercase , kernel_size=3 , stride=strides[i] , groups=lowercase , ) )
self.layer.append(
MobileNetVaConvLayer(
lowercase , in_channels=lowercase , out_channels=lowercase , kernel_size=1 , ) )
A__ = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
raise NotImplementedError
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowercase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCamelCase ( self , lowercase = None , lowercase = None , lowercase = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
'''simple docstring'''
A__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
A__ = 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" )
A__ = self.conv_stem(lowercase )
A__ = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
A__ = layer_module(lowercase )
if output_hidden_states:
A__ = all_hidden_states + (hidden_states,)
A__ = hidden_states
if self.pooler is not None:
A__ = torch.flatten(self.pooler(lowercase ) , start_dim=1 )
else:
A__ = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowercase , pooler_output=lowercase , hidden_states=lowercase , )
@add_start_docstrings(
'\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , snake_case , )
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase ) -> None:
'''simple docstring'''
super().__init__(lowercase )
A__ = config.num_labels
A__ = MobileNetVaModel(lowercase )
A__ = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
A__ = nn.Dropout(config.classifier_dropout_prob , inplace=lowercase )
A__ = nn.Linear(lowercase , 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(lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCamelCase ( self , lowercase = None , lowercase = None , lowercase = None , lowercase = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
'''simple docstring'''
A__ = return_dict if return_dict is not None else self.config.use_return_dict
A__ = self.mobilenet_va(lowercase , output_hidden_states=lowercase , return_dict=lowercase )
A__ = outputs.pooler_output if return_dict else outputs[1]
A__ = self.classifier(self.dropout(lowercase ) )
A__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
A__ = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
A__ = "single_label_classification"
else:
A__ = "multi_label_classification"
if self.config.problem_type == "regression":
A__ = MSELoss()
if self.num_labels == 1:
A__ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
A__ = loss_fct(lowercase , lowercase )
elif self.config.problem_type == "single_label_classification":
A__ = CrossEntropyLoss()
A__ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
A__ = BCEWithLogitsLoss()
A__ = loss_fct(lowercase , lowercase )
if not return_dict:
A__ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=lowercase , logits=lowercase , hidden_states=outputs.hidden_states , )
| 68 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCase__ = get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
lowerCAmelCase__ = json.load(f)
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return FSMTTokenizer.from_pretrained(lowercase )
def UpperCamelCase ( self , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = FSMTForConditionalGeneration.from_pretrained(lowercase ).to(lowercase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = F'facebook/wmt19-{pair}'
A__ = self.get_tokenizer(lowercase )
A__ = self.get_model(lowercase )
A__ = bleu_data[pair]["src"]
A__ = bleu_data[pair]["tgt"]
A__ = tokenizer(lowercase , return_tensors="pt" , truncation=lowercase , padding="longest" ).to(lowercase )
A__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
A__ = tokenizer.batch_decode(
lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase )
A__ = calculate_bleu(lowercase , lowercase )
print(lowercase )
self.assertGreaterEqual(scores["bleu"] , lowercase )
| 68 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
"""google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
"""google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'big_bird'
def __init__( self , lowercase=50358 , lowercase=768 , lowercase=12 , lowercase=12 , lowercase=3072 , lowercase="gelu_new" , lowercase=0.1 , lowercase=0.1 , lowercase=4096 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=True , lowercase=0 , lowercase=1 , lowercase=2 , lowercase=66 , lowercase="block_sparse" , lowercase=True , lowercase=False , lowercase=64 , lowercase=3 , lowercase=None , **lowercase , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , sep_token_id=lowercase , **lowercase , )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = initializer_range
A__ = type_vocab_size
A__ = layer_norm_eps
A__ = use_cache
A__ = rescale_embeddings
A__ = attention_type
A__ = use_bias
A__ = block_size
A__ = num_random_blocks
A__ = classifier_dropout
class a__ ( snake_case ):
"""simple docstring"""
@property
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
A__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 68 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> list:
'''simple docstring'''
A__ = int(SCREAMING_SNAKE_CASE_ )
if n_element < 1:
A__ = ValueError("a should be a positive number" )
raise my_error
A__ = [1]
A__ , A__ , A__ = (0, 0, 0)
A__ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
lowerCAmelCase__ = input("""Enter the last number (nth term) of the Hamming Number Series: """)
print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
lowerCAmelCase__ = hamming(int(n))
print("""-----------------------------------------------------""")
print(f"""The list with nth numbers is: {hamming_numbers}""")
print("""-----------------------------------------------------""")
| 68 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ['image_processor', 'tokenizer']
__lowerCamelCase = 'ViTImageProcessor'
__lowerCamelCase = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , lowercase=None , lowercase=None , **lowercase ) -> List[Any]:
'''simple docstring'''
A__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , lowercase , )
A__ = kwargs.pop("feature_extractor" )
A__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(lowercase , lowercase )
def __call__( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , **lowercase ) -> Any:
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError("You have to specify either text, visual prompt or images." )
if text is not None and visual_prompt is not None:
raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt." )
if text is not None:
A__ = self.tokenizer(lowercase , return_tensors=lowercase , **lowercase )
if visual_prompt is not None:
A__ = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if images is not None:
A__ = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if visual_prompt is not None and images is not None:
A__ = {
"pixel_values": image_features.pixel_values,
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
A__ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
A__ = {
"conditional_pixel_values": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def UpperCamelCase ( self , *lowercase , **lowercase ) -> List[str]:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def UpperCamelCase ( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , lowercase , )
return self.image_processor_class
@property
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , lowercase , )
return self.image_processor
| 68 |
import copy
import random
from transformers import CLIPTokenizer
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowercase , **lowercase )
A__ = {}
def UpperCamelCase ( self , lowercase , *lowercase , **lowercase ) -> str:
'''simple docstring'''
A__ = super().add_tokens(lowercase , *lowercase , **lowercase )
if num_added_tokens == 0:
raise ValueError(
F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
" `placeholder_token` that is not already in the tokenizer." )
def UpperCamelCase ( self , lowercase , *lowercase , lowercase=1 , **lowercase ) -> Any:
'''simple docstring'''
A__ = []
if num_vec_per_token == 1:
self.try_adding_tokens(lowercase , *lowercase , **lowercase )
output.append(lowercase )
else:
A__ = []
for i in range(lowercase ):
A__ = placeholder_token + F'_{i}'
self.try_adding_tokens(lowercase , *lowercase , **lowercase )
output.append(lowercase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F'The tokenizer already has placeholder token {token} that can get confused with'
F' {placeholder_token}keep placeholder tokens independent' )
A__ = output
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=1.0 ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = []
for i in range(len(lowercase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=lowercase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
A__ = self.token_map[placeholder_token]
A__ = tokens[: 1 + int(len(lowercase ) * prop_tokens_to_load )]
if vector_shuffle:
A__ = copy.copy(lowercase )
random.shuffle(lowercase )
A__ = text.replace(lowercase , " ".join(lowercase ) )
return text
def __call__( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase ) -> str:
'''simple docstring'''
return super().__call__(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase ) , *lowercase , **lowercase , )
def UpperCamelCase ( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase ) -> List[str]:
'''simple docstring'''
return super().encode(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase ) , *lowercase , **lowercase , )
| 68 | 1 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = AutoencoderKL
__lowerCamelCase = 'sample'
__lowerCamelCase = 1e-2
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = 4
A__ = 3
A__ = (32, 32)
A__ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase )
return {"sample": image}
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
A__ = self.dummy_input
return init_dict, inputs_dict
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skipIf(torch_device == "mps" , "Gradient checkpointing skipped on MPS" )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ , A__ = self.prepare_init_args_and_inputs_for_common()
A__ = self.model_class(**lowercase )
model.to(lowercase )
assert not model.is_gradient_checkpointing and model.training
A__ = model(**lowercase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
A__ = torch.randn_like(lowercase )
A__ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
A__ = self.model_class(**lowercase )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
A__ = model_a(**lowercase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
A__ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
A__ = dict(model.named_parameters() )
A__ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ , A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(lowercase )
A__ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" )
A__ = model.to(lowercase )
model.eval()
if torch_device == "mps":
A__ = torch.manual_seed(0 )
else:
A__ = torch.Generator(device=lowercase ).manual_seed(0 )
A__ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
A__ = image.to(lowercase )
with torch.no_grad():
A__ = model(lowercase , sample_posterior=lowercase , generator=lowercase ).sample
A__ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
A__ = torch.tensor(
[
-4.00_78e-01,
-3.83_23e-04,
-1.26_81e-01,
-1.14_62e-01,
2.00_95e-01,
1.08_93e-01,
-8.82_47e-02,
-3.03_61e-01,
-9.86_44e-03,
] )
elif torch_device == "cpu":
A__ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
A__ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase , lowercase , rtol=1e-2 ) )
@slow
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
return F'gaussian_noise_s={seed}_shape={"_".join([str(lowercase ) for s in shape] )}.npy'
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self , lowercase=0 , lowercase=(4, 3, 512, 512) , lowercase=False ) -> Optional[int]:
'''simple docstring'''
A__ = torch.floataa if fpaa else torch.floataa
A__ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase , lowercase ) ) ).to(lowercase ).to(lowercase )
return image
def UpperCamelCase ( self , lowercase="CompVis/stable-diffusion-v1-4" , lowercase=False ) -> Any:
'''simple docstring'''
A__ = "fp16" if fpaa else None
A__ = torch.floataa if fpaa else torch.floataa
A__ = AutoencoderKL.from_pretrained(
lowercase , subfolder="vae" , torch_dtype=lowercase , revision=lowercase , )
model.to(lowercase ).eval()
return model
def UpperCamelCase ( self , lowercase=0 ) -> List[str]:
'''simple docstring'''
if torch_device == "mps":
return torch.manual_seed(lowercase )
return torch.Generator(device=lowercase ).manual_seed(lowercase )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowercase , lowercase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , fpaa=lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Dict:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
with torch.no_grad():
A__ = model(lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowercase , lowercase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
A__ = sample[-1, -2:, :2, -2:].flatten().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def UpperCamelCase ( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase , lowercase , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model.encode(lowercase ).latent_dist
A__ = dist.sample(generator=lowercase )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
A__ = sample[0, -1, -3:, -3:].flatten().cpu()
A__ = torch.tensor(lowercase )
A__ = 3e-3 if torch_device != "mps" else 1e-2
assert torch_all_close(lowercase , lowercase , atol=lowercase )
| 68 |
from collections import deque
from math import floor
from random import random
from time import time
class a__ :
"""simple docstring"""
def __init__( self ) -> Dict:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase , lowercase=1 ) -> Tuple:
'''simple docstring'''
if self.graph.get(lowercase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
A__ = [[w, v]]
if not self.graph.get(lowercase ):
A__ = []
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return list(self.graph )
def UpperCamelCase ( self , lowercase , lowercase ) -> int:
'''simple docstring'''
if self.graph.get(lowercase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Any:
'''simple docstring'''
if s == d:
return []
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return visited
def UpperCamelCase ( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
if c == -1:
A__ = floor(random() * 10000 ) + 10
for i in range(lowercase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A__ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase , lowercase , 1 )
def UpperCamelCase ( self , lowercase=-2 ) -> Any:
'''simple docstring'''
A__ = deque()
A__ = []
if s == -2:
A__ = list(self.graph )[0]
d.append(lowercase )
visited.append(lowercase )
while d:
A__ = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return len(self.graph[u] )
def UpperCamelCase ( self , lowercase=-2 ) -> str:
'''simple docstring'''
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
A__ = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return sorted_nodes
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return list(lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return False
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Any:
'''simple docstring'''
A__ = time()
self.dfs(lowercase , lowercase )
A__ = time()
return end - begin
def UpperCamelCase ( self , lowercase=-2 ) -> int:
'''simple docstring'''
A__ = time()
self.bfs(lowercase )
A__ = time()
return end - begin
class a__ :
"""simple docstring"""
def __init__( self ) -> int:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase , lowercase=1 ) -> Union[str, Any]:
'''simple docstring'''
if self.graph.get(lowercase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
A__ = [[w, v]]
# add the other way
if self.graph.get(lowercase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
A__ = [[w, u]]
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
if self.graph.get(lowercase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase )
# the other way round
if self.graph.get(lowercase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowercase )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> List[str]:
'''simple docstring'''
if s == d:
return []
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return visited
def UpperCamelCase ( self , lowercase=-1 ) -> str:
'''simple docstring'''
if c == -1:
A__ = floor(random() * 10000 ) + 10
for i in range(lowercase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A__ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase , lowercase , 1 )
def UpperCamelCase ( self , lowercase=-2 ) -> Dict:
'''simple docstring'''
A__ = deque()
A__ = []
if s == -2:
A__ = list(self.graph )[0]
d.append(lowercase )
