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'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict , UpperCAmelCase__ :str , UpperCAmelCase__ :int ):
'''simple docstring'''
a = x
a = y
for step in range(UpperCAmelCase__ ): # noqa: B007
a = a * a - b * b + x
a = 2 * a * b + y
a = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if distance == 1:
return (0, 0, 0)
else:
return (2_55, 2_55, 2_55)
def UpperCAmelCase__ ( UpperCAmelCase__ :List[Any] ):
'''simple docstring'''
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 2_55 ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__ , 1 , 1 ) )
def UpperCAmelCase__ ( UpperCAmelCase__ :List[Any] = 8_00 , UpperCAmelCase__ :Tuple = 6_00 , UpperCAmelCase__ :Any = -0.6 , UpperCAmelCase__ :List[Any] = 0 , UpperCAmelCase__ :Union[str, Any] = 3.2 , UpperCAmelCase__ :List[Any] = 50 , UpperCAmelCase__ :int = True , ):
'''simple docstring'''
a = Image.new("RGB" , (image_width, image_height) )
a = img.load()
# loop through the image-coordinates
for image_x in range(UpperCAmelCase__ ):
for image_y in range(UpperCAmelCase__ ):
# determine the figure-coordinates based on the image-coordinates
a = figure_width / image_width * image_height
a = figure_center_x + (image_x / image_width - 0.5) * figure_width
a = figure_center_y + (image_y / image_height - 0.5) * figure_height
a = get_distance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
a = get_color_coded_rgb(UpperCAmelCase__ )
else:
a = get_black_and_white_rgb(UpperCAmelCase__ )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
A_ : List[str] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 713
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Optional[int] = {
'''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''switch_transformers'''
_UpperCAmelCase = ['''past_key_values''']
_UpperCAmelCase = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : str , __lowerCAmelCase : int=3_2128 , __lowerCAmelCase : Optional[Any]=768 , __lowerCAmelCase : Optional[Any]=64 , __lowerCAmelCase : Optional[int]=2048 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : int=12 , __lowerCAmelCase : List[Any]=3 , __lowerCAmelCase : Union[str, Any]=12 , __lowerCAmelCase : Optional[int]=3 , __lowerCAmelCase : str=12 , __lowerCAmelCase : Optional[int]=8 , __lowerCAmelCase : List[str]=False , __lowerCAmelCase : Any=0.0_1 , __lowerCAmelCase : Union[str, Any]="float32" , __lowerCAmelCase : List[str]=False , __lowerCAmelCase : int=32 , __lowerCAmelCase : Tuple=128 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : Union[str, Any]=1E-6 , __lowerCAmelCase : Union[str, Any]=0.0_0_1 , __lowerCAmelCase : Any=0.0_0_1 , __lowerCAmelCase : Dict=1.0 , __lowerCAmelCase : Optional[int]="relu" , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=False , __lowerCAmelCase : int=True , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : Optional[Any]=1 , **__lowerCAmelCase : Optional[int] , ) -> int:
"""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(__UpperCamelCase ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase ) > 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=__UpperCamelCase , eos_token_id=__UpperCamelCase , is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase , )
| 714
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
| 0
|
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
A_ : Union[str, Any] = {
'''E''': 12.70,
'''T''': 9.06,
'''A''': 8.17,
'''O''': 7.51,
'''I''': 6.97,
'''N''': 6.75,
'''S''': 6.33,
'''H''': 6.09,
'''R''': 5.99,
'''D''': 4.25,
'''L''': 4.03,
'''C''': 2.78,
'''U''': 2.76,
'''M''': 2.41,
'''W''': 2.36,
'''F''': 2.23,
'''G''': 2.02,
'''Y''': 1.97,
'''P''': 1.93,
'''B''': 1.29,
'''V''': 0.98,
'''K''': 0.77,
'''J''': 0.15,
'''X''': 0.15,
'''Q''': 0.10,
'''Z''': 0.07,
}
A_ : Optional[Any] = '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
A_ : int = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
a = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def UpperCAmelCase__ ( UpperCAmelCase__ :tuple ):
'''simple docstring'''
return x[0]
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
a = get_letter_count(_UpperCamelCase )
a = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_UpperCamelCase )
a = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_UpperCamelCase )
a = "".join(freq_to_letter[freq] )
a = list(freq_to_letter_str.items() )
freq_pairs.sort(key=_UpperCamelCase , reverse=_UpperCamelCase )
a = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(_UpperCamelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
a = get_frequency_order(_UpperCamelCase )
a = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 0
|
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] = 8 ):
'''simple docstring'''
a = ascii_letters + digits + punctuation
return "".join(secrets.choice(UpperCAmelCase__ ) for _ in range(UpperCAmelCase__ ) )
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :Optional[int] ):
'''simple docstring'''
i -= len(UpperCAmelCase__ )
a = i // 3
a = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
a = (
chars_incl
+ random(UpperCAmelCase__ , quotient + remainder )
+ random(UpperCAmelCase__ , UpperCAmelCase__ )
+ random(UpperCAmelCase__ , UpperCAmelCase__ )
)
a = list(UpperCAmelCase__ )
shuffle(UpperCAmelCase__ )
return "".join(UpperCAmelCase__ )
# random is a generalised function for letters, characters and numbers
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :Dict ):
'''simple docstring'''
return "".join(secrets.choice(UpperCAmelCase__ ) for _ in range(UpperCAmelCase__ ) )
def UpperCAmelCase__ ( UpperCAmelCase__ :List[Any] , UpperCAmelCase__ :Dict ):
'''simple docstring'''
pass # Put your code here...
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
pass # Put your code here...
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict , UpperCAmelCase__ :List[Any] ):
'''simple docstring'''
pass # Put your code here...
def UpperCAmelCase__ ( UpperCAmelCase__ :Any , UpperCAmelCase__ :Dict = 8 ):
'''simple docstring'''
if len(UpperCAmelCase__ ) < min_length:
# Your Password must be at least 8 characters long
return False
a = any(char in ascii_uppercase for char in password )
a = any(char in ascii_lowercase for char in password )
a = any(char in digits for char in password )
a = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = int(input("Please indicate the max length of your password: " ).strip() )
a = input(
"Please indicate the characters that must be in your password: " ).strip()
print("Password generated:" , password_generator(UpperCAmelCase__ ) )
print(
"Alternative Password generated:" , alternative_password_generator(UpperCAmelCase__ , UpperCAmelCase__ ) , )
print("[If you are thinking of using this passsword, You better save it.]" )
if __name__ == "__main__":
main()
| 716
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
a , a , a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
a , a , a = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 32
| 0
|
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
A_ : Tuple = logging.get_logger(__name__)
def UpperCAmelCase__ ( ):
a = os.getenv("SM_HP_MP_PARAMETERS" , "{}" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
a = json.loads(UpperCAmelCase__ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
a = os.getenv("SM_FRAMEWORK_PARAMS" , "{}" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
a = json.loads(UpperCAmelCase__ )
if not mpi_options.get("sagemaker_mpi_enabled" , UpperCAmelCase__ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("smdistributed" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class _lowercase ( __lowerCAmelCase ):
_UpperCAmelCase = field(
default='''''', metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}, )
def A ( self : List[Any] ) -> str:
"""simple docstring"""
super().__post_init__()
warnings.warn(
"`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
"`TrainingArguments` instead." , lowerCamelCase__ , )
@cached_property
def A ( self : Dict ) -> "torch.device":
"""simple docstring"""
logger.info("PyTorch: setting up devices" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"torch.distributed process group is initialized, but local_rank == -1. "
"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
if self.no_cuda:
a = torch.device("cpu" )
a = 0
elif is_sagemaker_model_parallel_available():
a = smp.local_rank()
a = torch.device("cuda" , lowerCamelCase__ )
a = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="smddp" , timeout=self.ddp_timeout_delta )
a = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
a = torch.device("cuda" , self.local_rank )
a = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
a = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
a = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="nccl" , timeout=self.ddp_timeout_delta )
a = torch.device("cuda" , self.local_rank )
a = 1
if device.type == "cuda":
torch.cuda.set_device(lowerCamelCase__ )
return device
@property
def A ( self : List[Any] ) -> int:
"""simple docstring"""
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
return not is_sagemaker_model_parallel_available()
@property
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return False
| 717
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 0
|
from __future__ import annotations
from random import choice
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return choice(snake_case_ )
def UpperCAmelCase__ ( UpperCAmelCase__ :list[int] , UpperCAmelCase__ :int ):
'''simple docstring'''
a = random_pivot(snake_case_ )
# partition based on pivot
# linear time
a = [e for e in lst if e < pivot]
a = [e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(snake_case_ ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(snake_case_ ) < k - 1:
return kth_number(snake_case_ , k - len(snake_case_ ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(snake_case_ , snake_case_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 0
|
import pytest
import datasets
# Import fixture modules as plugins
A_ : Any = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :str ):
'''simple docstring'''
for item in items:
if any(marker in item.keywords for marker in ["integration", "unit"] ):
continue
item.add_marker(pytest.mark.unit )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
config.addinivalue_line("markers" , "torchaudio_latest: mark test to run with torchaudio>=0.12" )
@pytest.fixture(autouse=UpperCamelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :Union[str, Any] ):
'''simple docstring'''
a = tmp_path_factory.getbasetemp() / "cache"
a = test_hf_cache_home / "datasets"
a = test_hf_cache_home / "metrics"
a = test_hf_cache_home / "modules"
monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE" , str(UpperCamelCase__ ) )
monkeypatch.setattr("datasets.config.HF_METRICS_CACHE" , str(UpperCamelCase__ ) )
monkeypatch.setattr("datasets.config.HF_MODULES_CACHE" , str(UpperCamelCase__ ) )
a = test_hf_datasets_cache / "downloads"
monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH" , str(UpperCamelCase__ ) )
a = test_hf_datasets_cache / "downloads" / "extracted"
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH" , str(UpperCamelCase__ ) )
@pytest.fixture(autouse=UpperCamelCase__ , scope="session" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
datasets.disable_progress_bar()
@pytest.fixture(autouse=UpperCamelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, Any] ):
'''simple docstring'''
monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS" , UpperCamelCase__ )
@pytest.fixture
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING" , UpperCamelCase__ )
| 719
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
| 0
|
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : Optional[Any] , __lowerCAmelCase : Optional[NestedDataStructureLike[PathLike]] = None , __lowerCAmelCase : Optional[NamedSplit] = None , __lowerCAmelCase : Optional[Features] = None , __lowerCAmelCase : str = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : Optional[int] = None , **__lowerCAmelCase : Optional[Any] , ) -> Tuple:
"""simple docstring"""
a = path_or_paths
a = split if split or isinstance(__lowerCAmelCase , __lowerCAmelCase ) else "train"
a = features
a = cache_dir
a = keep_in_memory
a = streaming
a = num_proc
a = kwargs
@abstractmethod
def A ( self : List[Any] ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
"""simple docstring"""
pass
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : Any , __lowerCAmelCase : Optional[Features] = None , __lowerCAmelCase : str = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : Optional[int] = None , **__lowerCAmelCase : int , ) -> Dict:
"""simple docstring"""
a = features
a = cache_dir
a = keep_in_memory
a = streaming
a = num_proc
a = kwargs
@abstractmethod
def A ( self : Optional[int] ) -> Union[Dataset, IterableDataset]:
"""simple docstring"""
pass
| 720
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 32
| 0
|
import numpy as np
from PIL import Image
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
a = np.array(UpperCAmelCase__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
a = 0
a = 0
a = 0
a = 0
# compute the shape of the output matrix
a = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
a = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
a = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
a = 0
a = 0
return updated_arr
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
a = np.array(UpperCAmelCase__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
a = 0
a = 0
a = 0
a = 0
# compute the shape of the output matrix
a = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
a = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
a = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
a = 0
a = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name='''avgpooling''', verbose=True)
# Loading the image
A_ : int = Image.open('''path_to_image''')
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 721
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Dict = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-large''': '''https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''luke'''
def __init__( self : Tuple , __lowerCAmelCase : Dict=5_0267 , __lowerCAmelCase : List[Any]=50_0000 , __lowerCAmelCase : Optional[Any]=768 , __lowerCAmelCase : Union[str, Any]=256 , __lowerCAmelCase : str=12 , __lowerCAmelCase : Dict=12 , __lowerCAmelCase : List[str]=3072 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : Union[str, Any]=512 , __lowerCAmelCase : str=2 , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-12 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Any=1 , __lowerCAmelCase : Optional[int]=0 , __lowerCAmelCase : Dict=2 , **__lowerCAmelCase : Tuple , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = entity_vocab_size
a = hidden_size
a = entity_emb_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = use_entity_aware_attention
a = classifier_dropout
| 700
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 0
|
import numpy as np
import datasets
A_ : Tuple = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''
A_ : List[Any] = '''\
@article{de2000mahalanobis,
title={The mahalanobis distance},
author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
journal={Chemometrics and intelligent laboratory systems},
volume={50},
number={1},
pages={1--18},
year={2000},
publisher={Elsevier}
}
'''
A_ : Optional[Any] = '''
Args:
X: List of datapoints to be compared with the `reference_distribution`.
reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:
>>> mahalanobis_metric = datasets.load_metric("mahalanobis")
>>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
>>> print(results)
{\'mahalanobis\': array([0.5])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"X": datasets.Sequence(datasets.Value("float" , id="sequence" ) , id="X" ),
} ) , )
def A ( self : Any , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = np.array(__lowerCAmelCase )
a = np.array(__lowerCAmelCase )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError("Expected `X` to be a 2D vector" )
if len(reference_distribution.shape ) != 2:
raise ValueError("Expected `reference_distribution` to be a 2D vector" )
if reference_distribution.shape[0] < 2:
raise ValueError(
"Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension" )
# Get mahalanobis distance for each prediction
a = X - np.mean(__lowerCAmelCase )
a = np.cov(reference_distribution.T )
try:
a = np.linalg.inv(__lowerCAmelCase )
except np.linalg.LinAlgError:
a = np.linalg.pinv(__lowerCAmelCase )
a = np.dot(__lowerCAmelCase , __lowerCAmelCase )
a = np.dot(__lowerCAmelCase , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 701
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 0
|
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
A_ : Any = logging.getLogger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''summarization'''
_UpperCAmelCase = ['''loss''']
_UpperCAmelCase = ROUGE_KEYS
_UpperCAmelCase = '''rouge2'''
def __init__( self : int , __lowerCAmelCase : Optional[Any] , **__lowerCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
a = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training" )
if hparams.sortish_sampler:
raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously" )
super().__init__(__lowerCAmelCase , num_labels=__lowerCAmelCase , mode=self.mode , **__lowerCAmelCase )
use_task_specific_params(self.model , "summarization" )
save_git_info(self.hparams.output_dir )
a = Path(self.output_dir ) / "metrics.json"
a = Path(self.output_dir ) / "hparams.pkl"
pickle_save(self.hparams , self.hparams_save_path )
a = 0
a = defaultdict(__lowerCAmelCase )
a = self.config.model_type
a = self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size
a = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
a = {
"train": self.hparams.n_train,
"val": self.hparams.n_val,
"test": self.hparams.n_test,
}
a = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
a = {
"train": self.hparams.max_target_length,
"val": self.hparams.val_max_target_length,
"test": self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
a = get_git_info()["repo_sha"]
a = hparams.num_workers
a = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , __lowerCAmelCase ):
a = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
a = self.decoder_start_token_id
a = (
SeqaSeqDataset if hasattr(self.tokenizer , "prepare_seq2seq_batch" ) else LegacySeqaSeqDataset
)
a = False
a = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
a = self.hparams.eval_max_gen_length
else:
a = self.model.config.max_length
a = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def A ( self : Tuple , __lowerCAmelCase : Dict[str, torch.Tensor] ) -> Dict[str, List[str]]:
"""simple docstring"""
a = {
k: self.tokenizer.batch_decode(v.tolist() ) if "mask" not in k else v.shape for k, v in batch.items()
}
save_json(__lowerCAmelCase , Path(self.output_dir ) / "text_batch.json" )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / "tok_batch.json" )
a = True
return readable_batch
def A ( self : str , __lowerCAmelCase : List[str] , **__lowerCAmelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return self.model(__lowerCAmelCase , **__lowerCAmelCase )
def A ( self : Dict , __lowerCAmelCase : List[int] ) -> Optional[Any]:
"""simple docstring"""
a = self.tokenizer.batch_decode(
__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
return lmap(str.strip , __lowerCAmelCase )
def A ( self : int , __lowerCAmelCase : dict ) -> Tuple:
"""simple docstring"""
a = self.tokenizer.pad_token_id
a , a = batch["input_ids"], batch["attention_mask"]
a = batch["labels"]
if isinstance(self.model , __lowerCAmelCase ):
a = self.model._shift_right(__lowerCAmelCase )
else:
a = shift_tokens_right(__lowerCAmelCase , __lowerCAmelCase )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
a = decoder_input_ids
self.save_readable_batch(__lowerCAmelCase )
a = self(__lowerCAmelCase , attention_mask=__lowerCAmelCase , decoder_input_ids=__lowerCAmelCase , use_cache=__lowerCAmelCase )
a = outputs["logits"]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
a = nn.CrossEntropyLoss(ignore_index=__lowerCAmelCase )
assert lm_logits.shape[-1] == self.vocab_size
a = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
a = nn.functional.log_softmax(__lowerCAmelCase , dim=-1 )
a , a = label_smoothed_nll_loss(
__lowerCAmelCase , __lowerCAmelCase , self.hparams.label_smoothing , ignore_index=__lowerCAmelCase )
return (loss,)
@property
def A ( self : str ) -> int:
"""simple docstring"""
return self.tokenizer.pad_token_id
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
a = self._step(__lowerCAmelCase )
a = dict(zip(self.loss_names , __lowerCAmelCase ) )
# tokens per batch
a = batch["input_ids"].ne(self.pad ).sum() + batch["labels"].ne(self.pad ).sum()
a = batch["input_ids"].shape[0]
a = batch["input_ids"].eq(self.pad ).sum()
a = batch["input_ids"].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def A ( self : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[str] ) -> Dict:
"""simple docstring"""
return self._generative_step(__lowerCAmelCase )
def A ( self : str , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str="val" ) -> Dict:
"""simple docstring"""
self.step_count += 1
a = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
a = losses["loss"]
a = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
}
a = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
a = torch.tensor(__lowerCAmelCase ).type_as(__lowerCAmelCase )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(__lowerCAmelCase )
a = {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
a = self.step_count
self.metrics[prefix].append(__lowerCAmelCase ) # callback writes this to self.metrics_save_path
a = flatten_list([x["preds"] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
f"""{prefix}_loss""": loss,
f"""{prefix}_{self.val_metric}""": metric_tensor,
}
def A ( self : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Dict:
"""simple docstring"""
return calculate_rouge(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : dict ) -> dict:
"""simple docstring"""
a = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
a = self.model.generate(
batch["input_ids"] , attention_mask=batch["attention_mask"] , use_cache=__lowerCAmelCase , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
a = (time.time() - ta) / batch["input_ids"].shape[0]
a = self.ids_to_clean_text(__lowerCAmelCase )
a = self.ids_to_clean_text(batch["labels"] )
a = self._step(__lowerCAmelCase )
a = dict(zip(self.loss_names , __lowerCAmelCase ) )
a = self.calc_generative_metrics(__lowerCAmelCase , __lowerCAmelCase )
a = np.mean(lmap(__lowerCAmelCase , __lowerCAmelCase ) )
base_metrics.update(gen_time=__lowerCAmelCase , gen_len=__lowerCAmelCase , preds=__lowerCAmelCase , target=__lowerCAmelCase , **__lowerCAmelCase )
return base_metrics
def A ( self : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
return self._generative_step(__lowerCAmelCase )
def A ( self : str , __lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return self.validation_epoch_end(__lowerCAmelCase , prefix="test" )
def A ( self : int , __lowerCAmelCase : List[Any] ) -> SeqaSeqDataset:
"""simple docstring"""
a = self.n_obs[type_path]
a = self.target_lens[type_path]
a = self.dataset_class(
self.tokenizer , type_path=__lowerCAmelCase , n_obs=__lowerCAmelCase , max_target_length=__lowerCAmelCase , **self.dataset_kwargs , )
return dataset
def A ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : bool = False ) -> DataLoader:
"""simple docstring"""
a = self.get_dataset(__lowerCAmelCase )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
a = dataset.make_sortish_sampler(__lowerCAmelCase , distributed=self.hparams.gpus > 1 )
return DataLoader(
__lowerCAmelCase , batch_size=__lowerCAmelCase , collate_fn=dataset.collate_fn , shuffle=__lowerCAmelCase , num_workers=self.num_workers , sampler=__lowerCAmelCase , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
a = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
__lowerCAmelCase , batch_sampler=__lowerCAmelCase , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
__lowerCAmelCase , batch_size=__lowerCAmelCase , collate_fn=dataset.collate_fn , shuffle=__lowerCAmelCase , num_workers=self.num_workers , sampler=__lowerCAmelCase , )
def A ( self : Union[str, Any] ) -> DataLoader:
"""simple docstring"""
a = self.get_dataloader("train" , batch_size=self.hparams.train_batch_size , shuffle=__lowerCAmelCase )
return dataloader
def A ( self : Any ) -> DataLoader:
"""simple docstring"""
return self.get_dataloader("val" , batch_size=self.hparams.eval_batch_size )
def A ( self : List[Any] ) -> DataLoader:
"""simple docstring"""
return self.get_dataloader("test" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def A ( __lowerCAmelCase : str , __lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
BaseTransformer.add_model_specific_args(__lowerCAmelCase , __lowerCAmelCase )
add_generic_args(__lowerCAmelCase , __lowerCAmelCase )
parser.add_argument(
"--max_source_length" , default=1024 , type=__lowerCAmelCase , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--max_target_length" , default=56 , type=__lowerCAmelCase , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--val_max_target_length" , default=142 , type=__lowerCAmelCase , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--test_max_target_length" , default=142 , type=__lowerCAmelCase , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument("--freeze_encoder" , action="store_true" )
parser.add_argument("--freeze_embeds" , action="store_true" )
parser.add_argument("--sortish_sampler" , action="store_true" , default=__lowerCAmelCase )
parser.add_argument("--overwrite_output_dir" , action="store_true" , default=__lowerCAmelCase )
parser.add_argument("--max_tokens_per_batch" , type=__lowerCAmelCase , default=__lowerCAmelCase )
parser.add_argument("--logger_name" , type=__lowerCAmelCase , choices=["default", "wandb", "wandb_shared"] , default="default" )
parser.add_argument("--n_train" , type=__lowerCAmelCase , default=-1 , required=__lowerCAmelCase , help="# examples. -1 means use all." )
parser.add_argument("--n_val" , type=__lowerCAmelCase , default=500 , required=__lowerCAmelCase , help="# examples. -1 means use all." )
parser.add_argument("--n_test" , type=__lowerCAmelCase , default=-1 , required=__lowerCAmelCase , help="# examples. -1 means use all." )
parser.add_argument(
"--task" , type=__lowerCAmelCase , default="summarization" , required=__lowerCAmelCase , help="# examples. -1 means use all." )
parser.add_argument("--label_smoothing" , type=__lowerCAmelCase , default=0.0 , required=__lowerCAmelCase )
parser.add_argument("--src_lang" , type=__lowerCAmelCase , default="" , required=__lowerCAmelCase )
parser.add_argument("--tgt_lang" , type=__lowerCAmelCase , default="" , required=__lowerCAmelCase )
parser.add_argument("--eval_beams" , type=__lowerCAmelCase , default=__lowerCAmelCase , required=__lowerCAmelCase )
parser.add_argument(
"--val_metric" , type=__lowerCAmelCase , default=__lowerCAmelCase , required=__lowerCAmelCase , choices=["bleu", "rouge2", "loss", None] )
parser.add_argument("--eval_max_gen_length" , type=__lowerCAmelCase , default=__lowerCAmelCase , help="never generate more than n tokens" )
parser.add_argument("--save_top_k" , type=__lowerCAmelCase , default=1 , required=__lowerCAmelCase , help="How many checkpoints to save" )
parser.add_argument(
"--early_stopping_patience" , type=__lowerCAmelCase , default=-1 , required=__lowerCAmelCase , help=(
"-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
" val_check_interval will effect it."
) , )
return parser
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''translation'''
_UpperCAmelCase = ['''loss''']
_UpperCAmelCase = ['''bleu''']
_UpperCAmelCase = '''bleu'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : str , **__lowerCAmelCase : Tuple ) -> List[str]:
"""simple docstring"""
super().__init__(__lowerCAmelCase , **__lowerCAmelCase )
a = hparams.src_lang
a = hparams.tgt_lang
def A ( self : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> dict:
"""simple docstring"""
return calculate_bleu(__lowerCAmelCase , __lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :int=None ):
'''simple docstring'''
Path(args.output_dir ).mkdir(exist_ok=UpperCAmelCase__ )
check_output_dir(UpperCAmelCase__ , expected_items=3 )
if model is None:
if "summarization" in args.task:
a = SummarizationModule(UpperCAmelCase__ )
else:
a = TranslationModule(UpperCAmelCase__ )
a = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith("/tmp" )
or str(args.output_dir ).startswith("/var" )
):
a = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
a = os.environ.get("WANDB_PROJECT" , UpperCAmelCase__ )
a = WandbLogger(name=model.output_dir.name , project=UpperCAmelCase__ )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
a = WandbLogger(name=model.output_dir.name , project=F"""hf_{dataset}""" )
if args.early_stopping_patience >= 0:
a = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
a = False
a = args.val_metric == "loss"
a = generic_train(
UpperCAmelCase__ , UpperCAmelCase__ , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , UpperCAmelCase__ ) , early_stopping_callback=UpperCAmelCase__ , logger=UpperCAmelCase__ , )
pickle_save(model.hparams , model.output_dir / "hparams.pkl" )
if not args.do_predict:
return model
a = ""
a = sorted(glob.glob(os.path.join(args.output_dir , "*.ckpt" ) , recursive=UpperCAmelCase__ ) )
if checkpoints:
a = checkpoints[-1]
a = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
A_ : str = argparse.ArgumentParser()
A_ : Union[str, Any] = pl.Trainer.add_argparse_args(parser)
A_ : Tuple = SummarizationModule.add_model_specific_args(parser, os.getcwd())
A_ : Any = parser.parse_args()
main(args)
| 702
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 0
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :list , UpperCAmelCase__ :list , UpperCAmelCase__ :list , UpperCAmelCase__ :list ):
'''simple docstring'''
a = np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase__ )] )
a = np.array(UpperCAmelCase__ )
a = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , UpperCAmelCase__ ) ) , x.transpose() ) , UpperCAmelCase__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :list , UpperCAmelCase__ :list ):
'''simple docstring'''
a = (1, 2, 1)
a = (1, 1, 0, 7)
a = SARIMAX(
UpperCAmelCase__ , exog=UpperCAmelCase__ , order=UpperCAmelCase__ , seasonal_order=UpperCAmelCase__ )
a = model.fit(disp=UpperCAmelCase__ , maxiter=6_00 , method="nm" )
a = model_fit.predict(1 , len(UpperCAmelCase__ ) , exog=[test_match] )
return result[0]
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :list , UpperCAmelCase__ :list ):
'''simple docstring'''
a = SVR(kernel="rbf" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(UpperCAmelCase__ , UpperCAmelCase__ )
a = regressor.predict(UpperCAmelCase__ )
return y_pred[0]
def UpperCAmelCase__ ( UpperCAmelCase__ :list ):
'''simple docstring'''
train_user.sort()
a = np.percentile(UpperCAmelCase__ , 25 )
a = np.percentile(UpperCAmelCase__ , 75 )
a = qa - qa
a = qa - (iqr * 0.1)
return low_lim
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :float ):
'''simple docstring'''
a = 0
a = 0
for i in list_vote:
if i > actual_result:
a = not_safe + 1
else:
if abs(abs(UpperCAmelCase__ ) - abs(UpperCAmelCase__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A_ : List[str] = [[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
A_ : Dict = pd.DataFrame(
data_input, columns=['''total_user''', '''total_even''', '''days''']
)
A_ : str = Normalizer().fit_transform(data_input_df.values)
# split data
A_ : int = normalize_df[:, 2].tolist()
A_ : List[str] = normalize_df[:, 0].tolist()
A_ : List[Any] = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A_ : Tuple = normalize_df[:, [1, 2]].tolist()
A_ : List[Any] = x[: len(x) - 1]
A_ : Optional[Any] = x[len(x) - 1 :]
# for linear regression & sarimax
A_ : Optional[int] = total_date[: len(total_date) - 1]
A_ : Any = total_user[: len(total_user) - 1]
A_ : Tuple = total_match[: len(total_match) - 1]
A_ : List[str] = total_date[len(total_date) - 1 :]
A_ : str = total_user[len(total_user) - 1 :]
A_ : Any = total_match[len(total_match) - 1 :]
# voting system with forecasting
A_ : Tuple = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A_ : Tuple = '''''' if data_safety_checker(res_vote, tst_user) else '''not '''
print('''Today\'s data is {not_str}safe.''')
| 703
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 0
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = StableDiffusionPanoramaPipeline
_UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
a = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
a = DDIMScheduler()
torch.manual_seed(0 )
a = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
a = CLIPTextModel(__lowerCAmelCase )
a = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
a = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def A ( self : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Dict=0 ) -> Optional[int]:
"""simple docstring"""
a = torch.manual_seed(__lowerCAmelCase )
a = {
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = sd_pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : str ) -> Dict:
"""simple docstring"""
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def A ( self : Any ) -> Optional[Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = "french fries"
a = sd_pipe(**__lowerCAmelCase , negative_prompt=__lowerCAmelCase )
a = output.images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : List[str] ) -> str:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = sd_pipe(**__lowerCAmelCase , view_batch_size=2 )
a = output.images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : str ) -> Any:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" )
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = sd_pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = PNDMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , skip_prk_steps=__lowerCAmelCase )
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = sd_pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Optional[Any] , __lowerCAmelCase : List[str]=0 ) -> Tuple:
"""simple docstring"""
a = torch.manual_seed(__lowerCAmelCase )
a = {
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = "stabilityai/stable-diffusion-2-base"
a = DDIMScheduler.from_pretrained(__lowerCAmelCase , subfolder="scheduler" )
a = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase , scheduler=__lowerCAmelCase , safety_checker=__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing()
a = self.get_inputs()
a = pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
a = np.array(
[
0.3_6_9_6_8_3_9_2,
0.2_7_0_2_5_3_7_2,
0.3_2_4_4_6_7_6_6,
0.2_8_3_7_9_3_8_7,
0.3_6_3_6_3_2_7_4,
0.3_0_7_3_3_3_4_7,
0.2_7_1_0_0_0_2_7,
0.2_7_0_5_4_1_2_5,
0.2_5_5_3_6_0_9_6,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = StableDiffusionPanoramaPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base" , safety_checker=__lowerCAmelCase )
a = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing()
a = self.get_inputs()
a = pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
a = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def A ( self : Dict ) -> int:
"""simple docstring"""
a = 0
def callback_fn(__lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : torch.FloatTensor ) -> None:
a = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
a = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
a = latents[0, -3:, -3:, -1]
a = np.array(
[
0.1_8_6_8_1_8_6_9,
0.3_3_9_0_7_8_1_6,
0.5_3_6_1_2_7_6,
0.1_4_4_3_2_8_6_5,
-0.0_2_8_5_6_6_1_1,
-0.7_3_9_4_1_1_2_3,
0.2_3_3_9_7_9_8_7,
0.4_7_3_2_2_6_8_2,
-0.3_7_8_2_3_1_6_4,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
a = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
a = latents[0, -3:, -3:, -1]
a = np.array(
[
0.1_8_5_3_9_6_4_5,
0.3_3_9_8_7_2_4_8,
0.5_3_7_8_5_5_9,
0.1_4_4_3_7_1_4_2,
-0.0_2_4_5_5_2_6_1,
-0.7_3_3_8_3_1_7,
0.2_3_9_9_0_7_5_5,
0.4_7_3_5_6_2_7_2,
-0.3_7_8_6_5_0_5,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
a = False
a = "stabilityai/stable-diffusion-2-base"
a = DDIMScheduler.from_pretrained(__lowerCAmelCase , subfolder="scheduler" )
a = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase , scheduler=__lowerCAmelCase , safety_checker=__lowerCAmelCase )
a = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing()
a = self.get_inputs()
pipe(**__lowerCAmelCase , callback=__lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def A ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
a = "stabilityai/stable-diffusion-2-base"
a = DDIMScheduler.from_pretrained(__lowerCAmelCase , subfolder="scheduler" )
a = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase , scheduler=__lowerCAmelCase , safety_checker=__lowerCAmelCase )
a = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
a = self.get_inputs()
a = pipe(**__lowerCAmelCase )
a = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 704
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 0
|
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : str ) -> str:
"""simple docstring"""
a = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
a = AutoTokenizer.from_pretrained("xlm-roberta-base" )
a = "The dog is cute and lives in the garden house"
a = jnp.array([tokenizer.encode(__lowerCAmelCase )] )
a = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
a = jnp.array(
[[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )
a = model(__lowerCAmelCase )["last_hidden_state"]
self.assertEqual(output.shape , __lowerCAmelCase )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , __lowerCAmelCase , atol=1E-3 ) )
| 705
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 0
|
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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 _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
a , a = FlaxStableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2" , revision="bf16" , dtype=jnp.bfloataa , )
a = "A painting of a squirrel eating a burger"
a = jax.device_count()
a = num_samples * [prompt]
a = sd_pipe.prepare_inputs(__lowerCAmelCase )
a = replicate(__lowerCAmelCase )
a = shard(__lowerCAmelCase )
a = jax.random.PRNGKey(0 )
a = jax.random.split(__lowerCAmelCase , jax.device_count() )
a = sd_pipe(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , num_inference_steps=25 , jit=__lowerCAmelCase )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
a = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
a = images[0, 253:256, 253:256, -1]
a = jnp.asarray(jax.device_get(image_slice.flatten() ) )
a = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = "stabilityai/stable-diffusion-2"
a , a = FlaxDPMSolverMultistepScheduler.from_pretrained(__lowerCAmelCase , subfolder="scheduler" )
a , a = FlaxStableDiffusionPipeline.from_pretrained(
__lowerCAmelCase , scheduler=__lowerCAmelCase , revision="bf16" , dtype=jnp.bfloataa , )
a = scheduler_params
a = "A painting of a squirrel eating a burger"
a = jax.device_count()
a = num_samples * [prompt]
a = sd_pipe.prepare_inputs(__lowerCAmelCase )
a = replicate(__lowerCAmelCase )
a = shard(__lowerCAmelCase )
a = jax.random.PRNGKey(0 )
a = jax.random.split(__lowerCAmelCase , jax.device_count() )
a = sd_pipe(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , num_inference_steps=25 , jit=__lowerCAmelCase )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
a = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
a = images[0, 253:256, 253:256, -1]
a = jnp.asarray(jax.device_get(image_slice.flatten() ) )
a = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 706
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 0
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ) -> Dict:
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 707
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 0
|
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray ):
'''simple docstring'''
a , a , a = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray ):
'''simple docstring'''
return (gray > 1_27) & (gray <= 2_55)
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ):
'''simple docstring'''
a = np.zeros_like(UpperCAmelCase__ )
a = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
a = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
a = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
a = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
A_ : int = Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
A_ : int = np.array(Image.open(lena_path))
# kernel to be applied
A_ : int = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
A_ : Any = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
A_ : str = Image.fromarray(output).convert('''RGB''')
pil_img.save('''result_dilation.png''')
| 708
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_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 = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""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
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
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 = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 0
|
from __future__ import annotations
from math import pow, sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :float , UpperCAmelCase__ :float ):
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
return {"resistance": sqrt(pow(UpperCAmelCase__ , 2 ) - pow(UpperCAmelCase__ , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(UpperCAmelCase__ , 2 ) - pow(UpperCAmelCase__ , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(UpperCAmelCase__ , 2 ) + pow(UpperCAmelCase__ , 2 ) )}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 709
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 0
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : Dict = {
'''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''levit'''
def __init__( self : str , __lowerCAmelCase : List[str]=224 , __lowerCAmelCase : int=3 , __lowerCAmelCase : List[Any]=3 , __lowerCAmelCase : List[str]=2 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : Union[str, Any]=16 , __lowerCAmelCase : Tuple=[128, 256, 384] , __lowerCAmelCase : Union[str, Any]=[4, 8, 12] , __lowerCAmelCase : List[str]=[4, 4, 4] , __lowerCAmelCase : Any=[16, 16, 16] , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : Any=[2, 2, 2] , __lowerCAmelCase : Tuple=[2, 2, 2] , __lowerCAmelCase : Dict=0.0_2 , **__lowerCAmelCase : int , ) -> Dict:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = num_channels
a = kernel_size
a = stride
a = padding
a = hidden_sizes
a = num_attention_heads
a = depths
a = key_dim
a = drop_path_rate
a = patch_size
a = attention_ratio
a = mlp_ratio
