code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['GLPNFeatureExtractor']
a_ = ['GLPNImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
'GLPNForDepthEstimation',
'GLPNLayer',
'GLPNModel',
'GLPNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3 | 25 | 1 |
"""simple docstring"""
def UpperCAmelCase_ ( ):
'''simple docstring'''
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def UpperCAmelCase_ ( __a : List[str] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = 1
_lowerCamelCase : Optional[Any] = 2
while i * i <= n:
_lowerCamelCase : int = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def UpperCAmelCase_ ( ):
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(__a ) > 5_00 )
if __name__ == "__main__":
print(solution())
| 349 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
"""umberto-commoncrawl-cased-v1""": (
"""https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
),
"""umberto-wikipedia-uncased-v1""": (
"""https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
),
}
class A_(SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
a_ : Optional[Any] = """camembert"""
def __init__( self , A=3_0522 , A=768 , A=12 , A=12 , A=3072 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=2 , A=0.0_2 , A=1E-12 , A=1 , A=0 , A=2 , A="absolute" , A=True , A=None , **A , ):
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A )
_lowerCamelCase : Tuple = vocab_size
_lowerCamelCase : List[str] = hidden_size
_lowerCamelCase : List[Any] = num_hidden_layers
_lowerCamelCase : str = num_attention_heads
_lowerCamelCase : Dict = hidden_act
_lowerCamelCase : int = intermediate_size
_lowerCamelCase : Any = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : Optional[Any] = max_position_embeddings
_lowerCamelCase : Any = type_vocab_size
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Tuple = layer_norm_eps
_lowerCamelCase : Tuple = position_embedding_type
_lowerCamelCase : Dict = use_cache
_lowerCamelCase : Dict = classifier_dropout
class A_(SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
@property
def _lowerCAmelCase ( self ):
if self.task == "multiple-choice":
_lowerCamelCase : List[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCamelCase : Any = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 349 | 1 |
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
a_ = 16
a_ = 32
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : int = 16 ,_UpperCamelCase : str = "bert-base-cased" ):
__lowerCamelCase = AutoTokenizer.from_pretrained(__a )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' )
def tokenize_function(_UpperCamelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=__a ,max_length=__a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__lowerCamelCase = datasets.map(
__a ,batched=__a ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,load_from_cache_file=__a )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase = 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.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__a ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return tokenizer.pad(__a ,padding='''longest''' ,return_tensors='''pt''' )
# Instantiate dataloaders.
__lowerCamelCase = DataLoader(
tokenized_datasets['''train'''] ,shuffle=__a ,collate_fn=__a ,batch_size=__a )
__lowerCamelCase = DataLoader(
tokenized_datasets['''validation'''] ,shuffle=__a ,collate_fn=__a ,batch_size=__a )
return train_dataloader, eval_dataloader
def a__ ( _UpperCamelCase : List[str] ,_UpperCamelCase : Union[str, Any] ,_UpperCamelCase : Any ,_UpperCamelCase : Optional[int] ):
model.eval()
__lowerCamelCase = 0
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase = model(**__a )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
__lowerCamelCase = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__a ) - 1:
__lowerCamelCase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
__lowerCamelCase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__a ,references=__a ,)
__lowerCamelCase = metric.compute()
return eval_metric["accuracy"]
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : Any ):
__lowerCamelCase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase = config['''lr''']
__lowerCamelCase = int(config['''num_epochs'''] )
__lowerCamelCase = int(config['''seed'''] )
__lowerCamelCase = int(config['''batch_size'''] )
__lowerCamelCase = args.model_name_or_path
set_seed(__a )
__lowerCamelCase = get_dataloaders(__a ,__a ,__a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(__a ,return_dict=__a )
# Instantiate optimizer
__lowerCamelCase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__lowerCamelCase = optimizer_cls(params=model.parameters() ,lr=__a )
if accelerator.state.deepspeed_plugin is not None:
__lowerCamelCase = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
__lowerCamelCase = 1
__lowerCamelCase = (len(__a ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
__lowerCamelCase = get_linear_schedule_with_warmup(
optimizer=__a ,num_warmup_steps=0 ,num_training_steps=__a ,)
else:
__lowerCamelCase = DummyScheduler(__a ,total_num_steps=__a ,warmup_num_steps=0 )
# 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.
__lowerCamelCase = accelerator.prepare(
__a ,__a ,__a ,__a ,__a )
# We need to keep track of how many total steps we have iterated over
__lowerCamelCase = 0
# We also need to keep track of the stating epoch so files are named properly
__lowerCamelCase = 0
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase = num_epochs
if args.partial_train_epoch is not None:
__lowerCamelCase = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
__lowerCamelCase = args.resume_from_checkpoint.split('''epoch_''' )[1]
__lowerCamelCase = ''''''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
__lowerCamelCase = int(__a ) + 1
__lowerCamelCase = evaluation_loop(__a ,__a ,__a ,__a )
accelerator.print('''resumed checkpoint performance:''' ,__a )
accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' ,lr_scheduler.get_lr()[0] )
accelerator.print('''resumed optimizers\'s lr:''' ,optimizer.param_groups[0]['''lr'''] )
with open(os.path.join(args.output_dir ,F"""state_{starting_epoch-1}.json""" ) ,'''r''' ) as f:
__lowerCamelCase = json.load(__a )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
__lowerCamelCase = {}
for epoch in range(__a ,__a ):
model.train()
for step, batch in enumerate(__a ):
__lowerCamelCase = model(**__a )
__lowerCamelCase = outputs.loss
__lowerCamelCase = loss / gradient_accumulation_steps
accelerator.backward(__a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
__lowerCamelCase = F"""epoch_{epoch}"""
__lowerCamelCase = os.path.join(args.output_dir ,__a )
accelerator.save_state(__a )
__lowerCamelCase = evaluation_loop(__a ,__a ,__a ,__a )
__lowerCamelCase = accuracy
__lowerCamelCase = lr_scheduler.get_lr()[0]
__lowerCamelCase = optimizer.param_groups[0]['''lr''']
__lowerCamelCase = epoch
__lowerCamelCase = overall_step
accelerator.print(F"""epoch {epoch}:""" ,__a )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,F"""state_{epoch}.json""" ) ,'''w''' ) as f:
json.dump(__a ,__a )
def a__ ( ):
__lowerCamelCase = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' ,type=__a ,default='''bert-base-cased''' ,help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,required=__a ,)
parser.add_argument(
'''--output_dir''' ,type=__a ,default='''.''' ,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,)
parser.add_argument(
'''--resume_from_checkpoint''' ,type=__a ,default=__a ,help='''If the training should continue from a checkpoint folder.''' ,)
parser.add_argument(
'''--partial_train_epoch''' ,type=__a ,default=__a ,help='''If passed, the training will stop after this number of epochs.''' ,)
parser.add_argument(
'''--num_epochs''' ,type=__a ,default=2 ,help='''Number of train epochs.''' ,)
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
training_function(__a ,__a )
if __name__ == "__main__":
main()
| 175 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
return series
if __name__ == "__main__":
a__ = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 14 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class UpperCAmelCase_ ( lowercase__ ):
snake_case_ = """audio-spectrogram-transformer"""
def __init__( self : Optional[Any] , _lowercase : Union[str, Any]=7_6_8 , _lowercase : Any=1_2 , _lowercase : List[Any]=1_2 , _lowercase : Tuple=3_0_7_2 , _lowercase : List[str]="gelu" , _lowercase : List[Any]=0.0 , _lowercase : Optional[Any]=0.0 , _lowercase : List[Any]=0.02 , _lowercase : Any=1e-1_2 , _lowercase : Any=1_6 , _lowercase : int=True , _lowercase : Optional[int]=1_0 , _lowercase : Optional[int]=1_0 , _lowercase : str=1_0_2_4 , _lowercase : Dict=1_2_8 , **_lowercase : Optional[int] , ) -> Any:
super().__init__(**_lowercase )
_lowercase = hidden_size
_lowercase = num_hidden_layers
_lowercase = num_attention_heads
_lowercase = intermediate_size
_lowercase = hidden_act
_lowercase = hidden_dropout_prob
_lowercase = attention_probs_dropout_prob
_lowercase = initializer_range
_lowercase = layer_norm_eps
_lowercase = patch_size
_lowercase = qkv_bias
_lowercase = frequency_stride
_lowercase = time_stride
_lowercase = max_length
_lowercase = num_mel_bins | 227 | """simple docstring"""
def __UpperCAmelCase ( _snake_case : list ):
_lowercase = len(_snake_case )
for _ in range(_snake_case ):
for i in range(_ % 2, arr_size - 1, 2 ):
if arr[i + 1] < arr[i]:
_lowercase , _lowercase = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
__UpperCamelCase : str = list(range(1_0, 0, -1))
print(f'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''') | 227 | 1 |
from __future__ import annotations
from math import gcd
def A ( _lowercase , _lowercase = 2 , _lowercase = 1 , _lowercase = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('''The input value cannot be less than 2''' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(_lowercase , _lowercase , _lowercase ) -> int:
return (pow(_lowercase , 2 ) + step) % modulus
for _ in range(_lowercase ):
# These track the position within the cycle detection logic.
SCREAMING_SNAKE_CASE : Any = seed
SCREAMING_SNAKE_CASE : List[Any] = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
SCREAMING_SNAKE_CASE : Optional[int] = rand_fn(_lowercase , _lowercase , _lowercase )
SCREAMING_SNAKE_CASE : str = rand_fn(_lowercase , _lowercase , _lowercase )
SCREAMING_SNAKE_CASE : Union[str, Any] = rand_fn(_lowercase , _lowercase , _lowercase )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
SCREAMING_SNAKE_CASE : Tuple = gcd(hare - tortoise , _lowercase )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
SCREAMING_SNAKE_CASE : Any = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__UpperCamelCase : Dict = argparse.ArgumentParser()
parser.add_argument(
'num',
type=int,
help='The value to find a divisor of',
)
parser.add_argument(
'--attempts',
type=int,
default=3,
help='The number of attempts before giving up',
)
__UpperCamelCase : Any = parser.parse_args()
__UpperCamelCase : List[str] = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(f"""{args.num} is probably prime""")
else:
__UpperCamelCase : List[str] = args.num // divisor
print(f"""{args.num} = {divisor} * {quotient}""")
| 248 | import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__UpperCamelCase : List[Any] = (
'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)
def A ( _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
return (preds == labels).mean()
def A ( _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
SCREAMING_SNAKE_CASE : int = simple_accuracy(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE : Tuple = fa_score(y_true=_lowercase , y_pred=_lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def A ( _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
SCREAMING_SNAKE_CASE : str = pearsonr(_lowercase , _lowercase )[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = spearmanr(_lowercase , _lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def A ( _lowercase , _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
assert len(_lowercase ) == len(_lowercase ), f"""Predictions and labels have mismatched lengths {len(_lowercase )} and {len(_lowercase )}"""
if task_name == "cola":
return {"mcc": matthews_corrcoef(_lowercase , _lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "mrpc":
return acc_and_fa(_lowercase , _lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(_lowercase , _lowercase )
elif task_name == "qqp":
return acc_and_fa(_lowercase , _lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
else:
raise KeyError(_lowercase )
def A ( _lowercase , _lowercase , _lowercase ):
warnings.warn(_lowercase , _lowercase )
requires_backends(_lowercase , '''sklearn''' )
if len(_lowercase ) != len(_lowercase ):
raise ValueError(f"""Predictions and labels have mismatched lengths {len(_lowercase )} and {len(_lowercase )}""" )
if task_name == "xnli":
return {"acc": simple_accuracy(_lowercase , _lowercase )}
else:
raise KeyError(_lowercase )
| 248 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __magic_name__ ( _UpperCamelCase ):
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__magic_name__ , 'tf_padding' ) )
self.parent.assertTrue(hasattr(__magic_name__ , 'depth_multiplier' ) )
class __magic_name__ :
def __init__( self , __magic_name__ , __magic_name__=1_3 , __magic_name__=3 , __magic_name__=3_2 , __magic_name__=0.25 , __magic_name__=8 , __magic_name__=True , __magic_name__=1_0_2_4 , __magic_name__=3_2 , __magic_name__="relu6" , __magic_name__=0.1 , __magic_name__=0.02 , __magic_name__=True , __magic_name__=True , __magic_name__=1_0 , __magic_name__=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = depth_multiplier
_lowerCAmelCase = min_depth
_lowerCAmelCase = tf_padding
_lowerCAmelCase = int(last_hidden_size * depth_multiplier )
_lowerCAmelCase = output_stride
_lowerCAmelCase = hidden_act
_lowerCAmelCase = classifier_dropout_prob
_lowerCAmelCase = use_labels
_lowerCAmelCase = is_training
_lowerCAmelCase = num_labels
_lowerCAmelCase = initializer_range
_lowerCAmelCase = scope
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_lowerCAmelCase = self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCamelCase ( self ):
"""simple docstring"""
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def _lowerCamelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
"""simple docstring"""
_lowerCAmelCase = MobileNetVaModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
_lowerCAmelCase = model(__magic_name__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _lowerCamelCase ( self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = MobileNetVaForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
_lowerCAmelCase = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( _UpperCamelCase ,_UpperCamelCase ,unittest.TestCase ):
UpperCamelCase : Optional[Any] = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
UpperCamelCase : Optional[int] = (
{"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase : Tuple = False
UpperCamelCase : Optional[int] = False
UpperCamelCase : Tuple = False
UpperCamelCase : str = False
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = MobileNetVaModelTester(self )
_lowerCAmelCase = MobileNetVaConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ )
def _lowerCamelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileNetV1 does not use inputs_embeds' )
def _lowerCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileNetV1 does not support input and output embeddings' )
def _lowerCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MobileNetV1 does not output attentions' )
def _lowerCamelCase ( self ):
"""simple docstring"""
pass
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(__magic_name__ )
_lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def _lowerCamelCase ( self ):
"""simple docstring"""
def check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ ):
_lowerCAmelCase = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
_lowerCAmelCase = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
_lowerCAmelCase = outputs.hidden_states
_lowerCAmelCase = 2_6
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = True
check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCAmelCase = True
check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = MobileNetVaModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def A__ ( ):
"""simple docstring"""
_lowerCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase ):
@cached_property
def _lowerCamelCase ( self ):
"""simple docstring"""
return (
MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224' ) if is_vision_available() else None
)
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224' ).to(__magic_name__ )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=__magic_name__ , return_tensors='pt' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
_lowerCAmelCase = model(**__magic_name__ )
# verify the logits
_lowerCAmelCase = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
_lowerCAmelCase = torch.tensor([-4.17_39, -1.12_33, 3.12_05] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a__ : str = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
"""SpeechT5Config""",
"""SpeechT5HifiGanConfig""",
],
"""feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
"""processing_speecht5""": ["""SpeechT5Processor"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] = ["""SpeechT5Tokenizer"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = [
"""SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SpeechT5ForSpeechToText""",
"""SpeechT5ForSpeechToSpeech""",
"""SpeechT5ForTextToSpeech""",
"""SpeechT5Model""",
"""SpeechT5PreTrainedModel""",
"""SpeechT5HifiGan""",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
a__ : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 309 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def SCREAMING_SNAKE_CASE__ ( ) -> Union[str, Any]:
_lowercase = ArgumentParser(
description=(
'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
) )
# Optional arguments for the launch helper
parser.add_argument('--num_cores' , type=snake_case__ , default=1 , help='Number of TPU cores to use (1 or 8).' )
# positional
parser.add_argument(
'training_script' , type=snake_case__ , help=(
'The full path to the single TPU training '
'program/script to be launched in parallel, '
'followed by all the arguments for the '
'training script'
) , )
# rest from the training program
parser.add_argument('training_script_args' , nargs=snake_case__ )
return parser.parse_args()
def SCREAMING_SNAKE_CASE__ ( ) -> int:
_lowercase = parse_args()
# Import training_script as a module.
_lowercase = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_lowercase = script_fpath.stem
_lowercase = importlib.import_module(snake_case__ )
# Patch sys.argv
_lowercase = [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main() | 67 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
_lowerCAmelCase : str = logging.get_logger(__name__)
# General docstring
_lowerCAmelCase : Optional[int] = "MobileNetV1Config"
# Base docstring
_lowerCAmelCase : Tuple = "google/mobilenet_v1_1.0_224"
_lowerCAmelCase : Optional[int] = [1, 1_0_2_4, 7, 7]
# Image classification docstring
_lowerCAmelCase : Tuple = "google/mobilenet_v1_1.0_224"
_lowerCAmelCase : List[Any] = "tabby, tabby cat"
_lowerCAmelCase : Tuple = [
"google/mobilenet_v1_1.0_224",
"google/mobilenet_v1_0.75_192",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__=None ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ = {}
if isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase__ = model.mobilenet_va
else:
lowerCAmelCase__ = model
lowerCAmelCase__ = 'MobilenetV1/Conv2d_0/'
lowerCAmelCase__ = backbone.conv_stem.convolution.weight
lowerCAmelCase__ = backbone.conv_stem.normalization.bias
lowerCAmelCase__ = backbone.conv_stem.normalization.weight
lowerCAmelCase__ = backbone.conv_stem.normalization.running_mean
lowerCAmelCase__ = backbone.conv_stem.normalization.running_var
for i in range(13 ):
lowerCAmelCase__ = i + 1
lowerCAmelCase__ = i * 2
lowerCAmelCase__ = backbone.layer[pt_index]
lowerCAmelCase__ = f'MobilenetV1/Conv2d_{tf_index}_depthwise/'
lowerCAmelCase__ = pointer.convolution.weight
lowerCAmelCase__ = pointer.normalization.bias
lowerCAmelCase__ = pointer.normalization.weight
lowerCAmelCase__ = pointer.normalization.running_mean
lowerCAmelCase__ = pointer.normalization.running_var
lowerCAmelCase__ = backbone.layer[pt_index + 1]
lowerCAmelCase__ = f'MobilenetV1/Conv2d_{tf_index}_pointwise/'
lowerCAmelCase__ = pointer.convolution.weight
lowerCAmelCase__ = pointer.normalization.bias
lowerCAmelCase__ = pointer.normalization.weight
lowerCAmelCase__ = pointer.normalization.running_mean
lowerCAmelCase__ = pointer.normalization.running_var
if isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase__ = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
lowerCAmelCase__ = model.classifier.weight
lowerCAmelCase__ = model.classifier.bias
return tf_to_pt_map
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ) -> List[str]:
"""simple docstring"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
lowerCAmelCase__ = tf.train.list_variables(snake_case__ )
lowerCAmelCase__ = {}
for name, shape in init_vars:
logger.info(f'Loading TF weight {name} with shape {shape}' )
lowerCAmelCase__ = tf.train.load_variable(snake_case__ , snake_case__ )
lowerCAmelCase__ = array
# Build TF to PyTorch weights loading map
lowerCAmelCase__ = _build_tf_to_pytorch_map(snake_case__ , snake_case__ , snake_case__ )
for name, pointer in tf_to_pt_map.items():
logger.info(f'Importing {name}' )
if name not in tf_weights:
logger.info(f'{name} not in tf pre-trained weights, skipping' )
continue
lowerCAmelCase__ = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
lowerCAmelCase__ = np.transpose(snake_case__ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
lowerCAmelCase__ = array.squeeze().transpose()
else:
lowerCAmelCase__ = np.transpose(snake_case__ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f'Pointer shape {pointer.shape} and array shape {array.shape} mismatched' )
logger.info(f'Initialize PyTorch weight {name} {array.shape}' )
lowerCAmelCase__ = torch.from_numpy(snake_case__ )
tf_weights.pop(snake_case__ , snake_case__ )
tf_weights.pop(name + '/RMSProp' , snake_case__ )
tf_weights.pop(name + '/RMSProp_1' , snake_case__ )
tf_weights.pop(name + '/ExponentialMovingAverage' , snake_case__ )
logger.info(f'Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}' )
return model
def UpperCAmelCase_ ( snake_case__ , snake_case__ ) -> torch.Tensor:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ = features.shape[-2:]
lowerCAmelCase__ , lowerCAmelCase__ = conv_layer.stride
lowerCAmelCase__ , lowerCAmelCase__ = conv_layer.kernel_size
if in_height % stride_height == 0:
lowerCAmelCase__ = max(kernel_height - stride_height , 0 )
else:
lowerCAmelCase__ = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
lowerCAmelCase__ = max(kernel_width - stride_width , 0 )
else:
lowerCAmelCase__ = max(kernel_width - (in_width % stride_width) , 0 )
lowerCAmelCase__ = pad_along_width // 2
lowerCAmelCase__ = pad_along_width - pad_left
lowerCAmelCase__ = pad_along_height // 2
lowerCAmelCase__ = pad_along_height - pad_top
lowerCAmelCase__ = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(snake_case__ , snake_case__ , 'constant' , 0.0 )
class __snake_case ( nn.Module ):
def __init__( self ,a_ ,a_ ,a_ ,a_ ,a_ = 1 ,a_ = 1 ,a_ = False ,a_ = True ,a_ = True ,):
"""simple docstring"""
super().__init__()
lowerCAmelCase__ = config
if in_channels % groups != 0:
raise ValueError(f'Input channels ({in_channels}) are not divisible by {groups} groups.' )
if out_channels % groups != 0:
raise ValueError(f'Output channels ({out_channels}) are not divisible by {groups} groups.' )
lowerCAmelCase__ = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
lowerCAmelCase__ = nn.Convad(
in_channels=a_ ,out_channels=a_ ,kernel_size=a_ ,stride=a_ ,padding=a_ ,groups=a_ ,bias=a_ ,padding_mode='zeros' ,)
if use_normalization:
lowerCAmelCase__ = nn.BatchNormad(
num_features=a_ ,eps=config.layer_norm_eps ,momentum=0.9997 ,affine=a_ ,track_running_stats=a_ ,)
else:
lowerCAmelCase__ = None
if use_activation:
if isinstance(a_ ,a_ ):
lowerCAmelCase__ = ACTaFN[use_activation]
elif isinstance(config.hidden_act ,a_ ):
lowerCAmelCase__ = ACTaFN[config.hidden_act]
else:
lowerCAmelCase__ = config.hidden_act
else:
lowerCAmelCase__ = None
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if self.config.tf_padding:
lowerCAmelCase__ = apply_tf_padding(a_ ,self.convolution )
lowerCAmelCase__ = self.convolution(a_ )
if self.normalization is not None:
lowerCAmelCase__ = self.normalization(a_ )
if self.activation is not None:
lowerCAmelCase__ = self.activation(a_ )
return features
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = MobileNetVaConfig
SCREAMING_SNAKE_CASE__ = load_tf_weights_in_mobilenet_va
SCREAMING_SNAKE_CASE__ = 'mobilenet_v1'
SCREAMING_SNAKE_CASE__ = 'pixel_values'
SCREAMING_SNAKE_CASE__ = False
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
if isinstance(a_ ,(nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(a_ ,nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
_lowerCAmelCase : Tuple = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
_lowerCAmelCase : Dict = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.' , SCREAMING_SNAKE_CASE , )
class __snake_case ( SCREAMING_SNAKE_CASE ):
def __init__( self ,a_ ,a_ = True ):
"""simple docstring"""
super().__init__(a_ )
lowerCAmelCase__ = config
lowerCAmelCase__ = 32
lowerCAmelCase__ = max(int(depth * config.depth_multiplier ) ,config.min_depth )
lowerCAmelCase__ = MobileNetVaConvLayer(
a_ ,in_channels=config.num_channels ,out_channels=a_ ,kernel_size=3 ,stride=2 ,)
lowerCAmelCase__ = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
lowerCAmelCase__ = nn.ModuleList()
for i in range(13 ):
lowerCAmelCase__ = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
lowerCAmelCase__ = max(int(depth * config.depth_multiplier ) ,config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
a_ ,in_channels=a_ ,out_channels=a_ ,kernel_size=3 ,stride=strides[i] ,groups=a_ ,) )
self.layer.append(
MobileNetVaConvLayer(
a_ ,in_channels=a_ ,out_channels=a_ ,kernel_size=1 ,) )
lowerCAmelCase__ = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(a_ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=a_ ,config_class=_CONFIG_FOR_DOC ,modality='vision' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def SCREAMING_SNAKE_CASE_ ( self ,a_ = None ,a_ = None ,a_ = None ,):
"""simple docstring"""
lowerCAmelCase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCAmelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
lowerCAmelCase__ = self.conv_stem(a_ )
lowerCAmelCase__ = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
lowerCAmelCase__ = layer_module(a_ )
if output_hidden_states:
lowerCAmelCase__ = all_hidden_states + (hidden_states,)
lowerCAmelCase__ = hidden_states
if self.pooler is not None:
lowerCAmelCase__ = torch.flatten(self.pooler(a_ ) ,start_dim=1 )
else:
lowerCAmelCase__ = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=a_ ,pooler_output=a_ ,hidden_states=a_ ,)
@add_start_docstrings(
'\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , SCREAMING_SNAKE_CASE , )
class __snake_case ( SCREAMING_SNAKE_CASE ):
def __init__( self ,a_ ):
"""simple docstring"""
super().__init__(a_ )
lowerCAmelCase__ = config.num_labels
lowerCAmelCase__ = MobileNetVaModel(a_ )
lowerCAmelCase__ = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
lowerCAmelCase__ = nn.Dropout(config.classifier_dropout_prob ,inplace=a_ )
lowerCAmelCase__ = nn.Linear(a_ ,config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(a_ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=a_ ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def SCREAMING_SNAKE_CASE_ ( self ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,):
"""simple docstring"""
lowerCAmelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase__ = self.mobilenet_va(a_ ,output_hidden_states=a_ ,return_dict=a_ )
lowerCAmelCase__ = outputs.pooler_output if return_dict else outputs[1]
lowerCAmelCase__ = self.classifier(self.dropout(a_ ) )
lowerCAmelCase__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowerCAmelCase__ = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowerCAmelCase__ = 'single_label_classification'
else:
lowerCAmelCase__ = 'multi_label_classification'
if self.config.problem_type == "regression":
lowerCAmelCase__ = MSELoss()
if self.num_labels == 1:
lowerCAmelCase__ = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowerCAmelCase__ = loss_fct(a_ ,a_ )
elif self.config.problem_type == "single_label_classification":
lowerCAmelCase__ = CrossEntropyLoss()
lowerCAmelCase__ = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowerCAmelCase__ = BCEWithLogitsLoss()
lowerCAmelCase__ = loss_fct(a_ ,a_ )
if not return_dict:
lowerCAmelCase__ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=a_ ,logits=a_ ,hidden_states=outputs.hidden_states ,)
| 193 | 0 |
def _lowerCAmelCase ( ) -> int:
"""simple docstring"""
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 219 |
from scipy.stats import spearmanr
import datasets
A__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''
A__ = '''
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric("spearmanr")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
'''
A__ = r'''\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a ( datasets.Metric ):
def __lowerCamelCase ( self :Union[str, Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) ,reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'''] ,)
def __lowerCamelCase ( self :Union[str, Any] ,__lowercase :Union[str, Any] ,__lowercase :Dict ,__lowercase :Dict=False ):
snake_case__ : str = spearmanr(__lowercase ,__lowercase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 219 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case = datasets.load_iris()
snake_case = np.array(data["""data"""])
snake_case = np.array(data["""target"""])
snake_case = data["""target_names"""]
snake_case , snake_case , snake_case , snake_case = train_test_split(X, y)
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return np.linalg.norm(np.array(lowercase ) - np.array(lowercase ) )
def lowerCamelCase__ ( lowercase , lowercase , lowercase , lowercase , lowercase=5 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = zip(lowercase , lowercase )
# List of distances of all points from the point to be classified
SCREAMING_SNAKE_CASE : Optional[int] = []
for data_point in data:
SCREAMING_SNAKE_CASE : Tuple = euclidean_distance(data_point[0] , lowercase )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
SCREAMING_SNAKE_CASE : List[Any] = [i[1] for i in sorted(lowercase )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
SCREAMING_SNAKE_CASE : List[Any] = Counter(lowercase ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 62 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
# TODO Update this
A = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : str = "esm"
def __init__( self : str ,UpperCamelCase : Tuple=None ,UpperCamelCase : Union[str, Any]=None ,UpperCamelCase : str=None ,UpperCamelCase : str=768 ,UpperCamelCase : List[str]=12 ,UpperCamelCase : Dict=12 ,UpperCamelCase : Any=3072 ,UpperCamelCase : List[str]=0.1 ,UpperCamelCase : int=0.1 ,UpperCamelCase : int=1026 ,UpperCamelCase : int=0.0_2 ,UpperCamelCase : Optional[Any]=1e-12 ,UpperCamelCase : str="absolute" ,UpperCamelCase : Tuple=True ,UpperCamelCase : int=None ,UpperCamelCase : Union[str, Any]=False ,UpperCamelCase : Tuple=False ,UpperCamelCase : Optional[int]=None ,UpperCamelCase : Any=None ,**UpperCamelCase : Dict ,) -> str:
super().__init__(pad_token_id=UpperCamelCase ,mask_token_id=UpperCamelCase ,**UpperCamelCase )
_lowercase : Any = vocab_size
_lowercase : List[Any] = hidden_size
_lowercase : Union[str, Any] = num_hidden_layers
_lowercase : Tuple = num_attention_heads
_lowercase : Optional[int] = intermediate_size
_lowercase : List[Any] = hidden_dropout_prob
_lowercase : Optional[int] = attention_probs_dropout_prob
_lowercase : str = max_position_embeddings
_lowercase : List[str] = initializer_range
_lowercase : Any = layer_norm_eps
_lowercase : Optional[int] = position_embedding_type
_lowercase : int = use_cache
_lowercase : Dict = emb_layer_norm_before
_lowercase : Optional[int] = token_dropout
_lowercase : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('No esmfold_config supplied for folding model, using default values.' )
_lowercase : str = EsmFoldConfig()
elif isinstance(UpperCamelCase ,UpperCamelCase ):
_lowercase : Tuple = EsmFoldConfig(**UpperCamelCase )
_lowercase : str = esmfold_config
if vocab_list is None:
logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!' )
_lowercase : Optional[int] = get_default_vocab_list()
else:
_lowercase : Optional[Any] = vocab_list
else:
_lowercase : Any = None
_lowercase : List[Any] = None
if self.esmfold_config is not None and getattr(self.esmfold_config ,'use_esm_attn_map' ,UpperCamelCase ):
raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!' )
def _lowerCamelCase ( self : str ) -> Tuple:
_lowercase : List[str] = super().to_dict()
if isinstance(self.esmfold_config ,UpperCamelCase ):
_lowercase : Union[str, Any] = self.esmfold_config.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCAmelCase__ : str = None
lowerCAmelCase__ : bool = True
lowerCAmelCase__ : bool = False
lowerCAmelCase__ : bool = False
lowerCAmelCase__ : bool = False
lowerCAmelCase__ : float = 0
lowerCAmelCase__ : bool = True
lowerCAmelCase__ : bool = False
lowerCAmelCase__ : int = 128
lowerCAmelCase__ : "TrunkConfig" = None
def _lowerCamelCase ( self : List[Any] ) -> str:
if self.trunk is None:
_lowercase : Optional[Any] = TrunkConfig()
elif isinstance(self.trunk ,UpperCamelCase ):
_lowercase : List[str] = TrunkConfig(**self.trunk )
def _lowerCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
_lowercase : Any = asdict(self )
_lowercase : Tuple = self.trunk.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCAmelCase__ : int = 48
lowerCAmelCase__ : int = 1_024
lowerCAmelCase__ : int = 128
lowerCAmelCase__ : int = 32
lowerCAmelCase__ : int = 32
lowerCAmelCase__ : int = 32
lowerCAmelCase__ : float = 0
lowerCAmelCase__ : float = 0
lowerCAmelCase__ : bool = False
lowerCAmelCase__ : int = 4
lowerCAmelCase__ : Optional[int] = 128
lowerCAmelCase__ : "StructureModuleConfig" = None
def _lowerCamelCase ( self : Dict ) -> Optional[Any]:
if self.structure_module is None:
_lowercase : Any = StructureModuleConfig()
elif isinstance(self.structure_module ,UpperCamelCase ):
_lowercase : int = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
_lowercase : Any = self.sequence_state_dim // self.sequence_head_width
_lowercase : Tuple = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _lowerCamelCase ( self : List[Any] ) -> str:
_lowercase : int = asdict(self )
_lowercase : Any = self.structure_module.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCAmelCase__ : int = 384
lowerCAmelCase__ : int = 128
lowerCAmelCase__ : int = 16
lowerCAmelCase__ : int = 128
lowerCAmelCase__ : int = 12
lowerCAmelCase__ : int = 4
lowerCAmelCase__ : int = 8
lowerCAmelCase__ : float = 0.1
lowerCAmelCase__ : int = 8
lowerCAmelCase__ : int = 1
lowerCAmelCase__ : int = 2
lowerCAmelCase__ : int = 7
lowerCAmelCase__ : int = 10
lowerCAmelCase__ : float = 1e-8
lowerCAmelCase__ : float = 1e5
def _lowerCamelCase ( self : List[str] ) -> Union[str, Any]:
return asdict(self )
def SCREAMING_SNAKE_CASE ( ) -> List[str]:
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
) | 125 | 0 |
import math
import sys
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
if number != int(__lowerCAmelCase ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a negative number''' )
if number == 0:
return 1
snake_case__ = [-1] * (number + 1)
snake_case__ = 0
for i in range(1 , number + 1 ):
snake_case__ = sys.maxsize
snake_case__ = int(math.sqrt(__lowerCAmelCase ) )
for j in range(1 , root + 1 ):
snake_case__ = 1 + answers[i - (j**2)]
snake_case__ = min(__lowerCAmelCase , __lowerCAmelCase )
snake_case__ = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 208 |
# 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.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
lowerCamelCase__ : Tuple = """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
snake_case__ = _ask_options(
'''In which compute environment are you running?''' , ['''This machine''', '''AWS (Amazon SageMaker)'''] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
snake_case__ = get_sagemaker_input()
else:
snake_case__ = get_cluster_input()
return config
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase=None ) -> int:
if subparsers is not None:
snake_case__ = subparsers.add_parser('''config''' , description=__lowerCAmelCase )
else:
snake_case__ = argparse.ArgumentParser('''Accelerate config command''' , description=__lowerCAmelCase )
parser.add_argument(
'''--config_file''' , default=__lowerCAmelCase , help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) , )
if subparsers is not None:
parser.set_defaults(func=__lowerCAmelCase )
return parser
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = get_user_input()
if args.config_file is not None:
snake_case__ = args.config_file
else:
if not os.path.isdir(__lowerCAmelCase ):
os.makedirs(__lowerCAmelCase )
snake_case__ = default_yaml_config_file
if config_file.endswith('''.json''' ):
config.to_json_file(__lowerCAmelCase )
else:
config.to_yaml_file(__lowerCAmelCase )
print(F"""accelerate configuration saved at {config_file}""" )
def SCREAMING_SNAKE_CASE ( ) -> Any:
snake_case__ = config_command_parser()
snake_case__ = parser.parse_args()
config_command(__lowerCAmelCase )
if __name__ == "__main__":
main()
| 208 | 1 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCamelCase = """sshleifer/bart-tiny-random"""
lowerCamelCase = """patrickvonplaten/t5-tiny-random"""
@require_torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowercase__ ( self : str ) -> int:
'''simple docstring'''
return AutoConfig.from_pretrained(_UpperCAmelCase )
def lowercase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ , *UpperCAmelCase_ = create_student_by_copying_alternating_layers(_UpperCAmelCase , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def lowercase__ ( self : str ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ , *UpperCAmelCase_ = create_student_by_copying_alternating_layers(_UpperCAmelCase , tempfile.mkdtemp() , e=1 , d=_UpperCAmelCase )
def lowercase__ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ , *UpperCAmelCase_ = create_student_by_copying_alternating_layers(_UpperCAmelCase , tempfile.mkdtemp() , e=1 , d=_UpperCAmelCase )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def lowercase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ , *UpperCAmelCase_ = create_student_by_copying_alternating_layers(_UpperCAmelCase , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def lowercase__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
with self.assertRaises(_UpperCAmelCase ):
create_student_by_copying_alternating_layers(_UpperCAmelCase , tempfile.mkdtemp() , e=_UpperCAmelCase , d=_UpperCAmelCase )
| 82 | """simple docstring"""
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class snake_case__ ( unittest.TestCase ):
def a__ ( self ):
__a = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase ) )
def a__ ( self ):
__a = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase ) )
def a__ ( self ):
__a = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowerCamelCase ) )
def a__ ( self ):
__a = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase ) )
def a__ ( self ):
__a = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(lowerCamelCase ) )
def a__ ( self ):
__a = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
__a = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def a__ ( self ):
__a = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
__a = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def a__ ( self ):
# pass variant but use the non-variant filenames
__a = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
__a = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def a__ ( self ):
__a = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
__a = "fp16"
self.assertFalse(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def a__ ( self ):
__a = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
__a = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def a__ ( self ):
# pass variant but use the non-variant filenames
__a = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
__a = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def a__ ( self ):
__a = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
__a = "fp16"
self.assertFalse(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
| 528 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
a : Dict = '0.12' # assumed parallelism: 8
@require_flax
@is_staging_test
class a ( unittest.TestCase ):
@classmethod
def A_ ( cls : Optional[int] ):
snake_case_ = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def A_ ( cls : Optional[int] ):
try:
delete_repo(token=cls._token , repo_id='''test-model-flax''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' )
except HTTPError:
pass
def A_ ( self : Tuple ):
snake_case_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
snake_case_ = FlaxBertModel(lowercase_ )
model.push_to_hub('''test-model-flax''' , use_auth_token=self._token )
snake_case_ = FlaxBertModel.from_pretrained(F"{USER}/test-model-flax" )
snake_case_ = flatten_dict(unfreeze(model.params ) )
snake_case_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
snake_case_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowercase_ , 1e-3 , msg=F"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id='''test-model-flax''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase_ , repo_id='''test-model-flax''' , push_to_hub=lowercase_ , use_auth_token=self._token )
snake_case_ = FlaxBertModel.from_pretrained(F"{USER}/test-model-flax" )
snake_case_ = flatten_dict(unfreeze(model.params ) )
snake_case_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
snake_case_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowercase_ , 1e-3 , msg=F"{key} not identical" )
def A_ ( self : Dict ):
snake_case_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
snake_case_ = FlaxBertModel(lowercase_ )
model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token )
snake_case_ = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
snake_case_ = flatten_dict(unfreeze(model.params ) )
snake_case_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
snake_case_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowercase_ , 1e-3 , msg=F"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
lowercase_ , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=lowercase_ , use_auth_token=self._token )
snake_case_ = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
snake_case_ = flatten_dict(unfreeze(model.params ) )
snake_case_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
snake_case_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowercase_ , 1e-3 , msg=F"{key} not identical" )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = True
snake_case_ = flatten_dict(modela.params )
snake_case_ = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
snake_case_ = False
return models_are_equal
@require_flax
class a ( unittest.TestCase ):
def A_ ( self : Optional[int] ):
snake_case_ = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
snake_case_ = FlaxBertModel(lowercase_ )
snake_case_ = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(lowercase_ , lowercase_ ) )
with self.assertRaises(lowercase_ ):
snake_case_ = FlaxBertModel.from_pretrained(lowercase_ )
snake_case_ = FlaxBertModel.from_pretrained(lowercase_ , subfolder=lowercase_ )
self.assertTrue(check_models_equal(lowercase_ , lowercase_ ) )
def A_ ( self : List[str] ):
snake_case_ = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
snake_case_ = FlaxBertModel(lowercase_ )
snake_case_ = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(lowercase_ , lowercase_ ) , max_shard_size='''10KB''' )
with self.assertRaises(lowercase_ ):
snake_case_ = FlaxBertModel.from_pretrained(lowercase_ )
snake_case_ = FlaxBertModel.from_pretrained(lowercase_ , subfolder=lowercase_ )