visited.append(lowercase )
while d:
A__ = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
return len(self.graph[u] )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return list(lowercase )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return False
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
return list(self.graph )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = time()
self.dfs(lowercase , lowercase )
A__ = time()
return end - begin
def UpperCamelCase ( self , lowercase=-2 ) -> List[Any]:
'''simple docstring'''
A__ = time()
self.bfs(lowercase )
A__ = time()
return end - begin
| 68 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import 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_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Tuple:
'''simple docstring'''
A__ = b.T
A__ = np.sum(np.square(SCREAMING_SNAKE_CASE_ ) , axis=1 )
A__ = np.sum(np.square(SCREAMING_SNAKE_CASE_ ) , axis=0 )
A__ = np.matmul(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A__ = aa[:, None] - 2 * ab + ba[None, :]
return d
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> Dict:
'''simple docstring'''
A__ = x.reshape(-1 , 3 )
A__ = squared_euclidean_distance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return np.argmin(SCREAMING_SNAKE_CASE_ , axis=1 )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ['pixel_values']
def __init__( self , lowercase = None , lowercase = True , lowercase = None , lowercase = PILImageResampling.BILINEAR , lowercase = True , lowercase = True , **lowercase , ) -> None:
'''simple docstring'''
super().__init__(**lowercase )
A__ = size if size is not None else {"height": 256, "width": 256}
A__ = get_size_dict(lowercase )
A__ = np.array(lowercase ) if clusters is not None else None
A__ = do_resize
A__ = size
A__ = resample
A__ = do_normalize
A__ = do_color_quantize
def UpperCamelCase ( self , lowercase , lowercase , lowercase = PILImageResampling.BILINEAR , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
A__ = get_size_dict(lowercase )
if "height" not in size or "width" not in size:
raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
lowercase , size=(size["height"], size["width"]) , resample=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , ) -> np.ndarray:
'''simple docstring'''
A__ = rescale(image=lowercase , scale=1 / 127.5 , data_format=lowercase )
A__ = image - 1
return image
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ) -> PIL.Image.Image:
'''simple docstring'''
A__ = do_resize if do_resize is not None else self.do_resize
A__ = size if size is not None else self.size
A__ = get_size_dict(lowercase )
A__ = resample if resample is not None else self.resample
A__ = do_normalize if do_normalize is not None else self.do_normalize
A__ = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
A__ = clusters if clusters is not None else self.clusters
A__ = np.array(lowercase )
A__ = make_list_of_images(lowercase )
if not valid_images(lowercase ):
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 or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
A__ = [to_numpy_array(lowercase ) for image in images]
if do_resize:
A__ = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_normalize:
A__ = [self.normalize(image=lowercase ) for image in images]
if do_color_quantize:
A__ = [to_channel_dimension_format(lowercase , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
A__ = np.array(lowercase )
A__ = color_quantize(lowercase , lowercase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
A__ = images.shape[0]
A__ = images.reshape(lowercase , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
A__ = list(lowercase )
else:
A__ = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
A__ = {"input_ids": images}
return BatchFeature(data=lowercase , tensor_type=lowercase )
| 68 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
"""
lowerCAmelCase__ = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""
lowerCAmelCase__ = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the CUAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'exact_match': Exact match (the normalized answer exactly match the gold answer)
'f1': The F-score of predicted tokens versus the gold answer
'aupr': Area Under the Precision-Recall curve
'prec_at_80_recall': Precision at 80% recall
'prec_at_90_recall': Precision at 90% recall
Examples:
>>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> cuad_metric = datasets.load_metric(\"cuad\")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def UpperCamelCase ( self , lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
A__ = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
A__ = evaluate(dataset=lowercase , predictions=lowercase )
return score
| 68 | 1 |
import requests
lowerCAmelCase__ = """YOUR API KEY"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str = giphy_api_key ) -> list:
'''simple docstring'''
A__ = "+".join(query.split() )
A__ = F'https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'
A__ = requests.get(SCREAMING_SNAKE_CASE_ ).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("""\n""".join(get_gifs("""space ship""")))
| 68 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
A__ = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
A__ = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
return torch.mm(SCREAMING_SNAKE_CASE_ , normalized_text_embeds.t() )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = CLIPConfig
__lowerCamelCase = ['CLIPEncoderLayer']
def __init__( self , lowercase ) -> Optional[int]:
'''simple docstring'''
super().__init__(lowercase )
A__ = CLIPVisionModel(config.vision_config )
A__ = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=lowercase )
A__ = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(17 ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(3 ) , requires_grad=lowercase )
@torch.no_grad()
def UpperCamelCase ( self , lowercase , lowercase ) -> Any:
'''simple docstring'''
A__ = self.vision_model(lowercase )[1] # pooled_output
A__ = self.visual_projection(lowercase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
A__ = cosine_distance(lowercase , self.special_care_embeds ).cpu().float().numpy()
A__ = cosine_distance(lowercase , self.concept_embeds ).cpu().float().numpy()
A__ = []
A__ = image_embeds.shape[0]
for i in range(lowercase ):
A__ = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
A__ = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
A__ = special_cos_dist[i][concept_idx]
A__ = self.special_care_embeds_weights[concept_idx].item()
A__ = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]} )
A__ = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
A__ = cos_dist[i][concept_idx]
A__ = self.concept_embeds_weights[concept_idx].item()
A__ = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(lowercase )
result.append(lowercase )
A__ = [len(res["bad_concepts"] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCamelCase ( self , lowercase , lowercase ) -> Any:
'''simple docstring'''
A__ = self.vision_model(lowercase )[1] # pooled_output
A__ = self.visual_projection(lowercase )
A__ = cosine_distance(lowercase , self.special_care_embeds )
A__ = cosine_distance(lowercase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
A__ = 0.0
A__ = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
A__ = torch.any(special_scores > 0 , dim=1 )
A__ = special_care * 0.01
A__ = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
A__ = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
A__ = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 68 | 1 |
import warnings
from functools import wraps
from typing import Callable
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Callable ) -> Callable:
'''simple docstring'''
@wraps(SCREAMING_SNAKE_CASE_ )
def _inner_fn(*SCREAMING_SNAKE_CASE_: int , **SCREAMING_SNAKE_CASE_: Union[str, Any] ):
warnings.warn(
(F'\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.') , SCREAMING_SNAKE_CASE_ , )
return fn(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
return _inner_fn
| 68 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 68 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ComputeEnvironment.AMAZON_SAGEMAKER
__lowerCamelCase = True
__lowerCamelCase = 'ml.p3.2xlarge'
__lowerCamelCase = 'accelerate_sagemaker_execution_role'
__lowerCamelCase = 'hf-sm'
__lowerCamelCase = 'us-east-1'
__lowerCamelCase = 1
__lowerCamelCase = 'accelerate-sagemaker-1'
__lowerCamelCase = '1.6'
__lowerCamelCase = '4.4'
__lowerCamelCase = 'train.py'
__lowerCamelCase = [
'--model_name_or_path',
'bert',
'--do_train',
'False',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
__lowerCamelCase = [
'--model_name_or_path',
'bert',
'--do_train',
'--do_test',
'False',
'--do_predict',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args["model_name_or_path"] , lowercase )
assert isinstance(converted_args["do_train"] , lowercase )
assert isinstance(converted_args["epochs"] , lowercase )
assert isinstance(converted_args["learning_rate"] , lowercase )
assert isinstance(converted_args["max_steps"] , lowercase )
with pytest.raises(lowercase ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 68 |
import string
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> None:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
A__ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
A__ = string.ascii_uppercase.find(SCREAMING_SNAKE_CASE_ )
A__ = num - key
if num < 0:
A__ = num + len(string.ascii_uppercase )
A__ = translated + string.ascii_uppercase[num]
else:
A__ = translated + symbol
print(F'Decryption using Key #{key}: {translated}' )
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
A__ = input("Encrypted message: " )
A__ = message.upper()
decrypt(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 68 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = ReformerTokenizer
__lowerCamelCase = ReformerTokenizerFast
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = True
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
super().setUp()
A__ = ReformerTokenizer(lowercase , keep_accents=lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = "<s>"
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(lowercase ) , 1000 )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer()
A__ = "I was born in 92000, and this is falsé."
A__ = tokenizer.tokenize(lowercase )
A__ = rust_tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
A__ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A__ = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
A__ = self.get_rust_tokenizer()
A__ = tokenizer.encode(lowercase )
A__ = rust_tokenizer.encode(lowercase )
self.assertListEqual(lowercase , lowercase )
def UpperCamelCase ( self , lowercase=15 ) -> Any:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
A__ = self.rust_tokenizer_class.from_pretrained(lowercase , **lowercase )
# Simple input
A__ = "This is a simple input"
A__ = ["This is a simple input 1", "This is a simple input 2"]
A__ = ("This is a simple input", "This is a pair")
A__ = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowercase , tokenizer_r.encode , lowercase , max_length=lowercase , padding="max_length" )
# Simple input
self.assertRaises(lowercase , tokenizer_r.encode_plus , lowercase , max_length=lowercase , padding="max_length" )
# Simple input
self.assertRaises(
lowercase , tokenizer_r.batch_encode_plus , lowercase , max_length=lowercase , padding="max_length" , )
# Pair input
self.assertRaises(lowercase , tokenizer_r.encode , lowercase , max_length=lowercase , padding="max_length" )
# Pair input
self.assertRaises(lowercase , tokenizer_r.encode_plus , lowercase , max_length=lowercase , padding="max_length" )
# Pair input
self.assertRaises(
lowercase , tokenizer_r.batch_encode_plus , lowercase , max_length=lowercase , padding="max_length" , )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = ReformerTokenizer(lowercase , keep_accents=lowercase )
A__ = tokenizer.tokenize("This is a test" )
self.assertListEqual(lowercase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [285, 46, 10, 170, 382] , )
A__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [
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",
".",
] , )
A__ = tokenizer.convert_tokens_to_ids(lowercase )
self.assertListEqual(
lowercase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
A__ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [
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>",
".",
] , )
@cached_property
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment" )
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = "Hello World!"
A__ = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(lowercase , self.big_tokenizer.encode(lowercase ) )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
A__ = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(lowercase , self.big_tokenizer.encode(lowercase ) )
@require_torch
@slow
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
A__ = list(self.big_tokenizer.get_vocab().keys() )[:10]
A__ = " ".join(lowercase )
A__ = self.big_tokenizer.encode_plus(lowercase , return_tensors="pt" )
A__ = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="pt" )
A__ = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
A__ = encoded_sequence["input_ids"].shape
A__ = ReformerModel(lowercase )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**lowercase )
model(**lowercase )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = {"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "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]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
A__ = [
"This is a very simple sentence.",
"The quick brown fox jumps over the lazy dog.",
]
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name="google/reformer-crime-and-punishment" , revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a" , padding=lowercase , sequences=lowercase , )
| 68 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = SpeechTaTokenizer
__lowerCamelCase = False
__lowerCamelCase = True
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
A__ = SpeechTaTokenizer(lowercase )
A__ = AddedToken("<mask>" , lstrip=lowercase , rstrip=lowercase )
A__ = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = "this is a test"
A__ = "this is a test"
return input_text, output_text
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=20 , lowercase=5 ) -> Optional[Any]:
'''simple docstring'''
A__ , A__ = self.get_input_output_texts(lowercase )
A__ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A__ = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
return text, ids
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = "<pad>"
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-4] , "œ" )
self.assertEqual(vocab_keys[-2] , "<mask>" )
self.assertEqual(vocab_keys[-1] , "<ctc_blank>" )
self.assertEqual(len(lowercase ) , 81 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.get_tokenizers(do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
A__ = tokenizer.add_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size + len(lowercase ) )
A__ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
A__ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
A__ = tokenizer.add_special_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size_a + len(lowercase ) )
A__ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.get_tokenizer()
A__ = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(lowercase , [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
A__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
A__ = tokenizer.convert_tokens_to_ids(lowercase )
# fmt: off
self.assertListEqual(lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
A__ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
A__ = {
"input_ids": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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, 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, 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],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name="microsoft/speecht5_asr" , revision="c5ef64c71905caeccde0e4462ef3f9077224c524" , sequences=lowercase , )
| 68 | 1 |
lowerCAmelCase__ = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
A__ = [False] * len(SCREAMING_SNAKE_CASE_ )
A__ = [s]
A__ = True
while queue:
A__ = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(SCREAMING_SNAKE_CASE_ )
A__ = True
A__ = u
return visited[t]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Any ) -> Optional[Any]:
'''simple docstring'''
A__ = [-1] * (len(SCREAMING_SNAKE_CASE_ ))
A__ = 0
A__ = []
A__ = [i[:] for i in graph] # Record original cut, copy.