a = initializer_range
a = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = version.parse('''1.11''' )
@property
def A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def A ( self : str ) -> float:
"""simple docstring"""
return 1E-4
| 710
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 0
|
from collections.abc import Iterable
from typing import Any
class _lowercase :
def __init__( self : Optional[int] , __lowerCAmelCase : int | None = None ) -> Optional[Any]:
"""simple docstring"""
a = value
a = None # Added in order to delete a node easier
a = None
a = None
def __repr__( self : str ) -> str:
"""simple docstring"""
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f"""{self.value}""": (self.left, self.right)} , indent=1 )
class _lowercase :
def __init__( self : int , __lowerCAmelCase : Node | None = None ) -> str:
"""simple docstring"""
a = root
def __str__( self : str ) -> str:
"""simple docstring"""
return str(self.root )
def A ( self : Any , __lowerCAmelCase : Node , __lowerCAmelCase : Node | None ) -> None:
"""simple docstring"""
if new_children is not None: # reset its kids
a = node.parent
if node.parent is not None: # reset its parent
if self.is_right(__lowerCAmelCase ): # If it is the right children
a = new_children
else:
a = new_children
else:
a = new_children
def A ( self : Tuple , __lowerCAmelCase : Node ) -> bool:
"""simple docstring"""
if node.parent and node.parent.right:
return node == node.parent.right
return False
def A ( self : str ) -> bool:
"""simple docstring"""
return self.root is None
def A ( self : int , __lowerCAmelCase : List[Any] ) -> None:
"""simple docstring"""
a = Node(__lowerCAmelCase ) # create a new Node
if self.empty(): # if Tree is empty
a = new_node # set its root
else: # Tree is not empty
a = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
a = new_node # We insert the new node in a leaf
break
else:
a = parent_node.left
else:
if parent_node.right is None:
a = new_node
break
else:
a = parent_node.right
a = parent_node
def A ( self : Any , *__lowerCAmelCase : Any ) -> None:
"""simple docstring"""
for value in values:
self.__insert(__lowerCAmelCase )
def A ( self : Tuple , __lowerCAmelCase : List[str] ) -> Node | None:
"""simple docstring"""
if self.empty():
raise IndexError("Warning: Tree is empty! please use another." )
else:
a = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
a = node.left if value < node.value else node.right
return node
def A ( self : Dict , __lowerCAmelCase : Node | None = None ) -> Node | None:
"""simple docstring"""
if node is None:
if self.root is None:
return None
a = self.root
if not self.empty():
while node.right is not None:
a = node.right
return node
def A ( self : Tuple , __lowerCAmelCase : Node | None = None ) -> Node | None:
"""simple docstring"""
if node is None:
a = self.root
if self.root is None:
return None
if not self.empty():
a = self.root
while node.left is not None:
a = node.left
return node
def A ( self : Any , __lowerCAmelCase : int ) -> None:
"""simple docstring"""
a = self.search(__lowerCAmelCase ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(__lowerCAmelCase , __lowerCAmelCase )
elif node.left is None: # Has only right children
self.__reassign_nodes(__lowerCAmelCase , node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(__lowerCAmelCase , node.left )
else:
a = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
a = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def A ( self : Any , __lowerCAmelCase : Node | None ) -> Iterable:
"""simple docstring"""
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def A ( self : Dict , __lowerCAmelCase : Tuple=None ) -> Any:
"""simple docstring"""
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def A ( self : str , __lowerCAmelCase : list , __lowerCAmelCase : Node | None ) -> None:
"""simple docstring"""
if node:
self.inorder(__lowerCAmelCase , node.left )
arr.append(node.value )
self.inorder(__lowerCAmelCase , node.right )
def A ( self : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Node ) -> int:
"""simple docstring"""
a = []
self.inorder(__lowerCAmelCase , __lowerCAmelCase ) # append all values to list using inorder traversal
return arr[k - 1]
def UpperCAmelCase__ ( UpperCAmelCase__ :Node | None ):
'''simple docstring'''
a = []
if curr_node is not None:
a = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = (8, 3, 6, 1, 10, 14, 13, 4, 7)
a = BinarySearchTree()
for i in testlist:
t.insert(UpperCAmelCase__ )
# Prints all the elements of the list in order traversal
print(UpperCAmelCase__ )
if t.search(6 ) is not None:
print("The value 6 exists" )
else:
print("The value 6 doesn't exist" )
if t.search(-1 ) is not None:
print("The value -1 exists" )
else:
print("The value -1 doesn't exist" )
if not t.empty():
print("Max Value: " , t.get_max().value ) # type: ignore
print("Min Value: " , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(UpperCAmelCase__ )
print(UpperCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 711
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
| 0
|
import logging
from transformers import PretrainedConfig
A_ : Tuple = logging.getLogger(__name__)
A_ : Dict = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''bertabs'''
def __init__( self : Optional[int] , __lowerCAmelCase : int=3_0522 , __lowerCAmelCase : Dict=512 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : Optional[Any]=512 , __lowerCAmelCase : Union[str, Any]=8 , __lowerCAmelCase : Any=512 , __lowerCAmelCase : List[Any]=0.2 , __lowerCAmelCase : List[str]=6 , __lowerCAmelCase : Tuple=768 , __lowerCAmelCase : int=8 , __lowerCAmelCase : str=2048 , __lowerCAmelCase : Any=0.2 , **__lowerCAmelCase : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = vocab_size
a = max_pos
a = enc_layers
a = enc_hidden_size
a = enc_heads
a = enc_ff_size
a = enc_dropout
a = dec_layers
a = dec_hidden_size
a = dec_heads
a = dec_ff_size
a = dec_dropout
| 712
|
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 32
| 0
|
'''simple docstring'''
from math import isclose, sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :float , UpperCAmelCase__ :float ):
'''simple docstring'''
a = point_y / 4 / point_x
a = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
a = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
a = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
a = outgoing_gradient**2 + 4
a = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
a = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
a = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
a = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
a = x_minus if isclose(UpperCAmelCase__ , UpperCAmelCase__ ) else x_plus
a = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def UpperCAmelCase__ ( UpperCAmelCase__ :float = 1.4 , UpperCAmelCase__ :float = -9.6 ):
'''simple docstring'''
a = 0
a = first_x_coord
a = first_y_coord
a = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
a , a , a = next_point(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(F"""{solution() = }""")
| 713
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32
| 0
|
from manim import *
class _lowercase ( UpperCAmelCase__ ):
def A ( self : int ) -> List[Any]:
"""simple docstring"""
a = Rectangle(height=0.5 , width=0.5 )
a = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
a = Rectangle(height=0.2_5 , width=0.2_5 )
a = [mem.copy() for i in range(6 )]
a = [mem.copy() for i in range(6 )]
a = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
a = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
a = VGroup(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
a = Text("CPU" , font_size=24 )
a = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__lowerCAmelCase )
a = [mem.copy() for i in range(4 )]
a = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
a = Text("GPU" , font_size=24 )
a = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(__lowerCAmelCase )
a = [mem.copy() for i in range(6 )]
a = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
a = Text("Model" , font_size=24 )
a = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(__lowerCAmelCase )
a = []
a = []
for i, rect in enumerate(__lowerCAmelCase ):
a = fill.copy().set_fill(__lowerCAmelCase , opacity=0.8 )
target.move_to(__lowerCAmelCase )
model_arr.append(__lowerCAmelCase )
a = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0.0 ).set_fill(__lowerCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(__lowerCAmelCase )
self.add(*__lowerCAmelCase , *__lowerCAmelCase )
a = [meta_mem.copy() for i in range(6 )]
a = [meta_mem.copy() for i in range(6 )]
a = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
a = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
a = VGroup(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
a = Text("Disk" , font_size=24 )
a = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
disk.move_to([-4, -1.2_5, 0] )
self.add(__lowerCAmelCase , __lowerCAmelCase )
a = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
a = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__lowerCAmelCase , __lowerCAmelCase )
a = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , )
blue_text.next_to(__lowerCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(__lowerCAmelCase )
a = MarkupText(
f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCAmelCase ) )
a = Square(0.3 )
input.set_fill(__lowerCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , __lowerCAmelCase , buff=0.5 )
self.play(Write(__lowerCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=__lowerCAmelCase , buff=0.0_2 )
self.play(MoveToTarget(__lowerCAmelCase ) )
self.play(FadeOut(__lowerCAmelCase ) )
a = Arrow(start=__lowerCAmelCase , end=__lowerCAmelCase , color=__lowerCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , __lowerCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
a = MarkupText(
f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCAmelCase , run_time=3 ) )
a = {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.0_2}
self.play(
Write(__lowerCAmelCase ) , Circumscribe(model_arr[0] , color=__lowerCAmelCase , **__lowerCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=__lowerCAmelCase , **__lowerCAmelCase ) , Circumscribe(gpu_rect[0] , color=__lowerCAmelCase , **__lowerCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
a = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.0_2 , __lowerCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.0_2 )
a = AnimationGroup(
FadeOut(__lowerCAmelCase , run_time=0.5 ) , MoveToTarget(__lowerCAmelCase , run_time=0.5 ) , FadeIn(__lowerCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(__lowerCAmelCase )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
a = 0.7
self.play(
Circumscribe(model_arr[i] , **__lowerCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **__lowerCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=__lowerCAmelCase , **__lowerCAmelCase ) , Circumscribe(gpu_rect[0] , color=__lowerCAmelCase , **__lowerCAmelCase ) , Circumscribe(model_arr[i + 1] , color=__lowerCAmelCase , **__lowerCAmelCase ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.0_2 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=__lowerCAmelCase , **__lowerCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=__lowerCAmelCase , **__lowerCAmelCase ) , Circumscribe(gpu_rect[0] , color=__lowerCAmelCase , **__lowerCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
a = a_c
a = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.0_2 , buff=0.5 )
self.play(
FadeOut(__lowerCAmelCase ) , FadeOut(__lowerCAmelCase , run_time=0.5 ) , )
a = MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed.""" , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCAmelCase , run_time=3 ) , MoveToTarget(__lowerCAmelCase ) )
self.wait()
| 714
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
| 0
|
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
A_ : List[Any] = logging.get_logger(__name__)
A_ : List[Any] = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
A_ : List[str] = {
'''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'''
)
},
}
A_ : List[str] = {
'''facebook/blenderbot_small-90M''': 5_12,
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = BlenderbotSmallTokenizer
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : Tuple=None , __lowerCAmelCase : Any="<|endoftext|>" , __lowerCAmelCase : Tuple="<|endoftext|>" , __lowerCAmelCase : str="<|endoftext|>" , __lowerCAmelCase : Any=False , __lowerCAmelCase : Union[str, Any]=True , **__lowerCAmelCase : int , ) -> str:
"""simple docstring"""
super().__init__(
ByteLevelBPETokenizer(
vocab=__lowerCAmelCase , merges=__lowerCAmelCase , add_prefix_space=__lowerCAmelCase , trim_offsets=__lowerCAmelCase , ) , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , **__lowerCAmelCase , )
a = add_prefix_space
def A ( self : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str]=None ) -> List[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 A ( self : int , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = 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]
| 715
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Tuple = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Dict = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 716
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
a , a , a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
a , a , a = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 32
| 0
|
import socket
def UpperCAmelCase__ ( ):
a = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
a = socket.gethostname()
a = 1_23_12
sock.connect((host, port) )
sock.send(b"Hello server!" )
with open("Received_file" , "wb" ) as out_file:
print("File opened" )
print("Receiving data..." )
while True:
a = sock.recv(10_24 )
if not data:
break
out_file.write(UpperCAmelCase__ )
print("Successfully received the file" )
sock.close()
print("Connection closed" )
if __name__ == "__main__":
main()
| 717
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 0
|
import unittest
from transformers import LiltConfig, 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 (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any]=13 , __lowerCAmelCase : Optional[Any]=7 , __lowerCAmelCase : Any=True , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Any=99 , __lowerCAmelCase : Optional[Any]=24 , __lowerCAmelCase : Any=2 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=37 , __lowerCAmelCase : Any="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Optional[Any]=512 , __lowerCAmelCase : Any=16 , __lowerCAmelCase : Optional[Any]=2 , __lowerCAmelCase : Optional[int]=0.0_2 , __lowerCAmelCase : List[str]=3 , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : str=1000 , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
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 = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = scope
a = range_bbox
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
a = bbox[i, j, 3]
a = bbox[i, j, 1]
a = t
if bbox[i, j, 2] < bbox[i, j, 0]:
a = bbox[i, j, 2]
a = bbox[i, j, 0]
a = t
a = None
if self.use_input_mask:
a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
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 = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def A ( self : Dict , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int , ) -> Optional[Any]:
"""simple docstring"""
a = LiltModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , bbox=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , bbox=__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = LiltForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : int , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
a = LiltForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : Any ) -> Any:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': LiltModel,
'''question-answering''': LiltForQuestionAnswering,
'''text-classification''': LiltForSequenceClassification,
'''token-classification''': LiltForTokenClassification,
'''zero-shot''': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : int , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int ) -> int:
"""simple docstring"""
return True
def A ( self : int ) -> int:
"""simple docstring"""
a = LiltModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : str ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a = type
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : List[Any] ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
@slow
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = LiltModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@require_torch
@slow
class _lowercase ( unittest.TestCase ):
def A ( self : int ) -> int:
"""simple docstring"""
a = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(__lowerCAmelCase )
a = torch.tensor([[1, 2]] , device=__lowerCAmelCase )
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(input_ids=__lowerCAmelCase , bbox=__lowerCAmelCase )
a = torch.Size([1, 2, 768] )
a = torch.tensor(
[[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]] , device=__lowerCAmelCase , )
self.assertTrue(outputs.last_hidden_state.shape , __lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , __lowerCAmelCase , atol=1E-3 ) )
| 718
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : List[Any] = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = ['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = [
'''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDistilBertForMaskedLM''',
'''TFDistilBertForMultipleChoice''',
'''TFDistilBertForQuestionAnswering''',
'''TFDistilBertForSequenceClassification''',
'''TFDistilBertForTokenClassification''',
'''TFDistilBertMainLayer''',
'''TFDistilBertModel''',
'''TFDistilBertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = [
'''FlaxDistilBertForMaskedLM''',
'''FlaxDistilBertForMultipleChoice''',
'''FlaxDistilBertForQuestionAnswering''',
'''FlaxDistilBertForSequenceClassification''',
'''FlaxDistilBertForTokenClassification''',
'''FlaxDistilBertModel''',
'''FlaxDistilBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
A_ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 719
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
| 0
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :list[int] , UpperCAmelCase__ :list[int] , UpperCAmelCase__ :int ):
'''simple docstring'''
a = list(range(len(UpperCAmelCase__ ) ) )
a = [v / w for v, w in zip(UpperCAmelCase__ , UpperCAmelCase__ )]
index.sort(key=lambda UpperCAmelCase__ : ratio[i] , reverse=UpperCAmelCase__ )
a = 0
a = [0] * len(UpperCAmelCase__ )
for i in index:
if weight[i] <= capacity:
a = 1
max_value += value[i]
capacity -= weight[i]
else:
a = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 720
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 32
| 0
|
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
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.
A_ : List[str] = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
_UpperCAmelCase = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
_UpperCAmelCase = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def A ( self : str , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Tuple ) -> Any:
"""simple docstring"""
a = ZeroShotClassificationPipeline(
model=__lowerCAmelCase , tokenizer=__lowerCAmelCase , candidate_labels=["polics", "health"] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def A ( self : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
a = classifier("Who are you voting for in 2020?" , candidate_labels="politics" )
self.assertEqual(__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase )]} )
# No kwarg
a = classifier("Who are you voting for in 2020?" , ["politics"] )
self.assertEqual(__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase )]} )
a = classifier("Who are you voting for in 2020?" , candidate_labels=["politics"] )
self.assertEqual(__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase )]} )
a = classifier("Who are you voting for in 2020?" , candidate_labels="politics, public health" )
self.assertEqual(
__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"] ) ) , 1.0 )
a = classifier("Who are you voting for in 2020?" , candidate_labels=["politics", "public health"] )
self.assertEqual(
__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"] ) ) , 1.0 )
a = classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template="This text is about {}" )
self.assertEqual(__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
a = classifier(["I am happy"] , ["positive", "negative"] )
self.assertEqual(
__lowerCAmelCase , [
{"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )]}
for i in range(1 )
] , )
a = classifier(["I am happy", "I am sad"] , ["positive", "negative"] )
self.assertEqual(
__lowerCAmelCase , [
{"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )]}
for i in range(2 )
] , )
with self.assertRaises(__lowerCAmelCase ):
classifier("" , candidate_labels="politics" )
with self.assertRaises(__lowerCAmelCase ):
classifier(__lowerCAmelCase , candidate_labels="politics" )
with self.assertRaises(__lowerCAmelCase ):
classifier("Who are you voting for in 2020?" , candidate_labels="" )
with self.assertRaises(__lowerCAmelCase ):
classifier("Who are you voting for in 2020?" , candidate_labels=__lowerCAmelCase )
with self.assertRaises(__lowerCAmelCase ):
classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template="Not formatting template" , )
with self.assertRaises(__lowerCAmelCase ):
classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template=__lowerCAmelCase , )
self.run_entailment_id(__lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : Pipeline ) -> Dict:
"""simple docstring"""
a = zero_shot_classifier.model.config
a = config.labelaid
a = zero_shot_classifier.entailment_id
a = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
a = {"entailment": 0, "neutral": 1, "contradiction": 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
a = {"ENTAIL": 0, "NON-ENTAIL": 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
a = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
a = original_labelaid
self.assertEqual(__lowerCAmelCase , zero_shot_classifier.entailment_id )
@require_torch
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
a = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="pt" , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"Who are you voting for in 2020?" * 100 , candidate_labels=["politics", "public health", "science"] )
@require_torch
def A ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
a = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="pt" , )
a = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@require_tf
def A ( self : int ) -> Union[str, Any]:
"""simple docstring"""
a = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="tf" , )
a = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@slow
@require_torch
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
a = pipeline("zero-shot-classification" , model="roberta-large-mnli" , framework="pt" )
a = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
a = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data." , candidate_labels=["machine learning", "statistics", "translation", "vision"] , multi_label=__lowerCAmelCase , )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
@slow
@require_tf
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = pipeline("zero-shot-classification" , model="roberta-large-mnli" , framework="tf" )
a = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
a = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data." , candidate_labels=["machine learning", "statistics", "translation", "vision"] , multi_label=__lowerCAmelCase , )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
| 721
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 0
|
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : Optional[int] = logging.get_logger(__name__)
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :List[Any] ):
'''simple docstring'''
a = WavaVecaForSequenceClassification.from_pretrained(UpperCAmelCase__ , config=UpperCAmelCase__ )
a = downstream_dict["projector.weight"]
a = downstream_dict["projector.bias"]
a = downstream_dict["model.post_net.linear.weight"]
a = downstream_dict["model.post_net.linear.bias"]
return model
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Dict ):
'''simple docstring'''
a = WavaVecaForAudioFrameClassification.from_pretrained(UpperCAmelCase__ , config=UpperCAmelCase__ )
a = downstream_dict["model.linear.weight"]
a = downstream_dict["model.linear.bias"]
return model
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[Any] , UpperCAmelCase__ :int , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = WavaVecaForXVector.from_pretrained(UpperCAmelCase__ , config=UpperCAmelCase__ )
a = downstream_dict["connector.weight"]
a = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
a = downstream_dict[
F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
]
a = downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
a = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
a = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
a = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
a = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
a = downstream_dict["objective.W"]
return model
@torch.no_grad()
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :str , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
a = torch.load(UpperCAmelCase__ , map_location="cpu" )
a = checkpoint["Downstream"]
a = WavaVecaConfig.from_pretrained(UpperCAmelCase__ )
a = WavaVecaFeatureExtractor.from_pretrained(
UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , do_normalize=UpperCAmelCase__ )
a = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
a = convert_classification(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
elif arch.endswith("ForAudioFrameClassification" ):
a = convert_diarization(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
elif arch.endswith("ForXVector" ):
a = convert_xvector(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
else:
raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}""" )
if hf_config.use_weighted_layer_sum:
a = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(UpperCAmelCase__ )
hf_model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Any = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
A_ : Any = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 700
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 0
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 701
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 0
|
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
A_ : Tuple = threading.Lock()
A_ : Optional[logging.Handler] = None
A_ : List[Any] = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
A_ : Optional[Any] = logging.WARNING
A_ : List[Any] = True
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = os.getenv("TRANSFORMERS_VERBOSITY" , UpperCAmelCase__ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
F"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def UpperCAmelCase__ ( ):
'''simple docstring'''
return __name__.split("." )[0]
def UpperCAmelCase__ ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
a = logging.StreamHandler() # Set sys.stderr as stream.