self.assertTrue(check_models_equal(lowercase_ , lowercase_ ) )
def A_ ( self : Any ):
snake_case_ = '''bert'''
snake_case_ = '''hf-internal-testing/tiny-random-bert-subfolder'''
with self.assertRaises(lowercase_ ):
snake_case_ = FlaxBertModel.from_pretrained(lowercase_ )
snake_case_ = FlaxBertModel.from_pretrained(lowercase_ , subfolder=lowercase_ )
self.assertIsNotNone(lowercase_ )
def A_ ( self : Dict ):
snake_case_ = '''bert'''
snake_case_ = '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
with self.assertRaises(lowercase_ ):
snake_case_ = FlaxBertModel.from_pretrained(lowercase_ )
snake_case_ = FlaxBertModel.from_pretrained(lowercase_ , subfolder=lowercase_ )
self.assertIsNotNone(lowercase_ )
| 719 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class a ( unittest.TestCase , _lowerCamelCase ):
def A_ ( self : List[str] ):
snake_case_ = load_tool('''text-to-speech''' )
self.tool.setup()
def A_ ( self : List[str] ):
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
snake_case_ = self.tool('''hey''' )
snake_case_ = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def A_ ( self : List[str] ):
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
snake_case_ = self.tool('''hey''' )
snake_case_ = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 593 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TapasForMaskedLM',
'TapasForQuestionAnswering',
'TapasForSequenceClassification',
'TapasModel',
'TapasPreTrainedModel',
'load_tf_weights_in_tapas',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFTapasForMaskedLM',
'TFTapasForQuestionAnswering',
'TFTapasForSequenceClassification',
'TFTapasModel',
'TFTapasPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 322 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, 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.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase : List[str] , UpperCamelCase : Optional[int]=99 , UpperCamelCase : Optional[Any]=13 , UpperCamelCase : List[Any]=16 , UpperCamelCase : Tuple=7 , UpperCamelCase : Any=True , UpperCamelCase : Union[str, Any]=True , UpperCamelCase : List[str]=True , UpperCamelCase : Dict=False , UpperCamelCase : str=True , UpperCamelCase : Any=2 , UpperCamelCase : Union[str, Any]=32 , UpperCamelCase : str=4 , UpperCamelCase : str=4 , UpperCamelCase : Union[str, Any]=30 , UpperCamelCase : Any=0 , UpperCamelCase : Union[str, Any]=1 , UpperCamelCase : int=2 , UpperCamelCase : int=None , ):
"""simple docstring"""
_lowercase : Tuple = parent
_lowercase : Optional[Any] = batch_size
_lowercase : Any = decoder_seq_length
# For common tests
_lowercase : Union[str, Any] = self.decoder_seq_length
_lowercase : str = is_training
_lowercase : int = use_attention_mask
_lowercase : Any = use_labels
_lowercase : List[str] = vocab_size
_lowercase : int = d_model
_lowercase : Optional[int] = d_model
_lowercase : Optional[int] = decoder_layers
_lowercase : str = decoder_layers
_lowercase : Dict = decoder_ffn_dim
_lowercase : Union[str, Any] = decoder_attention_heads
_lowercase : Optional[Any] = decoder_attention_heads
_lowercase : int = eos_token_id
_lowercase : Optional[Any] = bos_token_id
_lowercase : Any = pad_token_id
_lowercase : List[str] = decoder_start_token_id
_lowercase : str = use_cache
_lowercase : str = max_position_embeddings
_lowercase : Union[str, Any] = None
_lowercase : int = decoder_seq_length
_lowercase : List[Any] = 2
_lowercase : str = 1
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
_lowercase : Dict = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
_lowercase : Optional[Any] = None
if self.use_attention_mask:
_lowercase : str = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
_lowercase : int = None
if self.use_labels:
_lowercase : Union[str, Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
_lowercase : List[str] = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def lowerCAmelCase_ ( self : Optional[Any] , UpperCamelCase : Dict , UpperCamelCase : Optional[Any] , UpperCamelCase : List[str] , UpperCamelCase : int , ):
"""simple docstring"""
_lowercase : List[str] = True
_lowercase : int = TrOCRDecoder(config=UpperCamelCase ).to(UpperCamelCase ).eval()
_lowercase : List[Any] = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
_lowercase : List[str] = model(UpperCamelCase , use_cache=UpperCamelCase )
_lowercase : Optional[Any] = model(UpperCamelCase )
_lowercase : Tuple = model(UpperCamelCase , use_cache=UpperCamelCase )
self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) )
self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) + 1 )
_lowercase : int = outputs['''past_key_values''']
# create hypothetical next token and extent to next_input_ids
_lowercase : Any = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
_lowercase : Dict = torch.cat([input_ids, next_tokens] , dim=-1 )
_lowercase : str = model(UpperCamelCase )['''last_hidden_state''']
_lowercase : str = model(UpperCamelCase , past_key_values=UpperCamelCase )['''last_hidden_state''']
# select random slice
_lowercase : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_lowercase : Dict = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
_lowercase : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(UpperCamelCase , UpperCamelCase , atol=1E-3 )
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
_lowercase : Tuple = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase , _lowercase : List[Any] = config_and_inputs
_lowercase : str = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A_ , A_ , A_ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
UpperCAmelCase_ = (TrOCRForCausalLM,) if is_torch_available() else ()
UpperCAmelCase_ = {'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
UpperCAmelCase_ = True
UpperCAmelCase_ = False
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
_lowercase : int = TrOCRStandaloneDecoderModelTester(self , is_training=UpperCamelCase )
_lowercase : Union[str, Any] = ConfigTester(self , config_class=UpperCamelCase )
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
_lowercase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase )
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
return
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
pass | 322 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( __snake_case : int = 200_0000 ) -> int:
lowercase : list[int] = [0]
lowercase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
lowercase : int = 0
# the area corresponding to the grid that gives the product closest to target
lowercase : int = 0
# an estimate of b, using the quadratic formula
lowercase : float
# the largest integer less than b_estimate
lowercase : int
# the largest integer less than b_estimate
lowercase : int
# the triangle number corresponding to b_floor
lowercase : int
# the triangle number corresponding to b_ceil
lowercase : int
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
lowercase : Optional[int] = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
lowercase : List[str] = floor(__snake_case )
lowercase : Any = ceil(__snake_case )
lowercase : int = triangle_numbers[b_floor]
lowercase : Dict = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
lowercase : List[str] = triangle_b_first_guess * triangle_a
lowercase : Union[str, Any] = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
lowercase : Dict = triangle_b_second_guess * triangle_a
lowercase : List[str] = idx_a * b_ceil
return area
if __name__ == "__main__":
print(F"{solution() = }")
| 518 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_A : Tuple = logging.get_logger(__name__)
_A : Union[str, Any] = {"""vocab_file""": """spiece.model"""}
_A : Optional[Any] = {
"""vocab_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
}
}
# TODO(PVP) - this should be removed in Transformers v5
_A : str = {
"""t5-small""": 5_12,
"""t5-base""": 5_12,
"""t5-large""": 5_12,
"""t5-3b""": 5_12,
"""t5-11b""": 5_12,
}
_A : List[str] = """▁"""
class a__ ( a_ ):
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = ["""input_ids""", """attention_mask"""]
def __init__( self , _a , _a="</s>" , _a="<unk>" , _a="<pad>" , _a=100 , _a=None , _a = None , _a=True , **_a , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
lowercase : str = [f"""<extra_id_{i}>""" for i in range(_a )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
lowercase : str = len(set(filter(lambda _a : bool("extra_id" in str(_a ) ) , _a ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
" tokens" )
if legacy:
logger.warning_once(
f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
" read the related pull request available at https://github.com/huggingface/transformers/pull/24565" )
lowercase : Optional[Any] = legacy
lowercase : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_a , unk_token=_a , pad_token=_a , extra_ids=_a , additional_special_tokens=_a , sp_model_kwargs=self.sp_model_kwargs , legacy=_a , **_a , )
lowercase : Optional[Any] = vocab_file
lowercase : Union[str, Any] = extra_ids
lowercase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@staticmethod
def __magic_name__ ( _a , _a , _a ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
lowercase : str = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"This tokenizer was incorrectly instantiated with a model max length of"
f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
f""" {pretrained_model_name_or_path} automatically truncating your input to"""
f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
" instantiate this tokenizer with `model_max_length` set to your preferred value." , _a , )
return max_model_length
@property
def __magic_name__ ( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def __magic_name__ ( self ):
lowercase : Any = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __magic_name__ ( self , _a , _a = None , _a = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_a )) + [1]
return ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
def __magic_name__ ( self ):
return list(
set(filter(lambda _a : bool(re.search(R"<extra_id_\d+>" , _a ) ) is not None , self.additional_special_tokens ) ) )
def __magic_name__ ( self ):
return [self._convert_token_to_id(_a ) for token in self.get_sentinel_tokens()]
def __magic_name__ ( self , _a ):
if len(_a ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def __magic_name__ ( self , _a , _a = None ):
lowercase : Any = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def __magic_name__ ( self , _a , _a = None ):
lowercase : Any = self._add_eos_if_not_present(_a )
if token_ids_a is None:
return token_ids_a
else:
lowercase : str = self._add_eos_if_not_present(_a )
return token_ids_a + token_ids_a
def __getstate__( self ):
lowercase : List[str] = self.__dict__.copy()
lowercase : Optional[Any] = None
return state
def __setstate__( self , _a ):
lowercase : str = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase : str = {}
lowercase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __magic_name__ ( self , _a , **_a ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
lowercase : Optional[int] = SPIECE_UNDERLINE + text.replace(_a , " " )
return super().tokenize(_a , **_a )
def __magic_name__ ( self , _a , **_a ):
if not self.legacy:
lowercase : Dict = text.startswith(_a )
if is_first:
lowercase : Dict = text[1:]
lowercase : Tuple = self.sp_model.encode(_a , out_type=_a )
if not self.legacy and not is_first and not text.startswith(" " ) and tokens[0].startswith(_a ):
lowercase : Tuple = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def __magic_name__ ( self , _a ):
if token.startswith("<extra_id_" ):
lowercase : Optional[int] = re.match(R"<extra_id_(\d+)>" , _a )
lowercase : str = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_a )
def __magic_name__ ( self , _a ):
if index < self.sp_model.get_piece_size():
lowercase : Union[str, Any] = self.sp_model.IdToPiece(_a )
else:
lowercase : Union[str, Any] = f"""<extra_id_{self.vocab_size - 1 - index}>"""
return token
def __magic_name__ ( self , _a ):
lowercase : Tuple = []
lowercase : int = ""
lowercase : Optional[Any] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
lowercase : List[Any] = True
lowercase : Dict = []
else:
current_sub_tokens.append(_a )
lowercase : Dict = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def __magic_name__ ( self , _a , _a = None ):
if not os.path.isdir(_a ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase : int = os.path.join(
_a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , "wb" ) as fi:
lowercase : List[Any] = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 518 | 1 |
import argparse
import struct
import unittest
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase__ ) -> None:
'''simple docstring'''
a__ : str =data
# Initialize hash values
a__ : Dict =[
0X6A_09_E6_67,
0XBB_67_AE_85,
0X3C_6E_F3_72,
0XA5_4F_F5_3A,
0X51_0E_52_7F,
0X9B_05_68_8C,
0X1F_83_D9_AB,
0X5B_E0_CD_19,
]
# Initialize round constants
a__ : int =[
0X42_8A_2F_98,
0X71_37_44_91,
0XB5_C0_FB_CF,
0XE9_B5_DB_A5,
0X39_56_C2_5B,
0X59_F1_11_F1,
0X92_3F_82_A4,
0XAB_1C_5E_D5,
0XD8_07_AA_98,
0X12_83_5B_01,
0X24_31_85_BE,
0X55_0C_7D_C3,
0X72_BE_5D_74,
0X80_DE_B1_FE,
0X9B_DC_06_A7,
0XC1_9B_F1_74,
0XE4_9B_69_C1,
0XEF_BE_47_86,
0X0F_C1_9D_C6,
0X24_0C_A1_CC,
0X2D_E9_2C_6F,
0X4A_74_84_AA,
0X5C_B0_A9_DC,
0X76_F9_88_DA,
0X98_3E_51_52,
0XA8_31_C6_6D,
0XB0_03_27_C8,
0XBF_59_7F_C7,
0XC6_E0_0B_F3,
0XD5_A7_91_47,
0X06_CA_63_51,
0X14_29_29_67,
0X27_B7_0A_85,
0X2E_1B_21_38,
0X4D_2C_6D_FC,
0X53_38_0D_13,
0X65_0A_73_54,
0X76_6A_0A_BB,
0X81_C2_C9_2E,
0X92_72_2C_85,
0XA2_BF_E8_A1,
0XA8_1A_66_4B,
0XC2_4B_8B_70,
0XC7_6C_51_A3,
0XD1_92_E8_19,
0XD6_99_06_24,
0XF4_0E_35_85,
0X10_6A_A0_70,
0X19_A4_C1_16,
0X1E_37_6C_08,
0X27_48_77_4C,
0X34_B0_BC_B5,
0X39_1C_0C_B3,
0X4E_D8_AA_4A,
0X5B_9C_CA_4F,
0X68_2E_6F_F3,
0X74_8F_82_EE,
0X78_A5_63_6F,
0X84_C8_78_14,
0X8C_C7_02_08,
0X90_BE_FF_FA,
0XA4_50_6C_EB,
0XBE_F9_A3_F7,
0XC6_71_78_F2,
]
a__ : str =self.preprocessing(self.data )
self.final_hash()
@staticmethod
def _lowercase ( lowerCAmelCase__ ) -> bytes:
'''simple docstring'''
a__ : int =B"\x80" + (B"\x00" * (6_3 - (len(lowerCAmelCase__ ) + 8) % 6_4))
a__ : Any =struct.pack(">Q" , (len(lowerCAmelCase__ ) * 8) )
return data + padding + big_endian_integer
def _lowercase ( self ) -> None:
'''simple docstring'''
a__ : str =[
self.preprocessed_data[x : x + 6_4]
for x in range(0 , len(self.preprocessed_data ) , 6_4 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
a__ : Any =list(struct.unpack(">16L" , lowerCAmelCase__ ) )
# add 48 0-ed integers
words += [0] * 4_8
a__ , a__ , a__ , a__ , a__ , a__ , a__ , a__ : int =self.hashes
for index in range(0 , 6_4 ):
if index > 1_5:
# modify the zero-ed indexes at the end of the array
a__ : int =(
self.ror(words[index - 1_5] , 7 )
^ self.ror(words[index - 1_5] , 1_8 )
^ (words[index - 1_5] >> 3)
)
a__ : Any =(
self.ror(words[index - 2] , 1_7 )
^ self.ror(words[index - 2] , 1_9 )
^ (words[index - 2] >> 1_0)
)
a__ : Optional[Any] =(
words[index - 1_6] + sa + words[index - 7] + sa
) % 0X1_00_00_00_00
# Compression
a__ : Optional[Any] =self.ror(lowerCAmelCase__ , 6 ) ^ self.ror(lowerCAmelCase__ , 1_1 ) ^ self.ror(lowerCAmelCase__ , 2_5 )
a__ : Union[str, Any] =(e & f) ^ ((~e & 0XFF_FF_FF_FF) & g)
a__ : str =(
h + sa + ch + self.round_constants[index] + words[index]
) % 0X1_00_00_00_00
a__ : Optional[int] =self.ror(lowerCAmelCase__ , 2 ) ^ self.ror(lowerCAmelCase__ , 1_3 ) ^ self.ror(lowerCAmelCase__ , 2_2 )
a__ : Any =(a & b) ^ (a & c) ^ (b & c)
a__ : Dict =(sa + maj) % 0X1_00_00_00_00
a__ , a__ , a__ , a__ , a__ , a__ , a__ , a__ : List[str] =(
g,
f,
e,
((d + tempa) % 0X1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0X1_00_00_00_00),
)
a__ : Dict =[a, b, c, d, e, f, g, h]
# Modify final values
a__ : Any =[
((element + mutated_hash_values[index]) % 0X1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
a__ : Union[str, Any] ="".join([hex(lowerCAmelCase__ )[2:].zfill(8 ) for value in self.hashes] )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
return 0XFF_FF_FF_FF & (value << (3_2 - rotations)) | (value >> rotations)
class __lowerCAmelCase ( unittest.TestCase):
def _lowercase ( self ) -> None:
'''simple docstring'''
import hashlib
a__ : Dict =bytes("Test String" , "utf-8" )
self.assertEqual(SHAaaa(lowerCAmelCase__ ).hash , hashlib.shaaaa(lowerCAmelCase__ ).hexdigest() )
def _A ( ):
"""simple docstring"""
import doctest
doctest.testmod()
a__ : Tuple =argparse.ArgumentParser()
parser.add_argument(
"-s" , "--string" , dest="input_string" , default="Hello World!! Welcome to Cryptography" , help="Hash the string" , )
parser.add_argument(
"-f" , "--file" , dest="input_file" , help="Hash contents of a file" )
a__ : Dict =parser.parse_args()
a__ : Any =args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , "rb" ) as f:
a__ : List[Any] =f.read()
else:
a__ : Union[str, Any] =bytes(SCREAMING_SNAKE_CASE , "utf-8" )
print(SHAaaa(SCREAMING_SNAKE_CASE ).hash )
if __name__ == "__main__":
main()
| 563 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : List[str] = ["""input_values""", """attention_mask"""]
def __init__( self , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 1_6_0_0_0 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = False , lowerCAmelCase__ = 8_0 , lowerCAmelCase__ = 1_6 , lowerCAmelCase__ = 6_4 , lowerCAmelCase__ = "hann_window" , lowerCAmelCase__ = 1.0 , lowerCAmelCase__ = 8_0 , lowerCAmelCase__ = 7_6_0_0 , lowerCAmelCase__ = 1E-10 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = True , **lowerCAmelCase__ , ) -> str:
'''simple docstring'''
super().__init__(feature_size=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , padding_value=lowerCAmelCase__ , **lowerCAmelCase__ )
a__ : Tuple =do_normalize
a__ : Tuple =return_attention_mask
a__ : str =num_mel_bins
a__ : Any =hop_length
a__ : Optional[Any] =win_length
a__ : int =win_function
a__ : List[str] =frame_signal_scale
a__ : List[str] =fmin
a__ : str =fmax
a__ : Dict =mel_floor
a__ : Any =reduction_factor
a__ : str =win_length * sampling_rate // 1_0_0_0
a__ : List[str] =hop_length * sampling_rate // 1_0_0_0
a__ : Optional[Any] =optimal_fft_length(self.sample_size )
a__ : Any =(self.n_fft // 2) + 1
a__ : List[Any] =window_function(window_length=self.sample_size , name=self.win_function , periodic=lowerCAmelCase__ )
a__ : Optional[int] =mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm="slaney" , mel_scale="slaney" , )
if frame_signal_scale != 1.0:
warnings.warn(
"The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers" , lowerCAmelCase__ , )
if reduction_factor != 2.0:
warnings.warn(
"The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers" , lowerCAmelCase__ , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def _lowercase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 0.0 ) -> List[np.ndarray]:
'''simple docstring'''
if attention_mask is not None:
a__ : List[Any] =np.array(lowerCAmelCase__ , np.intaa )
a__ : Optional[Any] =[]
for vector, length in zip(lowerCAmelCase__ , attention_mask.sum(-1 ) ):
a__ : Tuple =(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
if length < normed_slice.shape[0]:
a__ : Any =padding_value
normed_input_values.append(lowerCAmelCase__ )
else:
a__ : Optional[int] =[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]
return normed_input_values
def _lowercase ( self , lowerCAmelCase__ , ) -> np.ndarray:
'''simple docstring'''
a__ : Dict =spectrogram(
lowerCAmelCase__ , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel="log10" , )
return log_mel_spec.T
def __call__( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> BatchFeature:
'''simple docstring'''
if audio is None and audio_target is None:
raise ValueError("You must provide either `audio` or `audio_target` values." )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
F''' {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." )
if audio is not None:
a__ : Dict =self._process_audio(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ , )
else:
a__ : str =None
if audio_target is not None:
a__ : int =self._process_audio(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ , )
if inputs is None:
return inputs_target
else:
a__ : Any =inputs_target["input_values"]
a__ : List[Any] =inputs_target.get("attention_mask" )
if decoder_attention_mask is not None:
a__ : Optional[int] =decoder_attention_mask
return inputs
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = False , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = False , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> BatchFeature:
'''simple docstring'''
a__ : List[Any] =isinstance(lowerCAmelCase__ , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
a__ : Optional[int] =is_batched_numpy or (
isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a__ : int =[np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(lowerCAmelCase__ , np.ndarray ):
a__ : List[Any] =np.asarray(lowerCAmelCase__ , dtype=np.floataa )
elif isinstance(lowerCAmelCase__ , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
a__ : Optional[Any] =speech.astype(np.floataa )
# always return batch
if not is_batched:
a__ : Union[str, Any] =[speech]
# needed to make pad() work on spectrogram inputs
a__ : Union[str, Any] =self.feature_size
# convert into correct format for padding
if is_target:
a__ : Dict =[self._extract_mel_features(lowerCAmelCase__ ) for waveform in speech]
a__ : str =BatchFeature({"input_values": features} )
a__ : List[str] =self.num_mel_bins
else:
a__ : List[str] =BatchFeature({"input_values": speech} )
a__ : Optional[int] =self.pad(
lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
a__ : Any =feature_size_hack
# convert input values to correct format
a__ : List[Any] =padded_inputs["input_values"]
if not isinstance(input_values[0] , np.ndarray ):
a__ : Union[str, Any] =[np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(lowerCAmelCase__ , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
a__ : str =[array.astype(np.floataa ) for array in input_values]
elif isinstance(lowerCAmelCase__ , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
a__ : Optional[int] =input_values.astype(np.floataa )
# convert attention_mask to correct format
a__ : str =padded_inputs.get("attention_mask" )
if attention_mask is not None:
a__ : str =[np.asarray(lowerCAmelCase__ , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
a__ : Union[str, Any] =(
attention_mask
if self._get_padding_strategies(lowerCAmelCase__ , max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
a__ : List[Any] =self.zero_mean_unit_var_norm(
padded_inputs["input_values"] , attention_mask=lowerCAmelCase__ , padding_value=self.padding_value )
if return_tensors is not None:
a__ : int =padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
def _lowercase ( self ) -> Dict[str, Any]:
'''simple docstring'''
a__ : Optional[int] =super().to_dict()
# Don't serialize these as they are derived from the other properties.
a__ : Optional[Any] =["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
for name in names:
if name in output:
del output[name]
return output
| 563 | 1 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class _lowercase ( lowerCAmelCase ):
def __init__( self : List[Any] , a : Union[str, "sqlalchemy.sql.Selectable"] , a : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , a : Optional[Features] = None , a : str = None , a : bool = False , **a : Any , ):
"""simple docstring"""
super().__init__(features=a , cache_dir=a , keep_in_memory=a , **a )
__snake_case : Optional[Any] =Sql(
cache_dir=a , features=a , sql=a , con=a , **a , )
def _UpperCamelCase ( self : List[str] ):
"""simple docstring"""
__snake_case : Any =None
__snake_case : Dict =None
__snake_case : int =None
__snake_case : str =None
self.builder.download_and_prepare(
download_config=a , download_mode=a , verification_mode=a , base_path=a , )
# Build dataset for splits
__snake_case : List[Any] =self.builder.as_dataset(
split='''train''' , verification_mode=a , in_memory=self.keep_in_memory )
return dataset
class _lowercase :
def __init__( self : Optional[int] , a : Dataset , a : str , a : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , a : Optional[int] = None , a : Optional[int] = None , **a : List[Any] , ):
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
__snake_case : List[Any] =dataset
__snake_case : List[str] =name
__snake_case : Any =con
__snake_case : Any =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
__snake_case : Optional[Any] =num_proc
__snake_case : Optional[int] =to_sql_kwargs
def _UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
__snake_case : List[Any] =self.to_sql_kwargs.pop('''sql''' , a )
__snake_case : Union[str, Any] =self.to_sql_kwargs.pop('''con''' , a )
__snake_case : Any =self.to_sql_kwargs.pop('''index''' , a )
__snake_case : Any =self._write(index=a , **self.to_sql_kwargs )
return written
def _UpperCamelCase ( self : Optional[Any] , a : Optional[Any] ):
"""simple docstring"""
__snake_case , __snake_case , __snake_case : int =args
__snake_case : str ={**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
__snake_case : List[Any] =query_table(
table=self.dataset.data , key=slice(a , offset + self.batch_size ) , indices=self.dataset._indices , )
__snake_case : Optional[int] =batch.to_pandas()
__snake_case : Tuple =df.to_sql(self.name , self.con , index=a , **a )
return num_rows or len(a )
def _UpperCamelCase ( self : Union[str, Any] , a : Optional[Any] , **a : str ):
"""simple docstring"""
__snake_case : int =0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
__snake_case , __snake_case : List[Any] =len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , a , a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += num_rows
return written
| 497 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
UpperCamelCase_ : List[str] = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def __lowercase ( ) -> Optional[Any]:
__snake_case : List[Any] =Github(os.environ['''GITHUB_TOKEN'''] )
__snake_case : int =g.get_repo('''huggingface/diffusers''' )
__snake_case : Any =repo.get_issues(state='''open''' )
for issue in open_issues:
__snake_case : Dict =sorted(issue.get_comments() , key=lambda a : i.created_at , reverse=a )
__snake_case : Dict =comments[0] if len(a ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 497 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class a__ ( _A ):
lowercase_ = "umt5"
lowercase_ = ["past_key_values"]
def __init__( self : Dict , UpperCamelCase_ : int=250112 , UpperCamelCase_ : Optional[Any]=512 , UpperCamelCase_ : List[Any]=64 , UpperCamelCase_ : Tuple=1024 , UpperCamelCase_ : Optional[Any]=8 , UpperCamelCase_ : int=None , UpperCamelCase_ : str=6 , UpperCamelCase_ : Dict=32 , UpperCamelCase_ : int=128 , UpperCamelCase_ : Union[str, Any]=0.1 , UpperCamelCase_ : List[Any]=1e-6 , UpperCamelCase_ : List[str]=1.0 , UpperCamelCase_ : str="gated-gelu" , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Any=True , UpperCamelCase_ : str="T5Tokenizer" , UpperCamelCase_ : str=True , UpperCamelCase_ : List[Any]=0 , UpperCamelCase_ : List[str]=1 , UpperCamelCase_ : str=0 , **UpperCamelCase_ : List[str] , ):
"""simple docstring"""
super().__init__(
is_encoder_decoder=UpperCamelCase__ , tokenizer_class=UpperCamelCase__ , tie_word_embeddings=UpperCamelCase__ , pad_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , decoder_start_token_id=UpperCamelCase__ , **UpperCamelCase__ , )
__UpperCAmelCase : Optional[Any] = vocab_size
__UpperCAmelCase : Union[str, Any] = d_model
__UpperCAmelCase : str = d_kv
__UpperCAmelCase : Optional[int] = d_ff
__UpperCAmelCase : Any = num_layers
__UpperCAmelCase : List[Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__UpperCAmelCase : Union[str, Any] = num_heads
__UpperCAmelCase : List[Any] = relative_attention_num_buckets
__UpperCAmelCase : List[str] = relative_attention_max_distance
__UpperCAmelCase : Any = dropout_rate
__UpperCAmelCase : List[Any] = layer_norm_epsilon
__UpperCAmelCase : Optional[Any] = initializer_factor
__UpperCAmelCase : Tuple = feed_forward_proj
__UpperCAmelCase : Optional[Any] = use_cache
__UpperCAmelCase : Dict = self.feed_forward_proj.split("-")
__UpperCAmelCase : Tuple = act_info[-1]
__UpperCAmelCase : Tuple = 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'")
if feed_forward_proj == "gated-gelu":
__UpperCAmelCase : str = "gelu_new"
@property
def a_ ( self : str):
"""simple docstring"""
return self.d_model
@property
def a_ ( self : int):
"""simple docstring"""
return self.num_heads
@property
def a_ ( self : List[Any]):
"""simple docstring"""
return self.num_layers
class a__ ( _A ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
__UpperCAmelCase : Optional[Any] = "past_encoder_sequence + sequence"
__UpperCAmelCase : Dict = {0: "batch"}
__UpperCAmelCase : List[str] = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
__UpperCAmelCase : int = {0: "batch", 1: "decoder_sequence"}
__UpperCAmelCase : Optional[int] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(UpperCamelCase__ , direction="inputs")
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def a_ ( self : Optional[int]):
"""simple docstring"""
return 13
@property
def a_ ( self : int):
"""simple docstring"""
return 5e-4
| 77 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ = """linear"""
a_ = """cosine"""
a_ = """cosine_with_restarts"""
a_ = """polynomial"""
a_ = """constant"""
a_ = """constant_with_warmup"""
a_ = """piecewise_constant"""
def lowerCamelCase__ ( A_ , A_ = -1 ):
return LambdaLR(A_ , lambda A_ : 1 , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1.0 , A_ ) )
return 1.0
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = step_rules.split("," )
for rule_str in rule_list[:-1]:
UpperCAmelCase_ , UpperCAmelCase_ = rule_str.split(":" )
UpperCAmelCase_ = int(A_ )
UpperCAmelCase_ = float(A_ )
UpperCAmelCase_ = value
UpperCAmelCase_ = float(rule_list[-1] )
def create_rules_function(A_ , A_ ):
def rule_func(A_ ) -> float:
UpperCAmelCase_ = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(A_ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
UpperCAmelCase_ = create_rules_function(A_ , A_ )
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=-1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 0.5 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(A_ ) * 2.0 * progress )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 1 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(A_ ) * progress) % 1.0) )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=1e-7 , A_=1.0 , A_=-1 ):
UpperCAmelCase_ = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
UpperCAmelCase_ = lr_init - lr_end
UpperCAmelCase_ = num_training_steps - num_warmup_steps
UpperCAmelCase_ = 1 - (current_step - num_warmup_steps) / decay_steps
UpperCAmelCase_ = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(A_ , A_ , A_ )
__snake_case : str = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCamelCase__ ( A_ , A_ , A_ = None , A_ = None , A_ = None , A_ = 1 , A_ = 1.0 , A_ = -1 , ):
UpperCAmelCase_ = SchedulerType(A_ )
UpperCAmelCase_ = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(A_ , last_epoch=A_ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(A_ , step_rules=A_ , last_epoch=A_ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(A_ , num_warmup_steps=A_ , last_epoch=A_ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , num_cycles=A_ , last_epoch=A_ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , power=A_ , last_epoch=A_ , )
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , last_epoch=A_ )
| 660 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
return None
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
return None
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
_a : Any = [
# (model_name, model_kwargs)
('''bert-base-cased''', {}),
('''gpt2''', {'''use_cache''': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def UpperCAmelCase__( self ) -> str:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , """tf""" , 12 , **lowerCamelCase__ )
@require_torch
@slow
def UpperCAmelCase__( self ) -> Optional[int]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , """pt""" , 12 , **lowerCamelCase__ )
@require_torch
@slow
def UpperCAmelCase__( self ) -> List[Any]:
from transformers import BertModel
lowercase : Optional[Any] = ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
with NamedTemporaryFile(mode="""w+t""" ) as vocab_file:
vocab_file.write("""\n""".join(lowerCamelCase__ ) )
vocab_file.flush()
lowercase : Any = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowercase : int = BertModel(BertConfig(vocab_size=len(lowerCamelCase__ ) ) )
model.save_pretrained(lowerCamelCase__ )
self._test_export(lowerCamelCase__ , """pt""" , 12 , lowerCamelCase__ )
@require_tf
@slow
def UpperCAmelCase__( self ) -> Optional[Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowercase : Optional[Any] = self._test_export(lowerCamelCase__ , """tf""" , 12 , **lowerCamelCase__ )
lowercase : int = quantize(Path(lowerCamelCase__ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
@require_torch
@slow
def UpperCAmelCase__( self ) -> Dict:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowercase : List[str] = self._test_export(lowerCamelCase__ , """pt""" , 12 , **lowerCamelCase__ )
lowercase : Optional[int] = quantize(lowerCamelCase__ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail("""Quantized model is bigger than initial ONNX model""" )
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , **lowerCamelCase__ ) -> int:
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowercase : Tuple = Path(lowerCamelCase__ ).joinpath("""model.onnx""" )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ )
return path
except Exception as e:
self.fail(lowerCamelCase__ )
@require_torch
@require_tokenizers
@slow
def UpperCAmelCase__( self ) -> List[str]:
from transformers import BertModel
lowercase : Union[str, Any] = BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
lowercase : Dict = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , """pt""" )
@require_tf
@require_tokenizers
@slow
def UpperCAmelCase__( self ) -> Optional[Any]:
from transformers import TFBertModel
lowercase : Dict = TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
lowercase : Any = BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , """tf""" )
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> str:
lowercase : Tuple = FeatureExtractionPipeline(lowerCamelCase__ , lowerCamelCase__ )
lowercase : Union[str, Any] = ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
lowercase : Dict = infer_shapes(lowerCamelCase__ , lowerCamelCase__ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , lowerCamelCase__ )
self.assertSequenceEqual(variable_names[3:] , lowerCamelCase__ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: """batch""", 1: """sequence"""} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes["""output_0"""] , {0: """batch""", 1: """sequence"""} )
self.assertDictEqual(shapes["""output_1"""] , {0: """batch"""} )
def UpperCAmelCase__( self ) -> Tuple:
lowercase : Tuple = ["""input_ids""", """attention_mask""", """token_type_ids"""]
lowercase : Optional[Any] = {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
lowercase : int = ensure_valid_input(FuncContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase__ ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase__ ) , set(lowerCamelCase__ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase__ , (tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowercase : str = ensure_valid_input(FuncNonContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase__ ) , 1 )
self.assertEqual(len(lowerCamelCase__ ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens["""input_ids"""] )
self.assertEqual(ordered_input_names[0] , """input_ids""" )
def UpperCAmelCase__( self ) -> Any:
lowercase : List[Any] = generate_identified_filename(Path("""/home/something/my_fake_model.onnx""" ) , """-test""" )
self.assertEqual("""/home/something/my_fake_model-test.onnx""" , generated.as_posix() ) | 716 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
__snake_case = get_logger(__name__)
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
_a : Union[str, Any] = '''dummy_data'''
_a : Any = '''datasets'''
_a : List[str] = False
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False , lowerCamelCase__ = True , lowerCamelCase__ = None , ) -> Union[str, Any]:
lowercase__ : Optional[Any] = 0
lowercase__ : Dict = dataset_name
lowercase__ : Optional[Any] = cache_dir
lowercase__ : Optional[int] = use_local_dummy_data
lowercase__ : Optional[Any] = config
# download_callbacks take a single url as input
lowercase__ : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowercase__ : List[str] = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowercase__ : int = str(lowerCamelCase__ )
# to be downloaded
lowercase__ : Tuple = None
lowercase__ : Dict = None
@property
def UpperCAmelCase__( self ) -> List[str]:
if self._dummy_file is None:
lowercase__ : Optional[int] = self.download_dummy_data()
return self._dummy_file
@property
def UpperCAmelCase__( self ) -> int:
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("""dummy""" , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join("""dummy""" , self.version_name )
@property
def UpperCAmelCase__( self ) -> Optional[int]:
return os.path.join(self.dummy_data_folder , """dummy_data.zip""" )
def UpperCAmelCase__( self ) -> int:
lowercase__ : int = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowercase__ : int = cached_path(
lowerCamelCase__ , cache_dir=self.cache_dir , extract_compressed_file=lowerCamelCase__ , force_extract=lowerCamelCase__ )
return os.path.join(lowerCamelCase__ , self.dummy_file_name )
@property
def UpperCAmelCase__( self ) -> Optional[int]:
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def UpperCAmelCase__( self ) -> Optional[Any]:
if self._bucket_url is None:
lowercase__ : Optional[Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , """/""" ) )
return self._bucket_url
@property
def UpperCAmelCase__( self ) -> Union[str, Any]:
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , """/""" ).split("""/""" )[:-1] )
def UpperCAmelCase__( self , lowerCamelCase__ , *lowerCamelCase__ ) -> Union[str, Any]:
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowercase__ : Dict = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowercase__ : Tuple = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
return self.create_dummy_data_dict(lowerCamelCase__ , lowerCamelCase__ )
elif isinstance(lowerCamelCase__ , (list, tuple) ):
return self.create_dummy_data_list(lowerCamelCase__ , lowerCamelCase__ )
else:
return self.create_dummy_data_single(lowerCamelCase__ , lowerCamelCase__ )
def UpperCAmelCase__( self , lowerCamelCase__ , *lowerCamelCase__ ) -> Optional[int]:
return self.download_and_extract(lowerCamelCase__ )
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ ) -> int:
return self.download_and_extract(lowerCamelCase__ )
def UpperCAmelCase__( self , lowerCamelCase__ , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
return path
def UpperCAmelCase__( self ) -> int:
return {}
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
lowercase__ : Optional[Any] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
for single_url in single_urls:
download_callback(lowerCamelCase__ )
else:
lowercase__ : Dict = single_urls
download_callback(lowerCamelCase__ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowercase__ : Any = [os.path.join(lowerCamelCase__ , urllib.parse.quote_plus(Path(lowerCamelCase__ ).name ) ) for x in single_urls]
else:
lowercase__ : Any = single_urls
lowercase__ : int = os.path.join(lowerCamelCase__ , urllib.parse.quote_plus(Path(lowerCamelCase__ ).name ) )
lowercase__ : Union[str, Any] = value
# make sure that values are unique
if all(isinstance(lowerCamelCase__ , lowerCamelCase__ ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowercase__ : Any = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ ) -> int:
lowercase__ : int = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowercase__ : Tuple = all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}""" , lowerCamelCase__ ) ) for url in data_url )
lowercase__ : Optional[Any] = all(
url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed""" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowercase__ : List[str] = [data_url[0]] * len(lowerCamelCase__ )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowerCamelCase__ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase__ : Dict = os.path.join(lowerCamelCase__ , urllib.parse.quote_plus(single_url.split("""/""" )[-1] ) )
dummy_data_list.append(lowerCamelCase__ )
return dummy_data_list
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ ) -> List[Any]:
for download_callback in self.download_callbacks:
download_callback(lowerCamelCase__ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase__ : Optional[Any] = os.path.join(lowerCamelCase__ , urllib.parse.quote_plus(data_url.split("""/""" )[-1] ) )
if os.path.exists(lowerCamelCase__ ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCAmelCase__( self ) -> str:
pass
def UpperCAmelCase__( self ) -> Optional[Any]:
pass
def UpperCAmelCase__( self , lowerCamelCase__ ) -> List[str]:
def _iter_archive_members(lowerCamelCase__ ):
# this preserves the order of the members inside the ZIP archive
lowercase__ : Optional[int] = Path(self.dummy_file ).parent
lowercase__ : int = path.relative_to(lowerCamelCase__ )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowercase__ : Tuple = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(lowerCamelCase__ )
lowercase__ : List[str] = Path(lowerCamelCase__ )
lowercase__ : Optional[int] = _iter_archive_members(lowerCamelCase__ ) if self.use_local_dummy_data else path.rglob("""*""" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((""".""", """__""") ):
yield file_path.relative_to(lowerCamelCase__ ).as_posix(), file_path.open("""rb""" )
def UpperCAmelCase__( self , lowerCamelCase__ ) -> List[Any]:
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowercase__ : Union[str, Any] = [paths]
for path in paths:
if os.path.isfile(lowerCamelCase__ ):
if os.path.basename(lowerCamelCase__ ).startswith((""".""", """__""") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowerCamelCase__ ):
if os.path.basename(lowerCamelCase__ ).startswith((""".""", """__""") ):
continue
dirnames.sort()
for filename in sorted(lowerCamelCase__ ):
if filename.startswith((""".""", """__""") ):
continue
yield os.path.join(lowerCamelCase__ , lowerCamelCase__ ) | 128 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {'vocab_file': 'spiece.model'}
_SCREAMING_SNAKE_CASE = {
'vocab_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
}
}
_SCREAMING_SNAKE_CASE = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
# Segments (not really needed)
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :List[Any] = VOCAB_FILES_NAMES
lowerCamelCase :Optional[int] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase :List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase :Tuple = '''left'''
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=False , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_="<s>" , lowerCAmelCase_="</s>" , lowerCAmelCase_="<unk>" , lowerCAmelCase_="<sep>" , lowerCAmelCase_="<pad>" , lowerCAmelCase_="<cls>" , lowerCAmelCase_="<mask>" , lowerCAmelCase_=["<eop>", "<eod>"] , lowerCAmelCase_ = None , **lowerCAmelCase_ , ) -> None:
_A = AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ ) if isinstance(lowercase_ , lowercase_ ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowercase_ , remove_space=lowercase_ , keep_accents=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , additional_special_tokens=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
_A = 3
_A = do_lower_case
_A = remove_space
_A = keep_accents
_A = vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase_ )
@property
def UpperCAmelCase ( self ) -> Dict:
return len(self.sp_model )
def UpperCAmelCase ( self ) -> Union[str, Any]:
_A = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Any:
_A = self.__dict__.copy()
_A = None
return state
def __setstate__( self , lowerCAmelCase_ ) -> Dict:
_A = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> Tuple:
if self.remove_space:
_A = """ """.join(inputs.strip().split() )
else:
_A = inputs
_A = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
_A = unicodedata.normalize("""NFKD""" , lowercase_ )
_A = """""".join([c for c in outputs if not unicodedata.combining(lowercase_ )] )
if self.do_lower_case:
_A = outputs.lower()
return outputs
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> List[str]:
_A = self.preprocess_text(lowercase_ )
_A = self.sp_model.encode(lowercase_ , out_type=lowercase_ )
_A = []
for piece in pieces:
if len(lowercase_ ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
_A = self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_ , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_A = cur_pieces[1:]
else:
_A = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(lowercase_ )
else:
new_pieces.append(lowercase_ )
return new_pieces
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> List[str]:
return self.sp_model.PieceToId(lowercase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> Any:
return self.sp_model.IdToPiece(lowercase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> Tuple:
_A = """""".join(lowercase_ ).replace(lowercase_ , """ """ ).strip()
return out_string
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None , lowerCAmelCase_ = True , **lowerCAmelCase_ , ) -> str:
_A = kwargs.pop("""use_source_tokenizer""" , lowercase_ )
_A = self.convert_ids_to_tokens(lowercase_ , skip_special_tokens=lowercase_ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
_A = []
_A = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowercase_ ) )
_A = []
sub_texts.append(lowercase_ )
else:
current_sub_text.append(lowercase_ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowercase_ ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
_A = """""".join(lowercase_ )
_A = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
_A = self.clean_up_tokenization(lowercase_ )
return clean_text
else:
return text
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ , token_ids_a=lowercase_ , already_has_special_tokens=lowercase_ )
if token_ids_a is not None:
return ([0] * len(lowercase_ )) + [1] + ([0] * len(lowercase_ )) + [1, 1]
return ([0] * len(lowercase_ )) + [1, 1]
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ) -> Tuple[str]:
if not os.path.isdir(lowercase_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
lowercase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ , """wb""" ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
| 401 | import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : int , lowercase_ : UNetaDModel , lowercase_ : UNetaDModel , lowercase_ : DDPMScheduler , lowercase_ : Any , ) -> Any:
"""simple docstring"""
super().__init__()
_UpperCamelCase = value_function
_UpperCamelCase = unet
_UpperCamelCase = scheduler
_UpperCamelCase = env
_UpperCamelCase = env.get_dataset()
_UpperCamelCase = {}
for key in self.data.keys():
try:
_UpperCamelCase = self.data[key].mean()
except: # noqa: E722
pass
_UpperCamelCase = {}
for key in self.data.keys():
try:
_UpperCamelCase = self.data[key].std()
except: # noqa: E722
pass
_UpperCamelCase = env.observation_space.shape[0]
_UpperCamelCase = env.action_space.shape[0]
def __UpperCAmelCase ( self : Any , lowercase_ : Optional[int] , lowercase_ : Optional[Any]) -> Tuple:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def __UpperCAmelCase ( self : Any , lowercase_ : Tuple , lowercase_ : Optional[Any]) -> List[Any]:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def __UpperCAmelCase ( self : List[Any] , lowercase_ : Optional[Any]) -> List[Any]:
"""simple docstring"""
if type(lowercase_) is dict:
return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
elif torch.is_tensor(lowercase_):
return x_in.to(self.unet.device)
return torch.tensor(lowercase_ , device=self.unet.device)
def __UpperCAmelCase ( self : Optional[Any] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : List[str]) -> Tuple:
"""simple docstring"""
for key, val in cond.items():
_UpperCamelCase = val.clone()
return x_in
def __UpperCAmelCase ( self : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : int) -> Dict:
"""simple docstring"""
_UpperCamelCase = x.shape[0]
_UpperCamelCase = None
for i in tqdm.tqdm(self.scheduler.timesteps):
# create batch of timesteps to pass into model
_UpperCamelCase = torch.full((batch_size,) , lowercase_ , device=self.unet.device , dtype=torch.long)
for _ in range(lowercase_):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
_UpperCamelCase = self.value_function(x.permute(0 , 2 , 1) , lowercase_).sample
_UpperCamelCase = torch.autograd.grad([y.sum()] , [x])[0]
_UpperCamelCase = self.scheduler._get_variance(lowercase_)
_UpperCamelCase = torch.exp(0.5 * posterior_variance)
_UpperCamelCase = model_std * grad
_UpperCamelCase = 0
_UpperCamelCase = x.detach()
_UpperCamelCase = x + scale * grad
_UpperCamelCase = self.reset_xa(lowercase_ , lowercase_ , self.action_dim)
_UpperCamelCase = self.unet(x.permute(0 , 2 , 1) , lowercase_).sample.permute(0 , 2 , 1)
# TODO: verify deprecation of this kwarg
_UpperCamelCase = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ , predict_epsilon=lowercase_)["prev_sample"]
# apply conditions to the trajectory (set the initial state)
_UpperCamelCase = self.reset_xa(lowercase_ , lowercase_ , self.action_dim)
_UpperCamelCase = self.to_torch(lowercase_)
return x, y
def __call__( self : Optional[int] , lowercase_ : str , lowercase_ : int=64 , lowercase_ : Any=32 , lowercase_ : List[Any]=2 , lowercase_ : str=0.1) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = self.normalize(lowercase_ , "observations")
_UpperCamelCase = obs[None].repeat(lowercase_ , axis=0)
_UpperCamelCase = {0: self.to_torch(lowercase_)}
_UpperCamelCase = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
_UpperCamelCase = randn_tensor(lowercase_ , device=self.unet.device)
_UpperCamelCase = self.reset_xa(lowercase_ , lowercase_ , self.action_dim)
_UpperCamelCase = self.to_torch(lowercase_)
# run the diffusion process
_UpperCamelCase , _UpperCamelCase = self.run_diffusion(lowercase_ , lowercase_ , lowercase_ , lowercase_)
# sort output trajectories by value
_UpperCamelCase = y.argsort(0 , descending=lowercase_).squeeze()
_UpperCamelCase = x[sorted_idx]
_UpperCamelCase = sorted_values[:, :, : self.action_dim]
_UpperCamelCase = actions.detach().cpu().numpy()
_UpperCamelCase = self.de_normalize(lowercase_ , key="actions")
# select the action with the highest value
if y is not None:
_UpperCamelCase = 0
else:
# if we didn't run value guiding, select a random action
_UpperCamelCase = np.random.randint(0 , lowercase_)
_UpperCamelCase = denorm_actions[selected_index, 0]
return denorm_actions
| 547 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : str = {
"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 ):
'''simple docstring'''
UpperCamelCase = """luke"""
def __init__( self :Optional[int] , lowerCamelCase_ :Union[str, Any]=5_02_67 , lowerCamelCase_ :int=50_00_00 , lowerCamelCase_ :Tuple=7_68 , lowerCamelCase_ :List[str]=2_56 , lowerCamelCase_ :Dict=12 , lowerCamelCase_ :Optional[int]=12 , lowerCamelCase_ :Optional[Any]=30_72 , lowerCamelCase_ :List[Any]="gelu" , lowerCamelCase_ :Any=0.1 , lowerCamelCase_ :Union[str, Any]=0.1 , lowerCamelCase_ :List[str]=5_12 , lowerCamelCase_ :Optional[int]=2 , lowerCamelCase_ :Tuple=0.0_2 , lowerCamelCase_ :Optional[int]=1E-12 , lowerCamelCase_ :List[Any]=True , lowerCamelCase_ :List[str]=None , lowerCamelCase_ :str=1 , lowerCamelCase_ :Any=0 , lowerCamelCase_ :str=2 , **lowerCamelCase_ :List[Any] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase_ , bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = vocab_size
SCREAMING_SNAKE_CASE : List[str] = entity_vocab_size
SCREAMING_SNAKE_CASE : str = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = entity_emb_size
SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Dict = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = type_vocab_size
SCREAMING_SNAKE_CASE : str = initializer_range
SCREAMING_SNAKE_CASE : Dict = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[int] = use_entity_aware_attention
SCREAMING_SNAKE_CASE : List[str] = classifier_dropout
| 18 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowercase__:
'''simple docstring'''
def __init__( self :Tuple , lowerCamelCase_ :Tuple , lowerCamelCase_ :Union[str, Any]=2 , lowerCamelCase_ :Any=3 , lowerCamelCase_ :Union[str, Any]=4 , lowerCamelCase_ :List[str]=2 , lowerCamelCase_ :str=7 , lowerCamelCase_ :Tuple=True , lowerCamelCase_ :Dict=True , lowerCamelCase_ :int=True , lowerCamelCase_ :List[Any]=True , lowerCamelCase_ :Optional[Any]=99 , lowerCamelCase_ :Any=36 , lowerCamelCase_ :Any=3 , lowerCamelCase_ :str=4 , lowerCamelCase_ :Tuple=37 , lowerCamelCase_ :Optional[int]="gelu" , lowerCamelCase_ :str=0.1 , lowerCamelCase_ :Optional[int]=0.1 , lowerCamelCase_ :Tuple=5_12 , lowerCamelCase_ :Optional[Any]=16 , lowerCamelCase_ :List[str]=2 , lowerCamelCase_ :Optional[int]=0.0_2 , lowerCamelCase_ :int=6 , lowerCamelCase_ :str=6 , lowerCamelCase_ :Optional[Any]=3 , lowerCamelCase_ :Union[str, Any]=4 , lowerCamelCase_ :List[Any]=None , lowerCamelCase_ :Tuple=10_00 , ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Optional[Any] = batch_size
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : Optional[int] = patch_size
SCREAMING_SNAKE_CASE : Tuple = text_seq_length
SCREAMING_SNAKE_CASE : Optional[int] = is_training
SCREAMING_SNAKE_CASE : Dict = use_input_mask
SCREAMING_SNAKE_CASE : Any = use_token_type_ids
SCREAMING_SNAKE_CASE : List[Any] = use_labels
SCREAMING_SNAKE_CASE : List[Any] = vocab_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE : Optional[int] = intermediate_size
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : int = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Dict = coordinate_size
SCREAMING_SNAKE_CASE : List[Any] = shape_size
SCREAMING_SNAKE_CASE : Dict = num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = num_choices
SCREAMING_SNAKE_CASE : List[str] = scope
SCREAMING_SNAKE_CASE : Optional[int] = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
SCREAMING_SNAKE_CASE : str = text_seq_length
SCREAMING_SNAKE_CASE : int = (image_size // patch_size) ** 2 + 1
SCREAMING_SNAKE_CASE : Optional[Any] = self.text_seq_length + self.image_seq_length
def __lowerCAmelCase ( self :List[Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : List[Any] = ids_tensor([self.batch_size, self.text_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]:
SCREAMING_SNAKE_CASE : str = bbox[i, j, 3]
SCREAMING_SNAKE_CASE : List[str] = bbox[i, j, 1]
SCREAMING_SNAKE_CASE : Any = t
if bbox[i, j, 2] < bbox[i, j, 0]:
SCREAMING_SNAKE_CASE : Any = bbox[i, j, 2]
SCREAMING_SNAKE_CASE : Any = bbox[i, j, 0]
SCREAMING_SNAKE_CASE : Optional[Any] = t
SCREAMING_SNAKE_CASE : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : Dict = random_attention_mask([self.batch_size, self.text_seq_length] )
SCREAMING_SNAKE_CASE : Any = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : Optional[int] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : List[str] = LayoutLMvaConfig(
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 , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def __lowerCAmelCase ( self :int , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :int , lowerCamelCase_ :str , lowerCamelCase_ :int , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Any , lowerCamelCase_ :Dict , lowerCamelCase_ :Union[str, Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = LayoutLMvaModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
# text + image
SCREAMING_SNAKE_CASE : Optional[Any] = model(lowerCamelCase_ , pixel_values=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = model(
lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = model(lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , token_type_ids=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = model(lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
SCREAMING_SNAKE_CASE : List[str] = model(lowerCamelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=lowerCamelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def __lowerCAmelCase ( self :str , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Tuple , lowerCamelCase_ :List[str] , lowerCamelCase_ :Any , lowerCamelCase_ :int , lowerCamelCase_ :List[Any] , lowerCamelCase_ :List[str] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.num_labels
SCREAMING_SNAKE_CASE : Dict = LayoutLMvaForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE : int = model(
lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self :Union[str, Any] , lowerCamelCase_ :Tuple , lowerCamelCase_ :Any , lowerCamelCase_ :Optional[int] , lowerCamelCase_ :Any , lowerCamelCase_ :Optional[int] , lowerCamelCase_ :int , lowerCamelCase_ :str , lowerCamelCase_ :Any ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.num_labels
SCREAMING_SNAKE_CASE : int = LayoutLMvaForTokenClassification(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def __lowerCAmelCase ( self :Optional[Any] , lowerCamelCase_ :Dict , lowerCamelCase_ :Dict , lowerCamelCase_ :Dict , lowerCamelCase_ :int , lowerCamelCase_ :List[str] , lowerCamelCase_ :int , lowerCamelCase_ :str , lowerCamelCase_ :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = LayoutLMvaForQuestionAnswering(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = model(
lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=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 __lowerCAmelCase ( self :Optional[int] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
),
) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''pixel_values''': pixel_values,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowercase__( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase = (
{"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
if is_torch_available()
else {}
)
def __lowerCAmelCase ( self :int , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :str , lowerCamelCase_ :str , lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :List[str] ) -> Union[str, Any]:
'''simple docstring'''
return True
def __lowerCAmelCase ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = LayoutLMvaModelTester(self )
SCREAMING_SNAKE_CASE : List[Any] = ConfigTester(self , config_class=lowerCamelCase_ , hidden_size=37 )
def __lowerCAmelCase ( self :Any , lowerCamelCase_ :Dict , lowerCamelCase_ :List[str] , lowerCamelCase_ :str=False ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = copy.deepcopy(lowerCamelCase_ )
if model_class in get_values(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Tuple = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCamelCase_ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
elif model_class in get_values(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
elif model_class in [
*get_values(lowerCamelCase_ ),
]:
SCREAMING_SNAKE_CASE : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
elif model_class in [
*get_values(lowerCamelCase_ ),
]:
SCREAMING_SNAKE_CASE : Dict = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCamelCase_ , )
return inputs_dict
def __lowerCAmelCase ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self :str ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def __lowerCAmelCase ( self :Any ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE : str = type
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def __lowerCAmelCase ( self :Union[str, Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def __lowerCAmelCase ( self :str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
def __lowerCAmelCase ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
@slow
def __lowerCAmelCase ( self :int ) -> Union[str, Any]:
'''simple docstring'''
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[int] = LayoutLMvaModel.from_pretrained(lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
def __A ( )-> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
class lowercase__( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __lowerCAmelCase ( self :str ) -> int:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_ ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self :Optional[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' ).to(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = self.default_image_processor
SCREAMING_SNAKE_CASE : List[Any] = prepare_img()
SCREAMING_SNAKE_CASE : Tuple = image_processor(images=lowerCamelCase_ , return_tensors='''pt''' ).pixel_values.to(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([[1, 2]] )
SCREAMING_SNAKE_CASE : Dict = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
SCREAMING_SNAKE_CASE : Tuple = model(
input_ids=input_ids.to(lowerCamelCase_ ) , bbox=bbox.to(lowerCamelCase_ ) , pixel_values=pixel_values.to(lowerCamelCase_ ) , )
# verify the logits
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Size((1, 1_99, 7_68) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCamelCase_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCamelCase_ , atol=1E-4 ) )
| 18 | 1 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any]=13 , _UpperCAmelCase : List[Any]=7 , _UpperCAmelCase : Dict=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Any=True , _UpperCAmelCase : str=False , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : List[Any]=2 , _UpperCAmelCase : List[Any]=99 , _UpperCAmelCase : Optional[Any]=0 , _UpperCAmelCase : List[str]=32 , _UpperCAmelCase : List[Any]=5 , _UpperCAmelCase : str=4 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : List[str]=512 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : Any=0.02 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Any=4 , _UpperCAmelCase : List[Any]="last" , _UpperCAmelCase : Any=True , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : List[str]=0 , ):
_A = parent
_A = batch_size
_A = seq_length
_A = is_training
_A = use_input_lengths
_A = use_token_type_ids
_A = use_labels
_A = gelu_activation
_A = sinusoidal_embeddings
_A = causal
_A = asm
_A = n_langs
_A = vocab_size
_A = n_special
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_sequence_label_size
_A = initializer_range
_A = num_labels
_A = num_choices
_A = summary_type
_A = use_proj
_A = scope
_A = bos_token_id
def lowerCAmelCase_ ( self : str ):
_A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A = random_attention_mask([self.batch_size, self.seq_length] )
_A = None
if self.use_input_lengths:
_A = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_A = None
if self.use_token_type_ids:
_A = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_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] , 2 ).float()
_A = ids_tensor([self.batch_size] , self.num_choices )
_A = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def lowerCAmelCase_ ( self : Tuple ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple , ):
_A = XLMModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , lengths=_UpperCAmelCase , langs=_UpperCAmelCase )
_A = model(_UpperCAmelCase , langs=_UpperCAmelCase )
_A = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Union[str, Any] , ):
_A = XLMWithLMHeadModel(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any] , ):
_A = XLMForQuestionAnsweringSimple(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase )
_A = model(_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase )
_A = outputs
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 lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , ):
_A = XLMForQuestionAnswering(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase )
_A = model(
_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase , cls_index=_UpperCAmelCase , is_impossible=_UpperCAmelCase , p_mask=_UpperCAmelCase , )
_A = model(
_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase , cls_index=_UpperCAmelCase , is_impossible=_UpperCAmelCase , )
((_A) , ) = result_with_labels.to_tuple()
_A = model(_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase )
((_A) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , ):
_A = XLMForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase )
_A = model(_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , ):
_A = self.num_labels
_A = XLMForTokenClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
_A = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Tuple , ):
_A = self.num_choices
_A = XLMForMultipleChoice(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
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(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.prepare_config_and_inputs()
(
(
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) , (
_A
) ,
) = config_and_inputs
_A = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : List[str] = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase : List[str] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
UpperCAmelCase : Optional[int] = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any]=False ):
_A = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
_A = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_UpperCAmelCase )
_A = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_UpperCAmelCase )
return inputs_dict
def lowerCAmelCase_ ( self : Tuple ):
_A = XLMModelTester(self )
_A = ConfigTester(self , config_class=_UpperCAmelCase , emb_dim=37 )
def lowerCAmelCase_ ( self : List[Any] ):
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : List[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Any ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : int=1 ):
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
self.assertListEqual(
[isinstance(_UpperCAmelCase , _UpperCAmelCase ) for iter_attentions in attentions] , [True] * len(_UpperCAmelCase ) )
self.assertEqual(len(_UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(_UpperCAmelCase ):
# adds PAD dummy token
_A = min_length + idx + 1
_A = min_length + idx + 1
_A = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Optional[int]=1 ):
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
self.assertListEqual(
[isinstance(_UpperCAmelCase , _UpperCAmelCase ) for iter_hidden_states in hidden_states] , [True] * len(_UpperCAmelCase ) , )
self.assertEqual(len(_UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(_UpperCAmelCase ):
# adds PAD dummy token
_A = min_length + idx + 1
_A = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(_UpperCAmelCase ) , )
pass
@slow
def lowerCAmelCase_ ( self : Dict ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = XLMModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@require_torch
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase_ ( self : str ):
_A = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(_UpperCAmelCase )
_A = torch.tensor([[14, 447]] , dtype=torch.long , device=_UpperCAmelCase ) # the president
_A = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
_A = model.generate(_UpperCAmelCase , do_sample=_UpperCAmelCase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , _UpperCAmelCase )
| 7 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCAmelCase ( a__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
SCREAMING_SNAKE_CASE = False
@property
def _lowerCAmelCase( self ) -> Optional[int]:
return 32
@property
def _lowerCAmelCase( self ) -> Optional[Any]:
return 32
@property
def _lowerCAmelCase( self ) -> List[Any]:
return self.time_input_dim
@property
def _lowerCAmelCase( self ) -> int:
return self.time_input_dim * 4
@property
def _lowerCAmelCase( self ) -> List[str]:
return 100
@property
def _lowerCAmelCase( self ) -> List[str]:
torch.manual_seed(0 )
lowercase__ : str = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase__ : Optional[int] = UNetaDConditionModel(**__lowerCAmelCase )
return model
@property
def _lowerCAmelCase( self ) -> str:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _lowerCAmelCase( self ) -> Any:
torch.manual_seed(0 )
lowercase__ : str = VQModel(**self.dummy_movq_kwargs )
return model
def _lowerCAmelCase( self ) -> Any:
lowercase__ : List[Any] = self.dummy_unet
lowercase__ : Optional[int] = self.dummy_movq
lowercase__ : List[str] = {
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_0_0_8_5,
'''beta_end''': 0.0_1_2,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase__ : Union[str, Any] = DDIMScheduler(**__lowerCAmelCase )
lowercase__ : List[Any] = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Dict:
lowercase__ : List[str] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
lowercase__ : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowerCAmelCase )
# create init_image
lowercase__ : List[str] = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
lowercase__ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ : int = Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert('''RGB''' ).resize((256, 256) )
# create hint
lowercase__ : Any = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
if str(__lowerCAmelCase ).startswith('''mps''' ):
lowercase__ : Dict = torch.manual_seed(__lowerCAmelCase )
else:
lowercase__ : Dict = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
lowercase__ : Dict = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ : Optional[int] = '''cpu'''
lowercase__ : Dict = self.get_dummy_components()
lowercase__ : List[str] = self.pipeline_class(**__lowerCAmelCase )
lowercase__ : Any = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : int = pipe(**self.get_dummy_inputs(__lowerCAmelCase ) )
lowercase__ : List[Any] = output.images
lowercase__ : str = pipe(
**self.get_dummy_inputs(__lowerCAmelCase ) , return_dict=__lowerCAmelCase , )[0]
lowercase__ : List[Any] = image[0, -3:, -3:, -1]
lowercase__ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ : int = np.array(
[0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
lowercase__ : int = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase__ : List[Any] = init_image.resize((512, 512) )
lowercase__ : Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
lowercase__ : str = torch.from_numpy(np.array(__lowerCAmelCase ) ).float() / 2_5_5.0
lowercase__ : List[str] = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase__ : Union[str, Any] = '''A robot, 4k photo'''
lowercase__ : int = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowerCAmelCase )
lowercase__ : Dict = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
lowercase__ : Optional[Any] = pipeline.to(__lowerCAmelCase )
pipeline.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : Any = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase__ , lowercase__ : Optional[Any] = pipe_prior(
__lowerCAmelCase , image=__lowerCAmelCase , strength=0.8_5 , generator=__lowerCAmelCase , negative_prompt='''''' , ).to_tuple()
lowercase__ : Tuple = pipeline(
image=__lowerCAmelCase , image_embeds=__lowerCAmelCase , negative_image_embeds=__lowerCAmelCase , hint=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=100 , height=512 , width=512 , strength=0.5 , output_type='''np''' , )
lowercase__ : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
| 152 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__: List[str] = logging.get_logger(__name__)
A__: Any = {
'microsoft/swin-tiny-patch4-window7-224': (
'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class A__ ( UpperCamelCase_ , UpperCamelCase_ ):
__UpperCamelCase : Any = "swin"
__UpperCamelCase : Optional[Any] = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self :List[str] , SCREAMING_SNAKE_CASE :List[str]=2_2_4 , SCREAMING_SNAKE_CASE :List[str]=4 , SCREAMING_SNAKE_CASE :List[str]=3 , SCREAMING_SNAKE_CASE :Any=9_6 , SCREAMING_SNAKE_CASE :Any=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE :Tuple=[3, 6, 1_2, 2_4] , SCREAMING_SNAKE_CASE :List[str]=7 , SCREAMING_SNAKE_CASE :Optional[int]=4.0 , SCREAMING_SNAKE_CASE :Tuple=True , SCREAMING_SNAKE_CASE :List[Any]=0.0 , SCREAMING_SNAKE_CASE :Any=0.0 , SCREAMING_SNAKE_CASE :List[Any]=0.1 , SCREAMING_SNAKE_CASE :Any="gelu" , SCREAMING_SNAKE_CASE :Optional[int]=False , SCREAMING_SNAKE_CASE :Optional[int]=0.02 , SCREAMING_SNAKE_CASE :Dict=1e-5 , SCREAMING_SNAKE_CASE :int=3_2 , SCREAMING_SNAKE_CASE :Optional[int]=None , SCREAMING_SNAKE_CASE :Any=None , **SCREAMING_SNAKE_CASE :Optional[Any] , ) -> Tuple:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : Any =image_size
_a : Optional[int] =patch_size
_a : Tuple =num_channels
_a : Union[str, Any] =embed_dim
_a : List[str] =depths
_a : Any =len(SCREAMING_SNAKE_CASE )
_a : Any =num_heads
_a : List[str] =window_size
_a : Any =mlp_ratio
_a : int =qkv_bias
_a : Dict =hidden_dropout_prob
_a : Union[str, Any] =attention_probs_dropout_prob
_a : Any =drop_path_rate
_a : Optional[int] =hidden_act
_a : Union[str, Any] =use_absolute_embeddings
_a : Tuple =layer_norm_eps
_a : str =initializer_range
_a : Dict =encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_a : Optional[Any] =int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE ) - 1) )
_a : List[Any] =['''stem'''] + [f"stage{idx}" for idx in range(1 , len(SCREAMING_SNAKE_CASE ) + 1 )]
_a : Optional[int] =get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE , out_indices=SCREAMING_SNAKE_CASE , stage_names=self.stage_names )
class A__ ( UpperCamelCase_ ):
__UpperCamelCase : List[str] = version.parse("1.11" )
@property
def __UpperCAmelCase ( self :Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __UpperCAmelCase ( self :Tuple ) -> float:
'''simple docstring'''
return 1e-4
| 719 |
'''simple docstring'''
A__: Dict = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',
'''dataclasses''': '''dataclasses''',
'''datasets''': '''datasets!=2.5.0''',
'''decord''': '''decord==0.6.0''',
'''deepspeed''': '''deepspeed>=0.9.3''',
'''diffusers''': '''diffusers''',
'''dill''': '''dill<0.3.5''',
'''evaluate''': '''evaluate>=0.2.0''',
'''fairscale''': '''fairscale>0.3''',
'''faiss-cpu''': '''faiss-cpu''',
'''fastapi''': '''fastapi''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1,<=0.7.0''',
'''ftfy''': '''ftfy''',
'''fugashi''': '''fugashi>=1.0''',
'''GitPython''': '''GitPython<3.1.19''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''ipadic''': '''ipadic>=1.0.0,<2.0''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
'''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
'''jieba''': '''jieba''',
'''kenlm''': '''kenlm''',
'''keras-nlp''': '''keras-nlp>=0.3.1''',
'''librosa''': '''librosa''',
'''nltk''': '''nltk''',
'''natten''': '''natten>=0.14.6''',
'''numpy''': '''numpy>=1.17''',
'''onnxconverter-common''': '''onnxconverter-common''',
'''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
'''onnxruntime''': '''onnxruntime>=1.4.0''',
'''opencv-python''': '''opencv-python''',
'''optuna''': '''optuna''',
'''optax''': '''optax>=0.0.8,<=0.1.4''',
'''packaging''': '''packaging>=20.0''',
'''parameterized''': '''parameterized''',
'''phonemizer''': '''phonemizer''',
'''protobuf''': '''protobuf''',
'''psutil''': '''psutil''',
'''pyyaml''': '''pyyaml>=5.1''',
'''pydantic''': '''pydantic<2''',
'''pytest''': '''pytest>=7.2.0''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''python''': '''python>=3.8.0''',
'''ray[tune]''': '''ray[tune]''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
'''rjieba''': '''rjieba''',
'''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
'''ruff''': '''ruff>=0.0.241,<=0.0.259''',
'''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
'''sacremoses''': '''sacremoses''',
'''safetensors''': '''safetensors>=0.3.1''',
'''sagemaker''': '''sagemaker>=2.31.0''',
'''scikit-learn''': '''scikit-learn''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''sigopt''': '''sigopt''',
'''starlette''': '''starlette''',
'''sudachipy''': '''sudachipy>=0.6.6''',
'''sudachidict_core''': '''sudachidict_core>=20220729''',
'''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
'''tensorflow''': '''tensorflow>=2.6,<2.14''',
'''tensorflow-text''': '''tensorflow-text<2.14''',
'''tf2onnx''': '''tf2onnx''',
'''timeout-decorator''': '''timeout-decorator''',
'''timm''': '''timm''',
'''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
'''torch''': '''torch>=1.9,!=1.12.0''',
'''torchaudio''': '''torchaudio''',
'''torchvision''': '''torchvision''',
'''pyctcdecode''': '''pyctcdecode>=0.4.0''',
'''tqdm''': '''tqdm>=4.27''',
'''unidic''': '''unidic>=1.0.2''',
'''unidic_lite''': '''unidic_lite>=1.0.7''',
'''urllib3''': '''urllib3<2.0.0''',
'''uvicorn''': '''uvicorn''',
}
| 506 | 0 |
"""simple docstring"""
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 snake_case ( lowerCAmelCase_ ) -> tuple:
return (data["data"], data["target"])
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> XGBClassifier:
_snake_case = XGBClassifier()
classifier.fit(lowerCAmelCase_ , lowerCAmelCase_ )
return classifier
def snake_case ( ) -> None:
_snake_case = load_iris()
_snake_case , _snake_case = data_handling(lowerCAmelCase_ )
_snake_case , _snake_case , _snake_case , _snake_case = train_test_split(
lowerCAmelCase_ , lowerCAmelCase_ , test_size=0.25 )
_snake_case = iris['''target_names''']
# Create an XGBoost Classifier from the training data
_snake_case = xgboost(lowerCAmelCase_ , lowerCAmelCase_ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , display_labels=lowerCAmelCase_ , cmap='''Blues''' , normalize='''true''' , )
plt.title('''Normalized Confusion Matrix - IRIS Dataset''' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 103 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
snake_case = random.Random()
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_=1.0 , lowerCAmelCase_=None , lowerCAmelCase_=None ) -> List[str]:
if rng is None:
_snake_case = global_rng
_snake_case = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[Any]=7 , __lowerCamelCase : List[Any]=4_0_0 , __lowerCamelCase : Any=2_0_0_0 , __lowerCamelCase : Any=2_0_4_8 , __lowerCamelCase : Any=1_2_8 , __lowerCamelCase : Any=1 , __lowerCamelCase : Optional[int]=5_1_2 , __lowerCamelCase : Tuple=3_0 , __lowerCamelCase : List[Any]=4_4_1_0_0 , ):
"""simple docstring"""
_snake_case = parent
_snake_case = batch_size
_snake_case = min_seq_length
_snake_case = max_seq_length
_snake_case = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_snake_case = spectrogram_length
_snake_case = feature_size
_snake_case = num_audio_channels
_snake_case = hop_length
_snake_case = chunk_length
_snake_case = sampling_rate
def __UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Any=False , __lowerCamelCase : int=False ):
"""simple docstring"""
def _flatten(__lowerCamelCase : List[str] ):
return list(itertools.chain(*__lowerCamelCase ) )
if equal_length:
_snake_case = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_snake_case = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_snake_case = [np.asarray(__lowerCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE,unittest.TestCase ):
A__ : Tuple = TvltFeatureExtractor
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = TvltFeatureExtractionTester(self )
def __UpperCAmelCase ( self : Any ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(__lowerCamelCase , '''spectrogram_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''feature_size''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''num_audio_channels''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''hop_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''chunk_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''sampling_rate''' ) )
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case = feat_extract_first.save_pretrained(__lowerCamelCase )[0]
check_json_file_has_correct_format(__lowerCamelCase )
_snake_case = self.feature_extraction_class.from_pretrained(__lowerCamelCase )
_snake_case = feat_extract_first.to_dict()
_snake_case = feat_extract_second.to_dict()
_snake_case = dict_first.pop('''mel_filters''' )
_snake_case = dict_second.pop('''mel_filters''' )
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase ) )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case = os.path.join(__lowerCamelCase , '''feat_extract.json''' )
feat_extract_first.to_json_file(__lowerCamelCase )
_snake_case = self.feature_extraction_class.from_json_file(__lowerCamelCase )
_snake_case = feat_extract_first.to_dict()
_snake_case = feat_extract_second.to_dict()
_snake_case = dict_first.pop('''mel_filters''' )
_snake_case = dict_second.pop('''mel_filters''' )
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase ) )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : Dict ):
"""simple docstring"""
# Initialize feature_extractor
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
_snake_case = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_snake_case = [np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]
# Test not batched input
_snake_case = feature_extractor(np_speech_inputs[0] , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
_snake_case = feature_extractor(
__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 , mask_audio=__lowerCamelCase ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
_snake_case = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
_snake_case = np.asarray(__lowerCamelCase )
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
_snake_case = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
_snake_case = ds.sort('''id''' ).select(range(__lowerCamelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def __UpperCAmelCase ( self : Dict ):
"""simple docstring"""
_snake_case = self._load_datasamples(1 )
_snake_case = TvltFeatureExtractor()
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''pt''' ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_9_2, 1_2_8) )
_snake_case = torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , __lowerCamelCase , atol=1E-4 ) )
| 103 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : Any = logging.get_logger(__name__)
UpperCamelCase : str = {"""vocab_file""": """sentencepiece.bpe.model"""}
UpperCamelCase : Optional[int] = {
"""vocab_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
}
}
UpperCamelCase : List[str] = {
"""camembert-base""": 512,
}
UpperCamelCase : List[Any] = """▁"""
class UpperCamelCase ( a_ ):
"""simple docstring"""
A : Tuple = VOCAB_FILES_NAMES
A : Any = PRETRAINED_VOCAB_FILES_MAP
A : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Any = ["input_ids", "attention_mask"]
def __init__( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int]="<s>" , UpperCAmelCase_ : Optional[Any]="</s>" , UpperCAmelCase_ : Tuple="</s>" , UpperCAmelCase_ : Optional[Any]="<s>" , UpperCAmelCase_ : int="<unk>" , UpperCAmelCase_ : int="<pad>" , UpperCAmelCase_ : Tuple="<mask>" , UpperCAmelCase_ : int=["<s>NOTUSED", "</s>NOTUSED"] , UpperCAmelCase_ : Optional[Dict[str, Any]] = None , **UpperCAmelCase_ : Optional[int] , ):
"""simple docstring"""
a : Dict = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_ , UpperCAmelCase_) else mask_token
a : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , sp_model_kwargs=self.sp_model_kwargs , **UpperCAmelCase_ , )
a : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(UpperCAmelCase_))
a : Tuple = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
a : str = {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
a : Optional[Any] = len(self.fairseq_tokens_to_ids)
a : List[str] = len(self.sp_model) + len(self.fairseq_tokens_to_ids)
a : Dict = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def SCREAMING_SNAKE_CASE_ ( self : Any , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
a : Optional[int] = [self.cls_token_id]
a : Optional[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None , UpperCAmelCase_ : bool = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase_ , token_ids_a=UpperCAmelCase_ , already_has_special_tokens=UpperCAmelCase_)
if token_ids_a is None:
return [1] + ([0] * len(UpperCAmelCase_)) + [1]
return [1] + ([0] * len(UpperCAmelCase_)) + [1, 1] + ([0] * len(UpperCAmelCase_)) + [1]
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None):
"""simple docstring"""
a : List[Any] = [self.sep_token_id]
a : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
@property
def SCREAMING_SNAKE_CASE_ ( self : Any):
"""simple docstring"""
return len(self.fairseq_tokens_to_ids) + len(self.sp_model)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any]):
"""simple docstring"""
a : int = {self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def SCREAMING_SNAKE_CASE_ ( self : int , UpperCAmelCase_ : str):
"""simple docstring"""
return self.sp_model.encode(UpperCAmelCase_ , out_type=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCAmelCase_ : List[str]):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(UpperCAmelCase_) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : str , UpperCAmelCase_ : List[Any]):
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
def SCREAMING_SNAKE_CASE_ ( self : int , UpperCAmelCase_ : List[Any]):
"""simple docstring"""
a : List[str] = []
a : List[str] = ''
a : Optional[Any] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCAmelCase_) + token
a : Tuple = True
a : Optional[Any] = []
else:
current_sub_tokens.append(UpperCAmelCase_)
a : int = False
out_string += self.sp_model.decode(UpperCAmelCase_)
return out_string.strip()
def __getstate__( self : Union[str, Any]):
"""simple docstring"""
a : str = self.__dict__.copy()
a : List[Any] = None
return state
def __setstate__( self : List[Any] , UpperCAmelCase_ : Optional[Any]):
"""simple docstring"""
a : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
a : Tuple = {}
a : str = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None):
"""simple docstring"""
if not os.path.isdir(UpperCAmelCase_):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
return
a : Optional[Any] = os.path.join(
UpperCAmelCase_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , UpperCAmelCase_)
elif not os.path.isfile(self.vocab_file):
with open(UpperCAmelCase_ , 'wb') as fi:
a : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(UpperCAmelCase_)
return (out_vocab_file,)
| 610 | '''simple docstring'''
UpperCamelCase : Union[str, Any] = """
# 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
"""
UpperCamelCase : List[Any] = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCamelCase : Tuple = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 610 | 1 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE( self :str ) ->str:
torch.manual_seed(0 )
lowercase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def SCREAMING_SNAKE_CASE( self :Tuple ) ->str:
lowercase = self.dummy_uncond_unet
lowercase = ScoreSdeVeScheduler()
lowercase = ScoreSdeVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
sde_ve.to(__SCREAMING_SNAKE_CASE )
sde_ve.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
lowercase = torch.manual_seed(0 )
lowercase = sde_ve(num_inference_steps=2 , output_type="numpy" , generator=__SCREAMING_SNAKE_CASE ).images
lowercase = torch.manual_seed(0 )
lowercase = sde_ve(num_inference_steps=2 , output_type="numpy" , generator=__SCREAMING_SNAKE_CASE , return_dict=__SCREAMING_SNAKE_CASE )[
0
]
lowercase = image[0, -3:, -3:, -1]
lowercase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE( self :int ) ->Tuple:
lowercase = "google/ncsnpp-church-256"
lowercase = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase = ScoreSdeVeScheduler.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase = ScoreSdeVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
sde_ve.to(__SCREAMING_SNAKE_CASE )
sde_ve.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
lowercase = torch.manual_seed(0 )
lowercase = sde_ve(num_inference_steps=10 , output_type="numpy" , generator=__SCREAMING_SNAKE_CASE ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 441 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : Optional[int] = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : List[str] ="""gptj"""
a : Optional[int] ={
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self,__SCREAMING_SNAKE_CASE=5_04_00,__SCREAMING_SNAKE_CASE=20_48,__SCREAMING_SNAKE_CASE=40_96,__SCREAMING_SNAKE_CASE=28,__SCREAMING_SNAKE_CASE=16,__SCREAMING_SNAKE_CASE=64,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="gelu_new",__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=1e-5,__SCREAMING_SNAKE_CASE=0.02,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_embd
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = rotary_dim
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = use_cache
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(
bos_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,tie_word_embeddings=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = "default",__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE,task=__SCREAMING_SNAKE_CASE,patching_specs=__SCREAMING_SNAKE_CASE,use_past=__SCREAMING_SNAKE_CASE )
if not getattr(self._config,"""pad_token_id""",__SCREAMING_SNAKE_CASE ):