while bfs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__ = float("Inf" )
A__ = sink
while s != source:
# Find the minimum value in select path
A__ = min(SCREAMING_SNAKE_CASE_ , graph[parent[s]][s] )
A__ = parent[s]
max_flow += path_flow
A__ = sink
while v != source:
A__ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
A__ = parent[v]
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 68 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> List[str]:
'''simple docstring'''
A__ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
A__ = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
A__ = F'{src_lang}-{tgt_lang}'
A__ = F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
A__ = os.path.join(SCREAMING_SNAKE_CASE_ , "README.md" )
print(F'Generating {path}' )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
# make sure we are under the root of the project
lowerCAmelCase__ = Path(__file__).resolve().parent.parent.parent
lowerCAmelCase__ = repo_dir / """model_cards"""
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = model_name.split("""-""")
lowerCAmelCase__ = model_cards_dir / """facebook""" / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 68 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> Optional[int]:
'''simple docstring'''
A__ = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> Union[str, Any]:
'''simple docstring'''
A__ = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
A__ = s_dict.pop(SCREAMING_SNAKE_CASE_ )
elif "subsample" in key:
A__ = s_dict.pop(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Union[str, Any]:
'''simple docstring'''
A__ , A__ = emb.weight.shape
A__ = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
A__ = emb.weight.data
return lin_layer
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: List[str] ) -> str:
'''simple docstring'''
A__ = torch.load(SCREAMING_SNAKE_CASE_ , map_location="cpu" )
A__ = mam_aaa["args"]
A__ = mam_aaa["model"]
A__ = state_dict["decoder.output_projection.weight"]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
rename_keys(SCREAMING_SNAKE_CASE_ )
A__ = state_dict["decoder.embed_tokens.weight"].shape[0]
A__ = args.share_decoder_input_output_embed
A__ = [int(SCREAMING_SNAKE_CASE_ ) for i in args.conv_kernel_sizes.split("," )]
A__ = SpeechaTextConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="relu" , num_conv_layers=len(SCREAMING_SNAKE_CASE_ ) , conv_channels=args.conv_channels , conv_kernel_sizes=SCREAMING_SNAKE_CASE_ , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , num_beams=5 , max_length=2_0_0 , use_cache=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=2 , early_stopping=SCREAMING_SNAKE_CASE_ , )
A__ = SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
A__ , A__ = model.model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0 and not set(SCREAMING_SNAKE_CASE_ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"
F' but all the following weights are missing {missing}' )
if tie_embeds:
A__ = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
A__ = lm_head_weights
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
lowerCAmelCase__ = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 68 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = feature_size
A__ = sampling_rate
A__ = padding_value
A__ = kwargs.pop("padding_side" , "right" )
A__ = kwargs.pop("return_attention_mask" , lowercase )
super().__init__(**lowercase )
def UpperCamelCase ( self , lowercase , lowercase = True , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , ) -> BatchFeature:
'''simple docstring'''
if isinstance(lowercase , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
A__ = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
F' to this method that includes {self.model_input_names[0]}, but you provided'
F' {list(processed_features.keys() )}' )
A__ = processed_features[self.model_input_names[0]]
A__ = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(lowercase ) == 0:
if return_attention_mask:
A__ = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
A__ = required_input[0]
if isinstance(lowercase , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
A__ = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(lowercase ):
A__ = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(lowercase ):
A__ = "tf"
elif is_torch_tensor(lowercase ):
A__ = "pt"
elif isinstance(lowercase , (int, float, list, tuple, np.ndarray) ):
A__ = "np"
else:
raise ValueError(
F'type of {first_element} unknown: {type(lowercase )}. '
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
A__ = to_numpy(lowercase )
else:
A__ = [to_numpy(lowercase ) for v in value]
# Convert padding_strategy in PaddingStrategy
A__ = self._get_padding_strategies(padding=lowercase , max_length=lowercase )
A__ = processed_features[self.model_input_names[0]]
A__ = len(lowercase )
if not all(len(lowercase ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
A__ = []
for i in range(lowercase ):
A__ = {k: v[i] for k, v in processed_features.items()}
# truncation
A__ = self._truncate(
lowercase , max_length=lowercase , pad_to_multiple_of=lowercase , truncation=lowercase , )
truncated_inputs.append(lowercase )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
A__ = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
A__ = PaddingStrategy.MAX_LENGTH
A__ = {}
for i in range(lowercase ):
# padding
A__ = self._pad(
truncated_inputs[i] , max_length=lowercase , padding_strategy=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , )
for key, value in outputs.items():
if key not in batch_outputs:
A__ = []
if value.dtype is np.dtype(np.floataa ):
A__ = value.astype(np.floataa )
batch_outputs[key].append(lowercase )
return BatchFeature(lowercase , tensor_type=lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = PaddingStrategy.DO_NOT_PAD , lowercase = None , lowercase = None , ) -> dict:
'''simple docstring'''
A__ = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
A__ = len(lowercase )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
A__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
A__ = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
A__ = np.ones(len(lowercase ) , dtype=np.intaa )
if needs_to_be_padded:
A__ = max_length - len(lowercase )
if self.padding_side == "right":
if return_attention_mask:
A__ = np.pad(
processed_features["attention_mask"] , (0, difference) )
A__ = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
A__ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
A__ = np.pad(
processed_features["attention_mask"] , (difference, 0) )
A__ = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
A__ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , ) -> Union[str, Any]:
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
A__ = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
A__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
A__ = len(lowercase ) > max_length
if needs_to_be_truncated:
A__ = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
A__ = processed_features["attention_mask"][:max_length]
return processed_features
def UpperCamelCase ( self , lowercase=False , lowercase=None ) -> Any:
'''simple docstring'''
if padding is not False:
if padding is True:
A__ = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(lowercase , lowercase ):
A__ = PaddingStrategy(lowercase )
elif isinstance(lowercase , lowercase ):
A__ = padding
else:
A__ = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 68 | 1 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> List[Any]:
'''simple docstring'''
return EnvironmentCommand()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> Tuple:
'''simple docstring'''
return EnvironmentCommand(args.accelerate_config_file )
class a__ ( snake_case ):
"""simple docstring"""
@staticmethod
def UpperCamelCase ( lowercase ) -> List[Any]:
'''simple docstring'''
A__ = parser.add_parser("env" )
download_parser.set_defaults(func=lowercase )
download_parser.add_argument(
"--accelerate-config_file" , default=lowercase , help="The accelerate config file to use for the default values in the launching script." , )
download_parser.set_defaults(func=lowercase )
def __init__( self , lowercase , *lowercase ) -> None:
'''simple docstring'''
A__ = accelerate_config_file
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = "not installed"
if is_safetensors_available():
import safetensors
A__ = safetensors.__version__
elif importlib.util.find_spec("safetensors" ) is not None:
import safetensors
A__ = F'{safetensors.__version__} but is ignored because of PyTorch version too old.'
A__ = "not installed"
A__ = A__ = "not found"
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
A__ = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(lowercase ):
A__ = load_config_from_file(self._accelerate_config_file ).to_dict()
A__ = (
"\n".join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()] )
if isinstance(lowercase , lowercase )
else F'\t{accelerate_config}'
)
A__ = "not installed"
A__ = "NA"
if is_torch_available():
import torch
A__ = torch.__version__
A__ = torch.cuda.is_available()
A__ = "not installed"
A__ = "NA"
if is_tf_available():
import tensorflow as tf
A__ = tf.__version__
try:
# deprecated in v2.1
A__ = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
A__ = bool(tf.config.list_physical_devices("GPU" ) )
A__ = "not installed"
A__ = "not installed"
A__ = "not installed"
A__ = "NA"
if is_flax_available():
import flax
import jax
import jaxlib
A__ = flax.__version__
A__ = jax.__version__
A__ = jaxlib.__version__
A__ = jax.lib.xla_bridge.get_backend().platform
A__ = {
"`transformers` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Huggingface_hub version": huggingface_hub.__version__,
"Safetensors version": F'{safetensors_version}',
"Accelerate version": F'{accelerate_version}',
"Accelerate config": F'{accelerate_config_str}',
"PyTorch version (GPU?)": F'{pt_version} ({pt_cuda_available})',
"Tensorflow version (GPU?)": F'{tf_version} ({tf_cuda_available})',
"Flax version (CPU?/GPU?/TPU?)": F'{flax_version} ({jax_backend})',
"Jax version": F'{jax_version}',
"JaxLib version": F'{jaxlib_version}',
"Using GPU in script?": "<fill in>",
"Using distributed or parallel set-up in script?": "<fill in>",
}
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
print(self.format_dict(lowercase ) )
return info
@staticmethod
def UpperCamelCase ( lowercase ) -> Optional[int]:
'''simple docstring'''
return "\n".join([F'- {prop}: {val}' for prop, val in d.items()] ) + "\n"
| 68 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
"""GroupViTTextConfig""",
"""GroupViTVisionConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GroupViTModel""",
"""GroupViTPreTrainedModel""",
"""GroupViTTextModel""",
"""GroupViTVisionModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFGroupViTModel""",
"""TFGroupViTPreTrainedModel""",
"""TFGroupViTTextModel""",
"""TFGroupViTVisionModel""",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 | 1 |
from itertools import count
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 5_0 ) -> int:
'''simple docstring'''
A__ = [1] * min_block_length
for n in count(SCREAMING_SNAKE_CASE_ ):
fill_count_functions.append(1 )
for block_length in range(SCREAMING_SNAKE_CASE_ , n + 1 ):
for block_start in range(n - block_length ):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 1_0_0_0_0_0_0:
break
return n
if __name__ == "__main__":
print(f"""{solution() = }""")
| 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'gpt_neox_japanese'
def __init__( self , lowercase=32000 , lowercase=2560 , lowercase=32 , lowercase=32 , lowercase=4 , lowercase="gelu" , lowercase=1.00 , lowercase=10000 , lowercase=2048 , lowercase=0.02 , lowercase=1e-5 , lowercase=True , lowercase=31996 , lowercase=31999 , lowercase=0.1 , lowercase=0.0 , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_multiple_size
A__ = hidden_act
A__ = rotary_pct
A__ = rotary_emb_base
A__ = initializer_range
A__ = layer_norm_eps
A__ = use_cache
A__ = attention_dropout
A__ = hidden_dropout
| 68 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
lowerCAmelCase__ = """3"""
print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
print("""Torch version:""", None)
try:
import deepspeed
print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
print("""DeepSpeed version:""", None)
try:
import tensorflow as tf
print("""TensorFlow version:""", tf.__version__)
print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
print("""TensorFlow version:""", None)
| 68 |
import warnings
from functools import wraps
from typing import Callable
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Callable ) -> Callable:
'''simple docstring'''
@wraps(SCREAMING_SNAKE_CASE_ )
def _inner_fn(*SCREAMING_SNAKE_CASE_: int , **SCREAMING_SNAKE_CASE_: Union[str, Any] ):
warnings.warn(
(F'\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.') , SCREAMING_SNAKE_CASE_ , )
return fn(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
return _inner_fn
| 68 | 1 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""nielsr/canine-s""": 2_0_4_8,
}
# Unicode defines 1,114,112 total “codepoints”
lowerCAmelCase__ = 1_1_1_4_1_1_2
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0xe0_00
lowerCAmelCase__ = 0xe0_01
lowerCAmelCase__ = 0xe0_02
lowerCAmelCase__ = 0xe0_03
lowerCAmelCase__ = 0xe0_04
# Maps special codepoints to human-readable names.
lowerCAmelCase__ = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
lowerCAmelCase__ = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=False , lowercase=2048 , **lowercase , ) -> List[Any]:
'''simple docstring'''
A__ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else bos_token
A__ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else eos_token
A__ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else sep_token
A__ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else cls_token
A__ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
A__ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else mask_token
super().__init__(
bos_token=lowercase , eos_token=lowercase , sep_token=lowercase , cls_token=lowercase , pad_token=lowercase , mask_token=lowercase , add_prefix_space=lowercase , model_max_length=lowercase , **lowercase , )
# Creates a mapping for looking up the IDs of special symbols.
A__ = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
A__ = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
A__ = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
A__ = UNICODE_VOCAB_SIZE
A__ = len(self._special_codepoints )
@property
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return self._unicode_vocab_size
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
return list(lowercase )
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
try:
return ord(lowercase )
except TypeError:
raise ValueError(F'invalid token: \'{token}\'' )
def UpperCamelCase ( self , lowercase ) -> str:
'''simple docstring'''
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(lowercase )
except TypeError:
raise ValueError(F'invalid id: {index}' )
def UpperCamelCase ( self , lowercase ) -> Dict:
'''simple docstring'''
return "".join(lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None ) -> List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [self.cls_token_id]
A__ = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
A__ = [1] + ([0] * len(lowercase )) + [1]
if token_ids_a is not None:
result += ([0] * len(lowercase )) + [1]
return result
def UpperCamelCase ( self , lowercase , lowercase = None ) -> List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [self.cls_token_id]
A__ = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def UpperCamelCase ( self , lowercase , lowercase = None ) -> Union[str, Any]:
'''simple docstring'''
return ()
| 68 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
lowerCAmelCase__ = """\
Text data.
Second line of data."""