a = sys.stderr.flush
# Apply our default configuration to the library root logger.
a = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
a = False
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _default_handler
with _lock:
if not _default_handler:
return
a = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
a = None
def UpperCAmelCase__ ( ):
'''simple docstring'''
return log_levels
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[str] = None ):
'''simple docstring'''
if name is None:
a = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
_configure_library_root_logger()
_get_library_root_logger().setLevel(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def UpperCAmelCase__ ( UpperCAmelCase__ :logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
a = False
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
a = True
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = _get_library_root_logger().handlers
for handler in handlers:
a = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s" )
handler.setFormatter(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(UpperCAmelCase__ )
def UpperCAmelCase__ ( self :Any , *UpperCAmelCase__ :List[str] , **UpperCAmelCase__ :List[str] ):
'''simple docstring'''
a = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS" , UpperCAmelCase__ )
if no_advisory_warnings:
return
self.warning(*UpperCAmelCase__ , **UpperCAmelCase__ )
A_ : List[str] = warning_advice
@functools.lru_cache(UpperCAmelCase__ )
def UpperCAmelCase__ ( self :Dict , *UpperCAmelCase__ :int , **UpperCAmelCase__ :int ):
'''simple docstring'''
self.warning(*UpperCAmelCase__ , **UpperCAmelCase__ )
A_ : List[Any] = warning_once
class _lowercase :
def __init__( self : str , *__lowerCAmelCase : Union[str, Any] , **__lowerCAmelCase : str ) -> Dict: # pylint: disable=unused-argument
"""simple docstring"""
a = args[0] if args else None
def __iter__( self : List[Any] ) -> Tuple:
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self : Dict , __lowerCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
def empty_fn(*__lowerCAmelCase : Optional[int] , **__lowerCAmelCase : Optional[int] ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : Tuple ) -> Optional[int]:
"""simple docstring"""
return self
def __exit__( self : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
return
class _lowercase :
def __call__( self : List[Any] , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm(*__lowerCAmelCase , **__lowerCAmelCase )
else:
return EmptyTqdm(*__lowerCAmelCase , **__lowerCAmelCase )
def A ( self : Dict , *__lowerCAmelCase : Any , **__lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*__lowerCAmelCase , **__lowerCAmelCase )
def A ( self : List[Any] ) -> Tuple:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
A_ : List[str] = _tqdm_cls()
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
a = True
hf_hub_utils.enable_progress_bars()
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
a = False
hf_hub_utils.disable_progress_bars()
| 702
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 0
|
from sklearn.metrics import mean_squared_error
import datasets
A_ : int = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
A_ : Optional[int] = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
A_ : str = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
def A ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
] , )
def A ( self : Dict ) -> Tuple:
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("float" ) ),
"references": datasets.Sequence(datasets.Value("float" ) ),
}
else:
return {
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
}
def A ( self : Dict , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : str="uniform_average" , __lowerCAmelCase : List[Any]=True ) -> Any:
"""simple docstring"""
a = mean_squared_error(
__lowerCAmelCase , __lowerCAmelCase , sample_weight=__lowerCAmelCase , multioutput=__lowerCAmelCase , squared=__lowerCAmelCase )
return {"mse": mse}
| 703
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 0
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("only integers accepted as input" )
else:
a = str(abs(UpperCAmelCase__ ) )
a = [list(UpperCAmelCase__ ) for char in range(len(UpperCAmelCase__ ) )]
for index in range(len(UpperCAmelCase__ ) ):
num_transpositions[index].pop(UpperCAmelCase__ )
return max(
int("".join(list(UpperCAmelCase__ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 704
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 0
|
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict , UpperCAmelCase__ :str , UpperCAmelCase__ :List[Any]=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
a = nn.Parameter(UpperCAmelCase__ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
a = nn.Parameter(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :List[Any] , UpperCAmelCase__ :Optional[Any] , UpperCAmelCase__ :int ):
'''simple docstring'''
a = np.asarray(weights[0] )
a = np.asarray(weights[1] )
a = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(UpperCAmelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCAmelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(UpperCAmelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCAmelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(UpperCAmelCase__ ).view(-1 , UpperCAmelCase__ ).contiguous().transpose(0 , 1 ) , )
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :Any , UpperCAmelCase__ :Tuple ):
'''simple docstring'''
a = np.asarray(weights[0] )
a = np.asarray(weights[1] )
a = np.asarray(weights[2] )
a = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(UpperCAmelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCAmelCase__ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(UpperCAmelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCAmelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(UpperCAmelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , UpperCAmelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(UpperCAmelCase__ ).view(-1 , UpperCAmelCase__ ).contiguous().transpose(0 , 1 ) , )
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :Tuple , UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
a = weights[0][0][0]
a = np.asarray(layer_norm_a[0] )
a = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(UpperCAmelCase__ ) , torch.tensor(UpperCAmelCase__ ) , )
# lsh weights + output
a = weights[0][1]
if len(UpperCAmelCase__ ) < 4:
set_layer_weights_in_torch_lsh(UpperCAmelCase__ , torch_block.attention , UpperCAmelCase__ )
else:
set_layer_weights_in_torch_local(UpperCAmelCase__ , torch_block.attention , UpperCAmelCase__ )
# intermediate weighs
a = weights[2][0][1][2]
# Chunked Feed Forward
if len(UpperCAmelCase__ ) == 4:
a = intermediate_weights[2]
# layernorm 2
a = np.asarray(intermediate_weights[0][0] )
a = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(UpperCAmelCase__ ) , torch.tensor(UpperCAmelCase__ ) , )
# intermediate dense
a = np.asarray(intermediate_weights[1][0] )
a = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(UpperCAmelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(UpperCAmelCase__ ) , )
# intermediate out
a = np.asarray(intermediate_weights[4][0] )
a = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(UpperCAmelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(UpperCAmelCase__ ) , )
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[Any] , UpperCAmelCase__ :int ):
'''simple docstring'''
a = torch_model.reformer
# word embeds
a = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(UpperCAmelCase__ ) , )
if isinstance(weights[3] , UpperCAmelCase__ ):
a = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
a = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F"""{position_embeddings[emb_idx]} emb does not match"""
a = nn.Parameter(torch.tensor(UpperCAmelCase__ ) )
a = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
UpperCAmelCase__ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
a = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# output layer norm
a = np.asarray(weights[7][0] )
a = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(UpperCAmelCase__ ) , torch.tensor(UpperCAmelCase__ ) , )
# output embeddings
a = np.asarray(weights[9][0] )
a = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(UpperCAmelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(UpperCAmelCase__ ) , )
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Dict ):
'''simple docstring'''
a = ReformerConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = ReformerModelWithLMHead(UpperCAmelCase__ )
with open(UpperCAmelCase__ , "rb" ) as f:
a = pickle.load(UpperCAmelCase__ )["weights"]
set_model_weights_in_torch(UpperCAmelCase__ , UpperCAmelCase__ , config.hidden_size )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Optional[Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 705
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 0
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 706
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 0
|
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A_ : Dict = logging.getLogger()
def UpperCAmelCase__ ( ) -> Union[str, Any]:
'''simple docstring'''
a = argparse.ArgumentParser()
parser.add_argument("-f" )
a = parser.parse_args()
return args.f
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] ) -> Dict:
'''simple docstring'''
a = {}
a = os.path.join(UpperCAmelCase__ , "all_results.json" )
if os.path.exists(UpperCAmelCase__ ):
with open(UpperCAmelCase__ , "r" ) as f:
a = json.load(UpperCAmelCase__ )
else:
raise ValueError(F"""can't find {path}""" )
return results
def UpperCAmelCase__ ( ) -> Tuple:
'''simple docstring'''
a = torch.cuda.is_available() and torch_device == "cuda"
return is_using_cuda and is_apex_available()
A_ : Tuple = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _lowercase ( UpperCAmelCase__ ):
@classmethod
def A ( cls : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a = tempfile.mkdtemp()
a = os.path.join(cls.tmpdir , "default_config.yml" )
write_basic_config(save_location=cls.configPath )
a = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def A ( cls : List[Any] ) -> Optional[Any]:
"""simple docstring"""
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "glue_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[str] ) -> Tuple:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertLess(result["perplexity"] , 100 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "clm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertLess(result["perplexity"] , 42 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "mlm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
a = 7 if get_gpu_count() > 1 else 2
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.7_5 )
self.assertLess(result["train_loss"] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "ner_no_trainer" ) ) )
@unittest.skip(reason="Fix me @muellerzr" )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result["eval_f1"] , 28 )
self.assertGreaterEqual(result["eval_exact"] , 28 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "qa_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "swag_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_rouge1"] , 10 )
self.assertGreaterEqual(result["eval_rouge2"] , 2 )
self.assertGreaterEqual(result["eval_rougeL"] , 7 )
self.assertGreaterEqual(result["eval_rougeLsum"] , 7 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "summarization_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_bleu"] , 30 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "translation_no_trainer" ) ) )
@slow
def A ( self : Tuple ) -> Tuple:
"""simple docstring"""
a = logging.StreamHandler(sys.stdout )
logger.addHandler(__lowerCAmelCase )
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_overall_accuracy"] , 0.1_0 )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[Any] ) -> List[str]:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
# The base model scores a 25%
self.assertGreaterEqual(result["eval_accuracy"] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "step_1" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "image_classification_no_trainer" ) ) )
| 707
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 0
|
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 708
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_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 = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""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
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
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 = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 0
|
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A_ : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
A_ : str = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
A_ : List[str] = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :str ):
'''simple docstring'''
a = len([g for position, g in enumerate(UpperCAmelCase__ ) if g == main_target[position]] )
return (item, float(UpperCAmelCase__ ))
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :str ):
'''simple docstring'''
a = random.randint(0 , len(UpperCAmelCase__ ) - 1 )
a = parent_a[:random_slice] + parent_a[random_slice:]
a = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :list[str] ):
'''simple docstring'''
a = list(UpperCAmelCase__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
a = random.choice(UpperCAmelCase__ )
return "".join(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :tuple[str, float] , UpperCAmelCase__ :list[tuple[str, float]] , UpperCAmelCase__ :list[str] , ):
'''simple docstring'''
a = []
# Generate more children proportionally to the fitness score.
a = int(parent_a[1] * 1_00 ) + 1
a = 10 if child_n >= 10 else child_n
for _ in range(UpperCAmelCase__ ):
a = population_score[random.randint(0 , UpperCAmelCase__ )][0]
a , a = crossover(parent_a[0] , UpperCAmelCase__ )
# Append new string to the population list.
pop.append(mutate(UpperCAmelCase__ , UpperCAmelCase__ ) )
pop.append(mutate(UpperCAmelCase__ , UpperCAmelCase__ ) )
return pop
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :list[str] , UpperCAmelCase__ :bool = True ):
'''simple docstring'''
if N_POPULATION < N_SELECTED:
a = F"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(UpperCAmelCase__ )
# Verify that the target contains no genes besides the ones inside genes variable.
a = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
a = F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(UpperCAmelCase__ )
# Generate random starting population.
a = []
for _ in range(UpperCAmelCase__ ):
population.append("".join([random.choice(UpperCAmelCase__ ) for i in range(len(UpperCAmelCase__ ) )] ) )
# Just some logs to know what the algorithms is doing.
a , a = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(UpperCAmelCase__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
a = [evaluate(UpperCAmelCase__ , UpperCAmelCase__ ) for item in population]
# Check if there is a matching evolution.
a = sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : x[1] , reverse=UpperCAmelCase__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F"""\nGeneration: {generation}"""
F"""\nTotal Population:{total_population}"""
F"""\nBest score: {population_score[0][1]}"""
F"""\nBest string: {population_score[0][0]}""" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
a = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(UpperCAmelCase__ )
# Normalize population score to be between 0 and 1.
a = [
(item, score / len(UpperCAmelCase__ )) for item, score in population_score
]
# This is selection
for i in range(UpperCAmelCase__ ):
population.extend(select(population_score[int(UpperCAmelCase__ )] , UpperCAmelCase__ , UpperCAmelCase__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(UpperCAmelCase__ ) > N_POPULATION:
break
if __name__ == "__main__":
A_ : int = (
'''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
)
A_ : Dict = list(
''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
'''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
)
A_ : int = basic(target_str, genes_list)
print(
F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
)
| 709
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 0
|
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def UpperCAmelCase__ ( ):
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
a = "__test_patch_submodule_mock__"
with patch_submodule(_test_patching , "os.path.join" , UpperCAmelCase__ ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def UpperCAmelCase__ ( ):
'''simple docstring'''
assert _test_patching.open is open
a = "__test_patch_submodule_builtin_mock__"
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , "open" , UpperCAmelCase__ ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = "__test_patch_submodule_missing_mock__"
with patch_submodule(_test_patching , "pandas.read_csv" , UpperCAmelCase__ ):
pass
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = "__test_patch_submodule_missing_builtin_mock__"
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , "len" , UpperCAmelCase__ ) is None
with patch_submodule(_test_patching , "len" , UpperCAmelCase__ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = "__test_patch_submodule_start_and_stop_mock__"
a = patch_submodule(_test_patching , "open" , UpperCAmelCase__ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def UpperCAmelCase__ ( ):
'''simple docstring'''
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
a = "__test_patch_submodule_successive_join__"
a = "__test_patch_submodule_successive_dirname__"
a = "__test_patch_submodule_successive_rename__"
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , "os.path.join" , UpperCAmelCase__ ):
with patch_submodule(_test_patching , "os.rename" , UpperCAmelCase__ ):
with patch_submodule(_test_patching , "os.path.dirname" , UpperCAmelCase__ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , "os.rename" , UpperCAmelCase__ ):
with patch_submodule(_test_patching , "os.path.join" , UpperCAmelCase__ ):
with patch_submodule(_test_patching , "os.path.dirname" , UpperCAmelCase__ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = "__test_patch_submodule_doesnt_exist_mock__"
with patch_submodule(_test_patching , "__module_that_doesn_exist__.__attribute_that_doesn_exist__" , UpperCAmelCase__ ):
pass
with patch_submodule(_test_patching , "os.__attribute_that_doesn_exist__" , UpperCAmelCase__ ):
pass
| 710
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 0
|
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
a = {}
a = tokenizer(example["content"] , truncation=UpperCAmelCase__ )["input_ids"]
a = len(example["content"] ) / len(output["input_ids"] )
return output
A_ : Tuple = HfArgumentParser(PretokenizationArguments)
A_ : List[str] = parser.parse_args()
if args.num_workers is None:
A_ : Union[str, Any] = multiprocessing.cpu_count()
A_ : Tuple = AutoTokenizer.from_pretrained(args.tokenizer_dir)
A_ : Optional[Any] = time.time()
A_ : Dict = load_dataset(args.dataset_name, split='''train''')
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")
A_ : Union[str, Any] = time.time()
A_ : str = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
'''repo_name''',
'''path''',
'''copies''',
'''size''',
'''content''',
'''license''',
'''hash''',
'''line_mean''',
'''line_max''',
'''alpha_frac''',
'''autogenerated''',
],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")
A_ : Optional[Any] = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")
| 711
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : Dict = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
'''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
'''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''funnel'''
_UpperCAmelCase = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
}
def __init__( self : Tuple , __lowerCAmelCase : Tuple=3_0522 , __lowerCAmelCase : Optional[Any]=[4, 4, 4] , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : List[Any]=768 , __lowerCAmelCase : Dict=12 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[int]=3072 , __lowerCAmelCase : str="gelu_new" , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : List[Any]=0.1 , __lowerCAmelCase : Optional[Any]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : List[str]=1E-9 , __lowerCAmelCase : Any="mean" , __lowerCAmelCase : List[str]="relative_shift" , __lowerCAmelCase : str=True , __lowerCAmelCase : int=True , __lowerCAmelCase : Dict=True , **__lowerCAmelCase : str , ) -> int:
"""simple docstring"""
a = vocab_size
a = block_sizes
a = [1] * len(__lowerCAmelCase ) if block_repeats is None else block_repeats
assert len(__lowerCAmelCase ) == len(
self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
a = num_decoder_layers
a = d_model
a = n_head
a = d_head
a = d_inner
a = hidden_act
a = hidden_dropout
a = attention_dropout
a = activation_dropout
a = initializer_range
a = initializer_std
a = layer_norm_eps
assert pooling_type in [
"mean",
"max",
], f"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
a = pooling_type
assert attention_type in [
"relative_shift",
"factorized",
], f"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
a = attention_type
a = separate_cls
a = truncate_seq
a = pool_q_only
super().__init__(**__lowerCAmelCase )
@property
def A ( self : Tuple ) -> Any:
"""simple docstring"""
return sum(self.block_sizes )
@num_hidden_layers.setter
def A ( self : str , __lowerCAmelCase : Dict ) -> Dict:
"""simple docstring"""
raise NotImplementedError(
"This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`." )
@property
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
return len(self.block_sizes )
@num_blocks.setter
def A ( self : List[str] , __lowerCAmelCase : int ) -> List[Any]:
"""simple docstring"""
raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`." )
| 712
|
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 32
| 0
|
'''simple docstring'''
A_ : Any = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
A_ : int = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = "Morse code here!"