# TODO: how to do that better?
__lowerCAmelCase = 0
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE,direction="""inputs""" )
__lowerCAmelCase = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_head
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,):
'''simple docstring'''
__lowerCAmelCase = super(__SCREAMING_SNAKE_CASE,self ).generate_dummy_inputs(
__SCREAMING_SNAKE_CASE,batch_size=__SCREAMING_SNAKE_CASE,seq_length=__SCREAMING_SNAKE_CASE,is_pair=__SCREAMING_SNAKE_CASE,framework=__SCREAMING_SNAKE_CASE )
# We need to order the input in the way they appears in the forward()
__lowerCAmelCase = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__lowerCAmelCase , __lowerCAmelCase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__lowerCAmelCase = seqlen + 2
__lowerCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__lowerCAmelCase = [
(torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers )
]
__lowerCAmelCase = common_inputs["""attention_mask"""]
if self.use_past:
__lowerCAmelCase = ordered_inputs["""attention_mask"""].dtype
__lowerCAmelCase = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE )],dim=1 )
return ordered_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 13
| 689 | 0 |
def lowerCAmelCase ( _lowerCAmelCase : Union[str, Any] = 5000_0000 ):
"""simple docstring"""
UpperCAmelCase__ = set()
UpperCAmelCase__ = int((limit - 24) ** (1 / 2) )
UpperCAmelCase__ = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , __snake_case ) ) )
for primea in primes:
UpperCAmelCase__ = primea * primea
for primea in primes:
UpperCAmelCase__ = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
UpperCAmelCase__ = primea * primea * primea * primea
UpperCAmelCase__ = square + cube + tetr
if total >= limit:
break
ret.add(__snake_case )
return len(__snake_case )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 707 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
_lowerCAmelCase : int = "facebook/wmt19-en-de"
_lowerCAmelCase : int = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase : Dict = FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
_lowerCAmelCase : List[Any] = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
_lowerCAmelCase : Optional[Any] = tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase : Optional[Any] = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
_lowerCAmelCase : Optional[Any] = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 364 | 0 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'spiece.model'}
UpperCamelCase = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
UpperCamelCase = {
'google/bigbird-roberta-base': 4096,
'google/bigbird-roberta-large': 4096,
'google/bigbird-base-trivia-itc': 4096,
}
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = PRETRAINED_VOCAB_FILES_MAP
snake_case__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = ["input_ids", "attention_mask"]
snake_case__ = []
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str]="<unk>" , SCREAMING_SNAKE_CASE__ : List[str]="<s>" , SCREAMING_SNAKE_CASE__ : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE__ : Tuple="<pad>" , SCREAMING_SNAKE_CASE__ : Any="[SEP]" , SCREAMING_SNAKE_CASE__ : Optional[int]="[MASK]" , SCREAMING_SNAKE_CASE__ : List[Any]="[CLS]" , SCREAMING_SNAKE_CASE__ : Optional[Dict[str, Any]] = None , **SCREAMING_SNAKE_CASE__ : List[Any] , ) -> None:
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else bos_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else eos_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else unk_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else pad_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else cls_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else mask_token
lowerCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , )
lowerCAmelCase__ = vocab_file
lowerCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(SCREAMING_SNAKE_CASE__ )
@property
def a ( self : List[str] ) -> List[str]:
return self.sp_model.get_piece_size()
def a ( self : List[str] ) -> Dict:
lowerCAmelCase__ = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Optional[int] ) -> Any:
lowerCAmelCase__ = self.__dict__.copy()
lowerCAmelCase__ = None
return state
def __setstate__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Any:
lowerCAmelCase__ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowerCAmelCase__ = {}
lowerCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : str ) -> List[str]:
return self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Tuple:
return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__ )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> List[str]:
lowerCAmelCase__ = self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__ )
return token
def a ( self : str , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> str:
lowerCAmelCase__ = []
lowerCAmelCase__ = ""
lowerCAmelCase__ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__ ) + token
lowerCAmelCase__ = True
lowerCAmelCase__ = []
else:
current_sub_tokens.append(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = False
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__ )
return out_string.strip()
def a ( self : Tuple , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : bool = None , SCREAMING_SNAKE_CASE__ : bool = True , **SCREAMING_SNAKE_CASE__ : int , ) -> str:
lowerCAmelCase__ = kwargs.pop("use_source_tokenizer" , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
lowerCAmelCase__ = []
lowerCAmelCase__ = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ ) )
lowerCAmelCase__ = []
sub_texts.append(SCREAMING_SNAKE_CASE__ )
else:
current_sub_text.append(SCREAMING_SNAKE_CASE__ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
lowerCAmelCase__ = re.sub(r" (\[(MASK|SEP)\])" , r"\1" , " ".join(SCREAMING_SNAKE_CASE__ ) )
else:
lowerCAmelCase__ = "".join(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
lowerCAmelCase__ = self.clean_up_tokenization(SCREAMING_SNAKE_CASE__ )
return clean_text
else:
return text
def a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase__ = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE__ , "wb" ) as fi:
lowerCAmelCase__ = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,)
def a ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
lowerCAmelCase__ = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def a ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None , SCREAMING_SNAKE_CASE__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
def a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None ) -> List[int]:
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
| 61 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class A :
def __init__( self: Any , _lowerCAmelCase: str , _lowerCAmelCase: Optional[Any]=13 , _lowerCAmelCase: List[str]=30 , _lowerCAmelCase: List[Any]=2 , _lowerCAmelCase: List[str]=3 , _lowerCAmelCase: Dict=True , _lowerCAmelCase: int=True , _lowerCAmelCase: Tuple=32 , _lowerCAmelCase: str=2 , _lowerCAmelCase: Dict=4 , _lowerCAmelCase: Dict=37 , _lowerCAmelCase: Optional[Any]="gelu" , _lowerCAmelCase: List[Any]=0.1 , _lowerCAmelCase: List[Any]=0.1 , _lowerCAmelCase: Union[str, Any]=10 , _lowerCAmelCase: str=0.02 , _lowerCAmelCase: Optional[Any]=3 , _lowerCAmelCase: Optional[int]=None , ) -> Any:
'''simple docstring'''
UpperCAmelCase_ =parent
UpperCAmelCase_ =batch_size
UpperCAmelCase_ =image_size
UpperCAmelCase_ =patch_size
UpperCAmelCase_ =num_channels
UpperCAmelCase_ =is_training
UpperCAmelCase_ =use_labels
UpperCAmelCase_ =hidden_size
UpperCAmelCase_ =num_hidden_layers
UpperCAmelCase_ =num_attention_heads
UpperCAmelCase_ =intermediate_size
UpperCAmelCase_ =hidden_act
UpperCAmelCase_ =hidden_dropout_prob
UpperCAmelCase_ =attention_probs_dropout_prob
UpperCAmelCase_ =type_sequence_label_size
UpperCAmelCase_ =initializer_range
UpperCAmelCase_ =scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase_ =(image_size // patch_size) ** 2
UpperCAmelCase_ =num_patches + 1
def lowerCAmelCase__ ( self: Any ) -> int:
'''simple docstring'''
UpperCAmelCase_ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase_ =None
if self.use_labels:
UpperCAmelCase_ =ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ =self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self: List[Any] ) -> Dict:
'''simple docstring'''
return ViTConfig(
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 , )
def lowerCAmelCase__ ( self: List[Any] , _lowerCAmelCase: int , _lowerCAmelCase: Any , _lowerCAmelCase: List[str] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ =TFViTModel(config=_lowerCAmelCase )
UpperCAmelCase_ =model(_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
UpperCAmelCase_ =self.image_size // 2
UpperCAmelCase_ =pixel_values[:, :, :image_size, :image_size]
UpperCAmelCase_ =model(_lowerCAmelCase , interpolate_pos_encoding=_lowerCAmelCase , training=_lowerCAmelCase )
UpperCAmelCase_ =(image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self: Optional[int] , _lowerCAmelCase: Optional[Any] , _lowerCAmelCase: List[Any] , _lowerCAmelCase: List[Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ =self.type_sequence_label_size
UpperCAmelCase_ =TFViTForImageClassification(_lowerCAmelCase )
UpperCAmelCase_ =model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
UpperCAmelCase_ =self.image_size // 2
UpperCAmelCase_ =pixel_values[:, :, :image_size, :image_size]
UpperCAmelCase_ =model(_lowerCAmelCase , interpolate_pos_encoding=_lowerCAmelCase , training=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase_ =1
UpperCAmelCase_ =TFViTForImageClassification(_lowerCAmelCase )
UpperCAmelCase_ =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase_ =model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase__ ( self: Any ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ =self.prepare_config_and_inputs()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ =config_and_inputs
UpperCAmelCase_ ={"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class A ( __lowercase , __lowercase , unittest.TestCase ):
_snake_case =(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_snake_case =(
{'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
if is_tf_available()
else {}
)
_snake_case =False
_snake_case =False
_snake_case =False
def lowerCAmelCase__ ( self: int ) -> int:
'''simple docstring'''
UpperCAmelCase_ =TFViTModelTester(self )
UpperCAmelCase_ =ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def lowerCAmelCase__ ( self: Optional[Any] ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def lowerCAmelCase__ ( self: Dict ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason="ViT does not use inputs_embeds" )
def lowerCAmelCase__ ( self: int ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCAmelCase__ ( self: List[Any] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ =model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
UpperCAmelCase_ =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , tf.keras.layers.Layer ) )
def lowerCAmelCase__ ( self: List[str] ) -> int:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ =model_class(_lowerCAmelCase )
UpperCAmelCase_ =inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ =[*signature.parameters.keys()]
UpperCAmelCase_ =["pixel_values"]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def lowerCAmelCase__ ( self: int ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def lowerCAmelCase__ ( self: List[Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def lowerCAmelCase__ ( self: Optional[Any] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ =TFViTModel.from_pretrained("google/vit-base-patch16-224" )
self.assertIsNotNone(_lowerCAmelCase )
def a__ ( ):
'''simple docstring'''
UpperCAmelCase_ =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class A ( unittest.TestCase ):
@cached_property
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224" ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self: Dict ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ =TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224" )
UpperCAmelCase_ =self.default_image_processor
UpperCAmelCase_ =prepare_img()
UpperCAmelCase_ =image_processor(images=_lowerCAmelCase , return_tensors="tf" )
# forward pass
UpperCAmelCase_ =model(**_lowerCAmelCase )
# verify the logits
UpperCAmelCase_ =tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
UpperCAmelCase_ =tf.constant([-0.27_44, 0.82_15, -0.08_36] )
tf.debugging.assert_near(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 )
| 54 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.17.0.dev0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')
__A : List[str] = logging.getLogger(__name__)
@dataclass
class _SCREAMING_SNAKE_CASE :
_UpperCamelCase:Optional[str] = field(
default="tab_fact" , metadata={"help": "The name of the dataset to use (via the datasets library)."})
_UpperCamelCase:Optional[str] = field(
default="tab_fact" , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} , )
_UpperCamelCase:int = field(
default=10_24 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
_UpperCamelCase:bool = field(
default=lowerCAmelCase__ , metadata={"help": "Overwrite the cached preprocessed datasets or not."})
_UpperCamelCase:bool = field(
default=lowerCAmelCase__ , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
_UpperCamelCase:Optional[int] = field(
default=lowerCAmelCase__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
_UpperCamelCase:Optional[int] = field(
default=lowerCAmelCase__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
_UpperCamelCase:Optional[int] = field(
default=lowerCAmelCase__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
} , )
_UpperCamelCase:Optional[str] = field(
default=lowerCAmelCase__ , metadata={"help": "A csv or a json file containing the training data."})
_UpperCamelCase:Optional[str] = field(
default=lowerCAmelCase__ , metadata={"help": "A csv or a json file containing the validation data."})
_UpperCamelCase:Optional[str] = field(default=lowerCAmelCase__ , metadata={"help": "A csv or a json file containing the test data."})
def _snake_case ( self )-> str:
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
lowerCamelCase_ =self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
lowerCamelCase_ =self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _SCREAMING_SNAKE_CASE :
_UpperCamelCase:str = field(
default=lowerCAmelCase__ , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
_UpperCamelCase:Optional[str] = field(
default=lowerCAmelCase__ , metadata={"help": "Pretrained config name or path if not the same as model_name"})
_UpperCamelCase:Optional[str] = field(
default=lowerCAmelCase__ , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
_UpperCamelCase:Optional[str] = field(
default=lowerCAmelCase__ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
_UpperCamelCase:bool = field(
default=lowerCAmelCase__ , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
_UpperCamelCase:str = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
_UpperCamelCase:bool = field(
default=lowerCAmelCase__ , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def __UpperCamelCase ( ) ->Any:
"""simple docstring"""
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCamelCase_ =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.
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ =parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
lowerCamelCase_ =training_args.get_process_log_level()
logger.setLevel(_A )
datasets.utils.logging.set_verbosity(_A )
transformers.utils.logging.set_verbosity(_A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
lowerCamelCase_ =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCamelCase_ =get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowerCamelCase_ =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
lowerCamelCase_ ={"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
lowerCamelCase_ =data_args.train_file.split(""".""" )[-1]
lowerCamelCase_ =data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
lowerCamelCase_ =data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(f'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
lowerCamelCase_ =load_dataset("""csv""" , data_files=_A , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
lowerCamelCase_ =load_dataset("""json""" , data_files=_A , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
lowerCamelCase_ =raw_datasets["""train"""].features["""label"""].names
lowerCamelCase_ =len(_A )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase_ =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
lowerCamelCase_ =TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=_A , )
lowerCamelCase_ =BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=_A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
lowerCamelCase_ ="""max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowerCamelCase_ =False
# Some models have set the order of the labels to use, so let's make sure we do use it.
lowerCamelCase_ ={"""Refused""": 0, """Entailed""": 1}
lowerCamelCase_ ={0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
lowerCamelCase_ =min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_A : Optional[Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_A : Any ):
lowerCamelCase_ =[_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
lowerCamelCase_ =pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
lowerCamelCase_ =examples["""statement"""]
lowerCamelCase_ =list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
lowerCamelCase_ =tokenizer(_A , _A , padding=_A , max_length=_A , truncation=_A )
lowerCamelCase_ =examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
lowerCamelCase_ =raw_datasets.map(
_A , batched=_A , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
lowerCamelCase_ =raw_datasets["""train"""]
if data_args.max_train_samples is not None:
lowerCamelCase_ =train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
lowerCamelCase_ =raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
lowerCamelCase_ =eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
lowerCamelCase_ =raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
lowerCamelCase_ =predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_A ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_A : EvalPrediction ):
lowerCamelCase_ =p.predictions[0] if isinstance(p.predictions , _A ) else p.predictions
lowerCamelCase_ =np.argmax(_A , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowerCamelCase_ =default_data_collator
elif training_args.fpaa:
lowerCamelCase_ =DataCollatorWithPadding(_A , pad_to_multiple_of=8 )
else:
lowerCamelCase_ =None
# Initialize our Trainer
lowerCamelCase_ =Trainer(
model=_A , args=_A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_A , tokenizer=_A , data_collator=_A , )
# Training
if training_args.do_train:
lowerCamelCase_ =None
if training_args.resume_from_checkpoint is not None:
lowerCamelCase_ =training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCamelCase_ =last_checkpoint
lowerCamelCase_ =trainer.train(resume_from_checkpoint=_A )
lowerCamelCase_ =train_result.metrics
lowerCamelCase_ =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(_A )
)
lowerCamelCase_ =min(_A , len(_A ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , _A )
trainer.save_metrics("""train""" , _A )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowerCamelCase_ =trainer.evaluate(eval_dataset=_A )
lowerCamelCase_ =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_A )
lowerCamelCase_ =min(_A , len(_A ) )
trainer.log_metrics("""eval""" , _A )
trainer.save_metrics("""eval""" , _A )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
lowerCamelCase_ =predict_dataset.remove_columns("""label""" )
lowerCamelCase_ =trainer.predict(_A , metric_key_prefix="""predict""" ).predictions
lowerCamelCase_ =np.argmax(_A , axis=1 )
lowerCamelCase_ =os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(_A , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(_A ):
lowerCamelCase_ =label_list[item]
writer.write(f'{index}\t{item}\n' )
lowerCamelCase_ ={"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**_A )
else:
trainer.create_model_card(**_A )
def __UpperCamelCase ( _A : List[str] ) ->Union[str, Any]:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 75 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__A : List[Any] = 'src/transformers'
__A : Tuple = 'docs/source/en'
__A : Optional[int] = '.'
def __UpperCamelCase ( _A : Tuple , _A : Tuple , _A : Optional[Any] ) ->Optional[Any]:
"""simple docstring"""
with open(_A , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase_ =f.readlines()
# Find the start prompt.
lowerCamelCase_ =0
while not lines[start_index].startswith(_A ):
start_index += 1
start_index += 1
lowerCamelCase_ =start_index
while not lines[end_index].startswith(_A ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
__A : Dict = 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A : Optional[int] = re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A : Optional[int] = re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A : str = re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# This is to make sure the transformers module imported is the one in the repo.
__A : List[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def __UpperCamelCase ( _A : List[Any] ) ->str:
"""simple docstring"""
lowerCamelCase_ =re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , _A )
return [m.group(0 ) for m in matches]
def __UpperCamelCase ( _A : Union[str, Any] , _A : List[str] ) ->Optional[int]:
"""simple docstring"""
lowerCamelCase_ =2 if text == """✅""" or text == """❌""" else len(_A )
lowerCamelCase_ =(width - text_length) // 2
lowerCamelCase_ =width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def __UpperCamelCase ( ) ->Any:
"""simple docstring"""
lowerCamelCase_ =transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowerCamelCase_ ={
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
lowerCamelCase_ ={name: config.replace("""Config""" , """""" ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
lowerCamelCase_ =collections.defaultdict(_A )
lowerCamelCase_ =collections.defaultdict(_A )
lowerCamelCase_ =collections.defaultdict(_A )
lowerCamelCase_ =collections.defaultdict(_A )
lowerCamelCase_ =collections.defaultdict(_A )
# Let's lookup through all transformers object (once).
for attr_name in dir(_A ):
lowerCamelCase_ =None
if attr_name.endswith("""Tokenizer""" ):
lowerCamelCase_ =slow_tokenizers
lowerCamelCase_ =attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
lowerCamelCase_ =fast_tokenizers
lowerCamelCase_ =attr_name[:-13]
elif _re_tf_models.match(_A ) is not None:
lowerCamelCase_ =tf_models
lowerCamelCase_ =_re_tf_models.match(_A ).groups()[0]
elif _re_flax_models.match(_A ) is not None:
lowerCamelCase_ =flax_models
lowerCamelCase_ =_re_flax_models.match(_A ).groups()[0]
elif _re_pt_models.match(_A ) is not None:
lowerCamelCase_ =pt_models
lowerCamelCase_ =_re_pt_models.match(_A ).groups()[0]
if lookup_dict is not None:
while len(_A ) > 0:
if attr_name in model_name_to_prefix.values():
lowerCamelCase_ =True
break
# Try again after removing the last word in the name
lowerCamelCase_ ="""""".join(camel_case_split(_A )[:-1] )
# Let's build that table!
lowerCamelCase_ =list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
lowerCamelCase_ =["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
lowerCamelCase_ =[len(_A ) + 2 for c in columns]
lowerCamelCase_ =max([len(_A ) for name in model_names] ) + 2
# Build the table per se
lowerCamelCase_ ="""|""" + """|""".join([_center_text(_A , _A ) for c, w in zip(_A , _A )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
lowerCamelCase_ ={True: """✅""", False: """❌"""}
for name in model_names:
lowerCamelCase_ =model_name_to_prefix[name]
lowerCamelCase_ =[
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(_A , _A ) for l, w in zip(_A , _A )] ) + "|\n"
return table
def __UpperCamelCase ( _A : str=False ) ->Optional[Any]:
"""simple docstring"""
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ =_find_text_in_file(
filename=os.path.join(_A , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
lowerCamelCase_ =get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(_A , """index.md""" ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
"""The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.""" )
if __name__ == "__main__":
__A : int = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__A : Any = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 75 | 1 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCamelCase : Tuple = False
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
pass
@slow
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
__lowercase : Tuple = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
__lowercase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase : Optional[Any] = torch.manual_seed(0 )
__lowercase : Optional[int] = pipe(
image=__a , generator=__a , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
__lowercase : Optional[Any] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase : List[str] = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 149 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase : List[str] = logging.get_logger(__name__)
def snake_case_ ( lowerCAmelCase_ : str ):
__lowercase : List[str] = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
__lowercase : Tuple = 192
__lowercase : List[Any] = 768
__lowercase : Tuple = 12
__lowercase : List[Any] = 3
__lowercase : str = [800, 1333]
__lowercase : List[Any] = False
elif yolos_name == "yolos_s_dWr":
__lowercase : Any = 330
__lowercase : int = 14
__lowercase : List[str] = 6
__lowercase : Tuple = 1320
elif "yolos_s" in yolos_name:
__lowercase : int = 384
__lowercase : Union[str, Any] = 1536
__lowercase : List[str] = 12
__lowercase : Optional[Any] = 6
elif "yolos_b" in yolos_name:
__lowercase : List[Any] = [800, 1344]
__lowercase : Tuple = 91
__lowercase : Union[str, Any] = """huggingface/label-files"""
__lowercase : Any = """coco-detection-id2label.json"""
__lowercase : Dict = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="""dataset""" ) , """r""" ) )
__lowercase : Union[str, Any] = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
__lowercase : Union[str, Any] = idalabel
__lowercase : List[str] = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ( lowerCAmelCase_ : dict , lowerCAmelCase_ : YolosConfig , lowerCAmelCase_ : bool = False ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowercase : int = state_dict.pop(F"blocks.{i}.attn.qkv.weight" )
__lowercase : str = state_dict.pop(F"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
__lowercase : Any = in_proj_weight[: config.hidden_size, :]
__lowercase : Tuple = in_proj_bias[: config.hidden_size]
__lowercase : Optional[int] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowercase : List[str] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowercase : Dict = in_proj_weight[-config.hidden_size :, :]
__lowercase : Optional[Any] = in_proj_bias[-config.hidden_size :]
def snake_case_ ( lowerCAmelCase_ : str ):
if "backbone" in name:
__lowercase : Union[str, Any] = name.replace("""backbone""" , """vit""" )
if "cls_token" in name:
__lowercase : Dict = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "det_token" in name:
__lowercase : str = name.replace("""det_token""" , """embeddings.detection_tokens""" )
if "mid_pos_embed" in name:
__lowercase : Dict = name.replace("""mid_pos_embed""" , """encoder.mid_position_embeddings""" )
if "pos_embed" in name:
__lowercase : str = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__lowercase : Optional[Any] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "blocks" in name:
__lowercase : List[str] = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
__lowercase : Optional[int] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
__lowercase : Optional[int] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
__lowercase : Dict = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__lowercase : List[Any] = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__lowercase : List[str] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__lowercase : str = name.replace("""mlp.fc2""" , """output.dense""" )
if "class_embed" in name:
__lowercase : Optional[Any] = name.replace("""class_embed""" , """class_labels_classifier""" )
if "bbox_embed" in name:
__lowercase : str = name.replace("""bbox_embed""" , """bbox_predictor""" )
if "vit.norm" in name:
__lowercase : List[str] = name.replace("""vit.norm""" , """vit.layernorm""" )
return name
def snake_case_ ( lowerCAmelCase_ : dict , lowerCAmelCase_ : YolosForObjectDetection ):
for key in orig_state_dict.copy().keys():
__lowercase : Optional[int] = orig_state_dict.pop(lowerCAmelCase_ )
if "qkv" in key:
__lowercase : int = key.split(""".""" )
__lowercase : List[str] = int(key_split[2] )
__lowercase : Optional[int] = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
__lowercase : Dict = val[:dim, :]
__lowercase : Union[str, Any] = val[
dim : dim * 2, :
]
__lowercase : Union[str, Any] = val[-dim:, :]
else:
__lowercase : str = val[:dim]
__lowercase : List[str] = val[dim : dim * 2]
__lowercase : Any = val[-dim:]
else:
__lowercase : List[str] = val
return orig_state_dict
def snake_case_ ( ):
__lowercase : str = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowercase : List[str] = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
return im
@torch.no_grad()
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str , lowerCAmelCase_ : str , lowerCAmelCase_ : bool = False ):
__lowercase : Optional[int] = get_yolos_config(lowerCAmelCase_ )
# load original state_dict
__lowercase : Any = torch.load(lowerCAmelCase_ , map_location="""cpu""" )["""model"""]
# load 🤗 model
__lowercase : Union[str, Any] = YolosForObjectDetection(lowerCAmelCase_ )
model.eval()
__lowercase : str = convert_state_dict(lowerCAmelCase_ , lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
# Check outputs on an image, prepared by YolosImageProcessor
__lowercase : str = 800 if yolos_name != """yolos_ti""" else 512
__lowercase : Dict = YolosImageProcessor(format="""coco_detection""" , size=lowerCAmelCase_ )
__lowercase : Dict = image_processor(images=prepare_img() , return_tensors="""pt""" )
__lowercase : Optional[int] = model(**lowerCAmelCase_ )
__lowercase , __lowercase : Tuple = outputs.logits, outputs.pred_boxes
__lowercase , __lowercase : Optional[Any] = None, None
if yolos_name == "yolos_ti":
__lowercase : Any = torch.tensor(
[[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]] )
__lowercase : Dict = torch.tensor(
[[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]] )
elif yolos_name == "yolos_s_200_pre":
__lowercase : List[Any] = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] )
__lowercase : Dict = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] )
elif yolos_name == "yolos_s_300_pre":
__lowercase : List[str] = torch.tensor(
[[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]] )
__lowercase : List[str] = torch.tensor(
[[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]] )
elif yolos_name == "yolos_s_dWr":
__lowercase : str = torch.tensor(
[[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]] )
__lowercase : List[str] = torch.tensor(
[[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]] )
elif yolos_name == "yolos_base":
__lowercase : List[Any] = torch.tensor(
[[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]] )
__lowercase : Optional[Any] = torch.tensor(
[[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]] )
else:
raise ValueError(F"Unknown yolos_name: {yolos_name}" )
assert torch.allclose(logits[0, :3, :3] , lowerCAmelCase_ , atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , lowerCAmelCase_ , atol=1e-4 )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
print(F"Saving model {yolos_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCAmelCase_ )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(lowerCAmelCase_ )
if push_to_hub:
__lowercase : Any = {
"""yolos_ti""": """yolos-tiny""",
"""yolos_s_200_pre""": """yolos-small""",
"""yolos_s_300_pre""": """yolos-small-300""",
"""yolos_s_dWr""": """yolos-small-dwr""",
"""yolos_base""": """yolos-base""",
}
print("""Pushing to the hub...""" )
__lowercase : List[str] = model_mapping[yolos_name]
image_processor.push_to_hub(lowerCAmelCase_ , organization="""hustvl""" )
model.push_to_hub(lowerCAmelCase_ , organization="""hustvl""" )
if __name__ == "__main__":
lowerCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--yolos_name''',
default='''yolos_s_200_pre''',
type=str,
help=(
'''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
lowerCamelCase : Optional[Any] = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub) | 149 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__)
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True])
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : int = tmp_path / "cache"
lowerCamelCase_ : Optional[int] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCamelCase_ : List[str] = ParquetDatasetReader(lowercase__ , cache_dir=lowercase__ , keep_in_memory=lowercase__).read()
_check_parquet_dataset(lowercase__ , lowercase__)
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = tmp_path / "cache"
lowerCamelCase_ : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase_ : int = features.copy() if features else default_expected_features
lowerCamelCase_ : Union[str, Any] = (
Features({feature: Value(lowercase__) for feature, dtype in features.items()}) if features is not None else None
)
lowerCamelCase_ : List[Any] = ParquetDatasetReader(lowercase__ , features=lowercase__ , cache_dir=lowercase__).read()
_check_parquet_dataset(lowercase__ , lowercase__)
@pytest.mark.parametrize("split" , [None, NamedSplit("train"), "train", "test"])
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : int = tmp_path / "cache"
lowerCamelCase_ : Any = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase_ : Tuple = ParquetDatasetReader(lowercase__ , cache_dir=lowercase__ , split=lowercase__).read()
_check_parquet_dataset(lowercase__ , lowercase__)
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list])
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
if issubclass(lowercase__ , lowercase__):
lowerCamelCase_ : Dict = parquet_path
elif issubclass(lowercase__ , lowercase__):
lowerCamelCase_ : int = [parquet_path]
lowerCamelCase_ : Optional[int] = tmp_path / "cache"
lowerCamelCase_ : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase_ : str = ParquetDatasetReader(lowercase__ , cache_dir=lowercase__).read()
_check_parquet_dataset(lowercase__ , lowercase__)
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=("train",)):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__)
for split in splits:
lowerCamelCase_ : int = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True])
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = tmp_path / "cache"
lowerCamelCase_ : Tuple = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCamelCase_ : str = ParquetDatasetReader(
{"train": parquet_path} , cache_dir=lowercase__ , keep_in_memory=lowercase__).read()
_check_parquet_datasetdict(lowercase__ , lowercase__)
@pytest.mark.parametrize(
"features" , [
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
] , )
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : List[Any] = tmp_path / "cache"
lowerCamelCase_ : List[str] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase_ : Any = features.copy() if features else default_expected_features
lowerCamelCase_ : int = (
Features({feature: Value(lowercase__) for feature, dtype in features.items()}) if features is not None else None
)
lowerCamelCase_ : Optional[int] = ParquetDatasetReader({"train": parquet_path} , features=lowercase__ , cache_dir=lowercase__).read()
_check_parquet_datasetdict(lowercase__ , lowercase__)
@pytest.mark.parametrize("split" , [None, NamedSplit("train"), "train", "test"])
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
if split:
lowerCamelCase_ : List[str] = {split: parquet_path}
else:
lowerCamelCase_ : Dict = "train"
lowerCamelCase_ : Dict = {"train": parquet_path, "test": parquet_path}
lowerCamelCase_ : Union[str, Any] = tmp_path / "cache"
lowerCamelCase_ : Union[str, Any] = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
lowerCamelCase_ : Optional[Any] = ParquetDatasetReader(lowercase__ , cache_dir=lowercase__).read()
_check_parquet_datasetdict(lowercase__ , lowercase__ , splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : str = ParquetDatasetWriter(lowercase__ , tmp_path / "foo.parquet")
assert writer.write() > 0
lowerCamelCase_ : Union[str, Any] = pq.ParquetFile(tmp_path / "foo.parquet")
lowerCamelCase_ : Dict = pf.read()
assert dataset.data.table == output_table
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : str = str(shared_datadir / "test_image_rgb.jpg")
lowerCamelCase_ : Tuple = {"image": [image_path]}
lowerCamelCase_ : int = Features({"image": Image()})
lowerCamelCase_ : List[Any] = Dataset.from_dict(lowercase__ , features=lowercase__)
lowerCamelCase_ : Optional[int] = ParquetDatasetWriter(lowercase__ , tmp_path / "foo.parquet")
assert writer.write() > 0
lowerCamelCase_ : Any = Dataset.from_parquet(str(tmp_path / "foo.parquet"))
assert dataset.features == reloaded_dataset.features
lowerCamelCase_ : Dict = ParquetDatasetReader(str(tmp_path / "foo.parquet") , streaming=lowercase__).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected" , [
(Features({"foo": Value("int32")}), None),
(Features({"image": Image(), "foo": Value("int32")}), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio())}), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
assert get_writer_batch_size(lowercase__) == expected
| 702 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Any = inspect.getfile(accelerate.test_utils )
__UpperCAmelCase : Union[str, Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
__UpperCAmelCase : Tuple = ['''accelerate''', '''launch''']
__UpperCAmelCase : Dict = Path.home() / '''.cache/huggingface/accelerate'''
__UpperCAmelCase : int = '''default_config.yaml'''
__UpperCAmelCase : Tuple = config_folder / config_file
__UpperCAmelCase : int = config_folder / '''_default_config.yaml'''
__UpperCAmelCase : int = Path('''tests/test_configs''' )
@classmethod
def _UpperCamelCase ( cls ):
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def _UpperCamelCase ( cls ):
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def _UpperCamelCase ( self ):
lowerCamelCase_ : List[Any] = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def _UpperCamelCase ( self ):
for config in sorted(self.test_config_path.glob("**/*.yaml" ) ):
with self.subTest(config_file=a_ ):
execute_subprocess_async(
self.base_cmd + ["--config_file", str(a_ ), self.test_file_path] , env=os.environ.copy() )
def _UpperCamelCase ( self ):
execute_subprocess_async(["accelerate", "test"] , env=os.environ.copy() )
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = '''test-tpu'''
__UpperCAmelCase : Tuple = '''us-central1-a'''
__UpperCAmelCase : Tuple = '''ls'''
__UpperCAmelCase : str = ['''accelerate''', '''tpu-config''']
__UpperCAmelCase : Dict = '''cd /usr/share'''
__UpperCAmelCase : Any = '''tests/test_samples/test_command_file.sh'''
__UpperCAmelCase : Dict = '''Running gcloud compute tpus tpu-vm ssh'''
def _UpperCamelCase ( self ):
lowerCamelCase_ : Any = run_command(
self.cmd
+ ["--command", self.command, "--tpu_zone", self.tpu_zone, "--tpu_name", self.tpu_name, "--debug"] , return_stdout=a_ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , a_ , )
def _UpperCamelCase ( self ):
lowerCamelCase_ : Tuple = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command",
self.command,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
] , return_stdout=a_ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , a_ , )
def _UpperCamelCase ( self ):
lowerCamelCase_ : Union[str, Any] = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--debug"] , return_stdout=a_ )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , a_ , )
def _UpperCamelCase ( self ):
lowerCamelCase_ : Any = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--command", self.command, "--debug"] , return_stdout=a_ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" , a_ , )
def _UpperCamelCase ( self ):
lowerCamelCase_ : List[Any] = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--command",
self.command,
"--command",
"echo \"Hello World\"",
"--debug",
] , return_stdout=a_ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all""" , a_ , )
def _UpperCamelCase ( self ):
lowerCamelCase_ : List[str] = run_command(
self.cmd
+ ["--config_file", "tests/test_configs/latest.yaml", "--command_file", self.command_file, "--debug"] , return_stdout=a_ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , a_ , )
def _UpperCamelCase ( self ):
lowerCamelCase_ : Dict = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command_file",
self.command_file,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
] , return_stdout=a_ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" , a_ , )
def _UpperCamelCase ( self ):
lowerCamelCase_ : str = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--install_accelerate", "--debug"] , return_stdout=a_ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all""" , a_ , )
def _UpperCamelCase ( self ):
lowerCamelCase_ : Any = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--install_accelerate",
"--accelerate_version",
"12.0.0",
"--debug",
] , return_stdout=a_ , )
self.assertIn(
F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all""" , a_ , )
| 73 | 0 |
'''simple docstring'''
import math
lowercase__ : List[str] = 10
lowercase__ : Optional[Any] = 7
lowercase__ : List[str] = BALLS_PER_COLOUR * NUM_COLOURS
def __lowerCamelCase ( _UpperCamelCase : int = 20 ):
'''simple docstring'''
UpperCAmelCase_ = math.comb(_UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ = math.comb(NUM_BALLS - BALLS_PER_COLOUR , _UpperCamelCase )
UpperCAmelCase_ = NUM_COLOURS * (1 - missing_colour / total)
return F"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 390 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, 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,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class a__ :
def __init__( self : List[Any] ,a__ : str ,a__ : Optional[Any]=13 ,a__ : Union[str, Any]=7 ,a__ : str=True ,a__ : Union[str, Any]=True ,a__ : Dict=True ,a__ : Optional[int]=True ,a__ : Optional[int]=99 ,a__ : Dict=64 ,a__ : Union[str, Any]=32 ,a__ : Union[str, Any]=5 ,a__ : Union[str, Any]=4 ,a__ : str=37 ,a__ : int="gelu" ,a__ : str=0.1 ,a__ : int=0.1 ,a__ : Optional[int]=512 ,a__ : Dict=16 ,a__ : Any=2 ,a__ : str=0.02 ,a__ : List[str]=3 ,a__ : Dict=4 ,a__ : Optional[Any]=None ,) -> int:
"""simple docstring"""
_lowerCAmelCase:Tuple = parent
_lowerCAmelCase:Union[str, Any] = batch_size
_lowerCAmelCase:Dict = seq_length
_lowerCAmelCase:Union[str, Any] = is_training
_lowerCAmelCase:Any = use_input_mask
_lowerCAmelCase:Dict = use_token_type_ids
_lowerCAmelCase:Optional[int] = use_labels
_lowerCAmelCase:List[Any] = vocab_size
_lowerCAmelCase:int = hidden_size
_lowerCAmelCase:Optional[int] = embedding_size
_lowerCAmelCase:List[Any] = num_hidden_layers
_lowerCAmelCase:Optional[Any] = num_attention_heads
_lowerCAmelCase:List[str] = intermediate_size
_lowerCAmelCase:List[str] = hidden_act
_lowerCAmelCase:Any = hidden_dropout_prob
_lowerCAmelCase:int = attention_probs_dropout_prob
_lowerCAmelCase:Tuple = max_position_embeddings
_lowerCAmelCase:Optional[Any] = type_vocab_size
_lowerCAmelCase:Any = type_sequence_label_size
_lowerCAmelCase:Optional[int] = initializer_range
_lowerCAmelCase:List[str] = num_labels
_lowerCAmelCase:List[str] = num_choices
_lowerCAmelCase:int = scope
def __UpperCamelCase ( self : Union[str, Any]) -> List[str]:
"""simple docstring"""
_lowerCAmelCase:Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size)
_lowerCAmelCase:Optional[int] = None
if self.use_input_mask:
_lowerCAmelCase:int = random_attention_mask([self.batch_size, self.seq_length])
_lowerCAmelCase:str = None
if self.use_token_type_ids:
_lowerCAmelCase:Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size)
_lowerCAmelCase:Tuple = None
_lowerCAmelCase:Optional[Any] = None
_lowerCAmelCase:Optional[Any] = None
if self.use_labels:
_lowerCAmelCase:List[str] = ids_tensor([self.batch_size] ,self.type_sequence_label_size)
_lowerCAmelCase:Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels)
_lowerCAmelCase:Dict = ids_tensor([self.batch_size] ,self.num_choices)
_lowerCAmelCase:Union[str, Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Optional[int]) -> List[Any]:
"""simple docstring"""
return MegatronBertConfig(
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=a__ ,initializer_range=self.initializer_range ,)
def __UpperCamelCase ( self : Any ,a__ : int ,a__ : Optional[Any] ,a__ : Dict ,a__ : Optional[Any] ,a__ : Dict ,a__ : int ,a__ : int) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase:List[str] = MegatronBertModel(config=a__)
model.to(a__)
model.eval()
_lowerCAmelCase:Optional[Any] = model(a__ ,attention_mask=a__ ,token_type_ids=a__)
_lowerCAmelCase:int = model(a__ ,token_type_ids=a__)
_lowerCAmelCase:Dict = model(a__)
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 __UpperCamelCase ( self : Union[str, Any] ,a__ : Tuple ,a__ : str ,a__ : List[str] ,a__ : Tuple ,a__ : List[Any] ,a__ : List[str] ,a__ : Dict) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase:Any = MegatronBertForMaskedLM(config=a__)
model.to(a__)
model.eval()
_lowerCAmelCase:List[Any] = model(a__ ,attention_mask=a__ ,token_type_ids=a__ ,labels=a__)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size))
def __UpperCamelCase ( self : Any ,a__ : Any ,a__ : Optional[Any] ,a__ : Tuple ,a__ : List[Any] ,a__ : Dict ,a__ : Tuple ,a__ : Any) -> str:
"""simple docstring"""
_lowerCAmelCase:List[Any] = MegatronBertForCausalLM(config=a__)
model.to(a__)
model.eval()
_lowerCAmelCase:int = model(a__ ,attention_mask=a__ ,token_type_ids=a__ ,labels=a__)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size))
def __UpperCamelCase ( self : List[Any] ,a__ : Union[str, Any] ,a__ : Any ,a__ : int ,a__ : Any ,a__ : int ,a__ : List[str] ,a__ : List[Any]) -> Dict:
"""simple docstring"""
_lowerCAmelCase:Tuple = MegatronBertForNextSentencePrediction(config=a__)
model.to(a__)
model.eval()
_lowerCAmelCase:Dict = model(
a__ ,attention_mask=a__ ,token_type_ids=a__ ,labels=a__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, 2))
def __UpperCamelCase ( self : Optional[int] ,a__ : Optional[int] ,a__ : List[Any] ,a__ : Tuple ,a__ : Optional[int] ,a__ : Any ,a__ : Optional[int] ,a__ : Optional[Any]) -> List[Any]:
"""simple docstring"""
_lowerCAmelCase:str = MegatronBertForPreTraining(config=a__)
model.to(a__)
model.eval()
_lowerCAmelCase:Optional[Any] = model(
a__ ,attention_mask=a__ ,token_type_ids=a__ ,labels=a__ ,next_sentence_label=a__ ,)
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 __UpperCamelCase ( self : Dict ,a__ : Any ,a__ : Any ,a__ : Optional[Any] ,a__ : Tuple ,a__ : Tuple ,a__ : Optional[Any] ,a__ : Union[str, Any]) -> Tuple:
"""simple docstring"""
_lowerCAmelCase:Optional[int] = MegatronBertForQuestionAnswering(config=a__)
model.to(a__)
model.eval()
_lowerCAmelCase:Union[str, Any] = model(
a__ ,attention_mask=a__ ,token_type_ids=a__ ,start_positions=a__ ,end_positions=a__ ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length))
def __UpperCamelCase ( self : Optional[Any] ,a__ : Dict ,a__ : Dict ,a__ : List[Any] ,a__ : int ,a__ : List[str] ,a__ : Union[str, Any] ,a__ : List[Any]) -> int:
"""simple docstring"""
_lowerCAmelCase:Union[str, Any] = self.num_labels
_lowerCAmelCase:str = MegatronBertForSequenceClassification(a__)
model.to(a__)
model.eval()
_lowerCAmelCase:Tuple = model(a__ ,attention_mask=a__ ,token_type_ids=a__ ,labels=a__)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels))
def __UpperCamelCase ( self : Dict ,a__ : Tuple ,a__ : Union[str, Any] ,a__ : List[str] ,a__ : Optional[Any] ,a__ : List[Any] ,a__ : Tuple ,a__ : str) -> List[str]:
"""simple docstring"""
_lowerCAmelCase:Union[str, Any] = self.num_labels
_lowerCAmelCase:Optional[int] = MegatronBertForTokenClassification(config=a__)
model.to(a__)
model.eval()
_lowerCAmelCase:Optional[Any] = model(a__ ,attention_mask=a__ ,token_type_ids=a__ ,labels=a__)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels))
def __UpperCamelCase ( self : Tuple ,a__ : Tuple ,a__ : List[str] ,a__ : List[Any] ,a__ : List[Any] ,a__ : Any ,a__ : List[str] ,a__ : int) -> int:
"""simple docstring"""
_lowerCAmelCase:str = self.num_choices
_lowerCAmelCase:Optional[int] = MegatronBertForMultipleChoice(config=a__)
model.to(a__)
model.eval()
_lowerCAmelCase:List[Any] = input_ids.unsqueeze(1).expand(-1 ,self.num_choices ,-1).contiguous()
_lowerCAmelCase:Union[str, Any] = token_type_ids.unsqueeze(1).expand(-1 ,self.num_choices ,-1).contiguous()
_lowerCAmelCase:Tuple = input_mask.unsqueeze(1).expand(-1 ,self.num_choices ,-1).contiguous()
_lowerCAmelCase:List[Any] = model(
a__ ,attention_mask=a__ ,token_type_ids=a__ ,labels=a__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices))
def __UpperCamelCase ( self : int) -> List[str]:
"""simple docstring"""
_lowerCAmelCase:List[Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
):Union[str, Any] = config_and_inputs
_lowerCAmelCase:List[Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class a__ ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
snake_case__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
snake_case__ = (
{
'''feature-extraction''': MegatronBertModel,
'''fill-mask''': MegatronBertForMaskedLM,
'''question-answering''': MegatronBertForQuestionAnswering,
'''text-classification''': MegatronBertForSequenceClassification,
'''text-generation''': MegatronBertForCausalLM,
'''token-classification''': MegatronBertForTokenClassification,
'''zero-shot''': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case__ = True
# test_resize_embeddings = False
snake_case__ = False
def __UpperCamelCase ( self : Tuple ,a__ : str ,a__ : Any ,a__ : Tuple=False) -> int:
"""simple docstring"""
_lowerCAmelCase:List[Any] = super()._prepare_for_class(a__ ,a__ ,return_labels=a__)
if return_labels:
if model_class in get_values(a__):
_lowerCAmelCase:Any = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) ,dtype=torch.long ,device=a__)
_lowerCAmelCase:Optional[Any] = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=a__)
return inputs_dict
def __UpperCamelCase ( self : Optional[Any]) -> Dict:
"""simple docstring"""
_lowerCAmelCase:Any = MegatronBertModelTester(self)
_lowerCAmelCase:List[str] = ConfigTester(self ,config_class=a__ ,hidden_size=37)
def __UpperCamelCase ( self : str) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Any) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase:Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*a__)
def __UpperCamelCase ( self : Optional[int]) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase:Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*a__)
def __UpperCamelCase ( self : List[str]) -> str:
"""simple docstring"""
_lowerCAmelCase:Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*a__)
def __UpperCamelCase ( self : Tuple) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase:Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*a__)
def __UpperCamelCase ( self : Tuple) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase:int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*a__)
def __UpperCamelCase ( self : Any) -> Dict:
"""simple docstring"""
_lowerCAmelCase:List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*a__)
def __UpperCamelCase ( self : int) -> List[str]:
"""simple docstring"""
_lowerCAmelCase:Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*a__)
def __UpperCamelCase ( self : Optional[Any]) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase:Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*a__)
def UpperCAmelCase ( snake_case : Any ):
return torch.tensor(
snake_case , dtype=torch.long , device=snake_case , )
UpperCamelCase__ = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
@slow
@unittest.skip('''Model is not available.''')