lowerCAmelCase__ = """file"""
@pytest.fixture(scope="session" )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> Optional[int]:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / (FILE_PATH + ".zstd")
A__ = bytes(SCREAMING_SNAKE_CASE_ , "utf-8" )
with zstd.open(SCREAMING_SNAKE_CASE_ , "wb" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any ) -> List[str]:
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , SCREAMING_SNAKE_CASE_ ) , "w" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return FILE_PATH
@pytest.mark.parametrize("compression_format" , ["gzip", "xz", "zstd"] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: int ) -> Any:
'''simple docstring'''
A__ = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
A__ = input_paths[compression_format]
A__ = tmp_path / "cache"
A__ = DownloadConfig(cache_dir=SCREAMING_SNAKE_CASE_ , extract_compressed_file=SCREAMING_SNAKE_CASE_ )
A__ = cached_path(SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("default_extracted" , [True, False] )
@pytest.mark.parametrize("default_cache_dir" , [True, False] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: str ) -> Dict:
'''simple docstring'''
A__ = "custom_cache"
A__ = "custom_extracted_dir"
A__ = tmp_path / "custom_extracted_path"
if default_extracted:
A__ = ("downloads" if default_cache_dir else custom_cache_dir, "extracted")
else:
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR" , SCREAMING_SNAKE_CASE_ )
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH" , str(SCREAMING_SNAKE_CASE_ ) )
A__ = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
A__ = xz_file
A__ = (
DownloadConfig(extract_compressed_file=SCREAMING_SNAKE_CASE_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=SCREAMING_SNAKE_CASE_ )
)
A__ = cached_path(SCREAMING_SNAKE_CASE_ , download_config=SCREAMING_SNAKE_CASE_ )
assert Path(SCREAMING_SNAKE_CASE_ ).parent.parts[-2:] == expected
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> Optional[int]:
'''simple docstring'''
A__ = str(Path(SCREAMING_SNAKE_CASE_ ).resolve() )
assert cached_path(SCREAMING_SNAKE_CASE_ ) == text_file
# relative path
A__ = str(Path(SCREAMING_SNAKE_CASE_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(SCREAMING_SNAKE_CASE_ ) == text_file
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[str]:
'''simple docstring'''
A__ = str(tmp_path.resolve() / "__missing_file__.txt" )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path(SCREAMING_SNAKE_CASE_ )
# relative path
A__ = "./__missing_file__.txt"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> Union[str, Any]:
'''simple docstring'''
A__ = get_from_cache(F'tmp://{tmpfs_file}' )
with open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read()
assert output_file_content == FILE_CONTENT
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( ) -> List[Any]:
'''simple docstring'''
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
cached_path("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> int:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_get("https://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[Any]:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
ftp_get("ftp://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
ftp_head("ftp://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> str:
'''simple docstring'''
A__ = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
fsspec_get("s3://huggingface.co" , temp_file=SCREAMING_SNAKE_CASE_ )
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
fsspec_head("s3://huggingface.co" )
| 68 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
"""xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
"""xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
"""xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
"""xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
"""xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
"""xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
"""xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
"""xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
"""xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'xlm'
__lowerCamelCase = {
'hidden_size': 'emb_dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
'n_words': 'vocab_size', # For backward compatibility
}
def __init__( self , lowercase=30145 , lowercase=2048 , lowercase=12 , lowercase=16 , lowercase=0.1 , lowercase=0.1 , lowercase=True , lowercase=False , lowercase=False , lowercase=False , lowercase=1 , lowercase=True , lowercase=512 , lowercase=2048**-0.5 , lowercase=1e-12 , lowercase=0.02 , lowercase=0 , lowercase=1 , lowercase=2 , lowercase=3 , lowercase=5 , lowercase=True , lowercase="first" , lowercase=True , lowercase=None , lowercase=True , lowercase=0.1 , lowercase=5 , lowercase=5 , lowercase=0 , lowercase=0 , lowercase=2 , lowercase=0 , **lowercase , ) -> Optional[int]:
'''simple docstring'''
A__ = vocab_size
A__ = emb_dim
A__ = n_layers
A__ = n_heads
A__ = dropout
A__ = attention_dropout
A__ = gelu_activation
A__ = sinusoidal_embeddings
A__ = causal
A__ = asm
A__ = n_langs
A__ = use_lang_emb
A__ = layer_norm_eps
A__ = bos_index
A__ = eos_index
A__ = pad_index
A__ = unk_index
A__ = mask_index
A__ = is_encoder
A__ = max_position_embeddings
A__ = embed_init_std
A__ = init_std
A__ = summary_type
A__ = summary_use_proj
A__ = summary_activation
A__ = summary_proj_to_labels
A__ = summary_first_dropout
A__ = start_n_top
A__ = end_n_top
A__ = mask_token_id
A__ = lang_id
if "n_words" in kwargs:
A__ = kwargs["n_words"]
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , **lowercase )
class a__ ( snake_case ):
"""simple docstring"""
@property
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
A__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 68 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class a__ :
"""simple docstring"""
__lowerCamelCase = BlenderbotSmallConfig
__lowerCamelCase = {}
__lowerCamelCase = 'gelu'
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=32 , lowercase=2 , lowercase=4 , lowercase=37 , lowercase=0.1 , lowercase=0.1 , lowercase=20 , lowercase=2 , lowercase=1 , lowercase=0 , ) -> Any:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = eos_token_id
A__ = pad_token_id
A__ = bos_token_id
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
A__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
A__ = tf.concat([input_ids, eos_tensor] , axis=1 )
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
A__ = prepare_blenderbot_small_inputs_dict(lowercase , lowercase , lowercase )
return config, inputs_dict
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = TFBlenderbotSmallModel(config=lowercase ).get_decoder()
A__ = inputs_dict["input_ids"]
A__ = input_ids[:1, :]
A__ = inputs_dict["attention_mask"][:1, :]
A__ = inputs_dict["head_mask"]
A__ = 1
# first forward pass
A__ = model(lowercase , attention_mask=lowercase , head_mask=lowercase , use_cache=lowercase )
A__ , A__ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
A__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
A__ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
A__ = tf.concat([input_ids, next_tokens] , axis=-1 )
A__ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
A__ = model(lowercase , attention_mask=lowercase )[0]
A__ = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
A__ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
A__ = output_from_no_past[:, -3:, random_slice_idx]
A__ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase , lowercase , rtol=1e-3 )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Optional[Any]=None , SCREAMING_SNAKE_CASE_: Optional[int]=None , SCREAMING_SNAKE_CASE_: Optional[int]=None , SCREAMING_SNAKE_CASE_: Dict=None , SCREAMING_SNAKE_CASE_: List[str]=None , ) -> List[Any]:
'''simple docstring'''
if attention_mask is None:
A__ = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
A__ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
A__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
A__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
A__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
__lowerCamelCase = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase = (
{
'conversational': TFBlenderbotSmallForConditionalGeneration,
'feature-extraction': TFBlenderbotSmallModel,
'summarization': TFBlenderbotSmallForConditionalGeneration,
'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
'translation': TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = TFBlenderbotSmallModelTester(self )
A__ = ConfigTester(self , config_class=lowercase )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase )
@require_tokenizers
@require_tf
class a__ ( unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = [
'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
' i\'m going to throw up.\nand why is that?'
]
__lowerCamelCase = 'facebook/blenderbot_small-90M'
@cached_property
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
@cached_property
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.tokenizer(self.src_text , return_tensors="tf" )
A__ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=lowercase , )
A__ = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowercase )[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 68 | 1 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""vocab_file""": """vocab.txt""",
"""merges_file""": """bpe.codes""",
}
lowerCAmelCase__ = {
"""vocab_file""": {
"""vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
"""vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
},
"""merges_file""": {
"""vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
"""vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
},
}
lowerCAmelCase__ = {
"""vinai/phobert-base""": 2_5_6,
"""vinai/phobert-large""": 2_5_6,
}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> str:
'''simple docstring'''
A__ = set()
A__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
A__ = char
A__ = set(SCREAMING_SNAKE_CASE_ )
return pairs
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , lowercase , lowercase , lowercase="<s>" , lowercase="</s>" , lowercase="</s>" , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , **lowercase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , sep_token=lowercase , cls_token=lowercase , pad_token=lowercase , mask_token=lowercase , **lowercase , )
A__ = vocab_file
A__ = merges_file
A__ = {}
A__ = 0
A__ = 1
A__ = 2
A__ = 3
self.add_from_file(lowercase )
A__ = {v: k for k, v in self.encoder.items()}
with open(lowercase , encoding="utf-8" ) as merges_handle:
A__ = merges_handle.read().split("\n" )[:-1]
A__ = [tuple(merge.split()[:-1] ) for merge in merges]
A__ = dict(zip(lowercase , range(len(lowercase ) ) ) )
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A__ = [self.cls_token_id]
A__ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
if token_ids_a is None:
return [1] + ([0] * len(lowercase )) + [1]
return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
def UpperCamelCase ( self , lowercase , lowercase = None ) -> List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [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]
@property
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase ( self , lowercase ) -> Dict:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
A__ = tuple(lowercase )
A__ = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
A__ = get_pairs(lowercase )
if not pairs:
return token
while True:
A__ = min(lowercase , key=lambda lowercase : self.bpe_ranks.get(lowercase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
A__ , A__ = bigram
A__ = []
A__ = 0
while i < len(lowercase ):
try:
A__ = word.index(lowercase , lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
A__ = j
if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
A__ = tuple(lowercase )
A__ = new_word
if len(lowercase ) == 1:
break
else:
A__ = get_pairs(lowercase )
A__ = "@@ ".join(lowercase )
A__ = word[:-4]
A__ = word
return word
def UpperCamelCase ( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = []
A__ = re.findall(R"\S+\n?" , lowercase )
for token in words:
split_tokens.extend(list(self.bpe(lowercase ).split(" " ) ) )
return split_tokens
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowercase , self.encoder.get(self.unk_token ) )
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
return self.decoder.get(lowercase , self.unk_token )
def UpperCamelCase ( self , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = " ".join(lowercase ).replace("@@ " , "" ).strip()
return out_string
def UpperCamelCase ( self , lowercase , lowercase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowercase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
A__ = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
A__ = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ):
copyfile(self.vocab_file , lowercase )
if os.path.abspath(self.merges_file ) != os.path.abspath(lowercase ):
copyfile(self.merges_file , lowercase )
return out_vocab_file, out_merge_file
def UpperCamelCase ( self , lowercase ) -> str:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
try:
with open(lowercase , "r" , encoding="utf-8" ) as fd:
self.add_from_file(lowercase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(F'Incorrect encoding detected in {f}, please rebuild the dataset' )
return
A__ = f.readlines()
for lineTmp in lines:
A__ = lineTmp.strip()
A__ = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
A__ = line[:idx]
A__ = len(self.encoder )
| 68 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ['pixel_values']
def __init__( self , lowercase = True , lowercase = None , lowercase = PILImageResampling.BICUBIC , lowercase = True , lowercase = 1 / 255 , lowercase = True , lowercase = None , lowercase = None , lowercase = True , **lowercase , ) -> None:
'''simple docstring'''
super().__init__(**lowercase )
A__ = size if size is not None else {"height": 384, "width": 384}
A__ = get_size_dict(lowercase , default_to_square=lowercase )
A__ = do_resize
A__ = size
A__ = resample
A__ = do_rescale
A__ = rescale_factor
A__ = do_normalize
A__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A__ = image_std if image_std is not None else OPENAI_CLIP_STD
A__ = do_convert_rgb
def UpperCamelCase ( self , lowercase , lowercase , lowercase = PILImageResampling.BICUBIC , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
A__ = get_size_dict(lowercase , default_to_square=lowercase )
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()}' )
A__ = (size["height"], size["width"])
return resize(lowercase , size=lowercase , resample=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase = None , **lowercase , ) -> Optional[Any]:
'''simple docstring'''
return rescale(lowercase , scale=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase , lowercase , lowercase = None , **lowercase , ) -> np.ndarray:
'''simple docstring'''
return normalize(lowercase , mean=lowercase , std=lowercase , data_format=lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ) -> PIL.Image.Image:
'''simple docstring'''
A__ = do_resize if do_resize is not None else self.do_resize
A__ = resample if resample is not None else self.resample
A__ = do_rescale if do_rescale is not None else self.do_rescale
A__ = rescale_factor if rescale_factor is not None else self.rescale_factor
A__ = do_normalize if do_normalize is not None else self.do_normalize
A__ = image_mean if image_mean is not None else self.image_mean
A__ = image_std if image_std is not None else self.image_std
A__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A__ = size if size is not None else self.size
A__ = get_size_dict(lowercase , default_to_square=lowercase )
A__ = make_list_of_images(lowercase )
if not valid_images(lowercase ):
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 or resample is None:
raise ValueError("Size and resample 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_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A__ = [convert_to_rgb(lowercase ) for image in images]
# All transformations expect numpy arrays.
A__ = [to_numpy_array(lowercase ) for image in images]
if do_resize:
A__ = [self.resize(image=lowercase , size=lowercase , resample=lowercase ) for image in images]
if do_rescale:
A__ = [self.rescale(image=lowercase , scale=lowercase ) for image in images]
if do_normalize:
A__ = [self.normalize(image=lowercase , mean=lowercase , std=lowercase ) for image in images]
A__ = [to_channel_dimension_format(lowercase , lowercase ) for image in images]
A__ = BatchFeature(data={"pixel_values": images} , tensor_type=lowercase )
return encoded_outputs
| 68 | 1 |
from PIL import Image
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Image ) -> Image:
'''simple docstring'''
A__ , A__ = image.size
A__ = 0
A__ = image.load()
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(SCREAMING_SNAKE_CASE_ ):
A__ = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(SCREAMING_SNAKE_CASE_ ):
for i in range(SCREAMING_SNAKE_CASE_ ):
A__ = 2_5_5 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
lowerCAmelCase__ = mean_threshold(Image.open("""path_to_image""").convert("""L"""))
image.save("""output_image_path""")
| 68 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
lowerCAmelCase__ = """hf-internal-testing/tiny-random-bert"""
lowerCAmelCase__ = os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
lowerCAmelCase__ = """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = cached_file(lowercase , lowercase )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(lowercase ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(lowercase , lowercase ) ) )
with open(os.path.join(lowercase , "refs" , "main" ) ) as f:
A__ = f.read()
self.assertEqual(lowercase , os.path.join(lowercase , "snapshots" , lowercase , lowercase ) )
self.assertTrue(os.path.isfile(lowercase ) )
# File is cached at the same place the second time.
A__ = cached_file(lowercase , lowercase )
self.assertEqual(lowercase , lowercase )
# Using a specific revision to test the full commit hash.
A__ = cached_file(lowercase , lowercase , revision="9b8c223" )
self.assertEqual(lowercase , os.path.join(lowercase , "snapshots" , lowercase , lowercase ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaisesRegex(lowercase , "is not a valid model identifier" ):
A__ = cached_file("tiny-random-bert" , lowercase )
with self.assertRaisesRegex(lowercase , "is not a valid git identifier" ):
A__ = cached_file(lowercase , lowercase , revision="aaaa" )
with self.assertRaisesRegex(lowercase , "does not appear to have a file named" ):
A__ = cached_file(lowercase , "conf" )
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(lowercase , "does not appear to have a file named" ):
A__ = cached_file(lowercase , "conf" )
with open(os.path.join(lowercase , "refs" , "main" ) ) as f:
A__ = f.read()
self.assertTrue(os.path.isfile(os.path.join(lowercase , ".no_exist" , lowercase , "conf" ) ) )
A__ = cached_file(lowercase , "conf" , _raise_exceptions_for_missing_entries=lowercase )
self.assertIsNone(lowercase )
A__ = cached_file(lowercase , "conf" , local_files_only=lowercase , _raise_exceptions_for_missing_entries=lowercase )
self.assertIsNone(lowercase )
A__ = mock.Mock()
A__ = 500
A__ = {}
A__ = HTTPError
A__ = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" , return_value=lowercase ) as mock_head:
A__ = cached_file(lowercase , "conf" , _raise_exceptions_for_connection_errors=lowercase )
self.assertIsNone(lowercase )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only" , lowercase ) )
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
self.assertIsNone(get_file_from_repo("bert-base-cased" , "ahah.txt" ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(lowercase , "is not a valid model identifier" ):
get_file_from_repo("bert-base-case" , lowercase )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(lowercase , "is not a valid git identifier" ):
get_file_from_repo("bert-base-cased" , lowercase , revision="ahaha" )
A__ = get_file_from_repo("bert-base-cased" , lowercase )