print(UpperCAmelCase__ )
a = encrypt(UpperCAmelCase__ )
print(UpperCAmelCase__ )
a = decrypt(UpperCAmelCase__ )
print(UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 713
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32
| 0
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
a = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
a = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
a = model(__lowerCAmelCase )["last_hidden_state"]
a = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , __lowerCAmelCase )
# compare the actual values for a slice.
a = tf.convert_to_tensor(
[[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 714
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
| 0
|
# Function to print upper half of diamond (pyramid)
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
for i in range(0 , UpperCAmelCase__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , end="" )
print()
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
for i in range(UpperCAmelCase__ , 0 , -1 ):
for _ in range(UpperCAmelCase__ , 0 , -1 ): # printing stars
print("* " , end="" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(" " , end="" )
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] ):
'''simple docstring'''
if n <= 0:
print(" ... .... nothing printing :(" )
return
floyd(UpperCAmelCase__ ) # upper half
reverse_floyd(UpperCAmelCase__ ) # lower half
if __name__ == "__main__":
print(r'''| /\ | |- | |- |--| |\ /| |-''')
print(r'''|/ \| |- |_ |_ |__| | \/ | |_''')
A_ : Optional[Any] = 1
while K:
A_ : int = int(input('''enter the number and , and see the magic : '''))
print()
pretty_print(user_number)
A_ : Optional[Any] = int(input('''press 0 to exit... and 1 to continue...'''))
print('''Good Bye...''')
| 715
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 0
|
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = 1
@register_to_config
def __init__( self : List[Any] , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : float = 0.1_5 , __lowerCAmelCase : float = 0.0_1 , __lowerCAmelCase : float = 1348.0 , __lowerCAmelCase : float = 1E-5 , __lowerCAmelCase : int = 1 , ) -> Optional[Any]:
"""simple docstring"""
a = sigma_max
# setable values
a = None
self.set_sigmas(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : int , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[int] = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def A ( self : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : float = None , __lowerCAmelCase : Union[str, torch.device] = None ) -> Optional[Any]:
"""simple docstring"""
a = sampling_eps if sampling_eps is not None else self.config.sampling_eps
a = torch.linspace(1 , __lowerCAmelCase , __lowerCAmelCase , device=__lowerCAmelCase )
def A ( self : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : float = None , __lowerCAmelCase : float = None , __lowerCAmelCase : float = None ) -> Union[str, Any]:
"""simple docstring"""
a = sigma_min if sigma_min is not None else self.config.sigma_min
a = sigma_max if sigma_max is not None else self.config.sigma_max
a = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(__lowerCAmelCase , __lowerCAmelCase )
a = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
a = torch.exp(torch.linspace(math.log(__lowerCAmelCase ) , math.log(__lowerCAmelCase ) , __lowerCAmelCase ) )
a = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def A ( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def A ( self : List[str] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : int , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[torch.Generator] = None , __lowerCAmelCase : bool = True , ) -> Union[SdeVeOutput, Tuple]:
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
a = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
a = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
a = timesteps.to(self.discrete_sigmas.device )
a = self.discrete_sigmas[timesteps].to(sample.device )
a = self.get_adjacent_sigma(__lowerCAmelCase , __lowerCAmelCase ).to(sample.device )
a = torch.zeros_like(__lowerCAmelCase )
a = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
a = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
a = diffusion.unsqueeze(-1 )
a = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
a = randn_tensor(
sample.shape , layout=sample.layout , generator=__lowerCAmelCase , device=sample.device , dtype=sample.dtype )
a = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
a = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=__lowerCAmelCase , prev_sample_mean=__lowerCAmelCase )
def A ( self : Optional[Any] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[torch.Generator] = None , __lowerCAmelCase : bool = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
a = randn_tensor(sample.shape , layout=sample.layout , generator=__lowerCAmelCase ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
a = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
a = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
a = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
a = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
a = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
a = step_size.unsqueeze(-1 )
a = sample + step_size * model_output
a = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , ) -> torch.FloatTensor:
"""simple docstring"""
a = timesteps.to(original_samples.device )
a = self.discrete_sigmas.to(original_samples.device )[timesteps]
a = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(__lowerCAmelCase ) * sigmas[:, None, None, None]
)
a = noise + original_samples
return noisy_samples
def __len__( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
return self.config.num_train_timesteps
| 716
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
a , a , a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
a , a , a = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 32
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : str = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
'''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
'''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
),
'''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''bert'''
def __init__( self : int , __lowerCAmelCase : Any=3_0522 , __lowerCAmelCase : List[Any]=768 , __lowerCAmelCase : int=12 , __lowerCAmelCase : List[str]=12 , __lowerCAmelCase : str=3072 , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Optional[Any]=0.1 , __lowerCAmelCase : str=0.1 , __lowerCAmelCase : str=512 , __lowerCAmelCase : List[Any]=2 , __lowerCAmelCase : Tuple=0.0_2 , __lowerCAmelCase : Any=1E-12 , __lowerCAmelCase : Any=0 , __lowerCAmelCase : Any="absolute" , __lowerCAmelCase : Dict=True , __lowerCAmelCase : str=None , **__lowerCAmelCase : str , ) -> Dict:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = use_cache
a = classifier_dropout
class _lowercase ( UpperCAmelCase__ ):
@property
def A ( self : Any ) -> 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),
] )
| 717
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 0
|
A_ : Optional[int] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
A_ : List[Any] = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
A_ : Optional[Any] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 718
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 0
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 719
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
| 0
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] ):
'''simple docstring'''
a = create_tensor(UpperCAmelCase__ )
a = gather(UpperCAmelCase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] ):
'''simple docstring'''
a = [state.process_index]
a = gather_object(UpperCAmelCase__ )
assert len(UpperCAmelCase__ ) == state.num_processes, F"""{gathered_obj}, {len(UpperCAmelCase__ )} != {state.num_processes}"""
assert gathered_obj == list(range(state.num_processes ) ), F"""{gathered_obj} != {list(range(state.num_processes ) )}"""
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
a = create_tensor(UpperCAmelCase__ )
a = broadcast(UpperCAmelCase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def UpperCAmelCase__ ( UpperCAmelCase__ :List[Any] ):
'''simple docstring'''
if state.is_main_process:
a = torch.arange(state.num_processes + 1 ).to(state.device )
else:
a = torch.arange(state.num_processes ).to(state.device )
a = pad_across_processes(UpperCAmelCase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def UpperCAmelCase__ ( UpperCAmelCase__ :List[Any] ):
'''simple docstring'''
if state.num_processes != 2:
return
a = create_tensor(UpperCAmelCase__ )
a = reduce(UpperCAmelCase__ , "sum" )
a = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(UpperCAmelCase__ , UpperCAmelCase__ ), F"""{reduced_tensor} != {truth_tensor}"""
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
if state.num_processes != 2:
return
a = create_tensor(UpperCAmelCase__ )
a = reduce(UpperCAmelCase__ , "mean" )
a = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(UpperCAmelCase__ , UpperCAmelCase__ ), F"""{reduced_tensor} != {truth_tensor}"""
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
main()
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = PartialState()
state.print(F"""State: {state}""" )
state.print("testing gather" )
test_gather(UpperCAmelCase__ )
state.print("testing gather_object" )
test_gather_object(UpperCAmelCase__ )
state.print("testing broadcast" )
test_broadcast(UpperCAmelCase__ )
state.print("testing pad_across_processes" )
test_pad_across_processes(UpperCAmelCase__ )
state.print("testing reduce_sum" )
test_reduce_sum(UpperCAmelCase__ )
state.print("testing reduce_mean" )
test_reduce_mean(UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 720
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 32
| 0
|
import os
import pytest
from transformers.dynamic_module_utils import get_imports
A_ : Tuple = '''
import os
'''
A_ : str = '''
def foo():
import os
return False
'''
A_ : Optional[int] = '''
def foo():
def bar():
if True:
import os
return False
return bar()
'''
A_ : Optional[int] = '''
import os
try:
import bar
except ImportError:
raise ValueError()
'''
A_ : Union[str, Any] = '''
import os
def foo():
try:
import bar
except ImportError:
raise ValueError()
'''
A_ : str = '''
import os
try:
import bar
except (ImportError, AttributeError):
raise ValueError()
'''
A_ : Tuple = '''
import os
try:
import bar
except ImportError as e:
raise ValueError()
'''
A_ : Optional[int] = '''
import os
try:
import bar
except:
raise ValueError()
'''
A_ : Union[str, Any] = '''
import os
try:
import bar
import baz
except ImportError:
raise ValueError()
'''
A_ : Union[str, Any] = '''
import os
try:
import bar
import baz
except ImportError:
x = 1
raise ValueError()
'''
A_ : Optional[int] = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("case" , UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict , UpperCAmelCase__ :Tuple ):
'''simple docstring'''
a = os.path.join(UpperCAmelCase__ , "test_file.py" )
with open(UpperCAmelCase__ , "w" ) as _tmp_file:
_tmp_file.write(UpperCAmelCase__ )
a = get_imports(UpperCAmelCase__ )
assert parsed_imports == ["os"]
| 721
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 0
|
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
#
########################################################################
A_ : Optional[Any] = 16
A_ : str = 32
def UpperCAmelCase__ ( UpperCAmelCase__ :Accelerator , UpperCAmelCase__ :int = 16 ):
'''simple docstring'''
a = AutoTokenizer.from_pretrained("bert-base-cased" )
a = load_dataset("glue" , "mrpc" )
def tokenize_function(UpperCAmelCase__ :str ):
# max_length=None => use the model max length (it's actually the default)
a = 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():
a = 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
a = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(UpperCAmelCase__ :List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
a = 1_28 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 = 16
elif accelerator.mixed_precision != "no":
a = 8
else:
a = None
return tokenizer.pad(
UpperCAmelCase__ , padding="longest" , max_length=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , return_tensors="pt" , )
# Instantiate dataloaders.
a = DataLoader(
tokenized_datasets["train"] , shuffle=UpperCAmelCase__ , collate_fn=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
a = 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
A_ : str = mocked_dataloaders # noqa: F811
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :int ):
'''simple docstring'''
if os.environ.get("TESTING_MOCKED_DATALOADERS" , UpperCAmelCase__ ) == "1":
a = 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:
a = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
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"] )
set_seed(UpperCAmelCase__ )
a , a = get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__ )
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
# 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=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).
a = model.to(accelerator.device )
# Instantiate optimizer
a = AdamW(params=model.parameters() , lr=UpperCAmelCase__ )
# Instantiate scheduler
a = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase__ , num_warmup_steps=1_00 , 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.
a , a , a , a , a = 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:
a = 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:
a = 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 )
a = model(**UpperCAmelCase__ )
a = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
a = 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():
a = model(**UpperCAmelCase__ )
a = outputs.logits.argmax(dim=-1 )
a , a = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=UpperCAmelCase__ , references=UpperCAmelCase__ , )
a = 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 UpperCAmelCase__ ( ):
'''simple docstring'''
a = 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" , )
a = parser.parse_args()
a = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 700
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 0
|
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
A_ : Optional[Any] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''input_features''']
def __init__( self : int , __lowerCAmelCase : Dict=80 , __lowerCAmelCase : int=1_6000 , __lowerCAmelCase : Optional[Any]=160 , __lowerCAmelCase : Union[str, Any]=30 , __lowerCAmelCase : Union[str, Any]=400 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Union[str, Any]=False , **__lowerCAmelCase : Union[str, Any] , ) -> str:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , **__lowerCAmelCase , )
a = n_fft
a = hop_length
a = chunk_length
a = chunk_length * sampling_rate
a = self.n_samples // hop_length
a = sampling_rate
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , )
def A ( self : Optional[Any] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel="log10" , )
a = log_spec[:, :-1]
a = np.maximum(__lowerCAmelCase , log_spec.max() - 8.0 )
a = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def A ( __lowerCAmelCase : List[np.ndarray] , __lowerCAmelCase : List[np.ndarray] , __lowerCAmelCase : float = 0.0 ) -> List[np.ndarray]:
"""simple docstring"""
if attention_mask is not None:
a = np.array(__lowerCAmelCase , np.intaa )
a = []
for vector, length in zip(__lowerCAmelCase , attention_mask.sum(-1 ) ):
a = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
a = padding_value
normed_input_values.append(__lowerCAmelCase )
else:
a = [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def __call__( self : str , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : bool = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[str] = "max_length" , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[bool] = None , **__lowerCAmelCase : str , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
a = BatchFeature({"input_features": raw_speech} )
# convert into correct format for padding
a = self.pad(
__lowerCAmelCase , padding=__lowerCAmelCase , max_length=max_length if max_length else self.n_samples , truncation=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
a = self.zero_mean_unit_var_norm(
padded_inputs["input_features"] , attention_mask=padded_inputs["attention_mask"] , padding_value=self.padding_value , )
a = np.stack(padded_inputs["input_features"] , axis=0 )
# make sure list is in array format
a = padded_inputs.get("input_features" ).transpose(2 , 0 , 1 )
a = [self._np_extract_fbank_features(__lowerCAmelCase ) for waveform in input_features[0]]
if isinstance(input_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in input_features]
else:
a = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
a = padded_inputs["attention_mask"][:, :: self.hop_length]
if return_tensors is not None:
a = padded_inputs.convert_to_tensors(__lowerCAmelCase )
return padded_inputs
def A ( self : str ) -> Dict[str, Any]:
"""simple docstring"""
a = copy.deepcopy(self.__dict__ )
a = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 701
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 0
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : Any = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''pix2struct_text_model'''
_UpperCAmelCase = ['''past_key_values''']
_UpperCAmelCase = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Optional[int] , __lowerCAmelCase : int=5_0244 , __lowerCAmelCase : str=768 , __lowerCAmelCase : List[Any]=64 , __lowerCAmelCase : Union[str, Any]=2048 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : str=128 , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : int=1E-6 , __lowerCAmelCase : str=1.0 , __lowerCAmelCase : Optional[int]="gelu_new" , __lowerCAmelCase : List[str]=0 , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : Optional[int]=0 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : int=True , **__lowerCAmelCase : List[str] , ) -> Any:
"""simple docstring"""
a = vocab_size
a = hidden_size
a = d_kv
a = d_ff
a = num_layers
a = num_heads
a = relative_attention_num_buckets
a = relative_attention_max_distance
a = dropout_rate
a = layer_norm_epsilon
a = initializer_factor
a = use_cache
a = eos_token_id
a = decoder_start_token_id
# for backwards compatibility
a = dense_act_fn
super().__init__(
pad_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , decoder_start_token_id=__lowerCAmelCase , tie_word_embeddings=__lowerCAmelCase , is_decoder=__lowerCAmelCase , **__lowerCAmelCase , )
@classmethod
def A ( cls : Dict , __lowerCAmelCase : Union[str, os.PathLike] , **__lowerCAmelCase : Dict ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__lowerCAmelCase )
a , a = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
a = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''pix2struct_vision_model'''
def __init__( self : List[Any] , __lowerCAmelCase : List[Any]=768 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Optional[int]=2048 , __lowerCAmelCase : int=64 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : str=12 , __lowerCAmelCase : Optional[Any]="gelu_new" , __lowerCAmelCase : Any=1E-6 , __lowerCAmelCase : Optional[Any]=0.0 , __lowerCAmelCase : Tuple=0.0 , __lowerCAmelCase : int=1E-10 , __lowerCAmelCase : int=1.0 , __lowerCAmelCase : Optional[Any]=4096 , __lowerCAmelCase : str=32 , __lowerCAmelCase : Any=128 , **__lowerCAmelCase : Optional[Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = hidden_size
a = patch_embed_hidden_size
a = d_ff
a = dropout_rate
a = num_hidden_layers
a = num_attention_heads
a = initializer_range
a = initializer_factor
a = attention_dropout
a = layer_norm_eps
a = dense_act_fn
a = seq_len
a = relative_attention_num_buckets
a = relative_attention_max_distance
a = d_kv
@classmethod
def A ( cls : List[str] , __lowerCAmelCase : Union[str, os.PathLike] , **__lowerCAmelCase : str ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__lowerCAmelCase )
a , a = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
a = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''pix2struct'''
_UpperCAmelCase = True
def __init__( self : Optional[Any] , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : Optional[Any]=1.0 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : Any , ) -> List[Any]:
"""simple docstring"""
super().__init__(tie_word_embeddings=__lowerCAmelCase , is_encoder_decoder=__lowerCAmelCase , **__lowerCAmelCase )
if text_config is None:
a = {}
logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values." )
if vision_config is None:
a = {}
logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values." )
a = PixaStructTextConfig(**__lowerCAmelCase )
a = PixaStructVisionConfig(**__lowerCAmelCase )
a = self.text_config.decoder_start_token_id
a = self.text_config.pad_token_id
a = self.text_config.eos_token_id
a = initializer_factor
a = initializer_range
a = self.initializer_range
a = self.initializer_range
a = is_vqa
@classmethod
def A ( cls : Optional[Any] , __lowerCAmelCase : PixaStructTextConfig , __lowerCAmelCase : PixaStructVisionConfig , **__lowerCAmelCase : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__lowerCAmelCase )
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
a = copy.deepcopy(self.__dict__ )
a = self.text_config.to_dict()
a = self.vision_config.to_dict()
a = self.__class__.model_type
return output
| 702
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[int] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
'''MobileNetV2OnnxConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[str] = ['''MobileNetV2FeatureExtractor''']
A_ : Tuple = ['''MobileNetV2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = [
'''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileNetV2ForImageClassification''',
'''MobileNetV2ForSemanticSegmentation''',
'''MobileNetV2Model''',
'''MobileNetV2PreTrainedModel''',
'''load_tf_weights_in_mobilenet_v2''',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 703
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 0
|
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCAmelCase__ ( UpperCAmelCase__ :Any , UpperCAmelCase__ :Union[str, Any] , UpperCAmelCase__ :Optional[Any] , UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :Optional[int] ):
'''simple docstring'''
a = StableDiffusionPipeline.from_pretrained(UpperCAmelCase__ , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
a = load_file(UpperCAmelCase__ )
a = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
a = key.split("." )[0].split(LORA_PREFIX_TEXT_ENCODER + "_" )[-1].split("_" )
a = pipeline.text_encoder
else:
a = key.split("." )[0].split(LORA_PREFIX_UNET + "_" )[-1].split("_" )
a = pipeline.unet
# find the target layer
a = layer_infos.pop(0 )
while len(UpperCAmelCase__ ) > -1:
try:
a = curr_layer.__getattr__(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) > 0:
a = layer_infos.pop(0 )
elif len(UpperCAmelCase__ ) == 0:
break
except Exception:
if len(UpperCAmelCase__ ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
a = layer_infos.pop(0 )
a = []
if "lora_down" in key:
pair_keys.append(key.replace("lora_down" , "lora_up" ) )
pair_keys.append(UpperCAmelCase__ )
else:
pair_keys.append(UpperCAmelCase__ )
pair_keys.append(key.replace("lora_up" , "lora_down" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
a = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
a = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase__ , UpperCAmelCase__ ).unsqueeze(2 ).unsqueeze(3 )
else:
a = state_dict[pair_keys[0]].to(torch.floataa )
a = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase__ , UpperCAmelCase__ )
# update visited list
for item in pair_keys:
visited.append(UpperCAmelCase__ )
return pipeline
if __name__ == "__main__":
A_ : int = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
)
parser.add_argument(
'''--lora_prefix_text_encoder''',
default='''lora_te''',
type=str,
help='''The prefix of text encoder weight in safetensors''',
)
parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
parser.add_argument(
'''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
)
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
A_ : Tuple = parser.parse_args()
A_ : int = args.base_model_path
A_ : int = args.checkpoint_path
A_ : Union[str, Any] = args.dump_path
A_ : str = args.lora_prefix_unet
A_ : Tuple = args.lora_prefix_text_encoder
A_ : Dict = args.alpha
A_ : Optional[int] = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
A_ : List[str] = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 704
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 0
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = field(default='''language-modeling''', metadata={'''include_in_asdict_even_if_is_default''': True} )
_UpperCAmelCase = Features({'''text''': Value('''string''' )} )
_UpperCAmelCase = Features({} )
_UpperCAmelCase = '''text'''
@property
def A ( self : int ) -> Dict[str, str]:
"""simple docstring"""
return {self.text_column: "text"}
| 705
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 0
|
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A_ : Any = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : Dict , *__lowerCAmelCase : Union[str, Any] , **__lowerCAmelCase : Union[str, Any] ) -> None:
"""simple docstring"""
warnings.warn(
"The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use LayoutLMv2ImageProcessor instead." , __lowerCAmelCase , )
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
| 706
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 0
|
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
A_ : Optional[int] = logging.getLogger()
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, Any] ) -> str:
'''simple docstring'''
a = {}
a = os.path.join(UpperCAmelCase__ , "all_results.json" )
if os.path.exists(UpperCAmelCase__ ):
with open(UpperCAmelCase__ , "r" ) as f:
a = json.load(UpperCAmelCase__ )
else:
raise ValueError(F"""can't find {path}""" )
return results
A_ : Any = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Any ) -> Tuple:
"""simple docstring"""
import xla_spawn
a = self.get_auto_remove_tmp_dir()
a = f"""
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(__lowerCAmelCase , "argv" , __lowerCAmelCase ):
a = time()
xla_spawn.main()
a = time()
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.7_5 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def A ( self : Tuple ) -> Tuple:
"""simple docstring"""
import xla_spawn
a = "\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n ".split()
with patch.object(__lowerCAmelCase , "argv" , __lowerCAmelCase ):
xla_spawn.main()
| 707
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 0
|
'''simple docstring'''
import os
from typing import Dict, List, Tuple, TypeVar, Union
A_ : List[Any] = TypeVar('''T''')
A_ : int = Union[List[T], Tuple[T, ...]]