def __UpperCamelCase ( self : List[str]) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase:Any = '''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
_lowerCAmelCase:List[str] = os.path.join(os.environ['''MYDIR'''] ,a__)
_lowerCAmelCase:Optional[Any] = MegatronBertModel.from_pretrained(a__)
model.to(a__)
model.half()
_lowerCAmelCase:int = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]])
with torch.no_grad():
_lowerCAmelCase:List[Any] = model(a__)[0]
_lowerCAmelCase:Dict = torch.Size((1, 9, 1024))
self.assertEqual(output.shape ,a__)
_lowerCAmelCase:Tuple = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3):
for jj in range(3):
_lowerCAmelCase:int = output[0, ii, jj]
_lowerCAmelCase:List[Any] = expected[3 * ii + jj]
_lowerCAmelCase:str = '''ii={} jj={} a={} b={}'''.format(a__ ,a__ ,a__ ,a__)
self.assertTrue(math.isclose(a__ ,a__ ,rel_tol=a__ ,abs_tol=a__) ,msg=a__)
| 227 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : torch.FloatTensor
class lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , lowerCAmelCase__=3 , lowerCAmelCase__=3 , lowerCAmelCase__=("DownEncoderBlock2D",) , lowerCAmelCase__=(64,) , lowerCAmelCase__=2 , lowerCAmelCase__=32 , lowerCAmelCase__="silu" , lowerCAmelCase__=True , ) -> List[str]:
super().__init__()
SCREAMING_SNAKE_CASE = layers_per_block
SCREAMING_SNAKE_CASE = torch.nn.Convad(
lowerCAmelCase__ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = nn.ModuleList([] )
# down
SCREAMING_SNAKE_CASE = block_out_channels[0]
for i, down_block_type in enumerate(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE = output_channel
SCREAMING_SNAKE_CASE = block_out_channels[i]
SCREAMING_SNAKE_CASE = i == len(lowerCAmelCase__ ) - 1
SCREAMING_SNAKE_CASE = get_down_block(
lowerCAmelCase__ , num_layers=self.layers_per_block , in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=lowerCAmelCase__ , resnet_groups=lowerCAmelCase__ , attention_head_dim=lowerCAmelCase__ , temb_channels=lowerCAmelCase__ , )
self.down_blocks.append(lowerCAmelCase__ )
# mid
SCREAMING_SNAKE_CASE = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=lowerCAmelCase__ , output_scale_factor=1 , resnet_time_scale_shift='default' , attention_head_dim=block_out_channels[-1] , resnet_groups=lowerCAmelCase__ , temb_channels=lowerCAmelCase__ , )
# out
SCREAMING_SNAKE_CASE = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=lowerCAmelCase__ , eps=1e-6 )
SCREAMING_SNAKE_CASE = nn.SiLU()
SCREAMING_SNAKE_CASE = 2 * out_channels if double_z else out_channels
SCREAMING_SNAKE_CASE = nn.Convad(block_out_channels[-1] , lowerCAmelCase__ , 3 , padding=1 )
SCREAMING_SNAKE_CASE = False
def __A ( self , lowerCAmelCase__ ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = x
SCREAMING_SNAKE_CASE = self.conv_in(lowerCAmelCase__ )
if self.training and self.gradient_checkpointing:
def create_custom_forward(lowerCAmelCase__ ):
def custom_forward(*lowerCAmelCase__ ):
return module(*lowerCAmelCase__ )
return custom_forward
# down
if is_torch_version('>=' , '1.11.0' ):
for down_block in self.down_blocks:
SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(lowerCAmelCase__ ) , lowerCAmelCase__ , use_reentrant=lowerCAmelCase__ )
# middle
SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowerCAmelCase__ , use_reentrant=lowerCAmelCase__ )
else:
for down_block in self.down_blocks:
SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase__ ) , lowerCAmelCase__ )
# middle
SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , lowerCAmelCase__ )
else:
# down
for down_block in self.down_blocks:
SCREAMING_SNAKE_CASE = down_block(lowerCAmelCase__ )
# middle
SCREAMING_SNAKE_CASE = self.mid_block(lowerCAmelCase__ )
# post-process
SCREAMING_SNAKE_CASE = self.conv_norm_out(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.conv_act(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.conv_out(lowerCAmelCase__ )
return sample
class lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , lowerCAmelCase__=3 , lowerCAmelCase__=3 , lowerCAmelCase__=("UpDecoderBlock2D",) , lowerCAmelCase__=(64,) , lowerCAmelCase__=2 , lowerCAmelCase__=32 , lowerCAmelCase__="silu" , lowerCAmelCase__="group" , ) -> int:
super().__init__()
SCREAMING_SNAKE_CASE = layers_per_block
SCREAMING_SNAKE_CASE = nn.Convad(
lowerCAmelCase__ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = nn.ModuleList([] )
SCREAMING_SNAKE_CASE = in_channels if norm_type == 'spatial' else None
# mid
SCREAMING_SNAKE_CASE = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=lowerCAmelCase__ , output_scale_factor=1 , resnet_time_scale_shift='default' if norm_type == 'group' else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=lowerCAmelCase__ , temb_channels=lowerCAmelCase__ , )
# up
SCREAMING_SNAKE_CASE = list(reversed(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE = reversed_block_out_channels[0]
for i, up_block_type in enumerate(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE = output_channel
SCREAMING_SNAKE_CASE = reversed_block_out_channels[i]
SCREAMING_SNAKE_CASE = i == len(lowerCAmelCase__ ) - 1
SCREAMING_SNAKE_CASE = get_up_block(
lowerCAmelCase__ , num_layers=self.layers_per_block + 1 , in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , prev_output_channel=lowerCAmelCase__ , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=lowerCAmelCase__ , resnet_groups=lowerCAmelCase__ , attention_head_dim=lowerCAmelCase__ , temb_channels=lowerCAmelCase__ , resnet_time_scale_shift=lowerCAmelCase__ , )
self.up_blocks.append(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = output_channel
# out
if norm_type == "spatial":
SCREAMING_SNAKE_CASE = SpatialNorm(block_out_channels[0] , lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=lowerCAmelCase__ , eps=1e-6 )
SCREAMING_SNAKE_CASE = nn.SiLU()
SCREAMING_SNAKE_CASE = nn.Convad(block_out_channels[0] , lowerCAmelCase__ , 3 , padding=1 )
SCREAMING_SNAKE_CASE = False
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__=None ) -> Tuple:
SCREAMING_SNAKE_CASE = z
SCREAMING_SNAKE_CASE = self.conv_in(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(lowerCAmelCase__ ):
def custom_forward(*lowerCAmelCase__ ):
return module(*lowerCAmelCase__ )
return custom_forward
if is_torch_version('>=' , '1.11.0' ):
# middle
SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowerCAmelCase__ , lowerCAmelCase__ , use_reentrant=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sample.to(lowerCAmelCase__ )
# up
for up_block in self.up_blocks:
SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(lowerCAmelCase__ ) , lowerCAmelCase__ , lowerCAmelCase__ , use_reentrant=lowerCAmelCase__ )
else:
# middle
SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sample.to(lowerCAmelCase__ )
# up
for up_block in self.up_blocks:
SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase__ ) , lowerCAmelCase__ , lowerCAmelCase__ )
else:
# middle
SCREAMING_SNAKE_CASE = self.mid_block(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = sample.to(lowerCAmelCase__ )
# up
for up_block in self.up_blocks:
SCREAMING_SNAKE_CASE = up_block(lowerCAmelCase__ , lowerCAmelCase__ )
# post-process
if latent_embeds is None:
SCREAMING_SNAKE_CASE = self.conv_norm_out(lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE = self.conv_norm_out(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.conv_act(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = self.conv_out(lowerCAmelCase__ )
return sample
class lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__="random" , lowerCAmelCase__=False , lowerCAmelCase__=True ) -> int:
super().__init__()
SCREAMING_SNAKE_CASE = n_e
SCREAMING_SNAKE_CASE = vq_embed_dim
SCREAMING_SNAKE_CASE = beta
SCREAMING_SNAKE_CASE = legacy
SCREAMING_SNAKE_CASE = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
SCREAMING_SNAKE_CASE = remap
if self.remap is not None:
self.register_buffer('used' , torch.tensor(np.load(self.remap ) ) )
SCREAMING_SNAKE_CASE = self.used.shape[0]
SCREAMING_SNAKE_CASE = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
SCREAMING_SNAKE_CASE = self.re_embed
SCREAMING_SNAKE_CASE = self.re_embed + 1
print(
F'Remapping {self.n_e} indices to {self.re_embed} indices. '
F'Using {self.unknown_index} for unknown indices.' )
else:
SCREAMING_SNAKE_CASE = n_e
SCREAMING_SNAKE_CASE = sane_index_shape
def __A ( self , lowerCAmelCase__ ) -> Dict:
SCREAMING_SNAKE_CASE = inds.shape
assert len(lowerCAmelCase__ ) > 1
SCREAMING_SNAKE_CASE = inds.reshape(ishape[0] , -1 )
SCREAMING_SNAKE_CASE = self.used.to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = (inds[:, :, None] == used[None, None, ...]).long()
SCREAMING_SNAKE_CASE = match.argmax(-1 )
SCREAMING_SNAKE_CASE = match.sum(2 ) < 1
if self.unknown_index == "random":
SCREAMING_SNAKE_CASE = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
SCREAMING_SNAKE_CASE = self.unknown_index
return new.reshape(lowerCAmelCase__ )
def __A ( self , lowerCAmelCase__ ) -> Optional[int]:
SCREAMING_SNAKE_CASE = inds.shape
assert len(lowerCAmelCase__ ) > 1
SCREAMING_SNAKE_CASE = inds.reshape(ishape[0] , -1 )
SCREAMING_SNAKE_CASE = self.used.to(lowerCAmelCase__ )
if self.re_embed > self.used.shape[0]: # extra token
SCREAMING_SNAKE_CASE = 0 # simply set to zero
SCREAMING_SNAKE_CASE = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , lowerCAmelCase__ )
return back.reshape(lowerCAmelCase__ )
def __A ( self , lowerCAmelCase__ ) -> Any:
# reshape z -> (batch, height, width, channel) and flatten
SCREAMING_SNAKE_CASE = z.permute(0 , 2 , 3 , 1 ).contiguous()
SCREAMING_SNAKE_CASE = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
SCREAMING_SNAKE_CASE = torch.argmin(torch.cdist(lowerCAmelCase__ , self.embedding.weight ) , dim=1 )
SCREAMING_SNAKE_CASE = self.embedding(lowerCAmelCase__ ).view(z.shape )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
# compute loss for embedding
if not self.legacy:
SCREAMING_SNAKE_CASE = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
SCREAMING_SNAKE_CASE = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
SCREAMING_SNAKE_CASE = z + (z_q - z).detach()
# reshape back to match original input shape
SCREAMING_SNAKE_CASE = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
SCREAMING_SNAKE_CASE = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
SCREAMING_SNAKE_CASE = self.remap_to_used(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
SCREAMING_SNAKE_CASE = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
# shape specifying (batch, height, width, channel)
if self.remap is not None:
SCREAMING_SNAKE_CASE = indices.reshape(shape[0] , -1 ) # add batch axis
SCREAMING_SNAKE_CASE = self.unmap_to_all(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
SCREAMING_SNAKE_CASE = self.embedding(lowerCAmelCase__ )
if shape is not None:
SCREAMING_SNAKE_CASE = z_q.view(lowerCAmelCase__ )
# reshape back to match original input shape
SCREAMING_SNAKE_CASE = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=False ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = parameters
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = torch.chunk(lowerCAmelCase__ , 2 , dim=1 )
SCREAMING_SNAKE_CASE = torch.clamp(self.logvar , -30.0 , 20.0 )
SCREAMING_SNAKE_CASE = deterministic
SCREAMING_SNAKE_CASE = torch.exp(0.5 * self.logvar )
SCREAMING_SNAKE_CASE = torch.exp(self.logvar )
if self.deterministic:
SCREAMING_SNAKE_CASE = SCREAMING_SNAKE_CASE = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def __A ( self , lowerCAmelCase__ = None ) -> torch.FloatTensor:
# make sure sample is on the same device as the parameters and has same dtype
SCREAMING_SNAKE_CASE = randn_tensor(
self.mean.shape , generator=lowerCAmelCase__ , device=self.parameters.device , dtype=self.parameters.dtype )
SCREAMING_SNAKE_CASE = self.mean + self.std * sample
return x
def __A ( self , lowerCAmelCase__=None ) -> Union[str, Any]:
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__=[1, 2, 3] ) -> Union[str, Any]:
if self.deterministic:
return torch.Tensor([0.0] )
SCREAMING_SNAKE_CASE = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=lowerCAmelCase__ )
def __A ( self ) -> Dict:
return self.mean
| 721 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowercase () -> List[Any]:
raise RuntimeError('CUDA out of memory.' )
class lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self ) -> Optional[int]:
super().__init__()
SCREAMING_SNAKE_CASE = nn.Linear(3 , 4 )
SCREAMING_SNAKE_CASE = nn.BatchNormad(4 )
SCREAMING_SNAKE_CASE = nn.Linear(4 , 5 )
def __A ( self , lowerCAmelCase__ ) -> Union[str, Any]:
return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase__ ) ) )
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __A ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(lowerCAmelCase__ ):
nonlocal batch_sizes
batch_sizes.append(lowerCAmelCase__ )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(lowerCAmelCase__ , [128, 64, 32, 16, 8] )
def __A ( self ) -> str:
SCREAMING_SNAKE_CASE = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(lowerCAmelCase__ , lowerCAmelCase__ ):
nonlocal batch_sizes
batch_sizes.append(lowerCAmelCase__ )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = mock_training_loop_function('hello' )
self.assertListEqual(lowerCAmelCase__ , [128, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, 'hello'] )
def __A ( self ) -> Optional[Any]:
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(lowerCAmelCase__ ):
pass
with self.assertRaises(lowerCAmelCase__ ) as cm:
mock_training_loop_function()
self.assertIn('No executable batch size found, reached zero.' , cm.exception.args[0] )
def __A ( self ) -> List[Any]:
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(lowerCAmelCase__ ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(lowerCAmelCase__ ) as cm:
mock_training_loop_function()
self.assertIn('No executable batch size found, reached zero.' , cm.exception.args[0] )
def __A ( self ) -> str:
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(lowerCAmelCase__ ) as cm:
mock_training_loop_function(128 , 'hello' , 'world' )
self.assertIn('Batch size was passed into `f`' , cm.exception.args[0] )
self.assertIn('`f(arg1=\'hello\', arg2=\'world\')' , cm.exception.args[0] )
def __A ( self ) -> Optional[int]:
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(lowerCAmelCase__ ):
raise ValueError('Oops, we had an error!' )
with self.assertRaises(lowerCAmelCase__ ) as cm:
mock_training_loop_function()
self.assertIn('Oops, we had an error!' , cm.exception.args[0] )
@require_cuda
def __A ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE = torch.cuda.memory_allocated()
SCREAMING_SNAKE_CASE = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = release_memory(lowerCAmelCase__ )
self.assertEqual(torch.cuda.memory_allocated() , lowerCAmelCase__ )
| 327 | 0 |
"""simple docstring"""
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__ ( A_ ,A_ ,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 UpperCamelCase_ ( self) -> str:
torch.manual_seed(0)
_lowerCamelCase : Optional[int] = 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 , )
_lowerCamelCase : Optional[Any] = DDIMScheduler()
torch.manual_seed(0)
_lowerCamelCase : int = 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)
_lowerCamelCase : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
_lowerCamelCase : Any = CLIPTextModel(SCREAMING_SNAKE_CASE)
_lowerCamelCase : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
_lowerCamelCase : List[str] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=0) -> Tuple:
_lowerCamelCase : Optional[int] = torch.manual_seed(SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[str] = {
"""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 UpperCamelCase_ ( self) -> Optional[int]:
_lowerCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Optional[int] = self.get_dummy_components()
_lowerCamelCase : Dict = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : Union[str, Any] = sd_pipe.to(SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
_lowerCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Union[str, Any] = sd_pipe(**SCREAMING_SNAKE_CASE).images
_lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCamelCase : Any = np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def UpperCamelCase_ ( self) -> Tuple:
super().test_inference_batch_consistent(batch_sizes=[1, 2])
def UpperCamelCase_ ( self) -> int:
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.2_5e-3)
def UpperCamelCase_ ( self) -> Tuple:
_lowerCamelCase : List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : int = self.get_dummy_components()
_lowerCamelCase : Union[str, Any] = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : Tuple = sd_pipe.to(SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
_lowerCamelCase : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_lowerCamelCase : int = """french fries"""
_lowerCamelCase : List[str] = sd_pipe(**SCREAMING_SNAKE_CASE , negative_prompt=SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[Any] = output.images
_lowerCamelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCamelCase : Optional[int] = np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def UpperCamelCase_ ( self) -> Optional[int]:
_lowerCamelCase : int = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Any = self.get_dummy_components()
_lowerCamelCase : Tuple = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : Dict = sd_pipe.to(SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
_lowerCamelCase : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[Any] = sd_pipe(**SCREAMING_SNAKE_CASE , view_batch_size=2)
_lowerCamelCase : List[Any] = output.images
_lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCamelCase : List[str] = np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def UpperCamelCase_ ( self) -> int:
_lowerCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : str = self.get_dummy_components()
_lowerCamelCase : Optional[Any] = EulerAncestralDiscreteScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="""scaled_linear""")
_lowerCamelCase : str = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : Dict = sd_pipe.to(SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_lowerCamelCase : int = sd_pipe(**SCREAMING_SNAKE_CASE).images
_lowerCamelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCamelCase : Dict = np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def UpperCamelCase_ ( self) -> int:
_lowerCamelCase : int = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Tuple = self.get_dummy_components()
_lowerCamelCase : Any = PNDMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="""scaled_linear""" , skip_prk_steps=SCREAMING_SNAKE_CASE)
_lowerCamelCase : Dict = StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[str] = sd_pipe.to(SCREAMING_SNAKE_CASE)
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
_lowerCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Dict = sd_pipe(**SCREAMING_SNAKE_CASE).images
_lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCamelCase : Union[str, Any] = np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
def UpperCamelCase_ ( self) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE=0) -> str:
_lowerCamelCase : List[str] = torch.manual_seed(SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[Any] = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase_ ( self) -> List[str]:
_lowerCamelCase : Tuple = """stabilityai/stable-diffusion-2-base"""
_lowerCamelCase : Optional[Any] = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE , subfolder="""scheduler""")
_lowerCamelCase : Union[str, Any] = StableDiffusionPanoramaPipeline.from_pretrained(SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE , safety_checker=SCREAMING_SNAKE_CASE)
pipe.to(SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing()
_lowerCamelCase : int = self.get_inputs()
_lowerCamelCase : Dict = pipe(**SCREAMING_SNAKE_CASE).images
_lowerCamelCase : Union[str, Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
_lowerCamelCase : Union[str, Any] = np.array(
[
0.36_96_83_92,
0.27_02_53_72,
0.32_44_67_66,
0.28_37_93_87,
0.36_36_32_74,
0.30_73_33_47,
0.27_10_00_27,
0.27_05_41_25,
0.25_53_60_96,
])
assert np.abs(expected_slice - image_slice).max() < 1e-2
def UpperCamelCase_ ( self) -> Dict:
_lowerCamelCase : int = StableDiffusionPanoramaPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-base""" , safety_checker=SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[int] = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing()
_lowerCamelCase : Optional[int] = self.get_inputs()
_lowerCamelCase : str = pipe(**SCREAMING_SNAKE_CASE).images
_lowerCamelCase : int = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
_lowerCamelCase : int = 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 UpperCamelCase_ ( self) -> Any:
_lowerCamelCase : List[str] = 0
def callback_fn(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> None:
_lowerCamelCase : str = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
_lowerCamelCase : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
_lowerCamelCase : Dict = latents[0, -3:, -3:, -1]
_lowerCamelCase : Tuple = np.array(
[
0.18_68_18_69,
0.33_90_78_16,
0.5_36_12_76,
0.14_43_28_65,
-0.02_85_66_11,
-0.73_94_11_23,
0.23_39_79_87,
0.47_32_26_82,
-0.37_82_31_64,
])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
_lowerCamelCase : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
_lowerCamelCase : int = latents[0, -3:, -3:, -1]
_lowerCamelCase : Tuple = np.array(
[
0.18_53_96_45,
0.33_98_72_48,
0.5_37_85_59,
0.14_43_71_42,
-0.02_45_52_61,
-0.7_33_83_17,
0.23_99_07_55,
0.47_35_62_72,
-0.3_78_65_05,
])
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
_lowerCamelCase : Any = False
_lowerCamelCase : Optional[Any] = """stabilityai/stable-diffusion-2-base"""
_lowerCamelCase : Optional[Any] = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE , subfolder="""scheduler""")
_lowerCamelCase : int = StableDiffusionPanoramaPipeline.from_pretrained(SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE , safety_checker=SCREAMING_SNAKE_CASE)
_lowerCamelCase : str = pipe.to(SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing()
_lowerCamelCase : Dict = self.get_inputs()
pipe(**SCREAMING_SNAKE_CASE , callback=SCREAMING_SNAKE_CASE , callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 3
def UpperCamelCase_ ( self) -> Tuple:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCamelCase : Tuple = """stabilityai/stable-diffusion-2-base"""
_lowerCamelCase : List[str] = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE , subfolder="""scheduler""")
_lowerCamelCase : Any = StableDiffusionPanoramaPipeline.from_pretrained(SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE , safety_checker=SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[str] = pipe.to(SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
_lowerCamelCase : int = self.get_inputs()
_lowerCamelCase : int = pipe(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[Any] = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 88 |
"""simple docstring"""
def _snake_case ( __snake_case : str , __snake_case : str ):
"""simple docstring"""
_lowerCamelCase : str = len(__snake_case )
_lowerCamelCase : Union[str, Any] = len(__snake_case )
_lowerCamelCase : int = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_lowerCamelCase : Union[str, Any] = True
for i in range(__snake_case ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
_lowerCamelCase : Tuple = True
if a[i].islower():
_lowerCamelCase : Tuple = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88 | 1 |
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Dict:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(__snake_case , __snake_case ):
raise TypeError("""Input value must be a 'int' type""" )
return bin(__snake_case ).count("""1""" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 705 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
__a: int = get_tests_dir('''fixtures''')
__a: str = get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__a: List[str] = get_tests_dir('''fixtures/dummy-config.json''')
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = 0
def lowerCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def lowerCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def lowerCamelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowerCamelCase ).to_dict()
config_dict.pop("""feature_extractor_type""" )
_UpperCAmelCase = WavaVecaFeatureExtractor(**lowerCamelCase )
# save in new folder
model_config.save_pretrained(lowerCamelCase )
config.save_pretrained(lowerCamelCase )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowerCamelCase )
# make sure private variable is not incorrectly saved
_UpperCAmelCase = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def lowerCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
def lowerCamelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCamelCase , """bert-base is not a local folder and is not a valid model identifier""" ):
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained("""bert-base""" )
def lowerCamelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCamelCase , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowerCamelCase , revision="""aaaaaa""" )
def lowerCamelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
with self.assertRaisesRegex(
lowerCamelCase , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model""" )
def lowerCamelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowerCamelCase ):
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase ):
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=lowerCamelCase )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=lowerCamelCase )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowerCamelCase )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowerCamelCase , trust_remote_code=lowerCamelCase )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
def lowerCamelCase ( self : str ) -> int:
"""simple docstring"""
try:
AutoConfig.register("""custom""" , lowerCamelCase )
AutoFeatureExtractor.register(lowerCamelCase , lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase ):
AutoFeatureExtractor.register(lowerCamelCase , lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
_UpperCAmelCase = CustomFeatureExtractor.from_pretrained(lowerCamelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowerCamelCase )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(lowerCamelCase )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def lowerCamelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
_lowerCamelCase = True
try:
AutoConfig.register("""custom""" , lowerCamelCase )
AutoFeatureExtractor.register(lowerCamelCase , lowerCamelCase )
# If remote code is not set, the default is to use local
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=lowerCamelCase )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=lowerCamelCase )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(not hasattr(lowerCamelCase , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig] | 402 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_A : List[Any] = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_A : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 361 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def __magic_name__ ( __snake_case : float , __snake_case : float , __snake_case : float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
return {"resistance": sqrt(pow(__snake_case , 2 ) - pow(__snake_case , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__snake_case , 2 ) - pow(__snake_case , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__snake_case , 2 ) + pow(__snake_case , 2 ) )}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 361 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( __lowercase ):
return len(set(lowerCamelCase__ ) ) == len(lowerCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a__ ( A__ ):
def __init__( self :List[str] , *_lowerCamelCase :int , **_lowerCamelCase :str ):
'''simple docstring'''
warnings.warn(
'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use VideoMAEImageProcessor instead.' , _lowerCamelCase , )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
| 395 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def A__ ( __lowerCAmelCase : list[list[int]] , __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] , __lowerCAmelCase : int , __lowerCAmelCase : list[list[int]] , ):
lowerCamelCase__ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__lowerCAmelCase ) )
] # the reference grid
lowerCamelCase__ = 1
lowerCamelCase__ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(__lowerCAmelCase ) )
] # the action grid
lowerCamelCase__ = init[0]
lowerCamelCase__ = init[1]
lowerCamelCase__ = 0
lowerCamelCase__ = g + heuristic[x][y] # cost from starting cell to destination cell
lowerCamelCase__ = [[f, g, x, y]]
lowerCamelCase__ = False # flag that is set when search is complete
lowerCamelCase__ = False # flag set if we can't find expand
while not found and not resign:
if len(__lowerCAmelCase ) == 0:
raise ValueError("""Algorithm is unable to find solution""" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
lowerCamelCase__ = cell.pop()
lowerCamelCase__ = next_cell[2]
lowerCamelCase__ = next_cell[3]
lowerCamelCase__ = next_cell[1]
if x == goal[0] and y == goal[1]:
lowerCamelCase__ = True
else:
for i in range(len(__lowerCAmelCase ) ): # to try out different valid actions
lowerCamelCase__ = x + DIRECTIONS[i][0]
lowerCamelCase__ = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(__lowerCAmelCase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
lowerCamelCase__ = g + cost
lowerCamelCase__ = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
lowerCamelCase__ = 1
lowerCamelCase__ = i
lowerCamelCase__ = []
lowerCamelCase__ = goal[0]
lowerCamelCase__ = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
lowerCamelCase__ = x - DIRECTIONS[action[x][y]][0]
lowerCamelCase__ = y - DIRECTIONS[action[x][y]][1]
lowerCamelCase__ = xa
lowerCamelCase__ = ya
invpath.append([x, y] )
lowerCamelCase__ = []
for i in range(len(__lowerCAmelCase ) ):
path.append(invpath[len(__lowerCAmelCase ) - 1 - i] )
return path, action
if __name__ == "__main__":
UpperCamelCase : List[Any] = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
UpperCamelCase : List[Any] = [0, 0]
# all coordinates are given in format [y,x]
UpperCamelCase : int = [len(grid) - 1, len(grid[0]) - 1]
UpperCamelCase : Dict = 1
# the cost map which pushes the path closer to the goal
UpperCamelCase : Tuple = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
UpperCamelCase : str = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
UpperCamelCase : Any = 99
UpperCamelCase , UpperCamelCase : int = search(grid, init, goal, cost, heuristic)
print('ACTION MAP')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 50 | """simple docstring"""
def lowercase ( a__ : float , a__ : int ) -> float:
if digit_amount > 0:
return round(number - int(a__ ) , a__ )
return number - int(a__ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.3_45, 1))
print(decimal_isolate(35.3_45, 2))
print(decimal_isolate(35.3_45, 3))
print(decimal_isolate(-14.7_89, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.1_23, 1))
print(decimal_isolate(-14.1_23, 2))
print(decimal_isolate(-14.1_23, 3))
| 420 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_A : Dict = """speech_to_text"""
_A : Any = ["""past_key_values"""]
_A : str = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__(self , lowerCAmelCase_=10000 , lowerCAmelCase_=12 , lowerCAmelCase_=2048 , lowerCAmelCase_=4 , lowerCAmelCase_=6 , lowerCAmelCase_=2048 , lowerCAmelCase_=4 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_="relu" , lowerCAmelCase_=256 , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.02 , lowerCAmelCase_=2 , lowerCAmelCase_=True , lowerCAmelCase_=1 , lowerCAmelCase_=0 , lowerCAmelCase_=2 , lowerCAmelCase_=6000 , lowerCAmelCase_=1024 , lowerCAmelCase_=2 , lowerCAmelCase_=(5, 5) , lowerCAmelCase_=1024 , lowerCAmelCase_=80 , lowerCAmelCase_=1 , **lowerCAmelCase_ , ):
A_ : Union[str, Any] = vocab_size
A_ : List[str] = d_model
A_ : int = encoder_ffn_dim
A_ : Tuple = encoder_layers
A_ : Tuple = encoder_attention_heads
A_ : Dict = decoder_ffn_dim
A_ : List[str] = decoder_layers
A_ : Dict = decoder_attention_heads
A_ : List[str] = dropout
A_ : int = attention_dropout
A_ : int = activation_dropout
A_ : List[str] = activation_function
A_ : List[str] = init_std
A_ : List[str] = encoder_layerdrop
A_ : Any = decoder_layerdrop
A_ : str = use_cache
A_ : Dict = encoder_layers
A_ : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : List[Any] = max_source_positions
A_ : Optional[int] = max_target_positions
A_ : List[Any] = num_conv_layers
A_ : Union[str, Any] = list(lowerCAmelCase_ )
A_ : Tuple = conv_channels
A_ : Dict = input_feat_per_channel
A_ : Union[str, Any] = input_channels
if len(self.conv_kernel_sizes ) != self.num_conv_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` """
f"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes )}`, """
f"""`config.num_conv_layers = {self.num_conv_layers}`.""" )
super().__init__(
pad_token_id=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , is_encoder_decoder=lowerCAmelCase_ , decoder_start_token_id=lowerCAmelCase_ , **lowerCAmelCase_ , )
| 480 |
"""simple docstring"""
def __UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(snake_case__ ) )
def __UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
# Base Case
if index == len(snake_case__ ):
return True
# Recursive Step
for i in range(snake_case__ ):
if valid_coloring(graph[index] , snake_case__ , snake_case__ ):
# Color current vertex
A_ : Dict = i
# Validate coloring
if util_color(snake_case__ , snake_case__ , snake_case__ , index + 1 ):
return True
# Backtrack
A_ : Union[str, Any] = -1
return False
def __UpperCamelCase ( snake_case__ , snake_case__ ):
A_ : int = [-1] * len(snake_case__ )
if util_color(snake_case__ , snake_case__ , snake_case__ , 0 ):
return colored_vertices
return []
| 480 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
# See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE_ ):
snake_case__ = "donut-swin"
snake_case__ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Any , __SCREAMING_SNAKE_CASE : List[str]=224 , __SCREAMING_SNAKE_CASE : Optional[Any]=4 , __SCREAMING_SNAKE_CASE : Dict=3 , __SCREAMING_SNAKE_CASE : Optional[Any]=96 , __SCREAMING_SNAKE_CASE : Optional[int]=[2, 2, 6, 2] , __SCREAMING_SNAKE_CASE : Optional[int]=[3, 6, 12, 24] , __SCREAMING_SNAKE_CASE : Tuple=7 , __SCREAMING_SNAKE_CASE : Union[str, Any]=4.0 , __SCREAMING_SNAKE_CASE : Any=True , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , __SCREAMING_SNAKE_CASE : List[Any]=0.0 , __SCREAMING_SNAKE_CASE : str=0.1 , __SCREAMING_SNAKE_CASE : List[str]="gelu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=False , __SCREAMING_SNAKE_CASE : str=0.02 , __SCREAMING_SNAKE_CASE : List[Any]=1e-5 , **__SCREAMING_SNAKE_CASE : str , ) -> Optional[int]:
super().__init__(**__SCREAMING_SNAKE_CASE )
a_ : str = image_size
a_ : Optional[Any] = patch_size
a_ : List[str] = num_channels
a_ : Any = embed_dim
a_ : Optional[int] = depths
a_ : Any = len(__SCREAMING_SNAKE_CASE )
a_ : Tuple = num_heads
a_ : Optional[Any] = window_size
a_ : Union[str, Any] = mlp_ratio
a_ : int = qkv_bias
a_ : Optional[int] = hidden_dropout_prob
a_ : Dict = attention_probs_dropout_prob
a_ : Union[str, Any] = drop_path_rate
a_ : Tuple = hidden_act
a_ : List[Any] = use_absolute_embeddings
a_ : Optional[int] = layer_norm_eps
a_ : Optional[int] = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
a_ : List[str] = int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE ) - 1) )
| 466 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
__lowerCAmelCase = object()
def _UpperCAmelCase ( __A : List[str] , __A : Tuple ):
a_ : List[Any] = tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(__A ) - len(__A ) + 1 ):
a_ : Union[str, Any] = [x.match(__A ) for x, y in zip(__A , ks[i:] )]
if matches and all(__A ):
return True
return False
def _UpperCAmelCase ( __A : List[str] ):
def replace(__A : int , __A : Union[str, Any] ):
for rule, replacement in rules:
if _match(__A , __A ):
return replacement
return val
return replace
def _UpperCAmelCase ( ):
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , __A )),
(("transformer", "wte", "embedding"), P('''mp''' , __A )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__A , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , __A )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__A , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , __A )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def _UpperCAmelCase ( __A : Union[str, Any] ):
a_ : Tuple = _get_partition_rules()
a_ : Tuple = _replacement_rules(__A )
a_ : Optional[Any] = {k: _unmatched for k in flatten_dict(__A )}
a_ : Optional[Any] = {k: replace(__A , __A ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__A ) )
| 466 | 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowercase_ ( _lowercase : bool = True , *_lowercase : Union[str, Any] , **_lowercase : Tuple ):
'''simple docstring'''
if not is_tqdm_available():
raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`." )
UpperCAmelCase : List[Any] = False
if main_process_only:
UpperCAmelCase : int = PartialState().local_process_index == 0
return _tqdm(*_lowercase , **_lowercase , disable=_lowercase )
| 292 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
snake_case_ : Tuple = {"""vocab_file""": """sentencepiece.bpe.model"""}
snake_case_ : List[str] = {
"""vocab_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
}
}
snake_case_ : List[Any] = {
"""camembert-base""": 5_1_2,
}
snake_case_ : Any = """▁"""
class snake_case__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE__ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ = ['''input_ids''', '''attention_mask''']
def __init__( self : int , lowercase : Union[str, Any] , lowercase : str="<s>" , lowercase : str="</s>" , lowercase : Optional[int]="</s>" , lowercase : Dict="<s>" , lowercase : Optional[Any]="<unk>" , lowercase : List[Any]="<pad>" , lowercase : Any="<mask>" , lowercase : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] , lowercase : Optional[Dict[str, Any]] = None , **lowercase : Dict , ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else mask_token
UpperCAmelCase : int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , sep_token=lowercase , cls_token=lowercase , pad_token=lowercase , mask_token=lowercase , additional_special_tokens=lowercase , sp_model_kwargs=self.sp_model_kwargs , **lowercase , )
UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowercase ) )
UpperCAmelCase : str = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
UpperCAmelCase : Union[str, Any] = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
UpperCAmelCase : Union[str, Any] = len(self.fairseq_tokens_to_ids )
UpperCAmelCase : Dict = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
UpperCAmelCase : List[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __lowerCAmelCase ( self : Union[str, Any] , lowercase : List[int] , lowercase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase : Dict = [self.cls_token_id]
UpperCAmelCase : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __lowerCAmelCase ( self : Dict , lowercase : List[int] , lowercase : Optional[List[int]] = None , lowercase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
if token_ids_a is None:
return [1] + ([0] * len(lowercase )) + [1]
return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
def __lowerCAmelCase ( self : Tuple , lowercase : List[int] , lowercase : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = [self.sep_token_id]
UpperCAmelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def __lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase : List[Any] = {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowerCAmelCase ( self : Optional[Any] , lowercase : str ):