# The name is the cached name which is not very easy to test, so instead we load the content.
A__ = json.loads(open(lowercase , "r" ).read() )
self.assertEqual(config["hidden_size"] , 768 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
A__ = Path(lowercase ) / "a.txt"
filename.touch()
self.assertEqual(get_file_from_repo(lowercase , "a.txt" ) , str(lowercase ) )
self.assertIsNone(get_file_from_repo(lowercase , "b.txt" ) )
| 68 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: List[str]=1 ) -> str:
'''simple docstring'''
if n_shave_prefix_segments >= 0:
return ".".join(path.split("." )[n_shave_prefix_segments:] )
else:
return ".".join(path.split("." )[:n_shave_prefix_segments] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: Any=0 ) -> Dict:
'''simple docstring'''
A__ = []
for old_item in old_list:
A__ = old_item.replace("in_layers.0" , "norm1" )
A__ = new_item.replace("in_layers.2" , "conv1" )
A__ = new_item.replace("out_layers.0" , "norm2" )
A__ = new_item.replace("out_layers.3" , "conv2" )
A__ = new_item.replace("emb_layers.1" , "time_emb_proj" )
A__ = new_item.replace("skip_connection" , "conv_shortcut" )
A__ = shave_segments(SCREAMING_SNAKE_CASE_ , n_shave_prefix_segments=SCREAMING_SNAKE_CASE_ )
mapping.append({"old": old_item, "new": new_item} )
return mapping
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: Tuple=0 ) -> Dict:
'''simple docstring'''
A__ = []
for old_item in old_list:
A__ = old_item
A__ = new_item.replace("norm.weight" , "group_norm.weight" )
A__ = new_item.replace("norm.bias" , "group_norm.bias" )
A__ = new_item.replace("proj_out.weight" , "proj_attn.weight" )
A__ = new_item.replace("proj_out.bias" , "proj_attn.bias" )
A__ = shave_segments(SCREAMING_SNAKE_CASE_ , n_shave_prefix_segments=SCREAMING_SNAKE_CASE_ )
mapping.append({"old": old_item, "new": new_item} )
return mapping
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: int=None , SCREAMING_SNAKE_CASE_: Tuple=None , SCREAMING_SNAKE_CASE_: List[str]=None ) -> int:
'''simple docstring'''
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
A__ = old_checkpoint[path]
A__ = old_tensor.shape[0] // 3
A__ = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
A__ = old_tensor.shape[0] // config["num_head_channels"] // 3
A__ = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
A__ , A__ , A__ = old_tensor.split(channels // num_heads , dim=1 )
A__ = query.reshape(SCREAMING_SNAKE_CASE_ )
A__ = key.reshape(SCREAMING_SNAKE_CASE_ )
A__ = value.reshape(SCREAMING_SNAKE_CASE_ )
for path in paths:
A__ = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
A__ = new_path.replace("middle_block.0" , "mid_block.resnets.0" )
A__ = new_path.replace("middle_block.1" , "mid_block.attentions.0" )
A__ = new_path.replace("middle_block.2" , "mid_block.resnets.1" )
if additional_replacements is not None:
for replacement in additional_replacements:
A__ = new_path.replace(replacement["old"] , replacement["new"] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
A__ = old_checkpoint[path["old"]][:, :, 0]
else:
A__ = old_checkpoint[path["old"]]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: str ) -> Union[str, Any]:
'''simple docstring'''
A__ = {}
A__ = checkpoint["time_embed.0.weight"]
A__ = checkpoint["time_embed.0.bias"]
A__ = checkpoint["time_embed.2.weight"]
A__ = checkpoint["time_embed.2.bias"]
A__ = checkpoint["input_blocks.0.0.weight"]
A__ = checkpoint["input_blocks.0.0.bias"]
A__ = checkpoint["out.0.weight"]
A__ = checkpoint["out.0.bias"]
A__ = checkpoint["out.2.weight"]
A__ = checkpoint["out.2.bias"]
# Retrieves the keys for the input blocks only
A__ = len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "input_blocks" in layer} )
A__ = {
layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
for layer_id in range(SCREAMING_SNAKE_CASE_ )
}
# Retrieves the keys for the middle blocks only
A__ = len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "middle_block" in layer} )
A__ = {
layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
for layer_id in range(SCREAMING_SNAKE_CASE_ )
}
# Retrieves the keys for the output blocks only
A__ = len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "output_blocks" in layer} )
A__ = {
layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
for layer_id in range(SCREAMING_SNAKE_CASE_ )
}
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
A__ = (i - 1) // (config["num_res_blocks"] + 1)
A__ = (i - 1) % (config["num_res_blocks"] + 1)
A__ = [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
A__ = [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
if F'input_blocks.{i}.0.op.weight' in checkpoint:
A__ = checkpoint[
F'input_blocks.{i}.0.op.weight'
]
A__ = checkpoint[
F'input_blocks.{i}.0.op.bias'
]
continue
A__ = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
A__ = {"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
A__ = {"old": "resnets.2.op", "new": "downsamplers.0.op"}
assign_to_checkpoint(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path, resnet_op] , config=SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ):
A__ = renew_attention_paths(SCREAMING_SNAKE_CASE_ )
A__ = {
"old": F'input_blocks.{i}.1',
"new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
}
A__ = {
F'input_blocks.{i}.1.qkv.bias': {
"key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
"query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
"value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
F'input_blocks.{i}.1.qkv.weight': {
"key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
"query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
"value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , attention_paths_to_split=SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ , )
A__ = middle_blocks[0]
A__ = middle_blocks[1]
A__ = middle_blocks[2]
A__ = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
A__ = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
A__ = renew_attention_paths(SCREAMING_SNAKE_CASE_ )
A__ = {
"middle_block.1.qkv.bias": {
"key": "mid_block.attentions.0.key.bias",
"query": "mid_block.attentions.0.query.bias",
"value": "mid_block.attentions.0.value.bias",
},
"middle_block.1.qkv.weight": {
"key": "mid_block.attentions.0.key.weight",
"query": "mid_block.attentions.0.query.weight",
"value": "mid_block.attentions.0.value.weight",
},
}
assign_to_checkpoint(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , attention_paths_to_split=SCREAMING_SNAKE_CASE_ , config=SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
A__ = i // (config["num_res_blocks"] + 1)
A__ = i % (config["num_res_blocks"] + 1)
A__ = [shave_segments(SCREAMING_SNAKE_CASE_ , 2 ) for name in output_blocks[i]]
A__ = {}
for layer in output_block_layers:
A__ , A__ = layer.split("." )[0], shave_segments(SCREAMING_SNAKE_CASE_ , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_ )
else:
A__ = [layer_name]
if len(SCREAMING_SNAKE_CASE_ ) > 1:
A__ = [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
A__ = [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
A__ = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
A__ = renew_resnet_paths(SCREAMING_SNAKE_CASE_ )
A__ = {"old": F'output_blocks.{i}.0', "new": F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , config=SCREAMING_SNAKE_CASE_ )
if ["conv.weight", "conv.bias"] in output_block_list.values():
A__ = list(output_block_list.values() ).index(["conv.weight", "conv.bias"] )
A__ = checkpoint[
F'output_blocks.{i}.{index}.conv.weight'
]
A__ = checkpoint[
F'output_blocks.{i}.{index}.conv.bias'
]
# Clear attentions as they have been attributed above.
if len(SCREAMING_SNAKE_CASE_ ) == 2:
A__ = []
if len(SCREAMING_SNAKE_CASE_ ):
A__ = renew_attention_paths(SCREAMING_SNAKE_CASE_ )
A__ = {
"old": F'output_blocks.{i}.1',
"new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
}
A__ = {
F'output_blocks.{i}.1.qkv.bias': {
"key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
"query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
"value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
F'output_blocks.{i}.1.qkv.weight': {
"key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
"query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
"value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any("qkv" in key for key in attentions ) else None , config=SCREAMING_SNAKE_CASE_ , )
else:
A__ = renew_resnet_paths(SCREAMING_SNAKE_CASE_ , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
A__ = ".".join(["output_blocks", str(SCREAMING_SNAKE_CASE_ ), path["old"]] )
A__ = ".".join(["up_blocks", str(SCREAMING_SNAKE_CASE_ ), "resnets", str(SCREAMING_SNAKE_CASE_ ), path["new"]] )
A__ = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the architecture.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
lowerCAmelCase__ = parser.parse_args()
lowerCAmelCase__ = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
lowerCAmelCase__ = json.loads(f.read())
lowerCAmelCase__ = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
lowerCAmelCase__ = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
lowerCAmelCase__ = DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
lowerCAmelCase__ = VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
lowerCAmelCase__ = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 68 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a__ ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = AutoencoderKL
__lowerCamelCase = 'sample'
__lowerCamelCase = 1e-2
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = 4
A__ = 3
A__ = (32, 32)
A__ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase )
return {"sample": image}
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
@property
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
A__ = self.dummy_input
return init_dict, inputs_dict
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skipIf(torch_device == "mps" , "Gradient checkpointing skipped on MPS" )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ , A__ = self.prepare_init_args_and_inputs_for_common()
A__ = self.model_class(**lowercase )
model.to(lowercase )
assert not model.is_gradient_checkpointing and model.training
A__ = model(**lowercase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
A__ = torch.randn_like(lowercase )
A__ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
A__ = self.model_class(**lowercase )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
A__ = model_a(**lowercase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
A__ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
A__ = dict(model.named_parameters() )
A__ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ , A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(lowercase )
A__ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" )
A__ = model.to(lowercase )
model.eval()
if torch_device == "mps":
A__ = torch.manual_seed(0 )
else:
A__ = torch.Generator(device=lowercase ).manual_seed(0 )
A__ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
A__ = image.to(lowercase )
with torch.no_grad():
A__ = model(lowercase , sample_posterior=lowercase , generator=lowercase ).sample
A__ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
A__ = torch.tensor(
[
-4.00_78e-01,
-3.83_23e-04,
-1.26_81e-01,
-1.14_62e-01,
2.00_95e-01,
1.08_93e-01,
-8.82_47e-02,
-3.03_61e-01,
-9.86_44e-03,
] )
elif torch_device == "cpu":
A__ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
A__ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase , lowercase , rtol=1e-2 ) )
@slow
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
return F'gaussian_noise_s={seed}_shape={"_".join([str(lowercase ) for s in shape] )}.npy'
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self , lowercase=0 , lowercase=(4, 3, 512, 512) , lowercase=False ) -> Optional[int]:
'''simple docstring'''
A__ = torch.floataa if fpaa else torch.floataa
A__ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase , lowercase ) ) ).to(lowercase ).to(lowercase )
return image
def UpperCamelCase ( self , lowercase="CompVis/stable-diffusion-v1-4" , lowercase=False ) -> Any:
'''simple docstring'''
A__ = "fp16" if fpaa else None
A__ = torch.floataa if fpaa else torch.floataa
A__ = AutoencoderKL.from_pretrained(
lowercase , subfolder="vae" , torch_dtype=lowercase , revision=lowercase , )
model.to(lowercase ).eval()
return model
def UpperCamelCase ( self , lowercase=0 ) -> List[str]:
'''simple docstring'''
if torch_device == "mps":
return torch.manual_seed(lowercase )
return torch.Generator(device=lowercase ).manual_seed(lowercase )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowercase , lowercase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , fpaa=lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model(lowercase , generator=lowercase , sample_posterior=lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Dict:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
with torch.no_grad():
A__ = model(lowercase ).sample
assert sample.shape == image.shape
A__ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
A__ = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(lowercase , lowercase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
A__ = sample[-1, -2:, :2, -2:].flatten().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
A__ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
A__ = torch.tensor(lowercase )
assert torch_all_close(lowercase , lowercase , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def UpperCamelCase ( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = self.get_sd_vae_model(fpaa=lowercase )
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) , fpaa=lowercase )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase , lowercase , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase , shape=(3, 4, 64, 64) )
with torch.no_grad():
A__ = model.decode(lowercase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
A__ = model.decode(lowercase ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def UpperCamelCase ( self , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = self.get_sd_vae_model()
A__ = self.get_sd_image(lowercase )
A__ = self.get_generator(lowercase )
with torch.no_grad():
A__ = model.encode(lowercase ).latent_dist
A__ = dist.sample(generator=lowercase )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
A__ = sample[0, -1, -3:, -3:].flatten().cpu()
A__ = torch.tensor(lowercase )
A__ = 3e-3 if torch_device != "mps" else 1e-2
assert torch_all_close(lowercase , lowercase , atol=lowercase )
| 68 | 1 |
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 a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase , lowercase ) -> Dict:
'''simple docstring'''
super().__init__()
# make sure scheduler can always be converted to DDIM
A__ = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=lowercase , scheduler=lowercase )
@torch.no_grad()
def __call__( self , lowercase = 1 , lowercase = None , lowercase = 0.0 , lowercase = 50 , lowercase = None , lowercase = "pil" , lowercase = True , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
if isinstance(self.unet.config.sample_size , lowercase ):
A__ = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
A__ = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(lowercase , lowercase ) and len(lowercase ) != batch_size:
raise ValueError(
F'You have passed a list of generators of length {len(lowercase )}, but requested an effective batch'
F' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
A__ = randn_tensor(lowercase , generator=lowercase , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(lowercase )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
A__ = self.unet(lowercase , lowercase ).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
A__ = self.scheduler.step(
lowercase , lowercase , lowercase , eta=lowercase , use_clipped_model_output=lowercase , generator=lowercase ).prev_sample
A__ = (image / 2 + 0.5).clamp(0 , 1 )
A__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A__ = self.numpy_to_pil(lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase )
| 68 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase__ = logging.getLogger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = label_idx
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
A__ = []
A__ = []
for line in f:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
A__ = []
A__ = []
else:
A__ = line.split(" " )
words.append(splits[0] )
if len(lowercase ) > 1:
labels.append(splits[self.label_idx].replace("\n" , "" ) )
else:
# Examples could have no label for mode = "test"
labels.append("O" )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
writer.write(lowercase )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
A__ = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(lowercase )
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0] )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(label_idx=-2 )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class a__ ( snake_case ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