A_ : Tuple = Union[T, List[T], Dict[str, T]]
A_ : int = Union[str, bytes, os.PathLike]
| 708
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_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 = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""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
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
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 = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 0
|
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
A_ : List[str] = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
- name: "Dataset Card for X" # First-level markdown heading
allow_empty: false
allow_empty_text: true
subsections:
- name: "Table of Contents"
allow_empty: false
allow_empty_text: false
subsections: null
- name: "Dataset Description"
allow_empty: false
allow_empty_text: false
subsections:
- name: "Dataset Summary"
allow_empty: false
allow_empty_text: false
subsections: null
- name: "Supported Tasks and Leaderboards"
allow_empty: true
allow_empty_text: true
subsections: null
- name: Languages
allow_empty: false
allow_empty_text: true
subsections: null
'''
)
A_ : List[Any] = {
'''name''': '''root''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{
'''name''': '''Dataset Card for My Dataset''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
{
'''name''': '''Dataset Description''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Dataset Summary''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [],
},
{
'''name''': '''Supported Tasks and Leaderboards''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
},
{'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
],
},
],
}
],
}
A_ : Dict = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Any = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : str = {
'''name''': '''root''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{
'''name''': '''Dataset Card for My Dataset''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
{
'''name''': '''Dataset Description''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Dataset Summary''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Extra Ignored Subsection''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
}
],
},
{
'''name''': '''Supported Tasks and Leaderboards''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
},
{'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
],
},
],
}
],
}
A_ : Tuple = '''\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Optional[int] = (
'''The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'''
)
A_ : Optional[int] = '''\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Tuple = (
'''The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'''
)
A_ : Optional[int] = '''\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : str = '''The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'''
A_ : Optional[Any] = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Dict = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored).'''
A_ : str = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
'''
A_ : List[str] = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found \'None\'.'''
A_ : int = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
'''
A_ : List[str] = '''The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'''
A_ : Optional[Any] = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
'''
A_ : int = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty.'''
A_ : Optional[Any] = '''\
---
language:
- zh
- en
---
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : Tuple = '''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.'''
A_ : int = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
'''
A_ : Dict = '''The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README.'''
A_ : Dict = '''\
---
language:
- zh
- en
---
# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : str = '''The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'''
A_ : str = ''''''
A_ : List[str] = '''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README.'''
A_ : Optional[Any] = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
A_ : List[Any] = '''The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections.'''
@pytest.mark.parametrize(
"readme_md, expected_dict" , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, Any] , UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
assert ReadMe.from_string(UpperCAmelCase__ , UpperCAmelCase__ ).to_dict() == expected_dict
@pytest.mark.parametrize(
"readme_md, expected_error" , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict , UpperCAmelCase__ :int ):
'''simple docstring'''
with pytest.raises(UpperCAmelCase__ , match=re.escape(expected_error.format(path="root" ) ) ):
a = ReadMe.from_string(UpperCAmelCase__ , UpperCAmelCase__ )
readme.validate()
@pytest.mark.parametrize(
"readme_md, expected_error" , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
with pytest.raises(UpperCAmelCase__ , match=re.escape(expected_error.format(path="root" ) ) ):
ReadMe.from_string(UpperCAmelCase__ , UpperCAmelCase__ )
@pytest.mark.parametrize(
"readme_md," , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
ReadMe.from_string(UpperCAmelCase__ , UpperCAmelCase__ , suppress_parsing_errors=UpperCAmelCase__ )
@pytest.mark.parametrize(
"readme_md, expected_dict" , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] , UpperCAmelCase__ :int ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
a = Path(UpperCAmelCase__ ) / "README.md"
with open(UpperCAmelCase__ , "w+" ) as readme_file:
readme_file.write(UpperCAmelCase__ )
a = ReadMe.from_readme(UpperCAmelCase__ , UpperCAmelCase__ ).to_dict()
assert out["name"] == path
assert out["text"] == ""
assert out["is_empty_text"]
assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
"readme_md, expected_error" , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :str ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
a = Path(UpperCAmelCase__ ) / "README.md"
with open(UpperCAmelCase__ , "w+" ) as readme_file:
readme_file.write(UpperCAmelCase__ )
a = expected_error.format(path=UpperCAmelCase__ )
with pytest.raises(UpperCAmelCase__ , match=re.escape(UpperCAmelCase__ ) ):
a = ReadMe.from_readme(UpperCAmelCase__ , UpperCAmelCase__ )
readme.validate()
@pytest.mark.parametrize(
"readme_md, expected_error" , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[Any] , UpperCAmelCase__ :int ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
a = Path(UpperCAmelCase__ ) / "README.md"
with open(UpperCAmelCase__ , "w+" ) as readme_file:
readme_file.write(UpperCAmelCase__ )
a = expected_error.format(path=UpperCAmelCase__ )
with pytest.raises(UpperCAmelCase__ , match=re.escape(UpperCAmelCase__ ) ):
ReadMe.from_readme(UpperCAmelCase__ , UpperCAmelCase__ )
@pytest.mark.parametrize(
"readme_md," , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
a = Path(UpperCAmelCase__ ) / "README.md"
with open(UpperCAmelCase__ , "w+" ) as readme_file:
readme_file.write(UpperCAmelCase__ )
ReadMe.from_readme(UpperCAmelCase__ , UpperCAmelCase__ , suppress_parsing_errors=UpperCAmelCase__ )
| 709
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 0
|
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
A_ : str = logging.get_logger(__name__)
A_ : Optional[int] = OrderedDict(
[
('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
('''beit''', '''BeitFeatureExtractor'''),
('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
('''clap''', '''ClapFeatureExtractor'''),
('''clip''', '''CLIPFeatureExtractor'''),
('''clipseg''', '''ViTFeatureExtractor'''),
('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
('''convnext''', '''ConvNextFeatureExtractor'''),
('''cvt''', '''ConvNextFeatureExtractor'''),
('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
('''data2vec-vision''', '''BeitFeatureExtractor'''),
('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
('''deit''', '''DeiTFeatureExtractor'''),
('''detr''', '''DetrFeatureExtractor'''),
('''dinat''', '''ViTFeatureExtractor'''),
('''donut-swin''', '''DonutFeatureExtractor'''),
('''dpt''', '''DPTFeatureExtractor'''),
('''encodec''', '''EncodecFeatureExtractor'''),
('''flava''', '''FlavaFeatureExtractor'''),
('''glpn''', '''GLPNFeatureExtractor'''),
('''groupvit''', '''CLIPFeatureExtractor'''),
('''hubert''', '''Wav2Vec2FeatureExtractor'''),
('''imagegpt''', '''ImageGPTFeatureExtractor'''),
('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
('''levit''', '''LevitFeatureExtractor'''),
('''maskformer''', '''MaskFormerFeatureExtractor'''),
('''mctct''', '''MCTCTFeatureExtractor'''),
('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
('''mobilevit''', '''MobileViTFeatureExtractor'''),
('''nat''', '''ViTFeatureExtractor'''),
('''owlvit''', '''OwlViTFeatureExtractor'''),
('''perceiver''', '''PerceiverFeatureExtractor'''),
('''poolformer''', '''PoolFormerFeatureExtractor'''),
('''regnet''', '''ConvNextFeatureExtractor'''),
('''resnet''', '''ConvNextFeatureExtractor'''),
('''segformer''', '''SegformerFeatureExtractor'''),
('''sew''', '''Wav2Vec2FeatureExtractor'''),
('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
('''speecht5''', '''SpeechT5FeatureExtractor'''),
('''swiftformer''', '''ViTFeatureExtractor'''),
('''swin''', '''ViTFeatureExtractor'''),
('''swinv2''', '''ViTFeatureExtractor'''),
('''table-transformer''', '''DetrFeatureExtractor'''),
('''timesformer''', '''VideoMAEFeatureExtractor'''),
('''tvlt''', '''TvltFeatureExtractor'''),
('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
('''van''', '''ConvNextFeatureExtractor'''),
('''videomae''', '''VideoMAEFeatureExtractor'''),
('''vilt''', '''ViltFeatureExtractor'''),
('''vit''', '''ViTFeatureExtractor'''),
('''vit_mae''', '''ViTFeatureExtractor'''),
('''vit_msn''', '''ViTFeatureExtractor'''),
('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
('''whisper''', '''WhisperFeatureExtractor'''),
('''xclip''', '''CLIPFeatureExtractor'''),
('''yolos''', '''YolosFeatureExtractor'''),
]
)
A_ : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
a = model_type_to_module_name(UpperCAmelCase__ )
a = importlib.import_module(F""".{module_name}""" , "transformers.models" )
try:
return getattr(UpperCAmelCase__ , UpperCAmelCase__ )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(UpperCAmelCase__ , "__name__" , UpperCAmelCase__ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
a = importlib.import_module("transformers" )
if hasattr(UpperCAmelCase__ , UpperCAmelCase__ ):
return getattr(UpperCAmelCase__ , UpperCAmelCase__ )
return None
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, os.PathLike] , UpperCAmelCase__ :Optional[Union[str, os.PathLike]] = None , UpperCAmelCase__ :bool = False , UpperCAmelCase__ :bool = False , UpperCAmelCase__ :Optional[Dict[str, str]] = None , UpperCAmelCase__ :Optional[Union[bool, str]] = None , UpperCAmelCase__ :Optional[str] = None , UpperCAmelCase__ :bool = False , **UpperCAmelCase__ :List[str] , ):
'''simple docstring'''
a = get_file_from_repo(
UpperCAmelCase__ , UpperCAmelCase__ , cache_dir=UpperCAmelCase__ , force_download=UpperCAmelCase__ , resume_download=UpperCAmelCase__ , proxies=UpperCAmelCase__ , use_auth_token=UpperCAmelCase__ , revision=UpperCAmelCase__ , local_files_only=UpperCAmelCase__ , )
if resolved_config_file is None:
logger.info(
"Could not locate the feature extractor configuration file, will try to use the model config instead." )
return {}
with open(UpperCAmelCase__ , encoding="utf-8" ) as reader:
return json.load(UpperCAmelCase__ )
class _lowercase :
def __init__( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
raise EnvironmentError(
"AutoFeatureExtractor is designed to be instantiated "
"using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method." )
@classmethod
@replace_list_option_in_docstrings(__lowerCAmelCase )
def A ( cls : List[str] , __lowerCAmelCase : Any , **__lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
a = kwargs.pop("config" , __lowerCAmelCase )
a = kwargs.pop("trust_remote_code" , __lowerCAmelCase )
a = True
a , a = FeatureExtractionMixin.get_feature_extractor_dict(__lowerCAmelCase , **__lowerCAmelCase )
a = config_dict.get("feature_extractor_type" , __lowerCAmelCase )
a = None
if "AutoFeatureExtractor" in config_dict.get("auto_map" , {} ):
a = config_dict["auto_map"]["AutoFeatureExtractor"]
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = AutoConfig.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
# It could be in `config.feature_extractor_type``
a = getattr(__lowerCAmelCase , "feature_extractor_type" , __lowerCAmelCase )
if hasattr(__lowerCAmelCase , "auto_map" ) and "AutoFeatureExtractor" in config.auto_map:
a = config.auto_map["AutoFeatureExtractor"]
if feature_extractor_class is not None:
a = feature_extractor_class_from_name(__lowerCAmelCase )
a = feature_extractor_auto_map is not None
a = feature_extractor_class is not None or type(__lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
a = resolve_trust_remote_code(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if has_remote_code and trust_remote_code:
a = get_class_from_dynamic_module(
__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
a = kwargs.pop("code_revision" , __lowerCAmelCase )
if os.path.isdir(__lowerCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__lowerCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
a = FEATURE_EXTRACTOR_MAPPING[type(__lowerCAmelCase )]
return feature_extractor_class.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
raise ValueError(
f"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
f"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
f"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def A ( __lowerCAmelCase : str , __lowerCAmelCase : List[str] ) -> Dict:
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(__lowerCAmelCase , __lowerCAmelCase )
| 710
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 0
|
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
A_ : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
A_ : Dict = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[Any] , UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :Optional[int]=8 ):
'''simple docstring'''
a = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
a = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDConditionModel , __lowerCAmelCase : DDPMScheduler , __lowerCAmelCase : VQModel , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
self.register_modules(
unet=__lowerCAmelCase , scheduler=__lowerCAmelCase , movq=__lowerCAmelCase , )
a = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def A ( self : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
if latents is None:
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase , device=__lowerCAmelCase , dtype=__lowerCAmelCase )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
a = latents.to(__lowerCAmelCase )
a = latents * scheduler.init_noise_sigma
return latents
def A ( self : Any , __lowerCAmelCase : Any=0 ) -> List[Any]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
a = torch.device(f"""cuda:{gpu_id}""" )
a = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : Dict , __lowerCAmelCase : Any=0 ) -> Any:
"""simple docstring"""
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
a = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=__lowerCAmelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
a = None
for cpu_offloaded_model in [self.unet, self.movq]:
a , a = cpu_offload_with_hook(__lowerCAmelCase , __lowerCAmelCase , prev_module_hook=__lowerCAmelCase )