'''simple docstring'''
return self.sp_model.encode(lowercase , out_type=lowercase )
def __lowerCAmelCase ( self : int , lowercase : Optional[Any] ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(lowercase ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(lowercase )
def __lowerCAmelCase ( self : Any , lowercase : Dict ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __lowerCAmelCase ( self : Tuple , lowercase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase : Tuple = []
UpperCAmelCase : Tuple = ""
UpperCAmelCase : Union[str, Any] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowercase ) + token
UpperCAmelCase : Optional[int] = True
UpperCAmelCase : str = []
else:
current_sub_tokens.append(lowercase )
UpperCAmelCase : Any = False
out_string += self.sp_model.decode(lowercase )
return out_string.strip()
def __getstate__( self : str ):
'''simple docstring'''
UpperCAmelCase : List[str] = self.__dict__.copy()
UpperCAmelCase : Dict = None
return state
def __setstate__( self : List[str] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase : List[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase : Any = {}
UpperCAmelCase : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self : Optional[Any] , lowercase : str , lowercase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowercase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase : Optional[Any] = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase , "wb" ) as fi:
UpperCAmelCase : List[Any] = self.sp_model.serialized_model_proto()
fi.write(lowercase )
return (out_vocab_file,)
| 292 | 1 |
'''simple docstring'''
import operator as op
def a ( _UpperCAmelCase ) -> str:
"""simple docstring"""
a_ = []
a_ = lambda _UpperCAmelCase , _UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation
a_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(1_2 ) , 'Stack' , sep=' | ' )
print('-' * (3_0 + len(__lowerCamelCase )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(__lowerCamelCase ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(1_2 ) , ','.join(__lowerCamelCase ) , sep=' | ' )
else:
a_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(1_2 ) , ','.join(__lowerCamelCase ) , sep=' | ' )
a_ = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(1_2 ) , ','.join(__lowerCamelCase ) , sep=' | ' )
stack.append(
str(opr[x](int(__lowerCamelCase ) , int(__lowerCamelCase ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(1_2 ) , ','.join(__lowerCamelCase ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
__lowerCAmelCase =input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| 697 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __lowerCAmelCase ( enum.Enum ):
UpperCamelCase__ = 0
UpperCamelCase__ = 1
@add_end_docstrings(__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''generated'''
def __init__( self :Any , *__magic_name__ :Tuple , **__magic_name__ :Tuple ):
'''simple docstring'''
super().__init__(*__magic_name__ , **__magic_name__ )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == """tf"""
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def lowerCamelCase__ ( self :Optional[int] , __magic_name__ :Any=None , __magic_name__ :Optional[Any]=None , __magic_name__ :Any=None , __magic_name__ :List[str]=None , __magic_name__ :Tuple=None , __magic_name__ :str=None , **__magic_name__ :List[Any] , ):
'''simple docstring'''
a = {}
if truncation is not None:
a = truncation
a = generate_kwargs
a = {}
if return_tensors is not None and return_type is None:
a = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
a = return_type
if clean_up_tokenization_spaces is not None:
a = clean_up_tokenization_spaces
if stop_sequence is not None:
a = self.tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
if len(__magic_name__ ) > 1:
warnings.warn(
"""Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
""" the stop sequence will be used as the stop sequence string in the interim.""" )
a = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def lowerCamelCase__ ( self :Optional[int] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
'''simple docstring'''
return True
def lowerCamelCase__ ( self :Dict , *__magic_name__ :Optional[int] , __magic_name__ :List[str] ):
'''simple docstring'''
a = self.model.config.prefix if self.model.config.prefix is not None else """"""
if isinstance(args[0] , __magic_name__ ):
if self.tokenizer.pad_token_id is None:
raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input""" )
a = ([prefix + arg for arg in args[0]],)
a = True
elif isinstance(args[0] , __magic_name__ ):
a = (prefix + args[0],)
a = False
else:
raise ValueError(
F' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`' )
a = self.tokenizer(*__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self :Tuple , *__magic_name__ :Any , **__magic_name__ :str ):
'''simple docstring'''
a = super().__call__(*__magic_name__ , **__magic_name__ )
if (
isinstance(args[0] , __magic_name__ )
and all(isinstance(__magic_name__ , __magic_name__ ) for el in args[0] )
and all(len(__magic_name__ ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def lowerCamelCase__ ( self :Dict , __magic_name__ :Optional[Any] , __magic_name__ :List[str]=TruncationStrategy.DO_NOT_TRUNCATE , **__magic_name__ :Any ):
'''simple docstring'''
a = self._parse_and_tokenize(__magic_name__ , truncation=__magic_name__ , **__magic_name__ )
return inputs
def lowerCamelCase__ ( self :Any , __magic_name__ :int , **__magic_name__ :int ):
'''simple docstring'''
if self.framework == "pt":
a , a = model_inputs["""input_ids"""].shape
elif self.framework == "tf":
a , a = tf.shape(model_inputs["""input_ids"""] ).numpy()
a = generate_kwargs.get("""min_length""" , self.model.config.min_length )
a = generate_kwargs.get("""max_length""" , self.model.config.max_length )
self.check_inputs(__magic_name__ , generate_kwargs["""min_length"""] , generate_kwargs["""max_length"""] )
a = self.model.generate(**__magic_name__ , **__magic_name__ )
a = output_ids.shape[0]
if self.framework == "pt":
a = output_ids.reshape(__magic_name__ , out_b // in_b , *output_ids.shape[1:] )
elif self.framework == "tf":
a = tf.reshape(__magic_name__ , (in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def lowerCamelCase__ ( self :Optional[int] , __magic_name__ :Dict , __magic_name__ :Any=ReturnType.TEXT , __magic_name__ :int=False ):
'''simple docstring'''
a = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
a = {F'{self.return_name}_token_ids': output_ids}
elif return_type == ReturnType.TEXT:
a = {
F'{self.return_name}_text': self.tokenizer.decode(
__magic_name__ , skip_special_tokens=__magic_name__ , clean_up_tokenization_spaces=__magic_name__ , )
}
records.append(__magic_name__ )
return records
@add_end_docstrings(__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''summary'''
def __call__( self :Any , *__magic_name__ :List[str] , **__magic_name__ :Optional[int] ):
'''simple docstring'''
return super().__call__(*__magic_name__ , **__magic_name__ )
def lowerCamelCase__ ( self :Any , __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
'''simple docstring'''
if max_length < min_length:
logger.warning(F'Your min_length={min_length} must be inferior than your max_length={max_length}.' )
if input_length < max_length:
logger.warning(
F'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
"""a summarization task, where outputs shorter than the input are typically wanted, you might """
F'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})' )
@add_end_docstrings(__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''translation'''
def lowerCamelCase__ ( self :List[Any] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
'''simple docstring'''
if input_length > 0.9 * max_length:
logger.warning(
F'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
"""increasing your max_length manually, e.g. translator('...', max_length=400)""" )
return True
def lowerCamelCase__ ( self :str , *__magic_name__ :Union[str, Any] , __magic_name__ :Any=TruncationStrategy.DO_NOT_TRUNCATE , __magic_name__ :Optional[Any]=None , __magic_name__ :List[str]=None ):
'''simple docstring'''
if getattr(self.tokenizer , """_build_translation_inputs""" , __magic_name__ ):
return self.tokenizer._build_translation_inputs(
*__magic_name__ , return_tensors=self.framework , truncation=__magic_name__ , src_lang=__magic_name__ , tgt_lang=__magic_name__ )
else:
return super()._parse_and_tokenize(*__magic_name__ , truncation=__magic_name__ )
def lowerCamelCase__ ( self :int , __magic_name__ :List[str]=None , __magic_name__ :Union[str, Any]=None , **__magic_name__ :Optional[int] ):
'''simple docstring'''
a , a , a = super()._sanitize_parameters(**__magic_name__ )
if src_lang is not None:
a = src_lang
if tgt_lang is not None:
a = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
a = kwargs.get("""task""" , self.task )
a = task.split("""_""" )
if task and len(__magic_name__ ) == 4:
# translation, XX, to YY
a = items[1]
a = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self :Optional[Any] , *__magic_name__ :Any , **__magic_name__ :str ):
'''simple docstring'''
return super().__call__(*__magic_name__ , **__magic_name__ )
| 468 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class a (unittest.TestCase ):
"""simple docstring"""
@slow
def __snake_case ( self : Any ) -> Any:
__snake_case : List[str] = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small" , return_dict=_A ).to(_A )
__snake_case : Tuple = AutoTokenizer.from_pretrained("google/mt5-small" )
__snake_case : Any = tokenizer("Hello there" , return_tensors="pt" ).input_ids
__snake_case : str = tokenizer("Hi I am" , return_tensors="pt" ).input_ids
__snake_case : Optional[int] = model(input_ids.to(_A ) , labels=labels.to(_A ) ).loss
__snake_case : Dict = -(labels.shape[-1] * loss.item())
__snake_case : Tuple = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 705 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowerCAmelCase_ ( __lowerCamelCase ):
if is_torch_version("<" , "2.0.0" ) or not hasattr(__lowerCamelCase , "_dynamo" ):
return False
return isinstance(__lowerCamelCase , torch._dynamo.eval_frame.OptimizedModule )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase = True ):
__snake_case : Optional[Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__snake_case : Tuple = is_compiled_module(__lowerCamelCase )
if is_compiled:
__snake_case : Tuple = model
__snake_case : List[str] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(__lowerCamelCase , __lowerCamelCase ):
__snake_case : List[Any] = model.module
if not keep_fpaa_wrapper:
__snake_case : Tuple = getattr(__lowerCamelCase , "forward" )
__snake_case : int = model.__dict__.pop("_original_forward" , __lowerCamelCase )
if original_forward is not None:
while hasattr(__lowerCamelCase , "__wrapped__" ):
__snake_case : str = forward.__wrapped__
if forward == original_forward:
break
__snake_case : Dict = forward
if getattr(__lowerCamelCase , "_converted_to_transformer_engine" , __lowerCamelCase ):
convert_model(__lowerCamelCase , to_transformer_engine=__lowerCamelCase )
if is_compiled:
__snake_case : Tuple = model
__snake_case : str = compiled_model
return model
def lowerCAmelCase_ ( ):
PartialState().wait_for_everyone()
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(__lowerCamelCase , __lowerCamelCase )
elif PartialState().local_process_index == 0:
torch.save(__lowerCamelCase , __lowerCamelCase )
@contextmanager
def lowerCAmelCase_ ( **__lowerCamelCase ):
for key, value in kwargs.items():
__snake_case : Union[str, Any] = str(__lowerCamelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowerCAmelCase_ ( __lowerCamelCase ):
if not hasattr(__lowerCamelCase , "__qualname__" ) and not hasattr(__lowerCamelCase , "__name__" ):
__snake_case : Dict = getattr(__lowerCamelCase , "__class__" , __lowerCamelCase )
if hasattr(__lowerCamelCase , "__qualname__" ):
return obj.__qualname__
if hasattr(__lowerCamelCase , "__name__" ):
return obj.__name__
return str(__lowerCamelCase )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
for key, value in source.items():
if isinstance(__lowerCamelCase , __lowerCamelCase ):
__snake_case : Tuple = destination.setdefault(__lowerCamelCase , {} )
merge_dicts(__lowerCamelCase , __lowerCamelCase )
else:
__snake_case : Union[str, Any] = value
return destination
def lowerCAmelCase_ ( __lowerCamelCase = None ):
if port is None:
__snake_case : List[str] = 2_9_5_0_0
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 203 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowercase (unittest.TestCase ):
def __UpperCamelCase ( self : Tuple):
UpperCamelCase__ : Dict = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100])
UpperCamelCase__ : Union[str, Any] = get_activation('gelu')
self.assertTrue(torch.allclose(gelu_python(UpperCAmelCase_) , torch_builtin(UpperCAmelCase_)))
self.assertFalse(torch.allclose(gelu_python(UpperCAmelCase_) , gelu_new(UpperCAmelCase_)))
def __UpperCamelCase ( self : List[Any]):
UpperCamelCase__ : Dict = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100])
UpperCamelCase__ : str = get_activation('gelu')
UpperCamelCase__ : List[Any] = get_activation('gelu_10')
UpperCamelCase__ : str = torch_builtin(UpperCAmelCase_)
UpperCamelCase__ : Optional[Any] = geluaa(UpperCAmelCase_)
UpperCamelCase__ : int = torch.where(y_gelu_aa < 10.0 , 1 , 0)
self.assertTrue(torch.max(UpperCAmelCase_).item() == 10.0)
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask))
def __UpperCamelCase ( self : Any):
get_activation('gelu')
get_activation('gelu_10')
get_activation('gelu_fast')
get_activation('gelu_new')
get_activation('gelu_python')
get_activation('gelu_pytorch_tanh')
get_activation('linear')
get_activation('mish')
get_activation('quick_gelu')
get_activation('relu')
get_activation('sigmoid')
get_activation('silu')
get_activation('swish')
get_activation('tanh')
with self.assertRaises(UpperCAmelCase_):
get_activation('bogus')
with self.assertRaises(UpperCAmelCase_):
get_activation(UpperCAmelCase_)
def __UpperCamelCase ( self : List[str]):
UpperCamelCase__ : Optional[int] = get_activation('gelu')
UpperCamelCase__ : int = 1
UpperCamelCase__ : List[str] = get_activation('gelu')
self.assertEqual(acta.a , 1)
with self.assertRaises(UpperCAmelCase_):
UpperCamelCase__ : List[str] = acta.a
| 596 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __lowercase (__lowerCamelCase ):
def __init__( self : Optional[int] , UpperCAmelCase_ : pyspark.sql.DataFrame , UpperCAmelCase_ : Optional[NamedSplit] = None , UpperCAmelCase_ : Optional[Features] = None , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : str = None , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : str = None , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : str = "arrow" , **UpperCAmelCase_ : str , ):
super().__init__(
split=UpperCAmelCase_ , features=UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , keep_in_memory=UpperCAmelCase_ , streaming=UpperCAmelCase_ , **UpperCAmelCase_ , )
UpperCamelCase__ : Union[str, Any] = load_from_cache_file
UpperCamelCase__ : int = file_format
UpperCamelCase__ : Any = Spark(
df=UpperCAmelCase_ , features=UpperCAmelCase_ , cache_dir=UpperCAmelCase_ , working_dir=UpperCAmelCase_ , **UpperCAmelCase_ , )
def __UpperCamelCase ( self : Optional[int]):
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split)
UpperCamelCase__ : Tuple = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=UpperCAmelCase_ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split)
| 596 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ :Optional[Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
snake_case__ : Optional[int] = XLMProphetNetTokenizer
snake_case__ : List[Any] = False
snake_case__ : Optional[Any] = True
def a_ ( self : List[Any] ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase : int = XLMProphetNetTokenizer(__lowerCamelCase , keep_accents=__lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def a_ ( self : int ):
"""simple docstring"""
__lowerCamelCase : Any = "[PAD]"
__lowerCamelCase : Optional[Any] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase ) , __lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase ) , __lowerCamelCase )
def a_ ( self : List[Any] ):
"""simple docstring"""
__lowerCamelCase : Optional[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """[PAD]""" )
self.assertEqual(vocab_keys[1] , """[CLS]""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(__lowerCamelCase ) , 1012 )
def a_ ( self : Any ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def a_ ( self : Any ):
"""simple docstring"""
__lowerCamelCase : Optional[Any] = XLMProphetNetTokenizer(__lowerCamelCase , keep_accents=__lowerCamelCase )
__lowerCamelCase : Tuple = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__lowerCamelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__lowerCamelCase : List[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__lowerCamelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
__lowerCamelCase : List[str] = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
__lowerCamelCase : str = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""[UNK]""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""[UNK]""",
""".""",
] , )
@cached_property
def a_ ( self : Optional[int] ):
"""simple docstring"""
return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
@slow
def a_ ( self : Tuple ):
"""simple docstring"""
__lowerCamelCase : str = "Hello World!"
__lowerCamelCase : str = [35389, 6672, 49, 2]
self.assertListEqual(__lowerCamelCase , self.big_tokenizer.encode(__lowerCamelCase ) )
@slow
def a_ ( self : List[str] ):
"""simple docstring"""
__lowerCamelCase : str = {"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCamelCase , model_name="""microsoft/xprophetnet-large-wiki100-cased""" , revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" , )
| 705 |
'''simple docstring'''
UpperCAmelCase__ :List[Any] = 256
# Modulus to hash a string
UpperCAmelCase__ :str = 1_000_003
def __lowercase (_lowercase, _lowercase ) -> bool:
"""simple docstring"""
__lowerCamelCase : str = len(_lowercase )
__lowerCamelCase : List[str] = len(_lowercase )
if p_len > t_len:
return False
__lowerCamelCase : Optional[Any] = 0
__lowerCamelCase : Tuple = 0
__lowerCamelCase : Dict = 1
# Calculating the hash of pattern and substring of text
for i in range(_lowercase ):
__lowerCamelCase : Optional[int] = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
__lowerCamelCase : Dict = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
__lowerCamelCase : Dict = (modulus_power * alphabet_size) % modulus
for i in range(0, t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
__lowerCamelCase : Dict = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def __lowercase () -> None:
"""simple docstring"""
__lowerCamelCase : List[Any] = """abc1abc12"""
__lowerCamelCase : Optional[Any] = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
__lowerCamelCase : List[Any] = """alskfjaldsk23adsfabcabc"""
assert rabin_karp(_lowercase, _lowercase ) and not rabin_karp(_lowercase, _lowercase )
# Test 2)
__lowerCamelCase : Optional[int] = """ABABX"""
__lowerCamelCase : Dict = """ABABZABABYABABX"""
assert rabin_karp(_lowercase, _lowercase )
# Test 3)
__lowerCamelCase : Any = """AAAB"""
__lowerCamelCase : int = """ABAAAAAB"""
assert rabin_karp(_lowercase, _lowercase )
# Test 4)
__lowerCamelCase : Any = """abcdabcy"""
__lowerCamelCase : Dict = """abcxabcdabxabcdabcdabcy"""
assert rabin_karp(_lowercase, _lowercase )
# Test 5)
__lowerCamelCase : str = """Lü"""
__lowerCamelCase : str = """Lüsai"""
assert rabin_karp(_lowercase, _lowercase )
__lowerCamelCase : Tuple = """Lue"""
assert not rabin_karp(_lowercase, _lowercase )
print("""Success.""" )
if __name__ == "__main__":
test_rabin_karp()
| 483 | 0 |
import re
from filelock import FileLock
try:
import nltk
__lowerCAmelCase = True
except (ImportError, ModuleNotFoundError):
__lowerCAmelCase = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __lowerCamelCase ( _lowerCAmelCase ) -> str:
re.sub("<n>" , "" , _lowerCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_lowerCAmelCase ) )
| 684 |
from itertools import permutations
def __lowerCamelCase ( _lowerCAmelCase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(_lowerCAmelCase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def __lowerCamelCase ( _lowerCAmelCase = 10 ) -> int:
return sum(
int("".join(map(_lowerCAmelCase , _lowerCAmelCase ) ) )
for num in permutations(range(_lowerCAmelCase ) )
if is_substring_divisible(_lowerCAmelCase ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 684 | 1 |
import argparse
import datetime
def __UpperCAmelCase ( UpperCAmelCase )-> str:
"""simple docstring"""
lowercase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thursday''',
'''5''': '''Friday''',
'''6''': '''Saturday''',
}
lowercase = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(UpperCAmelCase ) < 11:
raise ValueError('''Must be 10 characters long''' )
# Get month
lowercase = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError('''Month must be between 1 - 12''' )
lowercase = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get day
lowercase = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError('''Date must be between 1 - 31''' )
# Get second separator
lowercase = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get year
lowercase = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
'''Year out of range. There has to be some sort of limit...right?''' )
# Get datetime obj for validation
lowercase = datetime.date(int(UpperCAmelCase ), int(UpperCAmelCase ), int(UpperCAmelCase ) )
# Start math
if m <= 2:
lowercase = y - 1
lowercase = m + 12
# maths var
lowercase = int(str(UpperCAmelCase )[:2] )
lowercase = int(str(UpperCAmelCase )[2:] )
lowercase = int(2.6 * m - 5.39 )
lowercase = int(c / 4 )
lowercase = int(k / 4 )
lowercase = int(d + k )
lowercase = int(t + u + v + x )
lowercase = int(z - (2 * c) )
lowercase = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('''The date was evaluated incorrectly. Contact developer.''' )
# Response
lowercase = f'Your date {date_input}, is a {days[str(UpperCAmelCase )]}!'
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
A_ = parser.parse_args()
zeller(args.date_input)
| 479 | from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __UpperCAmelCase ( UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = int(number**0.5 )
return number == sq * sq
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[int, int]:
"""simple docstring"""
lowercase = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
lowercase = x_den * y_den * z_den
lowercase = gcd(UpperCAmelCase, UpperCAmelCase )
top //= hcf
bottom //= hcf
return top, bottom
def __UpperCAmelCase ( UpperCAmelCase = 35 )-> int:
"""simple docstring"""
lowercase = set()
lowercase = 42
lowercase = Fraction(0 )
lowercase = 42
for x_num in range(1, order + 1 ):
for x_den in range(x_num + 1, order + 1 ):
for y_num in range(1, order + 1 ):
for y_den in range(y_num + 1, order + 1 ):
# n=1
lowercase = x_num * y_den + x_den * y_num
lowercase = x_den * y_den
lowercase = gcd(UpperCAmelCase, UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase = add_three(
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
unique_s.add(UpperCAmelCase )
# n=2
lowercase = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
lowercase = x_den * x_den * y_den * y_den
if is_sq(UpperCAmelCase ) and is_sq(UpperCAmelCase ):
lowercase = int(sqrt(UpperCAmelCase ) )
lowercase = int(sqrt(UpperCAmelCase ) )
lowercase = gcd(UpperCAmelCase, UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase = add_three(
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
unique_s.add(UpperCAmelCase )
# n=-1
lowercase = x_num * y_num
lowercase = x_den * y_num + x_num * y_den
lowercase = gcd(UpperCAmelCase, UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase = add_three(
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
unique_s.add(UpperCAmelCase )
# n=2
lowercase = x_num * x_num * y_num * y_num
lowercase = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(UpperCAmelCase ) and is_sq(UpperCAmelCase ):
lowercase = int(sqrt(UpperCAmelCase ) )
lowercase = int(sqrt(UpperCAmelCase ) )
lowercase = gcd(UpperCAmelCase, UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase = add_three(
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
unique_s.add(UpperCAmelCase )
for num, den in unique_s:
total += Fraction(UpperCAmelCase, UpperCAmelCase )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F"{solution() = }")
| 479 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_lowercase : Tuple =logging.get_logger(__name__)
class UpperCamelCase_ ( snake_case__ ):
_a : List[str] = ['pixel_values']
def __init__( self : List[Any] , lowerCamelCase : bool = True , lowerCamelCase : Dict[str, int] = None , lowerCamelCase : PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase : bool = True , lowerCamelCase : Union[int, float] = 1 / 2_55 , lowerCamelCase : bool = True , lowerCamelCase : Optional[Union[float, List[float]]] = None , lowerCamelCase : Optional[Union[float, List[float]]] = None , lowerCamelCase : bool = True , **lowerCamelCase : Optional[Any] , ):
super().__init__(**lowerCamelCase )
lowerCamelCase_ : List[Any] = size if size is not None else {'height': 3_84, 'width': 3_84}
lowerCamelCase_ : List[str] = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
lowerCamelCase_ : Dict = do_resize
lowerCamelCase_ : int = size
lowerCamelCase_ : Union[str, Any] = resample
lowerCamelCase_ : Tuple = do_rescale
lowerCamelCase_ : Dict = rescale_factor
lowerCamelCase_ : int = do_normalize
lowerCamelCase_ : Any = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowerCamelCase_ : str = image_std if image_std is not None else OPENAI_CLIP_STD
lowerCamelCase_ : int = do_convert_rgb
def __a ( self : Tuple , lowerCamelCase : np.ndarray , lowerCamelCase : Dict[str, int] , lowerCamelCase : PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase : List[Any] , ):
lowerCamelCase_ : List[Any] = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}" )
lowerCamelCase_ : Optional[Any] = (size['height'], size['width'])
return resize(lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def __a ( self : Union[str, Any] , lowerCamelCase : np.ndarray , lowerCamelCase : Union[int, float] , lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase : Union[str, Any] , ):
return rescale(lowerCamelCase , scale=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def __a ( self : str , lowerCamelCase : np.ndarray , lowerCamelCase : Union[float, List[float]] , lowerCamelCase : Union[float, List[float]] , lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase : Optional[int] , ):
return normalize(lowerCamelCase , mean=lowerCamelCase , std=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def __a ( self : List[Any] , lowerCamelCase : ImageInput , lowerCamelCase : Optional[bool] = None , lowerCamelCase : Optional[Dict[str, int]] = None , lowerCamelCase : PILImageResampling = None , lowerCamelCase : Optional[bool] = None , lowerCamelCase : Optional[float] = None , lowerCamelCase : Optional[bool] = None , lowerCamelCase : Optional[Union[float, List[float]]] = None , lowerCamelCase : Optional[Union[float, List[float]]] = None , lowerCamelCase : Optional[Union[str, TensorType]] = None , lowerCamelCase : bool = None , lowerCamelCase : ChannelDimension = ChannelDimension.FIRST , **lowerCamelCase : Any , ):
lowerCamelCase_ : Optional[Any] = do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ : int = resample if resample is not None else self.resample
lowerCamelCase_ : Dict = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ : str = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ : List[str] = image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ : str = image_std if image_std is not None else self.image_std
lowerCamelCase_ : int = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCamelCase_ : Union[str, Any] = size if size is not None else self.size
lowerCamelCase_ : Optional[int] = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
lowerCamelCase_ : Dict = make_list_of_images(lowerCamelCase )
if not valid_images(lowerCamelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCamelCase_ : int = [convert_to_rgb(lowerCamelCase ) for image in images]
# All transformations expect numpy arrays.
lowerCamelCase_ : List[Any] = [to_numpy_array(lowerCamelCase ) for image in images]
if do_resize:
lowerCamelCase_ : Optional[int] = [self.resize(image=lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase ) for image in images]
if do_rescale:
lowerCamelCase_ : Dict = [self.rescale(image=lowerCamelCase , scale=lowerCamelCase ) for image in images]
if do_normalize:
lowerCamelCase_ : Optional[int] = [self.normalize(image=lowerCamelCase , mean=lowerCamelCase , std=lowerCamelCase ) for image in images]
lowerCamelCase_ : Optional[int] = [to_channel_dimension_format(lowerCamelCase , lowerCamelCase ) for image in images]
lowerCamelCase_ : Optional[int] = BatchFeature(data={'pixel_values': images} , tensor_type=lowerCamelCase )
return encoded_outputs
| 364 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_lowercase : int =logging.get_logger(__name__)
class UpperCamelCase_ ( snake_case__ ):
def __init__( self : Tuple , *lowerCamelCase : List[Any] , **lowerCamelCase : Union[str, Any] ):
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , lowerCamelCase , )
super().__init__(*lowerCamelCase , **lowerCamelCase )
| 364 | 1 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class __a ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
lowercase__: Optional[Any] = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
lowercase__: Optional[Any] = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(lowerCAmelCase__ )
from datasets import load_dataset
lowercase__: Optional[Any] = load_dataset('nielsr/rvlcdip-demo' )
lowercase__: Any = dataset['train'][0]['image'].convert('RGB' )
lowercase__: Optional[int] = image_processor(lowerCAmelCase__ , return_tensors='pt' ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
lowercase__: Any = model(**lowerCAmelCase__ )
lowercase__: Dict = outputs.logits
lowercase__: Optional[int] = torch.Size((1, 16) )
self.assertEqual(logits.shape , lowerCAmelCase__ )
lowercase__: Union[str, Any] = torch.tensor(
[-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7] , device=lowerCAmelCase__ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 706 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = ['''NllbTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = ['''NllbTokenizerFast''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 335 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
a_ = "1"
a_ = "0"
a_ = "1"
a_ = ort.SessionOptions()
a_ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
a_ = ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
a_ = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
a_ = ort.RunOptions()
a_ = 128
a_ = 1
a_ = np.ones((batch, sequence), dtype=np.intaa)
a_ = np.ones((batch, sequence), dtype=np.intaa)
a_ = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
a_ = time.time()
a_ = 2_000
a_ = {}
for iter in range(max_iters):
a_ = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1_000 / max_iters))
| 175 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __snake_case ( SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = SwinConfig(image_size=192 )
if "base" in model_name:
_UpperCAmelCase : Tuple = 6
_UpperCAmelCase : Optional[Any] = 128
_UpperCAmelCase : Dict = (2, 2, 18, 2)
_UpperCAmelCase : List[Any] = (4, 8, 16, 32)
elif "large" in model_name:
_UpperCAmelCase : int = 12
_UpperCAmelCase : Optional[int] = 192
_UpperCAmelCase : Optional[Any] = (2, 2, 18, 2)
_UpperCAmelCase : str = (6, 12, 24, 48)
else:
raise ValueError("Model not supported, only supports base and large variants" )
_UpperCAmelCase : Optional[int] = window_size
_UpperCAmelCase : Optional[int] = embed_dim
_UpperCAmelCase : List[Any] = depths
_UpperCAmelCase : Any = num_heads
return config
def __snake_case ( SCREAMING_SNAKE_CASE__ : Dict ) -> str:
'''simple docstring'''
if "encoder.mask_token" in name:
_UpperCAmelCase : Dict = name.replace("encoder.mask_token" , "embeddings.mask_token" )
if "encoder.patch_embed.proj" in name:
_UpperCAmelCase : Optional[int] = name.replace("encoder.patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "encoder.patch_embed.norm" in name:
_UpperCAmelCase : Any = name.replace("encoder.patch_embed.norm" , "embeddings.norm" )
if "attn.proj" in name:
_UpperCAmelCase : int = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
_UpperCAmelCase : Dict = name.replace("attn" , "attention.self" )
if "norm1" in name:
_UpperCAmelCase : List[str] = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
_UpperCAmelCase : int = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
_UpperCAmelCase : List[str] = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
_UpperCAmelCase : Any = name.replace("mlp.fc2" , "output.dense" )
if name == "encoder.norm.weight":
_UpperCAmelCase : Any = "layernorm.weight"
if name == "encoder.norm.bias":
_UpperCAmelCase : List[Any] = "layernorm.bias"
if "decoder" in name:
pass
else:
_UpperCAmelCase : Dict = "swin." + name
return name
def __snake_case ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int ) -> Optional[Any]:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
_UpperCAmelCase : Dict = orig_state_dict.pop(SCREAMING_SNAKE_CASE__ )
if "attn_mask" in key:
pass
elif "qkv" in key:
_UpperCAmelCase : int = key.split("." )
_UpperCAmelCase : List[str] = int(key_split[2] )
_UpperCAmelCase : Dict = int(key_split[4] )
_UpperCAmelCase : str = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_UpperCAmelCase : Optional[Any] = val[:dim, :]
_UpperCAmelCase : List[Any] = val[
dim : dim * 2, :
]
_UpperCAmelCase : Optional[Any] = val[-dim:, :]
else:
_UpperCAmelCase : Optional[int] = val[
:dim
]
_UpperCAmelCase : Dict = val[
dim : dim * 2
]
_UpperCAmelCase : Union[str, Any] = val[
-dim:
]
else:
_UpperCAmelCase : List[Any] = val
return orig_state_dict
def __snake_case ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : int ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : Any = torch.load(SCREAMING_SNAKE_CASE__ , map_location="cpu" )["model"]
_UpperCAmelCase : Union[str, Any] = get_swin_config(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : str = SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__ )
model.eval()
_UpperCAmelCase : Optional[Any] = convert_state_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : List[Any] = ViTImageProcessor(size={"height": 192, "width": 192} )
_UpperCAmelCase : int = Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw )
_UpperCAmelCase : str = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors="pt" )
with torch.no_grad():
_UpperCAmelCase : List[str] = model(**SCREAMING_SNAKE_CASE__ ).logits
print(outputs.keys() )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
print(f'Pushing model and image processor for {model_name} to hub' )
model.push_to_hub(f'microsoft/{model_name}' )
image_processor.push_to_hub(f'microsoft/{model_name}' )
if __name__ == "__main__":
_lowerCAmelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="swin-base-simmim-window6-192",
type=str,
choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
help="Name of the Swin SimMIM model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path",
default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
type=str,
help="Path to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
_lowerCAmelCase : str = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 289 | 0 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
def snake_case ( self : Any ):
lowerCamelCase :List[str] = tempfile.mkdtemp()
lowerCamelCase :List[str] = 8
# DPR tok
lowerCamelCase :str = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowerCamelCase :Dict = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase :List[str] = os.path.join(__snake_case , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
lowerCamelCase :Any = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
lowerCamelCase :List[str] = dict(zip(__snake_case , range(len(__snake_case ) ) ) )
lowerCamelCase :Any = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
lowerCamelCase :Any = {'''unk_token''': '''<unk>'''}
lowerCamelCase :Tuple = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCamelCase :Dict = os.path.join(__snake_case , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase :Optional[int] = os.path.join(__snake_case , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__snake_case ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__snake_case ) )
def snake_case ( self : List[Any] ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def snake_case ( self : Optional[Any] ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def snake_case ( self : List[str] ):
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def snake_case ( self : Optional[int] ):
lowerCamelCase :Tuple = os.path.join(self.tmpdirname , '''rag_tokenizer''' )
lowerCamelCase :Union[str, Any] = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
lowerCamelCase :Union[str, Any] = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(__snake_case )
rag_tokenizer.save_pretrained(__snake_case )
lowerCamelCase :int = RagTokenizer.from_pretrained(__snake_case , config=__snake_case )
self.assertIsInstance(new_rag_tokenizer.question_encoder , __snake_case )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , __snake_case )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def snake_case ( self : Any ):
lowerCamelCase :Optional[int] = RagTokenizer.from_pretrained('''facebook/rag-token-nq''' )
lowerCamelCase :Any = [
'''who got the first nobel prize in physics''',
'''when is the next deadpool movie being released''',
'''which mode is used for short wave broadcast service''',
'''who is the owner of reading football club''',
'''when is the next scandal episode coming out''',
'''when is the last time the philadelphia won the superbowl''',
'''what is the most current adobe flash player version''',
'''how many episodes are there in dragon ball z''',
'''what is the first step in the evolution of the eye''',
'''where is gall bladder situated in human body''',
'''what is the main mineral in lithium batteries''',
'''who is the president of usa right now''',
'''where do the greasers live in the outsiders''',
'''panda is a national animal of which country''',
'''what is the name of manchester united stadium''',
]
lowerCamelCase :Dict = tokenizer(__snake_case )
self.assertIsNotNone(__snake_case )
@slow
def snake_case ( self : Tuple ):
lowerCamelCase :Dict = RagTokenizer.from_pretrained('''facebook/rag-sequence-nq''' )
lowerCamelCase :List[Any] = [
'''who got the first nobel prize in physics''',
'''when is the next deadpool movie being released''',
'''which mode is used for short wave broadcast service''',
'''who is the owner of reading football club''',
'''when is the next scandal episode coming out''',
'''when is the last time the philadelphia won the superbowl''',
'''what is the most current adobe flash player version''',
'''how many episodes are there in dragon ball z''',
'''what is the first step in the evolution of the eye''',
'''where is gall bladder situated in human body''',
'''what is the main mineral in lithium batteries''',
'''who is the president of usa right now''',
'''where do the greasers live in the outsiders''',
'''panda is a national animal of which country''',
'''what is the name of manchester united stadium''',
]
lowerCamelCase :str = tokenizer(__snake_case )
self.assertIsNotNone(__snake_case )
| 49 | import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
A__ = logging.get_logger(__name__)
A__ = """Hello, World!"""