for sentence in parse_incr(lowercase ):
A__ = []
A__ = []
for token in sentence:
words.append(token["form"] )
labels.append(token["upos"] )
assert len(lowercase ) == len(lowercase )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = 0
for sentence in parse_incr(lowercase ):
A__ = preds_list[example_id]
A__ = ""
for token in sentence:
out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '
out += "\n"
writer.write(lowercase )
example_id += 1
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 68 | 1 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> str:
'''simple docstring'''
re.sub("<n>" , "" , SCREAMING_SNAKE_CASE_ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE_ ) )
| 68 |
import random
class a__ :
"""simple docstring"""
@staticmethod
def UpperCamelCase ( lowercase ) -> tuple[list[int], list[int]]:
'''simple docstring'''
A__ = [ord(lowercase ) for i in text]
A__ = []
A__ = []
for i in plain:
A__ = random.randint(1 , 300 )
A__ = (i + k) * k
cipher.append(lowercase )
key.append(lowercase )
return cipher, key
@staticmethod
def UpperCamelCase ( lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = []
for i in range(len(lowercase ) ):
A__ = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowercase ) )
return "".join(lowercase )
if __name__ == "__main__":
lowerCAmelCase__ , lowerCAmelCase__ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 68 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> float:
'''simple docstring'''
A__ = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowerCAmelCase__ ( ) -> Optional[Any]:
'''simple docstring'''
print(sum_of_series(1 , 1 , 1_0 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 68 |
def lowerCAmelCase__ ( ) -> Any:
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simple docstring'''
A__ = 1
A__ = 2
while i * i <= n:
A__ = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def lowerCAmelCase__ ( ) -> Dict:
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(SCREAMING_SNAKE_CASE_ ) > 5_0_0 )
if __name__ == "__main__":
print(solution())
| 68 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = (PNDMScheduler,)
__lowerCamelCase = (('num_inference_steps', 50),)
def UpperCamelCase ( self , **lowercase ) -> Tuple:
'''simple docstring'''
A__ = {
"num_train_timesteps": 1000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowercase )
return config
def UpperCamelCase ( self , lowercase=0 , **lowercase ) -> Any:
'''simple docstring'''
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop("num_inference_steps" , lowercase )
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config(**lowercase )
A__ = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# copy over dummy past residuals
A__ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase )
A__ = scheduler_class.from_pretrained(lowercase )
new_scheduler.set_timesteps(lowercase )
# copy over dummy past residuals
A__ = dummy_past_residuals[:]
A__ = scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample
A__ = new_scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
A__ = scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample
A__ = new_scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
def UpperCamelCase ( self , lowercase=0 , **lowercase ) -> Tuple:
'''simple docstring'''
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop("num_inference_steps" , lowercase )
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config()
A__ = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# copy over dummy past residuals (must be after setting timesteps)
A__ = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase )
A__ = scheduler_class.from_pretrained(lowercase )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase )
# copy over dummy past residual (must be after setting timesteps)
A__ = dummy_past_residuals[:]
A__ = scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample
A__ = new_scheduler.step_prk(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
A__ = scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample
A__ = new_scheduler.step_plms(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def UpperCamelCase ( self , **lowercase ) -> Dict:
'''simple docstring'''
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(**lowercase )
A__ = scheduler_class(**lowercase )
A__ = 10
A__ = self.dummy_model()
A__ = self.dummy_sample_deter
scheduler.set_timesteps(lowercase )
for i, t in enumerate(scheduler.prk_timesteps ):
A__ = model(lowercase , lowercase )
A__ = scheduler.step_prk(lowercase , lowercase , lowercase ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
A__ = model(lowercase , lowercase )
A__ = scheduler.step_plms(lowercase , lowercase , lowercase ).prev_sample
return sample
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop("num_inference_steps" , lowercase )
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config()
A__ = scheduler_class(**lowercase )
A__ = self.dummy_sample
A__ = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase , "set_timesteps" ):
scheduler.set_timesteps(lowercase )
elif num_inference_steps is not None and not hasattr(lowercase , "set_timesteps" ):
A__ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
A__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
A__ = dummy_past_residuals[:]
A__ = scheduler.step_prk(lowercase , 0 , lowercase , **lowercase ).prev_sample
A__ = scheduler.step_prk(lowercase , 1 , lowercase , **lowercase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
A__ = scheduler.step_plms(lowercase , 0 , lowercase , **lowercase ).prev_sample
A__ = scheduler.step_plms(lowercase , 1 , lowercase , **lowercase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=lowercase )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowercase )
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(steps_offset=1 )
A__ = scheduler_class(**lowercase )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=lowercase , beta_end=lowercase )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowercase )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowercase )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = 27
for scheduler_class in self.scheduler_classes:
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = self.get_scheduler_config()
A__ = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
A__ = scheduler.step_prk(lowercase , lowercase , lowercase ).prev_sample
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
with self.assertRaises(lowercase ):
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config()
A__ = scheduler_class(**lowercase )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.full_loop()
A__ = torch.sum(torch.abs(lowercase ) )
A__ = torch.mean(torch.abs(lowercase ) )
assert abs(result_sum.item() - 198.1318 ) < 1e-2
assert abs(result_mean.item() - 0.2580 ) < 1e-3
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.full_loop(prediction_type="v_prediction" )
A__ = torch.sum(torch.abs(lowercase ) )
A__ = torch.mean(torch.abs(lowercase ) )
assert abs(result_sum.item() - 67.3986 ) < 1e-2
assert abs(result_mean.item() - 0.0878 ) < 1e-3
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01 )
A__ = torch.sum(torch.abs(lowercase ) )
A__ = torch.mean(torch.abs(lowercase ) )
assert abs(result_sum.item() - 230.0399 ) < 1e-2
assert abs(result_mean.item() - 0.2995 ) < 1e-3
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01 )
A__ = torch.sum(torch.abs(lowercase ) )
A__ = torch.mean(torch.abs(lowercase ) )
assert abs(result_sum.item() - 186.9482 ) < 1e-2
assert abs(result_mean.item() - 0.2434 ) < 1e-3
| 68 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCase__ = get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
lowerCAmelCase__ = json.load(f)
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return FSMTTokenizer.from_pretrained(lowercase )
def UpperCamelCase ( self , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = FSMTForConditionalGeneration.from_pretrained(lowercase ).to(lowercase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def UpperCamelCase ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = F'facebook/wmt19-{pair}'
A__ = self.get_tokenizer(lowercase )
A__ = self.get_model(lowercase )
A__ = bleu_data[pair]["src"]
A__ = bleu_data[pair]["tgt"]
A__ = tokenizer(lowercase , return_tensors="pt" , truncation=lowercase , padding="longest" ).to(lowercase )
A__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
A__ = tokenizer.batch_decode(
lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase )
A__ = calculate_bleu(lowercase , lowercase )
print(lowercase )
self.assertGreaterEqual(scores["bleu"] , lowercase )
| 68 | 1 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
super().tearDown()
gc.collect()
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png" )
A__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" )
A__ = "xvjiarui/stable-diffusion-2-inpainting"
A__ , A__ = FlaxStableDiffusionInpaintPipeline.from_pretrained(lowercase , safety_checker=lowercase )
A__ = "Face of a yellow cat, high resolution, sitting on a park bench"
A__ = jax.random.PRNGKey(0 )
A__ = 50
A__ = jax.device_count()
A__ = num_samples * [prompt]
A__ = num_samples * [init_image]
A__ = num_samples * [mask_image]
A__ , A__ , A__ = pipeline.prepare_inputs(lowercase , lowercase , lowercase )
# shard inputs and rng
A__ = replicate(lowercase )
A__ = jax.random.split(lowercase , jax.device_count() )
A__ = shard(lowercase )
A__ = shard(lowercase )
A__ = shard(lowercase )
A__ = pipeline(
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , jit=lowercase )
A__ = output.images.reshape(lowercase , 512 , 512 , 3 )
A__ = images[0, 253:256, 253:256, -1]
A__ = jnp.asarray(jax.device_get(image_slice.flatten() ) )
A__ = jnp.array(
[0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084] )
print(F'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 68 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> list:
'''simple docstring'''
A__ = int(SCREAMING_SNAKE_CASE_ )
if n_element < 1:
A__ = ValueError("a should be a positive number" )
raise my_error
A__ = [1]
A__ , A__ , A__ = (0, 0, 0)
A__ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
lowerCAmelCase__ = input("""Enter the last number (nth term) of the Hamming Number Series: """)
print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
lowerCAmelCase__ = hamming(int(n))
print("""-----------------------------------------------------""")
print(f"""The list with nth numbers is: {hamming_numbers}""")
print("""-----------------------------------------------------""")
| 68 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowerCAmelCase__ = TypeVar("""T""")
lowerCAmelCase__ = Union[List[T], Tuple[T, ...]]
lowerCAmelCase__ = Union[T, List[T], Dict[str, T]]
lowerCAmelCase__ = Union[str, bytes, os.PathLike]
| 68 |
import copy
import random
from transformers import CLIPTokenizer
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowercase , **lowercase )
A__ = {}
def UpperCamelCase ( self , lowercase , *lowercase , **lowercase ) -> str:
'''simple docstring'''
A__ = super().add_tokens(lowercase , *lowercase , **lowercase )
if num_added_tokens == 0:
raise ValueError(
F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
" `placeholder_token` that is not already in the tokenizer." )
def UpperCamelCase ( self , lowercase , *lowercase , lowercase=1 , **lowercase ) -> Any:
'''simple docstring'''
A__ = []
if num_vec_per_token == 1:
self.try_adding_tokens(lowercase , *lowercase , **lowercase )
output.append(lowercase )
else:
A__ = []
for i in range(lowercase ):
A__ = placeholder_token + F'_{i}'
self.try_adding_tokens(lowercase , *lowercase , **lowercase )
output.append(lowercase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F'The tokenizer already has placeholder token {token} that can get confused with'
F' {placeholder_token}keep placeholder tokens independent' )
A__ = output
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=1.0 ) -> List[Any]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = []
for i in range(len(lowercase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=lowercase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
A__ = self.token_map[placeholder_token]
A__ = tokens[: 1 + int(len(lowercase ) * prop_tokens_to_load )]
if vector_shuffle:
A__ = copy.copy(lowercase )
random.shuffle(lowercase )
A__ = text.replace(lowercase , " ".join(lowercase ) )
return text
def __call__( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase ) -> str:
'''simple docstring'''
return super().__call__(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase ) , *lowercase , **lowercase , )
def UpperCamelCase ( self , lowercase , *lowercase , lowercase=False , lowercase=1.0 , **lowercase ) -> List[str]:
'''simple docstring'''
return super().encode(
self.replace_placeholder_tokens_in_text(
lowercase , vector_shuffle=lowercase , prop_tokens_to_load=lowercase ) , *lowercase , **lowercase , )
| 68 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> Dict:
'''simple docstring'''
def decorator(SCREAMING_SNAKE_CASE_: Optional[Any] ):
A__ = getattr(SCREAMING_SNAKE_CASE_ , "handle_key" , [] )
handle += [key]
setattr(SCREAMING_SNAKE_CASE_ , "handle_key" , SCREAMING_SNAKE_CASE_ )
return func
return decorator
def lowerCAmelCase__ ( *SCREAMING_SNAKE_CASE_: List[str] ) -> Any:
'''simple docstring'''
def decorator(SCREAMING_SNAKE_CASE_: int ):
A__ = getattr(SCREAMING_SNAKE_CASE_ , "handle_key" , [] )
handle += keys
setattr(SCREAMING_SNAKE_CASE_ , "handle_key" , SCREAMING_SNAKE_CASE_ )
return func
return decorator
class a__ ( snake_case ):
"""simple docstring"""
def __new__( cls , lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
A__ = super().__new__(cls , lowercase , lowercase , lowercase )
if not hasattr(lowercase , "key_handler" ):
setattr(lowercase , "key_handler" , {} )
setattr(lowercase , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
A__ = getattr(lowercase , "handle_key" , [] )
for key in handled_keys:
A__ = value
return new_cls
@staticmethod
def UpperCamelCase ( cls ) -> Optional[Any]:
'''simple docstring'''
A__ = get_character()
if char != KEYMAP["undefined"]:
A__ = ord(lowercase )
A__ = cls.key_handler.get(lowercase )
if handler:
A__ = char
return handler(cls )
else:
return None
def lowerCAmelCase__ ( cls: int ) -> Dict:
'''simple docstring'''
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 68 |
from collections import deque
from math import floor
from random import random
from time import time
class a__ :
"""simple docstring"""
def __init__( self ) -> Dict:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase , lowercase=1 ) -> Tuple:
'''simple docstring'''
if self.graph.get(lowercase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
A__ = [[w, v]]
if not self.graph.get(lowercase ):
A__ = []
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return list(self.graph )
def UpperCamelCase ( self , lowercase , lowercase ) -> int:
'''simple docstring'''
if self.graph.get(lowercase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Any:
'''simple docstring'''
if s == d:
return []
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return visited
def UpperCamelCase ( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
if c == -1:
A__ = floor(random() * 10000 ) + 10
for i in range(lowercase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A__ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase , lowercase , 1 )
def UpperCamelCase ( self , lowercase=-2 ) -> Any:
'''simple docstring'''
A__ = deque()
A__ = []
if s == -2:
A__ = list(self.graph )[0]
d.append(lowercase )
visited.append(lowercase )
while d:
A__ = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return len(self.graph[u] )
def UpperCamelCase ( self , lowercase=-2 ) -> str:
'''simple docstring'''
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
A__ = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return sorted_nodes
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return list(lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return False
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Any:
'''simple docstring'''
A__ = time()
self.dfs(lowercase , lowercase )
A__ = time()
return end - begin
def UpperCamelCase ( self , lowercase=-2 ) -> int:
'''simple docstring'''
A__ = time()
self.bfs(lowercase )
A__ = time()
return end - begin
class a__ :
"""simple docstring"""
def __init__( self ) -> int:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self , lowercase , lowercase , lowercase=1 ) -> Union[str, Any]:
'''simple docstring'''
if self.graph.get(lowercase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
A__ = [[w, v]]
# add the other way
if self.graph.get(lowercase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
A__ = [[w, u]]
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
if self.graph.get(lowercase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowercase )
# the other way round
if self.graph.get(lowercase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowercase )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> List[str]:
'''simple docstring'''
if s == d:
return []
A__ = []
A__ = []
if s == -2:
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowercase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return visited
def UpperCamelCase ( self , lowercase=-1 ) -> str:
'''simple docstring'''
if c == -1:
A__ = floor(random() * 10000 ) + 10
for i in range(lowercase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A__ = floor(random() * c ) + 1
if n != i:
self.add_pair(lowercase , lowercase , 1 )
def UpperCamelCase ( self , lowercase=-2 ) -> Dict:
'''simple docstring'''
A__ = deque()
A__ = []
if s == -2:
A__ = list(self.graph )[0]
d.append(lowercase )
visited.append(lowercase )
while d:
A__ = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
return len(self.graph[u] )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return list(lowercase )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = []
A__ = []
A__ = list(self.graph )[0]
stack.append(lowercase )
visited.append(lowercase )
A__ = -2
A__ = []
A__ = s
A__ = False
A__ = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A__ = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A__ = len(lowercase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A__ = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A__ = True
if len(lowercase ) != 0:
A__ = stack[len(lowercase ) - 1]
else:
A__ = False
indirect_parents.append(lowercase )
A__ = s
A__ = ss
# check if se have reached the starting point
if len(lowercase ) == 0:
return False
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
return list(self.graph )
def UpperCamelCase ( self , lowercase=-2 , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = time()
self.dfs(lowercase , lowercase )
A__ = time()
return end - begin
def UpperCamelCase ( self , lowercase=-2 ) -> List[Any]:
'''simple docstring'''
A__ = time()
self.bfs(lowercase )
A__ = time()
return end - begin
| 68 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
lowerCAmelCase__ = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
lowerCAmelCase__ = {
"""facebook/blenderbot_small-90M""": 5_1_2,
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = BlenderbotSmallTokenizer
def __init__( self , lowercase=None , lowercase=None , lowercase="<|endoftext|>" , lowercase="<|endoftext|>" , lowercase="<|endoftext|>" , lowercase=False , lowercase=True , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=lowercase , merges=lowercase , add_prefix_space=lowercase , trim_offsets=lowercase , ) , bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , **lowercase , )
A__ = add_prefix_space
def UpperCamelCase ( self , lowercase , lowercase=None ) -> Optional[Any]:
'''simple docstring'''
A__ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCamelCase ( self , lowercase , lowercase = None ) -> List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [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]
| 68 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
"""
lowerCAmelCase__ = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""
lowerCAmelCase__ = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the CUAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'exact_match': Exact match (the normalized answer exactly match the gold answer)
'f1': The F-score of predicted tokens versus the gold answer
'aupr': Area Under the Precision-Recall curve
'prec_at_80_recall': Precision at 80% recall
'prec_at_90_recall': Precision at 90% recall
Examples:
>>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> cuad_metric = datasets.load_metric(\"cuad\")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def UpperCamelCase ( self , lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
A__ = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
A__ = evaluate(dataset=lowercase , predictions=lowercase )
return score
| 68 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase__ = logging.getLogger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase=-1 ) -> Optional[Any]:
'''simple docstring'''
A__ = label_idx
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
A__ = []
A__ = []
for line in f:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
A__ = []
A__ = []
else:
A__ = line.split(" " )
words.append(splits[0] )
if len(lowercase ) > 1:
labels.append(splits[self.label_idx].replace("\n" , "" ) )
else:
# Examples could have no label for mode = "test"
labels.append("O" )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
writer.write(lowercase )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
A__ = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(lowercase )
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0] )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(label_idx=-2 )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
A__ = f.read().splitlines()
if "O" not in labels:
A__ = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class a__ ( snake_case ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> List[InputExample]:
'''simple docstring'''
if isinstance(lowercase , lowercase ):
A__ = mode.value
A__ = os.path.join(lowercase , F'{mode}.txt' )
A__ = 1
A__ = []
with open(lowercase , encoding="utf-8" ) as f:
for sentence in parse_incr(lowercase ):
A__ = []
A__ = []
for token in sentence:
words.append(token["form"] )
labels.append(token["upos"] )
assert len(lowercase ) == len(lowercase )
if words:
examples.append(InputExample(guid=F'{mode}-{guid_index}' , words=lowercase , labels=lowercase ) )
guid_index += 1
return examples
def UpperCamelCase ( self , lowercase , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = 0
for sentence in parse_incr(lowercase ):
A__ = preds_list[example_id]
A__ = ""
for token in sentence:
out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) '
out += "\n"
writer.write(lowercase )
example_id += 1
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
if path:
with open(lowercase , "r" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 68 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
A__ = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
A__ = nn.functional.normalize(SCREAMING_SNAKE_CASE_ )
return torch.mm(SCREAMING_SNAKE_CASE_ , normalized_text_embeds.t() )
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = CLIPConfig
__lowerCamelCase = ['CLIPEncoderLayer']
def __init__( self , lowercase ) -> Optional[int]:
'''simple docstring'''
super().__init__(lowercase )
A__ = CLIPVisionModel(config.vision_config )
A__ = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=lowercase )
A__ = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(17 ) , requires_grad=lowercase )
A__ = nn.Parameter(torch.ones(3 ) , requires_grad=lowercase )
@torch.no_grad()
def UpperCamelCase ( self , lowercase , lowercase ) -> Any:
'''simple docstring'''
A__ = self.vision_model(lowercase )[1] # pooled_output
A__ = self.visual_projection(lowercase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
A__ = cosine_distance(lowercase , self.special_care_embeds ).cpu().float().numpy()
A__ = cosine_distance(lowercase , self.concept_embeds ).cpu().float().numpy()
A__ = []
A__ = image_embeds.shape[0]
for i in range(lowercase ):
A__ = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
A__ = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
A__ = special_cos_dist[i][concept_idx]
A__ = self.special_care_embeds_weights[concept_idx].item()
A__ = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]} )
A__ = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
A__ = cos_dist[i][concept_idx]
A__ = self.concept_embeds_weights[concept_idx].item()
A__ = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(lowercase )
result.append(lowercase )
A__ = [len(res["bad_concepts"] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCamelCase ( self , lowercase , lowercase ) -> Any:
'''simple docstring'''
A__ = self.vision_model(lowercase )[1] # pooled_output
A__ = self.visual_projection(lowercase )
A__ = cosine_distance(lowercase , self.special_care_embeds )
A__ = cosine_distance(lowercase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
A__ = 0.0
A__ = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
A__ = torch.any(special_scores > 0 , dim=1 )
A__ = special_care * 0.01
A__ = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
A__ = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
A__ = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 68 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
lowerCAmelCase__ = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowerCAmelCase__ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModel)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_PRETRAINING_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_MASKED_LM_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
lowerCAmelCase__ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class a__ ( _BaseAutoModelClass ):
"""simple docstring"""
__lowerCamelCase = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
lowerCAmelCase__ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 68 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 68 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowercase , lowercase=7 , lowercase=3 , lowercase=18 , lowercase=30 , lowercase=400 , lowercase=True , lowercase=None , lowercase=True , lowercase=None , lowercase=True , lowercase=[0.4814_5466, 0.457_8275, 0.4082_1073] , lowercase=[0.2686_2954, 0.2613_0258, 0.2757_7711] , lowercase=True , ) -> Dict:
'''simple docstring'''
A__ = size if size is not None else {"height": 224, "width": 224}
A__ = crop_size if crop_size is not None else {"height": 18, "width": 18}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = image_size
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = do_center_crop
A__ = crop_size
A__ = do_normalize
A__ = image_mean
A__ = image_std
A__ = do_convert_rgb
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def UpperCamelCase ( self , lowercase=False , lowercase=False , lowercase=False ) -> Union[str, Any]:
'''simple docstring'''
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
A__ = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
A__ = []
for i in range(self.batch_size ):
A__ , A__ = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
A__ = [Image.fromarray(np.moveaxis(lowercase , 0 , -1 ) ) for x in image_inputs]
if torchify:
A__ = [torch.from_numpy(lowercase ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = ChineseCLIPImageProcessor if is_vision_available() else None
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = ChineseCLIPImageProcessingTester(self , do_center_crop=lowercase )
@property
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase , "do_resize" ) )
self.assertTrue(hasattr(lowercase , "size" ) )
self.assertTrue(hasattr(lowercase , "do_center_crop" ) )
self.assertTrue(hasattr(lowercase , "center_crop" ) )
self.assertTrue(hasattr(lowercase , "do_normalize" ) )
self.assertTrue(hasattr(lowercase , "image_mean" ) )
self.assertTrue(hasattr(lowercase , "image_std" ) )
self.assertTrue(hasattr(lowercase , "do_convert_rgb" ) )
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 224, "width": 224} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
A__ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , Image.Image )
# Test not batched input
A__ = 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
A__ = image_processing(lowercase , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A__ = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase , numpify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , np.ndarray )
# Test not batched input
A__ = 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
A__ = image_processing(lowercase , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A__ = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase , torchify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , torch.Tensor )
# Test not batched input
A__ = 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
A__ = image_processing(lowercase , 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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
@require_torch
@require_vision
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = ChineseCLIPImageProcessor if is_vision_available() else None
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=lowercase )
A__ = 3
@property
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase , "do_resize" ) )
self.assertTrue(hasattr(lowercase , "size" ) )
self.assertTrue(hasattr(lowercase , "do_center_crop" ) )
self.assertTrue(hasattr(lowercase , "center_crop" ) )
self.assertTrue(hasattr(lowercase , "do_normalize" ) )
self.assertTrue(hasattr(lowercase , "image_mean" ) )
self.assertTrue(hasattr(lowercase , "image_std" ) )
self.assertTrue(hasattr(lowercase , "do_convert_rgb" ) )
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = self.image_processor_tester.prepare_inputs(equal_resolution=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , Image.Image )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
A__ = image_processing(lowercase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 68 |
import string
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> None:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
A__ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
A__ = string.ascii_uppercase.find(SCREAMING_SNAKE_CASE_ )
A__ = num - key
if num < 0:
A__ = num + len(string.ascii_uppercase )
A__ = translated + string.ascii_uppercase[num]
else:
A__ = translated + symbol
print(F'Decryption using Key #{key}: {translated}' )
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
A__ = input("Encrypted message: " )
A__ = message.upper()
decrypt(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 68 | 1 |
from math import factorial
lowerCAmelCase__ = {str(d): factorial(d) for d in range(1_0)}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(SCREAMING_SNAKE_CASE_ ) )
def lowerCAmelCase__ ( ) -> int:
'''simple docstring'''
A__ = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , SCREAMING_SNAKE_CASE_ ) if sum_of_digit_factorial(SCREAMING_SNAKE_CASE_ ) == i )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 68 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = SpeechTaTokenizer
__lowerCamelCase = False
__lowerCamelCase = True
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
A__ = SpeechTaTokenizer(lowercase )
A__ = AddedToken("<mask>" , lstrip=lowercase , rstrip=lowercase )
A__ = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = "this is a test"
A__ = "this is a test"
return input_text, output_text
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=20 , lowercase=5 ) -> Optional[Any]:
'''simple docstring'''
A__ , A__ = self.get_input_output_texts(lowercase )
A__ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A__ = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
return text, ids
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = "<pad>"
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-4] , "œ" )
self.assertEqual(vocab_keys[-2] , "<mask>" )
self.assertEqual(vocab_keys[-1] , "<ctc_blank>" )
self.assertEqual(len(lowercase ) , 81 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.get_tokenizers(do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
A__ = tokenizer.add_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size + len(lowercase ) )
A__ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
A__ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
A__ = tokenizer.add_special_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size_a + len(lowercase ) )
A__ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.get_tokenizer()
A__ = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(lowercase , [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
A__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
A__ = tokenizer.convert_tokens_to_ids(lowercase )
# fmt: off
self.assertListEqual(lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
A__ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
A__ = {
"input_ids": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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, 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, 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],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name="microsoft/speecht5_asr" , revision="c5ef64c71905caeccde0e4462ef3f9077224c524" , sequences=lowercase , )
| 68 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Union[str, Any]=7 ) -> str:
'''simple docstring'''
A__ = None
if token is not None:
A__ = {"Accept": "application/vnd.github+json", "Authorization": F'Bearer {token}'}
# The id of a workflow (not of a workflow run)
A__ = "636036"
A__ = F'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'
A__ = requests.get(SCREAMING_SNAKE_CASE_ , headers=SCREAMING_SNAKE_CASE_ ).json()
return result["workflow_runs"]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Optional[int]:
'''simple docstring'''
A__ = get_daily_ci_runs(SCREAMING_SNAKE_CASE_ )
A__ = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
A__ = workflow_run["id"]
break
return workflow_run_id
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: str ) -> List[str]:
'''simple docstring'''
A__ = get_last_daily_ci_runs(SCREAMING_SNAKE_CASE_ )
if workflow_run_id is not None:
A__ = get_artifacts_links(worflow_run_id=SCREAMING_SNAKE_CASE_ , token=SCREAMING_SNAKE_CASE_ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
A__ = artifacts_links[artifact_name]
download_artifact(
artifact_name=SCREAMING_SNAKE_CASE_ , artifact_url=SCREAMING_SNAKE_CASE_ , output_dir=SCREAMING_SNAKE_CASE_ , token=SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: List[str] ) -> Optional[int]:
'''simple docstring'''
get_last_daily_ci_artifacts(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A__ = {}
for artifact_name in artifact_names:
A__ = os.path.join(SCREAMING_SNAKE_CASE_ , F'{artifact_name}.zip' )
if os.path.isfile(SCREAMING_SNAKE_CASE_ ):
A__ = {}
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ ) as z:
for filename in z.namelist():
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
# read the file
with z.open(SCREAMING_SNAKE_CASE_ ) as f:
A__ = f.read().decode("UTF-8" )
return results
| 68 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: int ) -> List[str]:
'''simple docstring'''
A__ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
A__ = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
A__ = F'{src_lang}-{tgt_lang}'
A__ = F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
A__ = os.path.join(SCREAMING_SNAKE_CASE_ , "README.md" )
print(F'Generating {path}' )
with open(SCREAMING_SNAKE_CASE_ , "w" , encoding="utf-8" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
# make sure we are under the root of the project
lowerCAmelCase__ = Path(__file__).resolve().parent.parent.parent
lowerCAmelCase__ = repo_dir / """model_cards"""
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = model_name.split("""-""")
lowerCAmelCase__ = model_cards_dir / """facebook""" / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 68 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'open-llama'
def __init__( self , lowercase=100000 , lowercase=4096 , lowercase=11008 , lowercase=32 , lowercase=32 , lowercase="silu" , lowercase=2048 , lowercase=0.02 , lowercase=1e-6 , lowercase=True , lowercase=0 , lowercase=1 , lowercase=2 , lowercase=False , lowercase=True , lowercase=0.1 , lowercase=0.1 , lowercase=True , lowercase=True , lowercase=None , **lowercase , ) -> Dict:
'''simple docstring'''
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = intermediate_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = hidden_act
A__ = initializer_range
A__ = rms_norm_eps
A__ = use_cache
A__ = kwargs.pop(
"use_memorry_efficient_attention" , lowercase )
A__ = hidden_dropout_prob
A__ = attention_dropout_prob
A__ = use_stable_embedding
A__ = shared_input_output_embedding
A__ = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , tie_word_embeddings=lowercase , **lowercase , )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , lowercase ) or len(self.rope_scaling ) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
F'got {self.rope_scaling}' )
A__ = self.rope_scaling.get("type" , lowercase )
A__ = self.rope_scaling.get("factor" , lowercase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(lowercase , lowercase ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 68 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = feature_size
A__ = sampling_rate
A__ = padding_value
A__ = kwargs.pop("padding_side" , "right" )
A__ = kwargs.pop("return_attention_mask" , lowercase )
super().__init__(**lowercase )
def UpperCamelCase ( self , lowercase , lowercase = True , lowercase = None , lowercase = False , lowercase = None , lowercase = None , lowercase = None , ) -> BatchFeature:
'''simple docstring'''
if isinstance(lowercase , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
A__ = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
F' to this method that includes {self.model_input_names[0]}, but you provided'
F' {list(processed_features.keys() )}' )
A__ = processed_features[self.model_input_names[0]]
A__ = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(lowercase ) == 0:
if return_attention_mask:
A__ = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
A__ = required_input[0]
if isinstance(lowercase , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
A__ = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(lowercase ):
A__ = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(lowercase ):
A__ = "tf"
elif is_torch_tensor(lowercase ):
A__ = "pt"
elif isinstance(lowercase , (int, float, list, tuple, np.ndarray) ):
A__ = "np"
else:
raise ValueError(
F'type of {first_element} unknown: {type(lowercase )}. '
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
A__ = to_numpy(lowercase )
else:
A__ = [to_numpy(lowercase ) for v in value]
# Convert padding_strategy in PaddingStrategy
A__ = self._get_padding_strategies(padding=lowercase , max_length=lowercase )
A__ = processed_features[self.model_input_names[0]]
A__ = len(lowercase )
if not all(len(lowercase ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
A__ = []
for i in range(lowercase ):
A__ = {k: v[i] for k, v in processed_features.items()}
# truncation
A__ = self._truncate(
lowercase , max_length=lowercase , pad_to_multiple_of=lowercase , truncation=lowercase , )
truncated_inputs.append(lowercase )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
A__ = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
A__ = PaddingStrategy.MAX_LENGTH
A__ = {}
for i in range(lowercase ):
# padding
A__ = self._pad(
truncated_inputs[i] , max_length=lowercase , padding_strategy=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , )
for key, value in outputs.items():
if key not in batch_outputs:
A__ = []
if value.dtype is np.dtype(np.floataa ):
A__ = value.astype(np.floataa )
batch_outputs[key].append(lowercase )
return BatchFeature(lowercase , tensor_type=lowercase )
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = PaddingStrategy.DO_NOT_PAD , lowercase = None , lowercase = None , ) -> dict:
'''simple docstring'''
A__ = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
A__ = len(lowercase )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
A__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
A__ = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
A__ = np.ones(len(lowercase ) , dtype=np.intaa )
if needs_to_be_padded:
A__ = max_length - len(lowercase )
if self.padding_side == "right":
if return_attention_mask:
A__ = np.pad(
processed_features["attention_mask"] , (0, difference) )
A__ = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
A__ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
A__ = np.pad(
processed_features["attention_mask"] , (difference, 0) )
A__ = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
A__ = np.pad(
lowercase , lowercase , "constant" , constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def UpperCamelCase ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , ) -> Union[str, Any]:
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
A__ = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
A__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
A__ = len(lowercase ) > max_length
if needs_to_be_truncated:
A__ = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
A__ = processed_features["attention_mask"][:max_length]
return processed_features
def UpperCamelCase ( self , lowercase=False , lowercase=None ) -> Any:
'''simple docstring'''
if padding is not False:
if padding is True:
A__ = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(lowercase , lowercase ):
A__ = PaddingStrategy(lowercase )
elif isinstance(lowercase , lowercase ):
A__ = padding
else:
A__ = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 68 | 1 |
import argparse
import os
import re
lowerCAmelCase__ = """src/diffusers"""
# Pattern that looks at the indentation in a line.
lowerCAmelCase__ = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCAmelCase__ = re.compile(R"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCAmelCase__ = re.compile(R"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCAmelCase__ = re.compile(R"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCAmelCase__ = re.compile(R"""\[([^\]]+)\]""")
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> List[str]:
'''simple docstring'''
A__ = _re_indent.search(SCREAMING_SNAKE_CASE_ )
return "" if search is None else search.groups()[0]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Dict="" , SCREAMING_SNAKE_CASE_: List[str]=None , SCREAMING_SNAKE_CASE_: List[str]=None ) -> int:
'''simple docstring'''
A__ = 0
A__ = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(SCREAMING_SNAKE_CASE_ ):
index += 1
A__ = ["\n".join(lines[:index] )]
else:
A__ = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
A__ = [lines[index]]
index += 1
while index < len(SCREAMING_SNAKE_CASE_ ) and (end_prompt is None or not lines[index].startswith(SCREAMING_SNAKE_CASE_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(SCREAMING_SNAKE_CASE_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(SCREAMING_SNAKE_CASE_ ) )
if index < len(SCREAMING_SNAKE_CASE_ ) - 1:
A__ = [lines[index + 1]]
index += 1
else:
A__ = []
else:
blocks.append("\n".join(SCREAMING_SNAKE_CASE_ ) )
A__ = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(SCREAMING_SNAKE_CASE_ ) > 0:
blocks.append("\n".join(SCREAMING_SNAKE_CASE_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(SCREAMING_SNAKE_CASE_ ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> int:
'''simple docstring'''
def _inner(SCREAMING_SNAKE_CASE_: List[Any] ):
return key(SCREAMING_SNAKE_CASE_ ).lower().replace("_" , "" )
return _inner
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Tuple=None ) -> int:
'''simple docstring'''
def noop(SCREAMING_SNAKE_CASE_: str ):
return x
if key is None:
A__ = noop
# Constants are all uppercase, they go first.
A__ = [obj for obj in objects if key(SCREAMING_SNAKE_CASE_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
A__ = [obj for obj in objects if key(SCREAMING_SNAKE_CASE_ )[0].isupper() and not key(SCREAMING_SNAKE_CASE_ ).isupper()]
# Functions begin with a lowercase, they go last.
A__ = [obj for obj in objects if not key(SCREAMING_SNAKE_CASE_ )[0].isupper()]
A__ = ignore_underscore(SCREAMING_SNAKE_CASE_ )
return sorted(SCREAMING_SNAKE_CASE_ , key=SCREAMING_SNAKE_CASE_ ) + sorted(SCREAMING_SNAKE_CASE_ , key=SCREAMING_SNAKE_CASE_ ) + sorted(SCREAMING_SNAKE_CASE_ , key=SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any ) -> str:
'''simple docstring'''
def _replace(SCREAMING_SNAKE_CASE_: Dict ):
A__ = match.groups()[0]
if "," not in imports:
return F'[{imports}]'
A__ = [part.strip().replace("\"" , "" ) for part in imports.split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
A__ = keys[:-1]
return "[" + ", ".join([F'"{k}"' for k in sort_objects(SCREAMING_SNAKE_CASE_ )] ) + "]"
A__ = import_statement.split("\n" )
if len(SCREAMING_SNAKE_CASE_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
A__ = 2 if lines[1].strip() == "[" else 1
A__ = [(i, _re_strip_line.search(SCREAMING_SNAKE_CASE_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
A__ = sort_objects(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : x[1] )
A__ = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(SCREAMING_SNAKE_CASE_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
A__ = _re_bracket_content.sub(_replace , lines[1] )
else:
A__ = [part.strip().replace("\"" , "" ) for part in lines[1].split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
A__ = keys[:-1]
A__ = get_indent(lines[1] ) + ", ".join([F'"{k}"' for k in sort_objects(SCREAMING_SNAKE_CASE_ )] )
return "\n".join(SCREAMING_SNAKE_CASE_ )
else:
# Finally we have to deal with imports fitting on one line
A__ = _re_bracket_content.sub(_replace , SCREAMING_SNAKE_CASE_ )
return import_statement
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: Optional[Any]=True ) -> List[Any]:
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE_ , "r" ) as f:
A__ = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
A__ = split_code_in_indented_blocks(
SCREAMING_SNAKE_CASE_ , start_prompt="_import_structure = {" , end_prompt="if TYPE_CHECKING:" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(SCREAMING_SNAKE_CASE_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
A__ = main_blocks[block_idx]
A__ = block.split("\n" )
# Get to the start of the imports.
A__ = 0
while line_idx < len(SCREAMING_SNAKE_CASE_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
A__ = len(SCREAMING_SNAKE_CASE_ )
else:
line_idx += 1
if line_idx >= len(SCREAMING_SNAKE_CASE_ ):
continue
# Ignore beginning and last line: they don't contain anything.
A__ = "\n".join(block_lines[line_idx:-1] )
A__ = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
A__ = split_code_in_indented_blocks(SCREAMING_SNAKE_CASE_ , indent_level=SCREAMING_SNAKE_CASE_ )
# We have two categories of import key: list or _import_structure[key].append/extend
A__ = _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
A__ = [(pattern.search(SCREAMING_SNAKE_CASE_ ).groups()[0] if pattern.search(SCREAMING_SNAKE_CASE_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
A__ = [(i, key) for i, key in enumerate(SCREAMING_SNAKE_CASE_ ) if key is not None]
A__ = [x[0] for x in sorted(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
A__ = 0
A__ = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
A__ = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(SCREAMING_SNAKE_CASE_ )
count += 1
# And we put our main block back together with its first and last line.
A__ = "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(SCREAMING_SNAKE_CASE_ ):
if check_only:
return True
else:
print(F'Overwriting {file}.' )
with open(SCREAMING_SNAKE_CASE_ , "w" ) as f:
f.write("\n".join(SCREAMING_SNAKE_CASE_ ) )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str]=True ) -> Optional[Any]:
'''simple docstring'''
A__ = []
for root, _, files in os.walk(SCREAMING_SNAKE_CASE_ ):
if "__init__.py" in files:
A__ = sort_imports(os.path.join(SCREAMING_SNAKE_CASE_ , "__init__.py" ) , check_only=SCREAMING_SNAKE_CASE_ )
if result:
A__ = [os.path.join(SCREAMING_SNAKE_CASE_ , "__init__.py" )]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise ValueError(F'Would overwrite {len(SCREAMING_SNAKE_CASE_ )} files, run `make style`.' )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
lowerCAmelCase__ = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 68 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
"""GroupViTTextConfig""",
"""GroupViTVisionConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GroupViTModel""",
"""GroupViTPreTrainedModel""",
"""GroupViTTextModel""",
"""GroupViTVisionModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFGroupViTModel""",
"""TFGroupViTPreTrainedModel""",
"""TFGroupViTTextModel""",
"""TFGroupViTVisionModel""",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 | 1 |
from __future__ import annotations
class a__ :
"""simple docstring"""
def __init__( self , lowercase=None ) -> Optional[int]:
'''simple docstring'''
A__ = data
A__ = None
def __repr__( self ) -> int:
'''simple docstring'''
A__ = []
A__ = self
while temp:
string_rep.append(F'{temp.data}' )
A__ = temp.next
return "->".join(lowercase )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list ) -> List[str]:
'''simple docstring'''
if not elements_list:
raise Exception("The Elements List is empty" )
A__ = A__ = Node(elements_list[0] )
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
A__ = Node(elements_list[i] )
A__ = current.next
return head
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Node ) -> None:
'''simple docstring'''
if head_node is not None and isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
print_reverse(head_node.next )
print(head_node.data )
def lowerCAmelCase__ ( ) -> List[Any]:
'''simple docstring'''
from doctest import testmod
testmod()
A__ = make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
print("Linked List:" )
print(SCREAMING_SNAKE_CASE_ )
print("Elements in Reverse:" )
print_reverse(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'gpt_neox_japanese'
def __init__( self , lowercase=32000 , lowercase=2560 , lowercase=32 , lowercase=32 , lowercase=4 , lowercase="gelu" , lowercase=1.00 , lowercase=10000 , lowercase=2048 , lowercase=0.02 , lowercase=1e-5 , lowercase=True , lowercase=31996 , lowercase=31999 , lowercase=0.1 , lowercase=0.0 , **lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_multiple_size
A__ = hidden_act
A__ = rotary_pct
A__ = rotary_emb_base
A__ = initializer_range
A__ = layer_norm_eps
A__ = use_cache
A__ = attention_dropout
A__ = hidden_dropout
| 68 | 1 |
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