# We'll offload the last model manually.
a = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A ( self : List[Any] ) -> str:
"""simple docstring"""
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__lowerCAmelCase , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__lowerCAmelCase )
def __call__( self : List[str] , __lowerCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , __lowerCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , __lowerCAmelCase : int = 512 , __lowerCAmelCase : int = 512 , __lowerCAmelCase : int = 100 , __lowerCAmelCase : float = 4.0 , __lowerCAmelCase : int = 1 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[torch.FloatTensor] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , ) -> List[str]:
"""simple docstring"""
a = self._execution_device
a = guidance_scale > 1.0
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = torch.cat(__lowerCAmelCase , dim=0 )
a = image_embeds.shape[0] * num_images_per_prompt
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = torch.cat(__lowerCAmelCase , dim=0 )
if do_classifier_free_guidance:
a = image_embeds.repeat_interleave(__lowerCAmelCase , dim=0 )
a = negative_image_embeds.repeat_interleave(__lowerCAmelCase , dim=0 )
a = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__lowerCAmelCase )
self.scheduler.set_timesteps(__lowerCAmelCase , device=__lowerCAmelCase )
a = self.scheduler.timesteps
a = self.unet.config.in_channels
a , a = downscale_height_and_width(__lowerCAmelCase , __lowerCAmelCase , self.movq_scale_factor )
# create initial latent
a = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(__lowerCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
a = {"image_embeds": image_embeds}
a = self.unet(
sample=__lowerCAmelCase , timestep=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , added_cond_kwargs=__lowerCAmelCase , return_dict=__lowerCAmelCase , )[0]
if do_classifier_free_guidance:
a , a = noise_pred.split(latents.shape[1] , dim=1 )
a , a = noise_pred.chunk(2 )
a , a = variance_pred.chunk(2 )
a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
a = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
a , a = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
a = self.scheduler.step(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase , )[0]
# post-processing
a = self.movq.decode(__lowerCAmelCase , force_not_quantize=__lowerCAmelCase )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
a = image * 0.5 + 0.5
a = image.clamp(0 , 1 )
a = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 711
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
| 0
|
from typing import List
from .keymap import KEYMAP, get_character
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
def decorator(UpperCAmelCase__ :Any ):
a = getattr(UpperCAmelCase__ , "handle_key" , [] )
handle += [key]
setattr(UpperCAmelCase__ , "handle_key" , UpperCAmelCase__ )
return func
return decorator
def UpperCAmelCase__ ( *UpperCAmelCase__ :List[str] ):
'''simple docstring'''
def decorator(UpperCAmelCase__ :str ):
a = getattr(UpperCAmelCase__ , "handle_key" , [] )
handle += keys
setattr(UpperCAmelCase__ , "handle_key" , UpperCAmelCase__ )
return func
return decorator
class _lowercase ( UpperCAmelCase__ ):
def __new__( cls : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Tuple , __lowerCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
a = super().__new__(cls , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not hasattr(__lowerCAmelCase , "key_handler" ):
setattr(__lowerCAmelCase , "key_handler" , {} )
setattr(__lowerCAmelCase , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
a = getattr(__lowerCAmelCase , "handle_key" , [] )
for key in handled_keys:
a = value
return new_cls
@staticmethod
def A ( cls : int ) -> Optional[Any]:
"""simple docstring"""
a = get_character()
if char != KEYMAP["undefined"]:
a = ord(__lowerCAmelCase )
a = cls.key_handler.get(__lowerCAmelCase )
if handler:
a = char
return handler(cls )
else:
return None
def UpperCAmelCase__ ( cls :Tuple ):
'''simple docstring'''
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 712
|
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
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'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :int ):
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(UpperCAmelCase__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 713
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
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def UpperCAmelCase__( UpperCAmelCase__ :list ):
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
a = grid[0]
for row_n in range(1 , len(UpperCAmelCase__ ) ):
a = grid[row_n]
a = fill_row(UpperCAmelCase__ , UpperCAmelCase__ )
a = grid[row_n]
return grid[-1][-1]
def UpperCAmelCase__( UpperCAmelCase__ :list , UpperCAmelCase__ :list ):
'''simple docstring'''
current_row[0] += row_above[0]
for cell_n in range(1 , len(UpperCAmelCase__ ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 714
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
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|
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 715
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
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| 0
|
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
A_ : List[str] = 2_99_79_24_58
# Symbols
A_ : Union[str, Any] = symbols('''ct x y z''')
def UpperCAmelCase__ ( UpperCAmelCase__ :float ):
'''simple docstring'''
if velocity > c:
raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("Speed must be greater than or equal to 1!" )
return velocity / c
def UpperCAmelCase__ ( UpperCAmelCase__ :float ):
'''simple docstring'''
return 1 / sqrt(1 - beta(UpperCAmelCase__ ) ** 2 )
def UpperCAmelCase__ ( UpperCAmelCase__ :float ):
'''simple docstring'''
return np.array(
[
[gamma(UpperCAmelCase__ ), -gamma(UpperCAmelCase__ ) * beta(UpperCAmelCase__ ), 0, 0],
[-gamma(UpperCAmelCase__ ) * beta(UpperCAmelCase__ ), gamma(UpperCAmelCase__ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :np.ndarray | None = None ):
'''simple docstring'''
if event is None:
a = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(UpperCAmelCase__ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
A_ : Tuple = transform(29_97_92_45)
print('''Example of four vector: ''')
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
A_ : List[str] = {ct: c, x: 1, y: 1, z: 1}
A_ : List[str] = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""")
| 716
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
a , a , a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
a , a , a = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
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|
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : int=64 , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Union[str, Any]=32 , __lowerCAmelCase : List[Any]=5 , __lowerCAmelCase : List[str]=4 , __lowerCAmelCase : List[Any]=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[Any]=0.1 , __lowerCAmelCase : List[Any]=10 , __lowerCAmelCase : Union[str, Any]=0.0_2 , __lowerCAmelCase : Optional[Any]=[1, 16, 4, 4] , __lowerCAmelCase : Tuple=None , ) -> int:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = patch_size
a = num_channels
a = is_training
a = use_labels
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 = type_sequence_label_size
a = initializer_range
a = scope
a = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
a = (self.image_size // 32) ** 2
a = num_patches + 1
def A ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Any ) -> List[str]:
"""simple docstring"""
a = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [4, 8, 16, 32],
"num_groups": 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__lowerCAmelCase , )
def A ( self : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = ViTHybridModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.type_sequence_label_size
a = ViTHybridForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A ( self : Dict ) -> Tuple:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = ViTHybridModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : str ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def A ( self : str ) -> Any:
"""simple docstring"""
pass
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowerCAmelCase , nn.Linear ) )
def A ( self : Tuple ) -> str:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : str ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : List[Any] ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
def A ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
a = _config_zero_init(__lowerCAmelCase )
for model_class in self.all_model_classes:
a = model_class(config=__lowerCAmelCase )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
a = [f"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@slow
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ViTHybridModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A ( self : str ) -> List[str]:
"""simple docstring"""
a = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
@slow
@require_accelerate
def A ( self : int ) -> int:
"""simple docstring"""
a = ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384" )
a = ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384" , device_map="auto" )
a = prepare_img()
a = image_processor(images=__lowerCAmelCase , return_tensors="pt" )
a = model(**__lowerCAmelCase )
a = outputs.logits
# model predicts one of the 1000 ImageNet classes
a = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , "tabby, tabby cat" )
| 717
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 0
|
A_ : List[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def UpperCAmelCase__ ( UpperCAmelCase__ :bytes ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
a = F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(UpperCAmelCase__ )
a = "".join(bin(UpperCAmelCase__ )[2:].zfill(8 ) for byte in data )
a = len(UpperCAmelCase__ ) % 6 != 0
if padding_needed:
# The padding that will be added later
a = b"=" * ((6 - len(UpperCAmelCase__ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(UpperCAmelCase__ ) % 6)
else:
a = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(UpperCAmelCase__ ) , 6 ) ).encode()
+ padding
)
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
a = (
"argument should be a bytes-like object or ASCII string, "
F"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(UpperCAmelCase__ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
try:
a = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
a = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(UpperCAmelCase__ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
a = encoded_data[:-padding]
a = "".join(
bin(B64_CHARSET.index(UpperCAmelCase__ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
a = "".join(
bin(B64_CHARSET.index(UpperCAmelCase__ ) )[2:].zfill(6 ) for char in encoded_data )
a = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(UpperCAmelCase__ ) , 8 )
]
return bytes(UpperCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Any = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''biogpt'''
def __init__( self : List[str] , __lowerCAmelCase : Optional[int]=4_2384 , __lowerCAmelCase : List[Any]=1024 , __lowerCAmelCase : Union[str, Any]=24 , __lowerCAmelCase : int=16 , __lowerCAmelCase : List[Any]=4096 , __lowerCAmelCase : List[str]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : Optional[int]=1024 , __lowerCAmelCase : List[str]=0.0_2 , __lowerCAmelCase : Tuple=1E-12 , __lowerCAmelCase : Any=True , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Any=0.0 , __lowerCAmelCase : int=0.0 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=0 , __lowerCAmelCase : Any=2 , **__lowerCAmelCase : int , ) -> Optional[int]:
"""simple docstring"""
a = vocab_size
a = max_position_embeddings
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = initializer_range
a = layer_norm_eps
a = scale_embedding
a = use_cache
a = layerdrop
a = activation_dropout
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 719
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
| 0
|
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCAmelCase__ ( UpperCAmelCase__ :dict ):
'''simple docstring'''
return (data["data"], data["target"])
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ):
'''simple docstring'''
a = XGBClassifier()
classifier.fit(UpperCAmelCase__ , UpperCAmelCase__ )
return classifier
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = load_iris()
a , a = data_handling(UpperCAmelCase__ )
a , a , a , a = train_test_split(
UpperCAmelCase__ , UpperCAmelCase__ , test_size=0.25 )
a = iris["target_names"]
# Create an XGBoost Classifier from the training data
a = xgboost(UpperCAmelCase__ , UpperCAmelCase__ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , display_labels=UpperCAmelCase__ , cmap="Blues" , normalize="true" , )
plt.title("Normalized Confusion Matrix - IRIS Dataset" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 720
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 32
| 0
|
from math import sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(UpperCAmelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 1_00_01 ):
'''simple docstring'''
a = 0
a = 1
while count != nth and number < 3:
number += 1
if is_prime(UpperCAmelCase__ ):
count += 1
while count != nth:
number += 2
if is_prime(UpperCAmelCase__ ):
count += 1
return number
if __name__ == "__main__":
print(F"""{solution() = }""")
| 721
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 0
|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase, UpperCAmelCase__ ):
def A ( self : List[Any] ) -> Tuple:
"""simple docstring"""
a = load_tool("text-classification" )
self.tool.setup()
a = load_tool("text-classification" , remote=__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__lowerCAmelCase , "positive" )
def A ( self : Any ) -> Dict:
"""simple docstring"""
a = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__lowerCAmelCase , "positive" )
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
a = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__lowerCAmelCase , "positive" )
def A ( self : Any ) -> Tuple:
"""simple docstring"""
a = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__lowerCAmelCase , "positive" )
| 700
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 0
|
from numpy import exp, pi, sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :float = 0.0 , UpperCAmelCase__ :float = 1.0 ):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 701
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 0
|
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Any , UpperCAmelCase__ :Tuple , UpperCAmelCase__ :str ):
'''simple docstring'''
a = MobileBertConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = MobileBertForPreTraining(UpperCAmelCase__ )
# Load weights from tf checkpoint
a = load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--mobilebert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained MobileBERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Optional[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 702
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
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|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :float , UpperCAmelCase__ :float ):
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * daily_interest_rate * days_between_payments
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :float , UpperCAmelCase__ :float , ):
'''simple docstring'''
if number_of_compounding_periods <= 0:
raise ValueError("number_of_compounding_periods must be > 0" )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError("nominal_annual_interest_rate_percentage must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :float , UpperCAmelCase__ :float , ):
'''simple docstring'''
if number_of_years <= 0:
raise ValueError("number_of_years must be > 0" )
if nominal_annual_percentage_rate < 0:
raise ValueError("nominal_annual_percentage_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return compound_interest(
UpperCAmelCase__ , nominal_annual_percentage_rate / 3_65 , number_of_years * 3_65 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 0
|
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 _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''image_processor''', '''tokenizer''']
_UpperCAmelCase = '''BridgeTowerImageProcessor'''
_UpperCAmelCase = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( self : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(__lowerCAmelCase , __lowerCAmelCase )
def __call__( self : Any , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __lowerCAmelCase : bool = True , __lowerCAmelCase : Union[bool, str, PaddingStrategy] = False , __lowerCAmelCase : Union[bool, str, TruncationStrategy] = None , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : int = 0 , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : Optional[bool] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = True , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , **__lowerCAmelCase : Any , ) -> BatchEncoding:
"""simple docstring"""
a = self.tokenizer(
text=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase , stride=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , return_overflowing_tokens=__lowerCAmelCase , return_special_tokens_mask=__lowerCAmelCase , return_offsets_mapping=__lowerCAmelCase , return_length=__lowerCAmelCase , verbose=__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase , )
# add pixel_values + pixel_mask
a = self.image_processor(
__lowerCAmelCase , return_tensors=__lowerCAmelCase , do_normalize=__lowerCAmelCase , do_center_crop=__lowerCAmelCase , **__lowerCAmelCase )
encoding.update(__lowerCAmelCase )
return encoding
def A ( self : Optional[int] , *__lowerCAmelCase : Optional[int] , **__lowerCAmelCase : Optional[int] ) -> Dict:
"""simple docstring"""
return self.tokenizer.batch_decode(*__lowerCAmelCase , **__lowerCAmelCase )
def A ( self : str , *__lowerCAmelCase : int , **__lowerCAmelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
return self.tokenizer.decode(*__lowerCAmelCase , **__lowerCAmelCase )
@property
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.tokenizer.model_input_names
a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 704
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 0
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
a = 0
a = str(UpperCAmelCase__ )
while len(UpperCAmelCase__ ) != 1:
a = [int(UpperCAmelCase__ ) for i in num_string]
a = 1
for i in range(0 , len(UpperCAmelCase__ ) ):
total *= numbers[i]
a = str(UpperCAmelCase__ )
steps += 1
return steps
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
a = 0
a = str(UpperCAmelCase__ )
while len(UpperCAmelCase__ ) != 1:
a = [int(UpperCAmelCase__ ) for i in num_string]
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
total += numbers[i]
a = str(UpperCAmelCase__ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 705
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 0
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 10_00 ):
'''simple docstring'''
a = 3
a = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F"""{solution() = }""")
| 706
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 0
|
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def UpperCAmelCase__ ( ) -> List[Any]:
'''simple docstring'''
print("Making key files..." )
make_key_files("rsa" , 10_24 )
print("Key files generation successful." )
def UpperCAmelCase__ ( UpperCAmelCase__ :int ) -> Dict:
'''simple docstring'''
print("Generating prime p..." )
a = rabinMiller.generate_large_prime(UpperCAmelCase__ )
print("Generating prime q..." )
a = rabinMiller.generate_large_prime(UpperCAmelCase__ )
a = p * q
print("Generating e that is relatively prime to (p - 1) * (q - 1)..." )
while True:
a = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(UpperCAmelCase__ , (p - 1) * (q - 1) ) == 1:
break
print("Calculating d that is mod inverse of e..." )
a = cryptoMath.find_mod_inverse(UpperCAmelCase__ , (p - 1) * (q - 1) )
a = (n, e)
a = (n, d)
return (public_key, private_key)
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :int ) -> str:
'''simple docstring'''
if os.path.exists(F"""{name}_pubkey.txt""" ) or os.path.exists(F"""{name}_privkey.txt""" ):
print("\nWARNING:" )
print(
F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
"Use a different name or delete these files and re-run this program." )
sys.exit()
a , a = generate_key(UpperCAmelCase__ )
print(F"""\nWriting public key to file {name}_pubkey.txt...""" )
with open(F"""{name}_pubkey.txt""" , "w" ) as out_file:
out_file.write(F"""{key_size},{public_key[0]},{public_key[1]}""" )
print(F"""Writing private key to file {name}_privkey.txt...""" )
with open(F"""{name}_privkey.txt""" , "w" ) as out_file:
out_file.write(F"""{key_size},{private_key[0]},{private_key[1]}""" )
if __name__ == "__main__":
main()
| 707
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''falcon'''
_UpperCAmelCase = ['''past_key_values''']
def __init__( self : int , __lowerCAmelCase : Tuple=6_5024 , __lowerCAmelCase : int=4544 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Union[str, Any]=71 , __lowerCAmelCase : Any=1E-5 , __lowerCAmelCase : Union[str, Any]=0.0_2 , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Any=0.0 , __lowerCAmelCase : Any=0.0 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : int=True , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=False , __lowerCAmelCase : Dict=11 , __lowerCAmelCase : Any=11 , **__lowerCAmelCase : Any , ) -> int:
"""simple docstring"""
a = vocab_size
# Backward compatibility with n_embed kwarg
a = kwargs.pop("n_embed" , __lowerCAmelCase )
a = hidden_size if n_embed is None else n_embed
a = num_hidden_layers
a = num_attention_heads
a = layer_norm_epsilon
a = initializer_range
a = use_cache
a = hidden_dropout
a = attention_dropout
a = bos_token_id
a = eos_token_id
a = num_attention_heads if num_kv_heads is None else num_kv_heads
a = alibi
a = new_decoder_architecture
a = multi_query # Ignored when new_decoder_architecture is True
a = parallel_attn
a = bias
super().__init__(bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
@property
def A ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def A ( self : Dict ) -> Tuple:
"""simple docstring"""
return not self.alibi
| 708
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_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 = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""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
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
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 = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 0
|
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :float ):
'''simple docstring'''
return np.where(vector > 0 , UpperCAmelCase__ , (alpha * (np.exp(UpperCAmelCase__ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 709
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 0
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
a = []
a , a = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
a = result + left + right
return input_list
def UpperCAmelCase__ ( UpperCAmelCase__ :list ):
'''simple docstring'''
if len(UpperCAmelCase__ ) <= 1:
return input_list
a = list(UpperCAmelCase__ )
# iteration for two-way merging
a = 2
while p <= len(UpperCAmelCase__ ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(UpperCAmelCase__ ) , UpperCAmelCase__ ):
a = i
a = i + p - 1
a = (low + high + 1) // 2
a = merge(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# final merge of last two parts
if p * 2 >= len(UpperCAmelCase__ ):
a = i
a = merge(UpperCAmelCase__ , 0 , UpperCAmelCase__ , len(UpperCAmelCase__ ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
A_ : List[str] = input('''Enter numbers separated by a comma:\n''').strip()
if user_input == "":
A_ : Dict = []
else:
A_ : int = [int(item.strip()) for item in user_input.split(''',''')]
print(iter_merge_sort(unsorted))
| 710
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 0
|
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Whether tp freeze the encoder.'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Whether to freeze the embeddings.'''} )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
_UpperCAmelCase = field(
default='''summarization''', metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}, )
_UpperCAmelCase = field(
default=1_024, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total sequence length for target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=142, metadata={
'''help''': (
'''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded. '''
'''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
'''during ``evaluate`` and ``predict``.'''