A__ = """en_XX"""
def _lowerCamelCase ( a_ : str , a_ : str , a_ : bool):
lowerCamelCase :int = Path('''data_bin''')
lowerCamelCase :Union[str, Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(a_).parent) , checkpoint_file=Path(a_).name , _name='''xmod_base''' , arch='''xmod_base''' , task='''multilingual_masked_lm''' , data_name_or_path=str(a_) , bpe='''sentencepiece''' , sentencepiece_model=str(Path(a_).parent / '''sentencepiece.bpe.model''') , src_dict=str(data_dir / '''dict.txt''') , )
xmod.eval() # disable dropout
print(a_)
lowerCamelCase :Any = xmod.model.encoder.sentence_encoder
lowerCamelCase :List[str] = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , '''bottleneck''' , 2) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
lowerCamelCase :Dict = xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
print('''Our X-MOD config:''' , a_)
lowerCamelCase :List[Any] = XmodForSequenceClassification(a_) if classification_head else XmodForMaskedLM(a_)
model.eval()
# Now let's copy all the weights.
# Embeddings
lowerCamelCase :Union[str, Any] = xmod_sent_encoder.embed_tokens.weight
lowerCamelCase :Tuple = xmod_sent_encoder.embed_positions.weight
lowerCamelCase :List[str] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight) # just zero them out b/c xmod doesn't use them.
lowerCamelCase :List[Any] = xmod_sent_encoder.layernorm_embedding.weight
lowerCamelCase :Optional[int] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers):
# Encoder: start of layer
lowerCamelCase :Union[str, Any] = model.roberta.encoder.layer[i]
lowerCamelCase :List[str] = xmod_sent_encoder.layers[i]
# self attention
lowerCamelCase :Optional[int] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size))
):
raise AssertionError('''Dimensions of self-attention weights do not match.''')
lowerCamelCase :Optional[int] = xmod_layer.self_attn.q_proj.weight
lowerCamelCase :List[str] = xmod_layer.self_attn.q_proj.bias
lowerCamelCase :str = xmod_layer.self_attn.k_proj.weight
lowerCamelCase :Optional[Any] = xmod_layer.self_attn.k_proj.bias
lowerCamelCase :Dict = xmod_layer.self_attn.v_proj.weight
lowerCamelCase :Optional[int] = xmod_layer.self_attn.v_proj.bias
# self-attention output
lowerCamelCase :Optional[int] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError('''Dimensions of self-attention output weights do not match.''')
lowerCamelCase :List[Any] = xmod_layer.self_attn.out_proj.weight
lowerCamelCase :Union[str, Any] = xmod_layer.self_attn.out_proj.bias
lowerCamelCase :str = xmod_layer.self_attn_layer_norm.weight
lowerCamelCase :List[Any] = xmod_layer.self_attn_layer_norm.bias
# intermediate
lowerCamelCase :Optional[int] = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('''Dimensions of intermediate weights do not match.''')
lowerCamelCase :int = xmod_layer.fca.weight
lowerCamelCase :Union[str, Any] = xmod_layer.fca.bias
# output
lowerCamelCase :List[str] = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('''Dimensions of feed-forward weights do not match.''')
lowerCamelCase :str = xmod_layer.fca.weight
lowerCamelCase :int = xmod_layer.fca.bias
lowerCamelCase :List[Any] = xmod_layer.final_layer_norm.weight
lowerCamelCase :List[str] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
lowerCamelCase :List[str] = xmod_layer.adapter_layer_norm.weight
lowerCamelCase :int = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys()) != sorted(xmod_layer.adapter_modules.keys()):
raise AssertionError('''Lists of language adapters do not match.''')
for lang_code, adapter in xmod_layer.adapter_modules.items():
lowerCamelCase :Optional[int] = bert_output.adapter_modules[lang_code]
lowerCamelCase :Dict = xmod_layer.adapter_modules[lang_code]
lowerCamelCase :List[Any] = from_adapter.fca.weight
lowerCamelCase :List[Any] = from_adapter.fca.bias
lowerCamelCase :Dict = from_adapter.fca.weight
lowerCamelCase :Optional[Any] = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
lowerCamelCase :Dict = xmod_sent_encoder.layer_norm.weight
lowerCamelCase :List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
lowerCamelCase :Union[str, Any] = xmod.model.classification_heads['''mnli'''].dense.weight
lowerCamelCase :Tuple = xmod.model.classification_heads['''mnli'''].dense.bias
lowerCamelCase :Optional[Any] = xmod.model.classification_heads['''mnli'''].out_proj.weight
lowerCamelCase :List[Any] = xmod.model.classification_heads['''mnli'''].out_proj.bias
else:
# LM Head
lowerCamelCase :int = xmod.model.encoder.lm_head.dense.weight
lowerCamelCase :List[Any] = xmod.model.encoder.lm_head.dense.bias
lowerCamelCase :Optional[int] = xmod.model.encoder.lm_head.layer_norm.weight
lowerCamelCase :List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
lowerCamelCase :List[Any] = xmod.model.encoder.lm_head.weight
lowerCamelCase :Any = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
lowerCamelCase :str = xmod.encode(a_).unsqueeze(0) # batch of size 1
model.roberta.set_default_language(a_)
lowerCamelCase :Any = model(a_)[0]
if classification_head:
lowerCamelCase :Dict = xmod.model.classification_heads['''mnli'''](xmod.extract_features(a_))
else:
lowerCamelCase :int = xmod.model(a_ , lang_id=[SAMPLE_LANGUAGE])[0]
print(our_output.shape , their_output.shape)
lowerCamelCase :List[str] = torch.max(torch.abs(our_output - their_output)).item()
print(F"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7
lowerCamelCase :str = torch.allclose(a_ , a_ , atol=1e-3)
print('''Do both models output the same tensors?''' , '''🔥''' if success else '''💩''')
if not success:
raise Exception('''Something went wRoNg''')
Path(a_).mkdir(parents=a_ , exist_ok=a_)
print(F"Saving model to {pytorch_dump_folder_path}")
model.save_pretrained(a_)
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
A__ = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 49 | 1 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class lowerCamelCase :
def snake_case_ ( self : List[str] , __snake_case : Union[str, Any] , __snake_case : List[str] , __snake_case : Union[str, Any] ) -> Any:
return None
class lowerCamelCase :
def snake_case_ ( self : Optional[Any] , __snake_case : Any , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] ) -> List[Any]:
return None
class lowerCamelCase ( unittest.TestCase ):
UpperCAmelCase : Tuple = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def snake_case_ ( self : Optional[Any] ) -> List[Any]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__snake_case , '''tf''' , 12 , **__snake_case )
@require_torch
@slow
def snake_case_ ( self : Tuple ) -> Dict:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(__snake_case , '''pt''' , 12 , **__snake_case )
@require_torch
@slow
def snake_case_ ( self : Any ) -> Dict:
from transformers import BertModel
_a : Tuple = ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
with NamedTemporaryFile(mode='''w+t''' ) as vocab_file:
vocab_file.write('''\n'''.join(__snake_case ) )
vocab_file.flush()
_a : Union[str, Any] = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
_a : List[Any] = BertModel(BertConfig(vocab_size=len(__snake_case ) ) )
model.save_pretrained(__snake_case )
self._test_export(__snake_case , '''pt''' , 12 , __snake_case )
@require_tf
@slow
def snake_case_ ( self : int ) -> Dict:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_a : Optional[Any] = self._test_export(__snake_case , '''tf''' , 12 , **__snake_case )
_a : List[Any] = quantize(Path(__snake_case ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__snake_case ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
@require_torch
@slow
def snake_case_ ( self : str ) -> Optional[int]:
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_a : Optional[Any] = self._test_export(__snake_case , '''pt''' , 12 , **__snake_case )
_a : List[str] = quantize(__snake_case )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(__snake_case ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
def snake_case_ ( self : List[str] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : Optional[int] , __snake_case : List[str]=None , **__snake_case : str ) -> Optional[Any]:
try:
# Compute path
with TemporaryDirectory() as tempdir:
_a : int = Path(__snake_case ).joinpath('''model.onnx''' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , **__snake_case )
return path
except Exception as e:
self.fail(__snake_case )
@require_torch
@require_tokenizers
@slow
def snake_case_ ( self : Union[str, Any] ) -> Tuple:
from transformers import BertModel
_a : List[Any] = BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_a : Optional[int] = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(__snake_case , __snake_case , '''pt''' )
@require_tf
@require_tokenizers
@slow
def snake_case_ ( self : List[Any] ) -> List[Any]:
from transformers import TFBertModel
_a : Optional[int] = TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_a : Optional[Any] = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(__snake_case , __snake_case , '''tf''' )
def snake_case_ ( self : List[Any] , __snake_case : Any , __snake_case : List[str] , __snake_case : List[str] ) -> Optional[Any]:
_a : Tuple = FeatureExtractionPipeline(__snake_case , __snake_case )
_a : str = ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
_a , _a , _a , _a : Optional[Any] = infer_shapes(__snake_case , __snake_case )
# Assert all variables are present
self.assertEqual(len(__snake_case ) , len(__snake_case ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , __snake_case )
self.assertSequenceEqual(variable_names[3:] , __snake_case )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: '''batch''', 1: '''sequence'''} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['''output_0'''] , {0: '''batch''', 1: '''sequence'''} )
self.assertDictEqual(shapes['''output_1'''] , {0: '''batch'''} )
def snake_case_ ( self : Any ) -> Optional[int]:
_a : Dict = ['''input_ids''', '''attention_mask''', '''token_type_ids''']
_a : Any = {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
_a , _a : Tuple = ensure_valid_input(FuncContiguousArgs() , __snake_case , __snake_case )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(__snake_case ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(__snake_case ) , set(__snake_case ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(__snake_case , (tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask''']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
_a , _a : Optional[Any] = ensure_valid_input(FuncNonContiguousArgs() , __snake_case , __snake_case )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(__snake_case ) , 1 )
self.assertEqual(len(__snake_case ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens['''input_ids'''] )
self.assertEqual(ordered_input_names[0] , '''input_ids''' )
def snake_case_ ( self : str ) -> Union[str, Any]:
_a : Dict = generate_identified_filename(Path('''/home/something/my_fake_model.onnx''' ) , '''-test''' )
self.assertEqual('''/home/something/my_fake_model-test.onnx''' , generated.as_posix() )
| 471 |
import math
def lowerCamelCase_ ( UpperCamelCase_ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(UpperCamelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase_ ( UpperCamelCase_ = 0.1 ):
_a : int = 3
_a : List[str] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(UpperCamelCase_ )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 471 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
a = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> List[Any]:
_UpperCAmelCase = WavaVecaForSequenceClassification.from_pretrained(snake_case , config=snake_case )
_UpperCAmelCase = downstream_dict["projector.weight"]
_UpperCAmelCase = downstream_dict["projector.bias"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.weight"]
_UpperCAmelCase = downstream_dict["model.post_net.linear.bias"]
return model
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = WavaVecaForAudioFrameClassification.from_pretrained(snake_case , config=snake_case )
_UpperCAmelCase = downstream_dict["model.linear.weight"]
_UpperCAmelCase = downstream_dict["model.linear.bias"]
return model
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = WavaVecaForXVector.from_pretrained(snake_case , config=snake_case )
_UpperCAmelCase = downstream_dict["connector.weight"]
_UpperCAmelCase = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_UpperCAmelCase = downstream_dict[
f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
]
_UpperCAmelCase = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
_UpperCAmelCase = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
_UpperCAmelCase = downstream_dict["objective.W"]
return model
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case ) -> Any:
_UpperCAmelCase = torch.load(snake_case , map_location="""cpu""" )
_UpperCAmelCase = checkpoint["Downstream"]
_UpperCAmelCase = WavaVecaConfig.from_pretrained(snake_case )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
snake_case , return_attention_mask=snake_case , do_normalize=snake_case )
_UpperCAmelCase = hf_config.architectures[0]
if arch.endswith("""ForSequenceClassification""" ):
_UpperCAmelCase = convert_classification(snake_case , snake_case , snake_case )
elif arch.endswith("""ForAudioFrameClassification""" ):
_UpperCAmelCase = convert_diarization(snake_case , snake_case , snake_case )
elif arch.endswith("""ForXVector""" ):
_UpperCAmelCase = convert_xvector(snake_case , snake_case , snake_case )
else:
raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}" )
if hf_config.use_weighted_layer_sum:
_UpperCAmelCase = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(snake_case )
hf_model.save_pretrained(snake_case )
if __name__ == "__main__":
a = 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 = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path) | 721 |
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> bool:
_UpperCAmelCase = len(snake_case ) + 1
_UpperCAmelCase = len(snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
_UpperCAmelCase = [[0 for i in range(snake_case )] for j in range(snake_case )]
# since string of zero length match pattern of zero length
_UpperCAmelCase = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , snake_case ):
_UpperCAmelCase = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , snake_case ):
_UpperCAmelCase = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , snake_case ):
for j in range(1 , snake_case ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
_UpperCAmelCase = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
_UpperCAmelCase = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
_UpperCAmelCase = dp[i - 1][j]
else:
_UpperCAmelCase = 0
else:
_UpperCAmelCase = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a = "aab"
a = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'{input_string} matches the given pattern {pattern}')
else:
print(F'{input_string} does not match with the given pattern {pattern}') | 175 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}
class lowerCamelCase__ ( UpperCAmelCase ):
"""simple docstring"""
_UpperCamelCase : Optional[int] = 'markuplm'
def __init__( self , snake_case=30522 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=2 , snake_case=0.02 , snake_case=1E-1_2 , snake_case=0 , snake_case=0 , snake_case=2 , snake_case=256 , snake_case=1024 , snake_case=216 , snake_case=1001 , snake_case=32 , snake_case=50 , snake_case="absolute" , snake_case=True , snake_case=None , **snake_case , ):
'''simple docstring'''
super().__init__(
pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , **snake_case , )
UpperCamelCase__ = vocab_size
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = hidden_act
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = type_vocab_size
UpperCamelCase__ = initializer_range
UpperCamelCase__ = layer_norm_eps
UpperCamelCase__ = position_embedding_type
UpperCamelCase__ = use_cache
UpperCamelCase__ = classifier_dropout
# additional properties
UpperCamelCase__ = max_depth
UpperCamelCase__ = max_xpath_tag_unit_embeddings
UpperCamelCase__ = max_xpath_subs_unit_embeddings
UpperCamelCase__ = tag_pad_id
UpperCamelCase__ = subs_pad_id
UpperCamelCase__ = xpath_unit_hidden_size
| 551 |
import numpy as np
def UpperCamelCase_( _A :np.array )-> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 551 | 1 |
'''simple docstring'''
import os
from math import logaa
def _lowercase ( lowerCamelCase__ : str = "base_exp.txt" ):
_a = 0
_a = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__ ), lowerCamelCase__ ) ) ):
_a , _a = list(map(lowerCamelCase__, line.split("," ) ) )
if x * logaa(lowerCamelCase__ ) > largest:
_a = x * logaa(lowerCamelCase__ )
_a = i + 1
return result
if __name__ == "__main__":
print(solution()) | 706 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__snake_case : Optional[int] = R"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `\" / \"`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `\" // \"`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `\"train\"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `\"compressed\"`)\n The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n `\"compressed\"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n"
@add_start_docstrings(a )
class A ( a ):
__UpperCAmelCase : Dict = """rag"""
__UpperCAmelCase : Dict = True
def __init__( self , snake_case_=None , snake_case_=True , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=" / " , snake_case_=" // " , snake_case_=5 , snake_case_=3_0_0 , snake_case_=7_6_8 , snake_case_=8 , snake_case_="wiki_dpr" , snake_case_="train" , snake_case_="compressed" , snake_case_=None , snake_case_=None , snake_case_=False , snake_case_=False , snake_case_=0.0 , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False , snake_case_=True , snake_case_=None , **snake_case_ , ) -> Optional[Any]:
super().__init__(
bos_token_id=snake_case_ , pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , forced_eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , prefix=snake_case_ , vocab_size=snake_case_ , **snake_case_ , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
_a = kwargs.pop("question_encoder" )
_a = question_encoder_config.pop("model_type" )
_a = kwargs.pop("generator" )
_a = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
_a = AutoConfig.for_model(snake_case_ , **snake_case_ )
_a = AutoConfig.for_model(snake_case_ , **snake_case_ )
_a = reduce_loss
_a = label_smoothing
_a = exclude_bos_score
_a = do_marginalize
_a = title_sep
_a = doc_sep
_a = n_docs
_a = max_combined_length
_a = dataset
_a = dataset_split
_a = index_name
_a = retrieval_vector_size
_a = retrieval_batch_size
_a = passages_path
_a = index_path
_a = use_dummy_dataset
_a = output_retrieved
_a = do_deduplication
_a = use_cache
if self.forced_eos_token_id is None:
_a = getattr(self.generator , "forced_eos_token_id" , snake_case_ )
@classmethod
def __lowerCAmelCase ( cls , snake_case_ , snake_case_ , **snake_case_ ) -> PretrainedConfig:
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **snake_case_ )
def __lowerCAmelCase ( self ) -> Optional[int]:
_a = copy.deepcopy(self.__dict__ )
_a = self.question_encoder.to_dict()
_a = self.generator.to_dict()
_a = self.__class__.model_type
return output
| 691 | 0 |
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ : str = 0
A_ : Dict = len(__lowerCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 ,__lowerCAmelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
if len(__lowerCAmelCase ) <= 1:
return arr, 0
A_ : Tuple = len(__lowerCAmelCase ) // 2
A_ : Optional[int] = arr[0:mid]
A_ : List[Any] = arr[mid:]
A_ , A_ : Any = count_inversions_recursive(__lowerCAmelCase )
A_ , A_ : List[Any] = count_inversions_recursive(__lowerCAmelCase )
A_ , A_ : Any = _count_cross_inversions(__lowerCAmelCase ,__lowerCAmelCase )
A_ : Optional[int] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : Optional[int] = []
A_ : str = 0
while i < len(__lowerCAmelCase ) and j < len(__lowerCAmelCase ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__lowerCAmelCase ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__lowerCAmelCase ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : Optional[int] = [1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
A_ : Tuple = count_inversions_bf(__lowerCAmelCase )
A_ , A_ : Optional[Any] = count_inversions_recursive(__lowerCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 8
print("""number of inversions = """ ,__lowerCAmelCase )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
A_ : Any = count_inversions_bf(__lowerCAmelCase )
A_ , A_ : Union[str, Any] = count_inversions_recursive(__lowerCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ ,__lowerCAmelCase )
# an empty list should also have zero inversions
A_ : Any = []
A_ : int = count_inversions_bf(__lowerCAmelCase )
A_ , A_ : List[Any] = count_inversions_recursive(__lowerCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ ,__lowerCAmelCase )
if __name__ == "__main__":
main()
| 569 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase : int = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
"post_extract_proj": "feature_projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.upsample.0": "encoder.upsample.projection",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
'''simple docstring'''
for attribute in key.split(""".""" ):
lowercase_ = getattr(__lowerCAmelCase , __lowerCAmelCase )
if weight_type is not None:
lowercase_ = getattr(__lowerCAmelCase , __lowerCAmelCase ).shape
else:
lowercase_ = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
lowercase_ = value
elif weight_type == "weight_g":
lowercase_ = value
elif weight_type == "weight_v":
lowercase_ = value
elif weight_type == "bias":
lowercase_ = value
else:
lowercase_ = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = []
lowercase_ = fairseq_model.state_dict()
lowercase_ = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
lowercase_ = False
if "conv_layers" in name:
load_conv_layer(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
lowercase_ = True
else:
for key, mapped_key in MAPPING.items():
lowercase_ = """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
lowercase_ = True
if "*" in mapped_key:
lowercase_ = name.split(__lowerCAmelCase )[0].split(""".""" )[-2]
lowercase_ = mapped_key.replace("""*""" , __lowerCAmelCase )
if "weight_g" in name:
lowercase_ = """weight_g"""
elif "weight_v" in name:
lowercase_ = """weight_v"""
elif "weight" in name:
lowercase_ = """weight"""
elif "bias" in name:
lowercase_ = """bias"""
else:
lowercase_ = None
set_recursively(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
continue
if not is_used:
unused_weights.append(__lowerCAmelCase )
logger.warning(F'''Unused weights: {unused_weights}''' )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = full_name.split("""conv_layers.""" )[-1]
lowercase_ = name.split(""".""" )
lowercase_ = int(items[0] )
lowercase_ = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
lowercase_ = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
lowercase_ = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
lowercase_ = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
lowercase_ = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = SEWConfig()
if is_finetuned:
lowercase_ = model.wav_encoder.wav_model.cfg
else:
lowercase_ = model.cfg
lowercase_ = fs_config.conv_bias
lowercase_ = eval(fs_config.conv_feature_layers )
lowercase_ = [x[0] for x in conv_layers]
lowercase_ = [x[1] for x in conv_layers]
lowercase_ = [x[2] for x in conv_layers]
lowercase_ = """gelu"""
lowercase_ = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
lowercase_ = 0.0
lowercase_ = fs_config.activation_fn.name
lowercase_ = fs_config.encoder_embed_dim
lowercase_ = 0.02
lowercase_ = fs_config.encoder_ffn_embed_dim
lowercase_ = 1E-5
lowercase_ = fs_config.encoder_layerdrop
lowercase_ = fs_config.encoder_attention_heads
lowercase_ = fs_config.conv_pos_groups
lowercase_ = fs_config.conv_pos
lowercase_ = len(__lowerCAmelCase )
lowercase_ = fs_config.encoder_layers
lowercase_ = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
lowercase_ = model.cfg
lowercase_ = fs_config.final_dropout
lowercase_ = fs_config.layerdrop
lowercase_ = fs_config.activation_dropout
lowercase_ = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
lowercase_ = fs_config.attention_dropout
lowercase_ = fs_config.dropout_input
lowercase_ = fs_config.dropout
lowercase_ = fs_config.mask_channel_length
lowercase_ = fs_config.mask_channel_prob
lowercase_ = fs_config.mask_length
lowercase_ = fs_config.mask_prob
lowercase_ = """Wav2Vec2FeatureExtractor"""
lowercase_ = """Wav2Vec2CTCTokenizer"""
return config
@torch.no_grad()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=True ) -> Union[str, Any]:
'''simple docstring'''
if is_finetuned:
lowercase_ , lowercase_ , lowercase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
lowercase_ , lowercase_ , lowercase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
lowercase_ = SEWConfig.from_pretrained(__lowerCAmelCase )
else:
lowercase_ = convert_config(model[0] , __lowerCAmelCase )
lowercase_ = model[0].eval()
lowercase_ = True if config.feat_extract_norm == """layer""" else False
lowercase_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , )
if is_finetuned:
if dict_path:
lowercase_ = Dictionary.load(__lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase_ = target_dict.pad_index
lowercase_ = target_dict.bos_index
lowercase_ = target_dict.pad_index
lowercase_ = target_dict.bos_index
lowercase_ = target_dict.eos_index
lowercase_ = len(target_dict.symbols )
lowercase_ = os.path.join(__lowerCAmelCase , """vocab.json""" )
if not os.path.isdir(__lowerCAmelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCAmelCase ) )
return
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , __lowerCAmelCase )
lowercase_ = WavaVecaCTCTokenizer(
__lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__lowerCAmelCase , )
lowercase_ = WavaVecaProcessor(feature_extractor=__lowerCAmelCase , tokenizer=__lowerCAmelCase )
processor.save_pretrained(__lowerCAmelCase )
lowercase_ = SEWForCTC(__lowerCAmelCase )
else:
lowercase_ = SEWModel(__lowerCAmelCase )
feature_extractor.save_pretrained(__lowerCAmelCase )
recursively_load_weights(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
hf_model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
UpperCAmelCase : int = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
UpperCAmelCase : str = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 567 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class UpperCAmelCase_ ( snake_case_ ):
"""simple docstring"""
lowercase = """blenderbot-small"""
lowercase = ["""past_key_values"""]
lowercase = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[Any] , snake_case_ : str=50_265 , snake_case_ : Dict=512 , snake_case_ : List[Any]=8 , snake_case_ : Optional[int]=2_048 , snake_case_ : Dict=16 , snake_case_ : Union[str, Any]=8 , snake_case_ : List[str]=2_048 , snake_case_ : Any=16 , snake_case_ : Any=0.0 , snake_case_ : Union[str, Any]=0.0 , snake_case_ : List[str]=True , snake_case_ : Optional[Any]=True , snake_case_ : int="gelu" , snake_case_ : str=512 , snake_case_ : Optional[int]=0.1 , snake_case_ : List[str]=0.0 , snake_case_ : List[str]=0.0 , snake_case_ : List[Any]=0.02 , snake_case_ : Optional[Any]=1 , snake_case_ : Optional[Any]=False , snake_case_ : Union[str, Any]=0 , snake_case_ : Tuple=1 , snake_case_ : Optional[int]=2 , snake_case_ : Tuple=2 , **snake_case_ : List[Any] , ):
snake_case__ : Dict = vocab_size
snake_case__ : Dict = max_position_embeddings
snake_case__ : Dict = d_model
snake_case__ : str = encoder_ffn_dim
snake_case__ : Tuple = encoder_layers
snake_case__ : Dict = encoder_attention_heads
snake_case__ : int = decoder_ffn_dim
snake_case__ : List[Any] = decoder_layers
snake_case__ : Any = decoder_attention_heads
snake_case__ : Union[str, Any] = dropout
snake_case__ : List[str] = attention_dropout
snake_case__ : Union[str, Any] = activation_dropout
snake_case__ : str = activation_function
snake_case__ : str = init_std
snake_case__ : int = encoder_layerdrop
snake_case__ : List[Any] = decoder_layerdrop
snake_case__ : Dict = use_cache
snake_case__ : Dict = encoder_layers
snake_case__ : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , decoder_start_token_id=snake_case_ , forced_eos_token_id=snake_case_ , **snake_case_ , )
class UpperCAmelCase_ ( snake_case_ ):
"""simple docstring"""
@property
def lowerCamelCase ( self : int ):
if self.task in ["default", "seq2seq-lm"]:
snake_case__ : List[Any] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
snake_case__ : List[str] = {0: """batch"""}
snake_case__ : int = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
snake_case__ : List[Any] = {0: """batch""", 1: """decoder_sequence"""}
snake_case__ : Optional[Any] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case__ : Dict = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
snake_case__ , snake_case__ : Dict = self.num_layers
for i in range(snake_case_ ):
snake_case__ : List[Any] = {0: """batch""", 2: """past_sequence + sequence"""}
snake_case__ : Union[str, Any] = {0: """batch""", 2: """past_sequence + sequence"""}
else:
snake_case__ : Any = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
def lowerCamelCase ( self : Tuple ):
if self.task in ["default", "seq2seq-lm"]:
snake_case__ : List[str] = super().outputs
else:
snake_case__ : Optional[Any] = super(snake_case_ , self ).outputs
if self.use_past:
snake_case__ , snake_case__ : Optional[Any] = self.num_layers
for i in range(snake_case_ ):
snake_case__ : Union[str, Any] = {0: """batch""", 2: """past_sequence + sequence"""}
snake_case__ : List[str] = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def lowerCamelCase ( self : Union[str, Any] , snake_case_ : str , snake_case_ : int = -1 , snake_case_ : Dict = -1 , snake_case_ : Dict = False , snake_case_ : List[str] = None , ):
snake_case__ : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Generate decoder inputs
snake_case__ : Optional[int] = seq_length if not self.use_past else 1
snake_case__ : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
snake_case__ : Any = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
snake_case__ : int = dict(**snake_case_ , **snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
snake_case__ , snake_case__ : Union[str, Any] = common_inputs["""input_ids"""].shape
snake_case__ : int = common_inputs["""decoder_input_ids"""].shape[1]
snake_case__ , snake_case__ : int = self.num_attention_heads
snake_case__ : Union[str, Any] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case__ : int = decoder_seq_length + 3
snake_case__ : int = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case__ : List[Any] = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(snake_case_ , snake_case_ )] , dim=1 )
snake_case__ : Union[str, Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case__ , snake_case__ : Any = self.num_layers
snake_case__ : str = min(snake_case_ , snake_case_ )
snake_case__ : Optional[int] = max(snake_case_ , snake_case_ ) - min_num_layers
snake_case__ : Optional[Any] = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(snake_case_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
torch.zeros(snake_case_ ),
) )
# TODO: test this.
snake_case__ : List[str] = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(snake_case_ , snake_case_ ):
common_inputs["past_key_values"].append((torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) )
return common_inputs
def lowerCamelCase ( self : Tuple , snake_case_ : Union[str, Any] , snake_case_ : List[Any] = -1 , snake_case_ : Optional[Any] = -1 , snake_case_ : str = False , snake_case_ : str = None , ):
snake_case__ : str = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
snake_case__ , snake_case__ : Union[str, Any] = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
snake_case__ : int = seqlen + 2
snake_case__ , snake_case__ : List[str] = self.num_layers
snake_case__ , snake_case__ : Union[str, Any] = self.num_attention_heads
snake_case__ : Dict = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case__ : Union[str, Any] = common_inputs["""attention_mask"""].dtype
snake_case__ : Union[str, Any] = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
snake_case__ : int = [
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(snake_case_ )
]
return common_inputs
def lowerCamelCase ( self : Union[str, Any] , snake_case_ : Union[str, Any] , snake_case_ : int = -1 , snake_case_ : List[str] = -1 , snake_case_ : Tuple = False , snake_case_ : Tuple = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case__ : int = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case__ : Tuple = tokenizer.num_special_tokens_to_add(snake_case_ )
snake_case__ : int = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
snake_case__ : Optional[int] = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case__ : Tuple = dict(tokenizer(snake_case_ , return_tensors=snake_case_ ) )
return common_inputs
def lowerCamelCase ( self : Optional[int] , snake_case_ : List[str] , snake_case_ : Tuple = -1 , snake_case_ : Any = -1 , snake_case_ : Optional[int] = False , snake_case_ : Any = None , ):
if self.task in ["default", "seq2seq-lm"]:
snake_case__ : Union[str, Any] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
elif self.task == "causal-lm":
snake_case__ : int = self._generate_dummy_inputs_for_causal_lm(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
else:
snake_case__ : Optional[int] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
snake_case_ , batch_size=snake_case_ , seq_length=snake_case_ , is_pair=snake_case_ , framework=snake_case_ )
return common_inputs
def lowerCamelCase ( self : Tuple , snake_case_ : Tuple , snake_case_ : Any , snake_case_ : Dict , snake_case_ : List[str] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case__ : Union[str, Any] = super()._flatten_past_key_values_(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
snake_case__ : Any = super(snake_case_ , self )._flatten_past_key_values_(
snake_case_ , snake_case_ , snake_case_ , snake_case_ )
| 701 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """filename with blanks.csv"""] )
@pytest.mark.parametrize("""revision""" , [None, """v2"""] )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Any = hf_hub_url(repo_id=_lowerCAmelCase , path=_lowerCAmelCase , revision=_lowerCAmelCase )
assert url == f"https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(_lowerCAmelCase )}"
| 301 | 0 |
'''simple docstring'''
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 TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowercase ( self : Any ):
'''simple docstring'''
_a : Any = TFXLMRobertaModel.from_pretrained('jplu/tf-xlm-roberta-base' )
_a : Tuple = {
'input_ids': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]] ,dtype=tf.intaa ), # "My dog is cute"
'attention_mask': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] ,dtype=tf.intaa ),
}
_a : Dict = model(_a )['last_hidden_state']
_a : int = tf.TensorShape((1, 6, 768) )
self.assertEqual(output.shape ,_a )
# compare the actual values for a slice.
_a : List[Any] = tf.convert_to_tensor(
[
[
[0.068_1762, 0.1089_4451, 0.0677_2504],
[-0.0642_3668, 0.0236_6615, 0.0432_9344],
[-0.0605_7295, 0.0997_4135, -0.0007_0584],
]
] ,dtype=tf.floataa ,)
self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,expected_slice.numpy() ,atol=1E-4 ) )
| 229 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class UpperCAmelCase__ :
"""simple docstring"""
__UpperCAmelCase : int = LEDConfig
__UpperCAmelCase : Union[str, Any] = {}
__UpperCAmelCase : Dict = '''gelu'''
def __init__( self : Optional[Any] ,_a : str ,_a : List[str]=13 ,_a : List[str]=7 ,_a : Optional[Any]=True ,_a : Dict=False ,_a : str=99 ,_a : List[Any]=32 ,_a : int=2 ,_a : str=4 ,_a : List[Any]=37 ,_a : Union[str, Any]=0.1 ,_a : Tuple=0.1 ,_a : str=20 ,_a : List[Any]=2 ,_a : Optional[Any]=1 ,_a : int=0 ,_a : Dict=4 ,):
'''simple docstring'''
_a : Dict = parent
_a : List[Any] = batch_size
_a : str = seq_length
_a : Dict = is_training
_a : Tuple = use_labels
_a : List[str] = vocab_size
_a : Union[str, Any] = hidden_size
_a : Union[str, Any] = num_hidden_layers
_a : Any = num_attention_heads
_a : Union[str, Any] = intermediate_size
_a : Any = hidden_dropout_prob
_a : Optional[Any] = attention_probs_dropout_prob
_a : Optional[Any] = max_position_embeddings
_a : Dict = eos_token_id
_a : Tuple = pad_token_id
_a : Optional[int] = bos_token_id
_a : Tuple = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
_a : List[str] = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
_a : Optional[Any] = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowercase ( self : Dict ):
'''simple docstring'''
_a : List[str] = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
_a : Any = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
_a : List[Any] = tf.concat([input_ids, eos_tensor] ,axis=1 )
_a : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_a : str = self.config_cls(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,attention_window=self.attention_window ,**self.config_updates ,)
_a : Dict = prepare_led_inputs_dict(_a ,_a ,_a )
_a : Dict = tf.concat(
[tf.zeros_like(_a )[:, :-1], tf.ones_like(_a )[:, -1:]] ,axis=-1 ,)
_a : int = global_attention_mask
return config, inputs_dict
def __lowercase ( self : int ,_a : Tuple ,_a : Union[str, Any] ):
'''simple docstring'''
_a : int = TFLEDModel(config=_a ).get_decoder()
_a : Union[str, Any] = inputs_dict['input_ids']
_a : str = input_ids[:1, :]
_a : Optional[int] = inputs_dict['attention_mask'][:1, :]
_a : Dict = 1
# first forward pass
_a : Tuple = model(_a ,attention_mask=_a ,use_cache=_a )
_a, _a : List[Any] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_a : List[Any] = ids_tensor((self.batch_size, 3) ,config.vocab_size )
_a : Union[str, Any] = tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )
# append to next input_ids and
_a : Any = tf.concat([input_ids, next_tokens] ,axis=-1 )
_a : Optional[Any] = tf.concat([attention_mask, next_attn_mask] ,axis=-1 )
_a : Tuple = model(_a ,attention_mask=_a )[0]
_a : List[Any] = model(_a ,attention_mask=_a ,past_key_values=_a )[0]
self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )
# select random slice
_a : List[str] = int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
_a : Dict = output_from_no_past[:, -3:, random_slice_idx]
_a : Any = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_a ,_a ,rtol=1E-3 )
def UpperCAmelCase_ (__a : Union[str, Any] , __a : List[Any] , __a : Union[str, Any] , __a : Optional[int]=None , __a : Any=None , __a : Tuple=None , __a : List[str]=None , ):
"""simple docstring"""
if attention_mask is None:
_a : str = tf.cast(tf.math.not_equal(__a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_a : Any = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_a : str = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_a : List[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class UpperCAmelCase__ ( lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : List[Any] = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
__UpperCAmelCase : str = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
__UpperCAmelCase : Union[str, Any] = (
{
'''conversational''': TFLEDForConditionalGeneration,
'''feature-extraction''': TFLEDModel,
'''summarization''': TFLEDForConditionalGeneration,
'''text2text-generation''': TFLEDForConditionalGeneration,
'''translation''': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
__UpperCAmelCase : Optional[Any] = True
__UpperCAmelCase : Optional[int] = False
__UpperCAmelCase : List[Any] = False
__UpperCAmelCase : str = False
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
_a : List[Any] = TFLEDModelTester(self )
_a : Optional[int] = ConfigTester(self ,config_class=_a )
def __lowercase ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
_a : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
def __lowercase ( self : Tuple ):
'''simple docstring'''
_a, _a : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
_a : Tuple = tf.zeros_like(inputs_dict['attention_mask'] )
_a : List[Any] = 2
_a : Dict = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices ,1 ,inputs_dict['global_attention_mask'] ,)
_a : Tuple = True
_a : List[Any] = self.model_tester.seq_length
_a : str = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_a : str ):
_a : Union[str, Any] = outputs.decoder_attentions
self.assertEqual(len(_a ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, seq_length, seq_length] ,)
def check_encoder_attentions_output(_a : Tuple ):
_a : Optional[Any] = [t.numpy() for t in outputs.encoder_attentions]
_a : Optional[Any] = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_a ) ,self.model_tester.num_hidden_layers )
self.assertEqual(len(_a ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, seq_length, seq_length] ,)
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] ,)
for model_class in self.all_model_classes:
_a : Union[str, Any] = True
_a : Union[str, Any] = False
_a : Tuple = False
_a : Union[str, Any] = model_class(_a )
_a : Union[str, Any] = model(self._prepare_for_class(_a ,_a ) )
_a : Dict = len(_a )
self.assertEqual(config.output_hidden_states ,_a )
check_encoder_attentions_output(_a )
if self.is_encoder_decoder:
_a : Any = model_class(_a )
_a : Tuple = model(self._prepare_for_class(_a ,_a ) )
self.assertEqual(config.output_hidden_states ,_a )
check_decoder_attentions_output(_a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
_a : str = True
_a : Optional[Any] = model_class(_a )
_a : Dict = model(self._prepare_for_class(_a ,_a ) )
self.assertEqual(config.output_hidden_states ,_a )
check_encoder_attentions_output(_a )
# Check attention is always last and order is fine
_a : Dict = True
_a : Dict = True
_a : Optional[int] = model_class(_a )
_a : Union[str, Any] = model(self._prepare_for_class(_a ,_a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) ,len(_a ) )
self.assertEqual(model.config.output_hidden_states ,_a )
check_encoder_attentions_output(_a )
@unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.' )
def __lowercase ( self : Any ):
'''simple docstring'''
pass
def __lowercase ( self : str ):
'''simple docstring'''
pass
def UpperCAmelCase_ (__a : Union[str, Any] ):
"""simple docstring"""
return tf.constant(__a , dtype=tf.intaa )
__lowerCAmelCase = 1e-4
@slow
@require_tf
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
_a : Tuple = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' ).led
# change to intended input here
_a : Optional[int] = _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
_a : str = _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
_a : Tuple = prepare_led_inputs_dict(model.config ,_a ,_a )
_a : Optional[int] = model(**_a )[0]
_a : List[Any] = (1, 1024, 768)
self.assertEqual(output.shape ,_a )
# change to expected output here
_a : Optional[int] = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] ,)
tf.debugging.assert_near(output[:, :3, :3] ,_a ,atol=1E-3 )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
_a : int = TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384' )
# change to intended input here
_a : Dict = _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
_a : List[str] = _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
_a : Union[str, Any] = prepare_led_inputs_dict(model.config ,_a ,_a )
_a : Union[str, Any] = model(**_a )[0]
_a : Optional[Any] = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape ,_a )
# change to expected output here
_a : Optional[Any] = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] ,)
tf.debugging.assert_near(output[:, :3, :3] ,_a ,atol=1E-3 ,rtol=1E-3 )
| 229 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE = F'''The input value of [n={number}] has to be > 0'''
raise ValueError(_UpperCAmelCase)
else:
SCREAMING_SNAKE_CASE = sylvester(number - 1)
SCREAMING_SNAKE_CASE = num - 1
SCREAMING_SNAKE_CASE = num
return lower * upper + 1
if __name__ == "__main__":
print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")
| 444 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
a_ : Optional[int] = logging.get_logger(__name__)
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = b.T
SCREAMING_SNAKE_CASE = np.sum(np.square(_UpperCAmelCase) , axis=1)
SCREAMING_SNAKE_CASE = np.sum(np.square(_UpperCAmelCase) , axis=0)
SCREAMING_SNAKE_CASE = np.matmul(_UpperCAmelCase , _UpperCAmelCase)
SCREAMING_SNAKE_CASE = aa[:, None] - 2 * ab + ba[None, :]
return d
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = x.reshape(-1 , 3)
SCREAMING_SNAKE_CASE = squared_euclidean_distance(_UpperCAmelCase , _UpperCAmelCase)
return np.argmin(_UpperCAmelCase , axis=1)
class _snake_case ( A__ ):
_lowercase : str = ['''pixel_values''']
def __init__( self , a = None , a = True , a = None , a = PILImageResampling.BILINEAR , a = True , a = True , **a , ) -> None:
super().__init__(**a)
SCREAMING_SNAKE_CASE = size if size is not None else {'height': 256, 'width': 256}
SCREAMING_SNAKE_CASE = get_size_dict(a)
SCREAMING_SNAKE_CASE = np.array(a) if clusters is not None else None
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = resample
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = do_color_quantize
def SCREAMING_SNAKE_CASE__ ( self , a , a , a = PILImageResampling.BILINEAR , a = None , **a , ) -> np.ndarray:
SCREAMING_SNAKE_CASE = get_size_dict(a)
if "height" not in size or "width" not in size:
raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''')
return resize(
a , size=(size['height'], size['width']) , resample=a , data_format=a , **a)
def SCREAMING_SNAKE_CASE__ ( self , a , a = None , ) -> np.ndarray:
SCREAMING_SNAKE_CASE = rescale(image=a , scale=1 / 1_27.5 , data_format=a)
SCREAMING_SNAKE_CASE = image - 1
return image
def SCREAMING_SNAKE_CASE__ ( self , a , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = ChannelDimension.FIRST , **a , ) -> PIL.Image.Image:
SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE = size if size is not None else self.size
SCREAMING_SNAKE_CASE = get_size_dict(a)
SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
SCREAMING_SNAKE_CASE = clusters if clusters is not None else self.clusters
SCREAMING_SNAKE_CASE = np.array(a)
SCREAMING_SNAKE_CASE = make_list_of_images(a)
if not valid_images(a):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.')