)
}, )
_UpperCAmelCase = field(
default=142, metadata={
'''help''': (
'''The maximum total sequence length for test target text after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(default=-1, metadata={'''help''': '''# training examples. -1 means use all.'''} )
_UpperCAmelCase = field(default=-1, metadata={'''help''': '''# validation examples. -1 means use all.'''} )
_UpperCAmelCase = field(default=-1, metadata={'''help''': '''# test examples. -1 means use all.'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Source language id for translation.'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Target language id for translation.'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''# num_beams to use for evaluation.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}, )
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[Any] , UpperCAmelCase__ :Union[str, Any] ):
'''simple docstring'''
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , F"""{split}_results.json""" ) )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
a , a , a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
a , a , a = parser.parse_args_into_dataclasses()
check_output_dir(UpperCAmelCase__ )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
a = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
assert hasattr(UpperCAmelCase__ , UpperCAmelCase__ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(UpperCAmelCase__ , UpperCAmelCase__ , getattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
a = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=".ckpt" in model_args.model_name_or_path , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(UpperCAmelCase__ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
a = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(UpperCAmelCase__ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
a = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
a = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(UpperCAmelCase__ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
a = SeqaSeqDataset
# Get datasets
a = (
dataset_class(
UpperCAmelCase__ , type_path="train" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_train
else None
)
a = (
dataset_class(
UpperCAmelCase__ , type_path="val" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
a = (
dataset_class(
UpperCAmelCase__ , type_path="test" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or "" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
a = (
build_compute_metrics_fn(data_args.task , UpperCAmelCase__ ) if training_args.predict_with_generate else None
)
a = SeqaSeqTrainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , data_args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , data_collator=SeqaSeqDataCollator(
UpperCAmelCase__ , UpperCAmelCase__ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , )
a = {}
# Training
if training_args.do_train:
logger.info("*** Train ***" )
a = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
a = train_result.metrics
a = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("train" , UpperCAmelCase__ , training_args.output_dir )
all_metrics.update(UpperCAmelCase__ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate(metric_key_prefix="val" )
a = data_args.n_val
a = round(metrics["val_loss"] , 4 )
if trainer.is_world_process_zero():
handle_metrics("val" , UpperCAmelCase__ , training_args.output_dir )
all_metrics.update(UpperCAmelCase__ )
if training_args.do_predict:
logger.info("*** Predict ***" )
a = trainer.predict(test_dataset=UpperCAmelCase__ , metric_key_prefix="test" )
a = test_output.metrics
a = data_args.n_test
if trainer.is_world_process_zero():
a = round(metrics["test_loss"] , 4 )
handle_metrics("test" , UpperCAmelCase__ , training_args.output_dir )
all_metrics.update(UpperCAmelCase__ )
if training_args.predict_with_generate:
a = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ )
a = lmap(str.strip , UpperCAmelCase__ )
write_txt_file(UpperCAmelCase__ , os.path.join(training_args.output_dir , "test_generations.txt" ) )
if trainer.is_world_process_zero():
save_json(UpperCAmelCase__ , os.path.join(training_args.output_dir , "all_results.json" ) )
return all_metrics
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 711
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
| 0
|
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import 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 import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any]=13 , __lowerCAmelCase : Optional[int]=7 , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : List[Any]=99 , __lowerCAmelCase : str=32 , __lowerCAmelCase : List[str]=5 , __lowerCAmelCase : List[str]=4 , __lowerCAmelCase : List[str]=37 , __lowerCAmelCase : int="gelu" , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Any=16 , __lowerCAmelCase : Any=2 , __lowerCAmelCase : Dict=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : List[str]=4 , __lowerCAmelCase : List[Any]=None , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
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 = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : int ) -> Dict:
"""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
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
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 = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str] ) -> Optional[Any]:
"""simple docstring"""
a = NystromformerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
a = NystromformerForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = NystromformerForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
a = self.num_labels
a = NystromformerForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
a = self.num_labels
a = NystromformerForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = self.num_choices
a = NystromformerForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': NystromformerModel,
'''fill-mask''': NystromformerForMaskedLM,
'''question-answering''': NystromformerForQuestionAnswering,
'''text-classification''': NystromformerForSequenceClassification,
'''token-classification''': NystromformerForTokenClassification,
'''zero-shot''': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
a = NystromformerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> List[str]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a = type
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def A ( self : str ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase )
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
def A ( self : Any ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )
def A ( self : List[str] ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
@slow
def A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = NystromformerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
a = NystromformerModel.from_pretrained("uw-madison/nystromformer-512" )
a = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowerCAmelCase , atol=1E-4 ) )
@slow
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = "the [MASK] of Belgium is Brussels"
a = AutoTokenizer.from_pretrained("uw-madison/nystromformer-512" )
a = NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512" )
a = tokenizer(__lowerCAmelCase , return_tensors="pt" )
with torch.no_grad():
a = model(encoding.input_ids ).logits
a = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(__lowerCAmelCase ) , "capital" )
| 712
|
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
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|
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :int ):
'''simple docstring'''
a = [0 for i in range(r + 1 )]
# nc0 = 1
a = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
a = min(UpperCAmelCase__ , UpperCAmelCase__ )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=10, r=5))
| 713
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32
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|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Dict = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = [
'''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WavLMForAudioFrameClassification''',
'''WavLMForCTC''',
'''WavLMForSequenceClassification''',
'''WavLMForXVector''',
'''WavLMModel''',
'''WavLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 714
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
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|
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 _lowercase ( unittest.TestCase ):
def A ( self : str ) -> Optional[int]:
"""simple docstring"""
a = "ylacombe/bark-small"
a = tempfile.mkdtemp()
a = "en_speaker_1"
a = "This is a test string"
a = "speaker_embeddings_path.json"
a = "speaker_embeddings"
def A ( self : Any , **__lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint , **__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def A ( self : Any ) -> List[Any]:
"""simple docstring"""
a = self.get_tokenizer()
a = BarkProcessor(tokenizer=__lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
a = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = 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 , )
a = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
a = 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 A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
a = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
a = 35
a = 2
a = 8
a = {
"semantic_prompt": np.ones(__lowerCAmelCase ),
"coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
"fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
a = processor(text=self.input_string , voice_preset=__lowerCAmelCase )
a = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__lowerCAmelCase , np.array([] ) ).tolist() )
# test loading voice preset from npz file
a = os.path.join(self.tmpdirname , "file.npz" )
np.savez(__lowerCAmelCase , **__lowerCAmelCase )
a = processor(text=self.input_string , voice_preset=__lowerCAmelCase )
a = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__lowerCAmelCase , np.array([] ) ).tolist() )
# test loading voice preset from the hub
a = processor(text=self.input_string , voice_preset=self.voice_preset )
def A ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
a = self.get_tokenizer()
a = BarkProcessor(tokenizer=__lowerCAmelCase )
a = processor(text=self.input_string )
a = tokenizer(
self.input_string , padding="max_length" , max_length=256 , add_special_tokens=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 715
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 0
|
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : int = logging.get_logger(__name__)
A_ : List[Any] = 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'''),
]
)
A_ : str = 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'''),
]
)
A_ : List[Any] = 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'''),
]
)
A_ : Dict = 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'''),
]
)
A_ : Optional[Any] = OrderedDict(
[
# Model for Image-classsification
('''beit''', '''FlaxBeitForImageClassification'''),
('''regnet''', '''FlaxRegNetForImageClassification'''),
('''resnet''', '''FlaxResNetForImageClassification'''),
('''vit''', '''FlaxViTForImageClassification'''),
]
)
A_ : List[str] = OrderedDict(
[
('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
]
)
A_ : Optional[int] = 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'''),
]
)
A_ : List[str] = 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'''),
]
)
A_ : int = 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'''),
]
)
A_ : str = 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'''),
]
)
A_ : int = 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'''),
]
)
A_ : Dict = OrderedDict(
[
('''bert''', '''FlaxBertForNextSentencePrediction'''),
]
)
A_ : Optional[Any] = OrderedDict(
[
('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
]
)
A_ : Any = OrderedDict(
[
('''whisper''', '''FlaxWhisperForAudioClassification'''),
]
)
A_ : Dict = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
A_ : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
A_ : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
A_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
A_ : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
A_ : Union[str, Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
A_ : Union[str, Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
A_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
A_ : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
A_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
A_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
A_ : str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
A_ : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
A_ : int = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_MAPPING
A_ : List[str] = auto_class_update(FlaxAutoModel)
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_PRETRAINING_MAPPING
A_ : List[Any] = auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
A_ : Optional[int] = auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_MASKED_LM_MAPPING
A_ : int = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
A_ : Tuple = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
A_ : List[str] = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
A_ : int = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
A_ : Any = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
A_ : Tuple = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
A_ : Union[str, Any] = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
A_ : Tuple = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='''image classification'''
)
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
A_ : str = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')
class _lowercase ( _BaseAutoModelClass ):
_UpperCAmelCase = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
A_ : Dict = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)
| 716
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
a , a , a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
a , a , a = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 32
| 0
|
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
A_ : Dict = argparse.ArgumentParser()
parser.add_argument('''--user''', type=str, default='''ubuntu''')
parser.add_argument('''--host''', type=str, default='''localhost''')
parser.add_argument('''--key_path''', type=str, default=None)
parser.add_argument('''--instance''', type=str, default='''V100:1''')
parser.add_argument('''--provider''', type=str, default='''cheapest''')
parser.add_argument('''--use_spot''', type=bool, default=False)
parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
A_ : List[Any] = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
A_ : Optional[Any] = rh.cluster(
name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
)
else:
A_ : List[Any] = rh.cluster(
name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
A_ : Tuple = args.example.rsplit('''/''', 1)[0]
# Set up remote environment
cluster.install_packages(['''pip:./''']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([F"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 717
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 718
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 0
|
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowercase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
_UpperCAmelCase = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def UpperCAmelCase__ ( ):
'''simple docstring'''
if os.name == "nt":
a = CursorInfo()
a = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
a = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def UpperCAmelCase__ ( ):
'''simple docstring'''
if os.name == "nt":
a = CursorInfo()
a = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
a = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def UpperCAmelCase__ ( ):
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 719
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
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|
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 10_00 ):
'''simple docstring'''
a , a = 1, 1
a = []
for i in range(1 , n + 1 ):
a = prev_numerator + 2 * prev_denominator
a = prev_numerator + prev_denominator
if len(str(UpperCAmelCase__ ) ) > len(str(UpperCAmelCase__ ) ):
result.append(UpperCAmelCase__ )
a = numerator
a = denominator
return len(UpperCAmelCase__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 720
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
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| 0
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=UpperCAmelCase__ )
a = parser.add_subparsers(help="accelerate command helpers" )
# Register commands
get_config_parser(subparsers=UpperCAmelCase__ )
env_command_parser(subparsers=UpperCAmelCase__ )
launch_command_parser(subparsers=UpperCAmelCase__ )
tpu_command_parser(subparsers=UpperCAmelCase__ )
test_command_parser(subparsers=UpperCAmelCase__ )
# Let's go
a = parser.parse_args()
if not hasattr(UpperCAmelCase__ , "func" ):
parser.print_help()
exit(1 )
# Run
args.func(UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 721
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 0
|
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : List[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A_ : str = {
'''vocab_file''': {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
),
},
}
A_ : List[str] = {
'''vocab_file''': {
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
),
},
}
A_ : Optional[Any] = {
'''vocab_file''': {
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
),
},
}
A_ : str = {
'''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
'''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
A_ : Dict = {
'''facebook/dpr-question_encoder-single-nq-base''': 5_12,
'''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
A_ : int = {
'''facebook/dpr-reader-single-nq-base''': 5_12,
'''facebook/dpr-reader-multiset-base''': 5_12,
}
A_ : Optional[Any] = {
'''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
A_ : int = {
'''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
A_ : int = {
'''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
A_ : Optional[int] = collections.namedtuple(
'''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)
A_ : Union[str, Any] = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])
A_ : List[Any] = r'''
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
```
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
```
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `\'tf\'`: Return TensorFlow `tf.constant` objects.
- `\'pt\'`: Return PyTorch `torch.Tensor` objects.
- `\'np\'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer\'s default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Returns:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
'''
@add_start_docstrings(UpperCAmelCase__ )
class _lowercase :
def __call__( self : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Union[bool, str] = False , __lowerCAmelCase : Union[bool, str] = False , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = None , **__lowerCAmelCase : int , ) -> BatchEncoding:
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
__lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase , return_tensors=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , **__lowerCAmelCase , )
elif titles is None or texts is None:
a = titles if texts is None else texts
return super().__call__(
__lowerCAmelCase , __lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase , return_tensors=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , **__lowerCAmelCase , )
a = titles if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) else [titles]
a = texts if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) else [texts]
a = len(__lowerCAmelCase )
a = questions if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) else [questions] * n_passages
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
raise ValueError(
f"""There should be as many titles than texts but got {len(__lowerCAmelCase )} titles and {len(__lowerCAmelCase )} texts.""" )
a = super().__call__(__lowerCAmelCase , __lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase )["input_ids"]
a = super().__call__(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase )["input_ids"]
a = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(__lowerCAmelCase , __lowerCAmelCase )
]
}
if return_attention_mask is not False:
a = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
a = attention_mask
return self.pad(__lowerCAmelCase , padding=__lowerCAmelCase , max_length=__lowerCAmelCase , return_tensors=__lowerCAmelCase )
def A ( self : Optional[int] , __lowerCAmelCase : BatchEncoding , __lowerCAmelCase : DPRReaderOutput , __lowerCAmelCase : int = 16 , __lowerCAmelCase : int = 64 , __lowerCAmelCase : int = 4 , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
a = reader_input["input_ids"]
a , a , a = reader_output[:3]
a = len(__lowerCAmelCase )
a = sorted(range(__lowerCAmelCase ) , reverse=__lowerCAmelCase , key=relevance_logits.__getitem__ )
a = []
for doc_id in sorted_docs:
a = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
a = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
a = sequence_ids.index(self.pad_token_id )
else:
a = len(__lowerCAmelCase )
a = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=__lowerCAmelCase , top_spans=__lowerCAmelCase , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=__lowerCAmelCase , start_index=__lowerCAmelCase , end_index=__lowerCAmelCase , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(__lowerCAmelCase ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def A ( self : List[str] , __lowerCAmelCase : List[int] , __lowerCAmelCase : List[int] , __lowerCAmelCase : int , __lowerCAmelCase : int , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
a = []
for start_index, start_score in enumerate(__lowerCAmelCase ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
a = sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : x[1] , reverse=__lowerCAmelCase )
a = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]""" )
a = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(f"""Span is too long: {length} > {max_answer_length}""" )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(__lowerCAmelCase ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(UpperCAmelCase__ )
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = READER_PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = READER_PRETRAINED_INIT_CONFIGURATION
_UpperCAmelCase = ['''input_ids''', '''attention_mask''']
| 700
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 0
|
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _lowercase ( UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = DanceDiffusionPipeline
_UpperCAmelCase = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
_UpperCAmelCase = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
a = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=1_6000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=__lowerCAmelCase , use_timestep_embedding=__lowerCAmelCase , time_embedding_type="fourier" , mid_block_type="UNetMidBlock1D" , down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , )
a = IPNDMScheduler()
a = {
"unet": unet,
"scheduler": scheduler,
}
return components
def A ( self : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int]=0 ) -> Tuple:
"""simple docstring"""
if str(__lowerCAmelCase ).startswith("mps" ):
a = torch.manual_seed(__lowerCAmelCase )
else:
a = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
a = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 4,
}
return inputs
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = DanceDiffusionPipeline(**__lowerCAmelCase )
a = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = pipe(**__lowerCAmelCase )
a = output.audios
a = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
a = np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def A ( self : Dict ) -> List[str]:
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def A ( self : int ) -> List[str]:
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return super().test_save_load_optional_components()
@skip_mps
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return super().test_attention_slicing_forward_pass()
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
def A ( self : Any ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
a = torch_device
a = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" )
a = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = torch.manual_seed(0 )
a = pipe(generator=__lowerCAmelCase , num_inference_steps=100 , audio_length_in_s=4.0_9_6 )
a = output.audios
a = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
a = np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
a = torch_device
a = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" , torch_dtype=torch.floataa )
a = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = torch.manual_seed(0 )
a = pipe(generator=__lowerCAmelCase , num_inference_steps=100 , audio_length_in_s=4.0_9_6 )
a = output.audios
a = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
a = np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 701
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 0
|
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 _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict=13 , __lowerCAmelCase : Optional[Any]=7 , __lowerCAmelCase : Optional[Any]=True , __lowerCAmelCase : str=True , __lowerCAmelCase : str=True , __lowerCAmelCase : List[Any]=99 , __lowerCAmelCase : str=32 , __lowerCAmelCase : int=5 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : List[str]=37 , __lowerCAmelCase : List[str]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : Optional[int]=512 , __lowerCAmelCase : Dict=16 , __lowerCAmelCase : Dict=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : str=3 , __lowerCAmelCase : Tuple=4 , __lowerCAmelCase : Dict=None , ) -> Optional[int]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_token_type_ids
a = use_labels
a = vocab_size
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 = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
a = self.vocab_size - 1
def A ( self : Dict ) -> str:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
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 = 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 , )
a = 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 A ( self : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] , *__lowerCAmelCase : Tuple ) -> str:
"""simple docstring"""
a = OpenAIGPTModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase , head_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , *__lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = OpenAIGPTLMHeadModel(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , *__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = OpenAIGPTDoubleHeadsModel(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , *__lowerCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
a = self.num_labels
a = OpenAIGPTForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_UpperCAmelCase = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def A ( self : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""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 A ( self : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : List[str]=False ) -> Union[str, Any]:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase , )
a = inputs_dict["labels"]
a = inputs_dict["labels"]
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=__lowerCAmelCase , )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : List[str] ) -> str:
"""simple docstring"""
a = OpenAIGPTModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , n_embd=37 )
def A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*__lowerCAmelCase )
def A ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*__lowerCAmelCase )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCAmelCase )
@slow
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = OpenAIGPTModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
a = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
model.to(__lowerCAmelCase )
a = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=__lowerCAmelCase ) # the president is
a = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
a = model.generate(__lowerCAmelCase , do_sample=__lowerCAmelCase )
self.assertListEqual(output_ids[0].tolist() , __lowerCAmelCase )
| 702
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 0
|
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