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.')
if do_color_quantize and clusters is None:
raise ValueError('Clusters must be specified if do_color_quantize is True.')
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE = [to_numpy_array(a) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE = [self.resize(image=a , size=a , resample=a) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE = [self.normalize(image=a) for image in images]
if do_color_quantize:
SCREAMING_SNAKE_CASE = [to_channel_dimension_format(a , ChannelDimension.LAST) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
SCREAMING_SNAKE_CASE = np.array(a)
SCREAMING_SNAKE_CASE = color_quantize(a , a).reshape(images.shape[:-1])
# flatten to (batch_size, height*width)
SCREAMING_SNAKE_CASE = images.shape[0]
SCREAMING_SNAKE_CASE = images.reshape(a , -1)
# We need to convert back to a list of images to keep consistent behaviour across processors.
SCREAMING_SNAKE_CASE = list(a)
else:
SCREAMING_SNAKE_CASE = [to_channel_dimension_format(a , a) for image in images]
SCREAMING_SNAKE_CASE = {'input_ids': images}
return BatchFeature(data=a , tensor_type=a)
| 444 | 1 |
'''simple docstring'''
from __future__ import annotations
def a_ ( lowerCamelCase : list[list[int]] ):
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(lowerCamelCase ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(lowerCamelCase ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 133 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( UpperCamelCase : str , UpperCamelCase : list[str] | None = None ) -> list[list[str]]:
a__ = word_bank or []
# create a table
a__ = len(UpperCamelCase ) + 1
a__ = []
for _ in range(UpperCamelCase ):
table.append([] )
# seed value
a__ = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(UpperCamelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(UpperCamelCase )] == word:
a__ = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(UpperCamelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(UpperCamelCase )]:
combination.reverse()
return table[len(UpperCamelCase )]
if __name__ == "__main__":
print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
print(
all_construct(
'hexagonosaurus',
['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
)
)
| 273 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = 384
if "tiny" in model_name:
A_ : Union[str, Any] = [3, 3, 9, 3]
A_ : str = [96, 192, 384, 768]
if "small" in model_name:
A_ : List[Any] = [3, 3, 27, 3]
A_ : List[Any] = [96, 192, 384, 768]
if "base" in model_name:
A_ : Dict = [3, 3, 27, 3]
A_ : Tuple = [128, 256, 512, 1024]
A_ : Union[str, Any] = 512
if "large" in model_name:
A_ : Dict = [3, 3, 27, 3]
A_ : Dict = [192, 384, 768, 1536]
A_ : List[str] = 768
if "xlarge" in model_name:
A_ : Dict = [3, 3, 27, 3]
A_ : List[Any] = [256, 512, 1024, 2048]
A_ : Any = 1024
# set label information
A_ : List[str] = 150
A_ : int = 'huggingface/label-files'
A_ : int = 'ade20k-id2label.json'
A_ : str = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type='dataset' ) , 'r' ) )
A_ : Any = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
A_ : Optional[Any] = {v: k for k, v in idalabel.items()}
A_ : List[Any] = ConvNextConfig(
depths=_UpperCAmelCase , hidden_sizes=_UpperCAmelCase , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
A_ : Tuple = UperNetConfig(
backbone_config=_UpperCAmelCase , auxiliary_in_channels=_UpperCAmelCase , num_labels=_UpperCAmelCase , idalabel=_UpperCAmelCase , labelaid=_UpperCAmelCase , )
return config
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Optional[int] = []
# fmt: off
# stem
rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight') )
rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias') )
rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight') )
rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.stages.{i}.{j}.gamma""", f"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.norm.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.norm.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""") )
rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""") )
if i > 0:
rename_keys.append((f"""backbone.downsample_layers.{i}.0.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.0.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.1.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""") )
rename_keys.append((f"""backbone.downsample_layers.{i}.1.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""") )
rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
] )
# fmt: on
return rename_keys
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = dct.pop(_UpperCAmelCase )
A_ : Union[str, Any] = val
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : int = {
'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
}
A_ : Optional[int] = model_name_to_url[model_name]
A_ : List[str] = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location='cpu' )['state_dict']
A_ : Optional[Any] = get_upernet_config(_UpperCAmelCase )
A_ : List[str] = UperNetForSemanticSegmentation(_UpperCAmelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
A_ : Optional[Any] = state_dict.pop(_UpperCAmelCase )
if "bn" in key:
A_ : Optional[Any] = key.replace('bn' , 'batch_norm' )
A_ : Dict = val
# rename keys
A_ : Dict = create_rename_keys(_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
# verify on image
A_ : int = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
A_ : str = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('RGB' )
A_ : Optional[int] = SegformerImageProcessor()
A_ : int = processor(_UpperCAmelCase , return_tensors='pt' ).pixel_values
with torch.no_grad():
A_ : str = model(_UpperCAmelCase )
if model_name == "upernet-convnext-tiny":
A_ : Optional[int] = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
A_ : Optional[int] = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
A_ : List[str] = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
A_ : Dict = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
A_ : Dict = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
print('Logits:' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , _UpperCAmelCase , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_UpperCAmelCase )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print(f"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(f"""openmmlab/{model_name}""" )
processor.push_to_hub(f"""openmmlab/{model_name}""" )
if __name__ == "__main__":
lowerCamelCase_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='upernet-convnext-tiny',
type=str,
choices=[F"upernet-convnext-{size}" for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
help='Name of the ConvNext UperNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
lowerCamelCase_ : int = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 302 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : Tuple = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class _UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
lowercase_ : Union[str, Any] = """informer"""
lowercase_ : str = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__( self , snake_case_ = None , snake_case_ = None , snake_case_ = "student_t" , snake_case_ = "nll" , snake_case_ = 1 , snake_case_ = None , snake_case_ = "mean" , snake_case_ = 0 , snake_case_ = 0 , snake_case_ = 0 , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = 6_4 , snake_case_ = 3_2 , snake_case_ = 3_2 , snake_case_ = 2 , snake_case_ = 2 , snake_case_ = 2 , snake_case_ = 2 , snake_case_ = True , snake_case_ = "gelu" , snake_case_ = 0.05 , snake_case_ = 0.1 , snake_case_ = 0.1 , snake_case_ = 0.1 , snake_case_ = 0.1 , snake_case_ = 1_0_0 , snake_case_ = 0.02 , snake_case_=True , snake_case_ = "prob" , snake_case_ = 5 , snake_case_ = True , **snake_case_ , ):
"""simple docstring"""
A_ : Tuple = prediction_length
A_ : Any = context_length or prediction_length
A_ : Tuple = distribution_output
A_ : Union[str, Any] = loss
A_ : Any = input_size
A_ : Dict = num_time_features
A_ : int = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
A_ : Optional[Any] = scaling
A_ : Optional[Any] = num_dynamic_real_features
A_ : Union[str, Any] = num_static_real_features
A_ : Optional[int] = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(snake_case_ ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
A_ : Dict = cardinality
else:
A_ : Tuple = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(snake_case_ ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
A_ : Dict = embedding_dimension
else:
A_ : List[Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
A_ : Optional[Any] = num_parallel_samples
# Transformer architecture configuration
A_ : Any = input_size * len(self.lags_sequence ) + self._number_of_features
A_ : List[Any] = d_model
A_ : Tuple = encoder_attention_heads
A_ : int = decoder_attention_heads
A_ : Any = encoder_ffn_dim
A_ : Optional[Any] = decoder_ffn_dim
A_ : List[str] = encoder_layers
A_ : str = decoder_layers
A_ : Any = dropout
A_ : Optional[Any] = attention_dropout
A_ : Optional[int] = activation_dropout
A_ : Union[str, Any] = encoder_layerdrop
A_ : Optional[int] = decoder_layerdrop
A_ : Optional[Any] = activation_function
A_ : Any = init_std
A_ : str = use_cache
# Informer
A_ : List[str] = attention_type
A_ : Optional[int] = sampling_factor
A_ : Any = distil
super().__init__(is_encoder_decoder=snake_case_ , **snake_case_ )
@property
def lowerCamelCase_ ( self ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
) | 302 | 1 |
a = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
a = [{'type': 'code', 'content': INSTALL_CONTENT}]
a = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 412 |
import argparse
from collections import defaultdict
import yaml
a = 'docs/source/en/_toctree.yml'
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = defaultdict(UpperCAmelCase__ )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase_ = [key for key, value in counts.items() if value > 1]
lowercase_ = []
for duplicate_key in duplicates:
lowercase_ = list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key} )
if len(UpperCAmelCase__ ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1] )
# Sort
return sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : s["title"].lower() )
def UpperCAmelCase_ ( UpperCAmelCase__=False ):
with open(UpperCAmelCase__ , encoding="""utf-8""" ) as f:
lowercase_ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase_ = content[api_idx]["""sections"""]
# Then to the model doc
lowercase_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase_ = api_doc[model_idx]["""sections"""]
lowercase_ = [(idx, section) for idx, section in enumerate(UpperCAmelCase__ ) if """sections""" in section]
lowercase_ = False
for idx, modality_doc in modalities_docs:
lowercase_ = modality_doc["""sections"""]
lowercase_ = clean_model_doc_toc(UpperCAmelCase__ )
if old_modality_doc != new_modality_doc:
lowercase_ = True
if overwrite:
lowercase_ = new_modality_doc
if diff:
if overwrite:
lowercase_ = model_doc
lowercase_ = api_doc
with open(UpperCAmelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(UpperCAmelCase__ , allow_unicode=UpperCAmelCase__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
a = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 412 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class UpperCAmelCase ( a__ ):
_A : Tuple = "Salesforce/blip-image-captioning-base"
_A : List[Any] = (
"This is a tool that generates a description of an image. It takes an input named `image` which should be the "
"image to caption, and returns a text that contains the description in English."
)
_A : List[str] = "image_captioner"
_A : Optional[int] = AutoModelForVisionaSeq
_A : Union[str, Any] = ["image"]
_A : Optional[int] = ["text"]
def __init__( self , *__A , **__A ):
requires_backends(self , ['vision'] )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_ )
def __lowerCamelCase ( self , __A ):
return self.pre_processor(images=lowerCamelCase_ , return_tensors='pt' )
def __lowerCamelCase ( self , __A ):
return self.model.generate(**lowerCamelCase_ )
def __lowerCamelCase ( self , __A ):
return self.pre_processor.batch_decode(lowerCamelCase_ , skip_special_tokens=lowerCamelCase_ )[0].strip()
| 710 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , __A , __A=13 , __A=7 , __A=True , __A=True , __A=True , __A=True , __A=99 , __A=32 , __A=5 , __A=4 , __A=37 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=16 , __A=2 , __A=0.0_2 , __A=False , __A=True , __A="None" , __A=3 , __A=4 , __A=None , ):
__UpperCAmelCase = parent
__UpperCAmelCase = batch_size
__UpperCAmelCase = seq_length
__UpperCAmelCase = is_training
__UpperCAmelCase = use_input_mask
__UpperCAmelCase = use_token_type_ids
__UpperCAmelCase = use_labels
__UpperCAmelCase = vocab_size
__UpperCAmelCase = hidden_size
__UpperCAmelCase = num_hidden_layers
__UpperCAmelCase = num_attention_heads
__UpperCAmelCase = intermediate_size
__UpperCAmelCase = hidden_act
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = max_position_embeddings
__UpperCAmelCase = type_vocab_size
__UpperCAmelCase = type_sequence_label_size
__UpperCAmelCase = initializer_range
__UpperCAmelCase = num_labels
__UpperCAmelCase = num_choices
__UpperCAmelCase = relative_attention
__UpperCAmelCase = position_biased_input
__UpperCAmelCase = pos_att_type
__UpperCAmelCase = scope
def __lowerCamelCase ( self ):
__UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase = None
if self.use_input_mask:
__UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__UpperCAmelCase = None
if self.use_token_type_ids:
__UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCAmelCase = None
__UpperCAmelCase = None
__UpperCAmelCase = None
if self.use_labels:
__UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
__UpperCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self ):
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __lowerCamelCase ( self , __A ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __lowerCamelCase ( self , __A , __A , __A , __A , __A , __A , __A ):
__UpperCAmelCase = DebertaVaModel(config=__A )
model.to(__A )
model.eval()
__UpperCAmelCase = model(__A , attention_mask=__A , token_type_ids=__A )[0]
__UpperCAmelCase = model(__A , token_type_ids=__A )[0]
__UpperCAmelCase = model(__A )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __lowerCamelCase ( self , __A , __A , __A , __A , __A , __A , __A ):
__UpperCAmelCase = DebertaVaForMaskedLM(config=__A )
model.to(__A )
model.eval()
__UpperCAmelCase = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , __A , __A , __A , __A , __A , __A , __A ):
__UpperCAmelCase = self.num_labels
__UpperCAmelCase = DebertaVaForSequenceClassification(__A )
model.to(__A )
model.eval()
__UpperCAmelCase = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(__A )
def __lowerCamelCase ( self , __A , __A , __A , __A , __A , __A , __A ):
__UpperCAmelCase = self.num_labels
__UpperCAmelCase = DebertaVaForTokenClassification(config=__A )
model.to(__A )
model.eval()
__UpperCAmelCase = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self , __A , __A , __A , __A , __A , __A , __A ):
__UpperCAmelCase = DebertaVaForQuestionAnswering(config=__A )
model.to(__A )
model.eval()
__UpperCAmelCase = model(
__A , attention_mask=__A , token_type_ids=__A , start_positions=__A , end_positions=__A , )
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 __lowerCamelCase ( self , __A , __A , __A , __A , __A , __A , __A ):
__UpperCAmelCase = DebertaVaForMultipleChoice(config=__A )
model.to(__A )
model.eval()
__UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.prepare_config_and_inputs()
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) = config_and_inputs
__UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
_A : Optional[Any] = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
_A : int = (
{
"""feature-extraction""": DebertaVaModel,
"""fill-mask""": DebertaVaForMaskedLM,
"""question-answering""": DebertaVaForQuestionAnswering,
"""text-classification""": DebertaVaForSequenceClassification,
"""token-classification""": DebertaVaForTokenClassification,
"""zero-shot""": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
_A : List[str] = True
_A : Union[str, Any] = False
_A : int = False
_A : Dict = False
_A : int = False
def __lowerCamelCase ( self ):
__UpperCAmelCase = DebertaVaModelTester(self )
__UpperCAmelCase = ConfigTester(self , config_class=__A , hidden_size=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__A )
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__A )
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__A )
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__A )
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__A )
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*__A )
@slow
def __lowerCamelCase ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase = DebertaVaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __lowerCamelCase ( self ):
pass
@slow
def __lowerCamelCase ( self ):
__UpperCAmelCase = DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
__UpperCAmelCase = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
__UpperCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__UpperCAmelCase = model(__A , attention_mask=__A )[0]
# compare the actual values for a slice.
__UpperCAmelCase = torch.tensor(
[[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __A , atol=1E-4 ) , f'{output[:, 1:4, 1:4]}' )
| 617 | 0 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _lowerCAmelCase :
"""simple docstring"""
_lowerCamelCase = None
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : Tuple = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , _lowercase )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[str] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = os.path.join(_lowercase , """feat_extract.json""" )
feat_extract_first.to_json_file(_lowercase )
snake_case_ : str = self.feature_extraction_class.from_json_file(_lowercase )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : str = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Tuple = feat_extract_first.save_pretrained(_lowercase )[0]
check_json_file_has_correct_format(_lowercase )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(_lowercase )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : int = self.feature_extraction_class()
self.assertIsNotNone(_lowercase )
| 58 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class A_ (a_ ):
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
class A_ (a_ ):
def __init__( self , _A=1 , _A=0 , _A=2 , _A=5_1_2 , _A="cls" , _A=False , _A=True , **_A , ):
'''simple docstring'''
super().__init__(pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , **_A )
UpperCAmelCase = project_dim
UpperCAmelCase = pooler_fn
UpperCAmelCase = learn_encoder
UpperCAmelCase = use_attention_mask
class A_ (a_ ):
UpperCAmelCase__ = [r'''pooler''', r'''logit_scale''']
UpperCAmelCase__ = [r'''position_ids''', r'''predictions.decoder.bias''']
UpperCAmelCase__ = '''roberta'''
UpperCAmelCase__ = RobertaSeriesConfig
def __init__( self , _A ):
'''simple docstring'''
super().__init__(_A )
UpperCAmelCase = XLMRobertaModel(_A )
UpperCAmelCase = nn.Linear(config.hidden_size , config.project_dim )
UpperCAmelCase = getattr(_A , '''has_pre_transformation''' , _A )
if self.has_pre_transformation:
UpperCAmelCase = nn.Linear(config.hidden_size , config.project_dim )
UpperCAmelCase = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def _lowercase ( self , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , ):
'''simple docstring'''
UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase = self.base_model(
input_ids=_A , attention_mask=_A , token_type_ids=_A , position_ids=_A , head_mask=_A , inputs_embeds=_A , encoder_hidden_states=_A , encoder_attention_mask=_A , output_attentions=_A , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=_A , )
if self.has_pre_transformation:
UpperCAmelCase = outputs['''hidden_states'''][-2]
UpperCAmelCase = self.pre_LN(_A )
UpperCAmelCase = self.transformation_pre(_A )
return TransformationModelOutput(
projection_state=_A , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
UpperCAmelCase = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=_A , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 130 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__UpperCAmelCase = 'Usage of script: script_name <size_of_canvas:int>'
__UpperCAmelCase = [0] * 1_00 + [1] * 10
random.shuffle(choice)
def _snake_case ( lowercase__ : str ) -> list[list[bool]]:
'''simple docstring'''
lowerCAmelCase_ :Dict = [[False for i in range(a__ )] for j in range(a__ )]
return canvas
def _snake_case ( lowercase__ : Optional[Any] ) -> None:
'''simple docstring'''
for i, row in enumerate(a__ ):
for j, _ in enumerate(a__ ):
lowerCAmelCase_ :str = bool(random.getrandbits(1 ) )
def _snake_case ( lowercase__ : Optional[Any] ) -> list[list[bool]]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = np.array(a__ )
lowerCAmelCase_ :Union[str, Any] = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(a__ ):
for c, pt in enumerate(a__ ):
lowerCAmelCase_ :Optional[int] = __judge_point(
a__ , current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
lowerCAmelCase_ :Optional[int] = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
lowerCAmelCase_ :int = current_canvas.tolist()
return return_canvas
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple = 0
lowerCAmelCase_ :List[str] = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
lowerCAmelCase_ :Dict = pt
if pt:
if alive < 2:
lowerCAmelCase_ :Optional[Any] = False
elif alive == 2 or alive == 3:
lowerCAmelCase_ :Dict = True
elif alive > 3:
lowerCAmelCase_ :Optional[Any] = False
else:
if alive == 3:
lowerCAmelCase_ :int = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
__UpperCAmelCase = int(sys.argv[1])
# main working structure of this module.
__UpperCAmelCase = create_canvas(canvas_size)
seed(c)
__UpperCAmelCase , __UpperCAmelCase = plt.subplots()
fig.show()
__UpperCAmelCase = ListedColormap(['w', 'k'])
try:
while True:
__UpperCAmelCase = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 716 |
"""simple docstring"""
from __future__ import annotations
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A=None ) -> Tuple:
lowerCAmelCase_ :Optional[int] = data
lowerCAmelCase_ :List[Any] = None
def __repr__( self ) -> Union[str, Any]:
lowerCAmelCase_ :int = []
lowerCAmelCase_ :int = self
while temp:
string_rep.append(f"""{temp.data}""" )
lowerCAmelCase_ :List[str] = temp.next
return "->".join(__A )
def _snake_case ( lowercase__ : list ) -> Union[str, Any]:
'''simple docstring'''
if not elements_list:
raise Exception("""The Elements List is empty""" )
lowerCAmelCase_ :int = Node(elements_list[0] )
for i in range(1 , len(lowercase__ ) ):
lowerCAmelCase_ :Tuple = Node(elements_list[i] )
lowerCAmelCase_ :Union[str, Any] = current.next
return head
def _snake_case ( lowercase__ : Node ) -> None:
'''simple docstring'''
if head_node is not None and isinstance(lowercase__ , lowercase__ ):
print_reverse(head_node.next )
print(head_node.data )
def _snake_case ( ) -> Optional[int]:
'''simple docstring'''
from doctest import testmod
testmod()
lowerCAmelCase_ :Union[str, Any] = make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
print("""Linked List:""" )
print(lowercase__ )
print("""Elements in Reverse:""" )
print_reverse(lowercase__ )
if __name__ == "__main__":
main()
| 256 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "ClapFeatureExtractor"
SCREAMING_SNAKE_CASE = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
super().__init__(__lowerCAmelCase , __lowerCAmelCase )
def __call__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , **__lowerCAmelCase ) -> Dict:
lowercase__ : List[Any] = kwargs.pop('''sampling_rate''' , __lowerCAmelCase )
if text is None and audios is None:
raise ValueError('''You have to specify either text or audios. Both cannot be none.''' )
if text is not None:
lowercase__ : List[Any] = self.tokenizer(__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase )
if audios is not None:
lowercase__ : Dict = self.feature_extractor(
__lowerCAmelCase , sampling_rate=__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase )
if text is not None and audios is not None:
lowercase__ : str = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__lowerCAmelCase ) , tensor_type=__lowerCAmelCase )
def _lowerCAmelCase( self , *__lowerCAmelCase , **__lowerCAmelCase ) -> Any:
return self.tokenizer.batch_decode(*__lowerCAmelCase , **__lowerCAmelCase )
def _lowerCAmelCase( self , *__lowerCAmelCase , **__lowerCAmelCase ) -> List[str]:
return self.tokenizer.decode(*__lowerCAmelCase , **__lowerCAmelCase )
@property
def _lowerCAmelCase( self ) -> int:
lowercase__ : Union[str, Any] = self.tokenizer.model_input_names
lowercase__ : Union[str, Any] = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 152 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Union[str, Any] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase : str = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
]
)
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
lowercase : List[str] = state_dict.pop(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = val
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> Any:
lowercase : str = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase : Dict = key.replace("""backbone.0.body""" , """backbone.conv_encoder.model""" )
lowercase : Union[str, Any] = value
else:
lowercase : Tuple = value
return new_state_dict
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ) -> List[Any]:
lowercase : str = """"""
if is_panoptic:
lowercase : Tuple = """conditional_detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase : int = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight" )
lowercase : Union[str, Any] = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
lowercase : Dict = in_proj_weight[:256, :]
lowercase : Optional[int] = in_proj_bias[:256]
lowercase : Tuple = in_proj_weight[256:512, :]
lowercase : Any = in_proj_bias[256:512]
lowercase : Any = in_proj_weight[-256:, :]
lowercase : Dict = in_proj_bias[-256:]
def _snake_case( ) -> Tuple:
lowercase : Any = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase : Tuple = Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw )
return im
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Tuple:
lowercase : str = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
lowercase : Tuple = """resnet101"""
if "dc5" in model_name:
lowercase : List[Any] = True
lowercase : Optional[Any] = """panoptic""" in model_name
if is_panoptic:
lowercase : Optional[int] = 250
else:
lowercase : Tuple = 91
lowercase : Any = """huggingface/label-files"""
lowercase : int = """coco-detection-id2label.json"""
lowercase : str = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type="""dataset""" ) , """r""" ) )
lowercase : Any = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
lowercase : int = idalabel
lowercase : List[Any] = {v: k for k, v in idalabel.items()}
# load image processor
lowercase : int = """coco_panoptic""" if is_panoptic else """coco_detection"""
lowercase : List[Any] = ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE__ )
# prepare image
lowercase : Dict = prepare_img()
lowercase : List[str] = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" )
lowercase : List[str] = encoding["""pixel_values"""]
logger.info(f"Converting model {model_name}..." )
# load original model from torch hub
lowercase : Union[str, Any] = torch.hub.load("""DeppMeng/ConditionalDETR""" , SCREAMING_SNAKE_CASE__ , pretrained=SCREAMING_SNAKE_CASE__ ).eval()
lowercase : Any = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
lowercase : str = """conditional_detr.""" + src
rename_key(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = rename_backbone_keys(SCREAMING_SNAKE_CASE__ )
# query, key and value matrices need special treatment
read_in_q_k_v(SCREAMING_SNAKE_CASE__ , is_panoptic=SCREAMING_SNAKE_CASE__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase : Optional[int] = """conditional_detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""conditional_detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
lowercase : Union[str, Any] = state_dict.pop(SCREAMING_SNAKE_CASE__ )
lowercase : Dict = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
lowercase : Optional[int] = state_dict.pop(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
lowercase : List[Any] = state_dict.pop(SCREAMING_SNAKE_CASE__ )
lowercase : Optional[int] = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
lowercase : Dict = state_dict.pop(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = val
# finally, create HuggingFace model and load state dict
lowercase : str = ConditionalDetrForSegmentation(SCREAMING_SNAKE_CASE__ ) if is_panoptic else ConditionalDetrForObjectDetection(SCREAMING_SNAKE_CASE__ )
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
model.eval()
model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE__ , organization="""DepuMeng""" , commit_message="""Add model""" )
# verify our conversion
lowercase : List[Any] = conditional_detr(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = model(SCREAMING_SNAKE_CASE__ )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1e-4 )
# Save model and image processor
logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..." )
Path(SCREAMING_SNAKE_CASE__ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
lowercase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""conditional_detr_resnet50""",
type=str,
help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
lowercase : Any = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 336 | 0 |
from ... import PretrainedConfig
snake_case_ : Dict = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class snake_case_ ( __A ):
'''simple docstring'''
lowerCamelCase = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
lowerCamelCase = "nezha"
def __init__( self : Optional[Any] , __magic_name__ : Tuple=2_1128 , __magic_name__ : Optional[Any]=768 , __magic_name__ : Optional[Any]=12 , __magic_name__ : Union[str, Any]=12 , __magic_name__ : Dict=3072 , __magic_name__ : List[Any]="gelu" , __magic_name__ : Union[str, Any]=0.1 , __magic_name__ : List[str]=0.1 , __magic_name__ : int=512 , __magic_name__ : List[str]=64 , __magic_name__ : List[str]=2 , __magic_name__ : Union[str, Any]=0.02 , __magic_name__ : Tuple=1e-12 , __magic_name__ : Tuple=0.1 , __magic_name__ : Tuple=0 , __magic_name__ : Tuple=2 , __magic_name__ : Optional[Any]=3 , __magic_name__ : Optional[Any]=True , **__magic_name__ : int , ) -> Optional[Any]:
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
lowerCamelCase_ : int = vocab_size
lowerCamelCase_ : Any = hidden_size
lowerCamelCase_ : Tuple = num_hidden_layers
lowerCamelCase_ : Any = num_attention_heads
lowerCamelCase_ : str = hidden_act
lowerCamelCase_ : List[str] = intermediate_size
lowerCamelCase_ : List[Any] = hidden_dropout_prob
lowerCamelCase_ : Union[str, Any] = attention_probs_dropout_prob
lowerCamelCase_ : Union[str, Any] = max_position_embeddings
lowerCamelCase_ : Any = max_relative_position
lowerCamelCase_ : str = type_vocab_size
lowerCamelCase_ : List[str] = initializer_range
lowerCamelCase_ : Union[str, Any] = layer_norm_eps
lowerCamelCase_ : List[Any] = classifier_dropout
lowerCamelCase_ : Union[str, Any] = use_cache
| 253 |
from collections.abc import Generator
from math import sin
def __a ( __UpperCAmelCase : bytes ) -> bytes:
"""simple docstring"""
if len(__UpperCAmelCase ) != 32:
raise ValueError("Input must be of length 32" )
lowerCamelCase_ : Optional[Any] = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def __a ( __UpperCAmelCase : int ) -> bytes:
"""simple docstring"""
if i < 0:
raise ValueError("Input must be non-negative" )
lowerCamelCase_ : Tuple = format(__UpperCAmelCase , "08x" )[-8:]
lowerCamelCase_ : int = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
return little_endian_hex
def __a ( __UpperCAmelCase : bytes ) -> bytes:
"""simple docstring"""
lowerCamelCase_ : int = b""
for char in message:
bit_string += format(__UpperCAmelCase , "08b" ).encode("utf-8" )
lowerCamelCase_ : Optional[int] = format(len(__UpperCAmelCase ) , "064b" ).encode("utf-8" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__UpperCAmelCase ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def __a ( __UpperCAmelCase : bytes ) -> Generator[list[int], None, None]:
"""simple docstring"""
if len(__UpperCAmelCase ) % 512 != 0:
raise ValueError("Input must have length that's a multiple of 512" )
for pos in range(0 , len(__UpperCAmelCase ) , 512 ):
lowerCamelCase_ : Union[str, Any] = bit_string[pos : pos + 512]
lowerCamelCase_ : Any = []
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def __a ( __UpperCAmelCase : int ) -> int:
"""simple docstring"""
if i < 0:
raise ValueError("Input must be non-negative" )
lowerCamelCase_ : Dict = format(__UpperCAmelCase , "032b" )
lowerCamelCase_ : Dict = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__UpperCAmelCase , 2 )
def __a ( __UpperCAmelCase : int , __UpperCAmelCase : int ) -> int:
"""simple docstring"""
return (a + b) % 2**32
def __a ( __UpperCAmelCase : int , __UpperCAmelCase : int ) -> int:
"""simple docstring"""
if i < 0:
raise ValueError("Input must be non-negative" )
if shift < 0:
raise ValueError("Shift must be non-negative" )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def __a ( __UpperCAmelCase : bytes ) -> bytes:
"""simple docstring"""
lowerCamelCase_ : int = preprocess(__UpperCAmelCase )
lowerCamelCase_ : Union[str, Any] = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
lowerCamelCase_ : List[str] = 0X67_452_301
lowerCamelCase_ : Optional[int] = 0XEF_CDA_B89
lowerCamelCase_ : str = 0X98_BAD_CFE
lowerCamelCase_ : Optional[int] = 0X10_325_476
lowerCamelCase_ : Union[str, Any] = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__UpperCAmelCase ):
lowerCamelCase_ : Optional[int] = aa
lowerCamelCase_ : List[str] = ba
lowerCamelCase_ : Optional[int] = ca
lowerCamelCase_ : List[Any] = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCamelCase_ : Dict = d ^ (b & (c ^ d))
lowerCamelCase_ : Any = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCamelCase_ : Any = c ^ (d & (b ^ c))
lowerCamelCase_ : List[Any] = (5 * i + 1) % 16
elif i <= 47:
lowerCamelCase_ : List[Any] = b ^ c ^ d
lowerCamelCase_ : int = (3 * i + 5) % 16
else:
lowerCamelCase_ : str = c ^ (b | not_aa(__UpperCAmelCase ))
lowerCamelCase_ : int = (7 * i) % 16
lowerCamelCase_ : List[str] = (f + a + added_consts[i] + block_words[g]) % 2**32
lowerCamelCase_ : Union[str, Any] = d
lowerCamelCase_ : Optional[int] = c
lowerCamelCase_ : Union[str, Any] = b
lowerCamelCase_ : List[str] = sum_aa(__UpperCAmelCase , left_rotate_aa(__UpperCAmelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
lowerCamelCase_ : Tuple = sum_aa(__UpperCAmelCase , __UpperCAmelCase )
lowerCamelCase_ : List[str] = sum_aa(__UpperCAmelCase , __UpperCAmelCase )
lowerCamelCase_ : Dict = sum_aa(__UpperCAmelCase , __UpperCAmelCase )
lowerCamelCase_ : Optional[int] = sum_aa(__UpperCAmelCase , __UpperCAmelCase )
lowerCamelCase_ : Optional[int] = reformat_hex(__UpperCAmelCase ) + reformat_hex(__UpperCAmelCase ) + reformat_hex(__UpperCAmelCase ) + reformat_hex(__UpperCAmelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 253 | 1 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __magic_name__ :
"""simple docstring"""
def __init__( self :int , snake_case :Any , snake_case :Tuple=13 , snake_case :Any=30 , snake_case :Any=2 , snake_case :str=3 , snake_case :Any=True , snake_case :Any=True , snake_case :Optional[Any]=32 , snake_case :int=5 , snake_case :int=4 , snake_case :Tuple=37 , snake_case :Dict="gelu" , snake_case :Tuple=0.1 , snake_case :Optional[Any]=0.1 , snake_case :Optional[int]=10 , snake_case :Optional[int]=0.02 , snake_case :str=None , snake_case :int=2 , ):
'''simple docstring'''
A_ : Dict = parent
A_ : Optional[int] = batch_size
A_ : int = image_size
A_ : Optional[int] = patch_size
A_ : str = num_channels
A_ : List[str] = is_training
A_ : List[Any] = use_labels
A_ : Any = hidden_size
A_ : List[str] = num_hidden_layers
A_ : List[str] = num_attention_heads
A_ : Tuple = intermediate_size
A_ : Tuple = hidden_act
A_ : Optional[Any] = hidden_dropout_prob
A_ : str = attention_probs_dropout_prob
A_ : Any = type_sequence_label_size
A_ : List[Any] = initializer_range
A_ : str = scope
A_ : Optional[Any] = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A_ : Any = (image_size // patch_size) ** 2
A_ : Tuple = num_patches + 1
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
A_ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Optional[int] = None
if self.use_labels:
A_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : int = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def SCREAMING_SNAKE_CASE ( self :Optional[int] , snake_case :Union[str, Any] , snake_case :Union[str, Any] , snake_case :List[str] ):
'''simple docstring'''
A_ : Tuple = ViTModel(config=snake_case )
model.to(snake_case )
model.eval()
A_ : Dict = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self :List[Any] , snake_case :Optional[Any] , snake_case :Optional[int] , snake_case :int ):
'''simple docstring'''
A_ : Optional[Any] = ViTForMaskedImageModeling(config=snake_case )
model.to(snake_case )
model.eval()
A_ : Any = model(snake_case )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A_ : Union[str, Any] = 1
A_ : Optional[int] = ViTForMaskedImageModeling(snake_case )
model.to(snake_case )
model.eval()
A_ : Any = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : List[Any] = model(snake_case )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def SCREAMING_SNAKE_CASE ( self :Optional[int] , snake_case :Tuple , snake_case :Optional[int] , snake_case :List[str] ):
'''simple docstring'''
A_ : Optional[int] = self.type_sequence_label_size
A_ : Optional[Any] = ViTForImageClassification(snake_case )
model.to(snake_case )
model.eval()
A_ : Any = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A_ : int = 1
A_ : List[str] = ViTForImageClassification(snake_case )
model.to(snake_case )
model.eval()
A_ : Any = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : Dict = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE ( self :int ):
'''simple docstring'''
A_ : Dict = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) ,
) : int = config_and_inputs
A_ : str = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (
{'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
if is_torch_available()
else {}
)
__UpperCamelCase = True
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
A_ : List[Any] = ViTModelTester(self )
A_ : List[Any] = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE ( self :int ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE ( self :int ):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self :Tuple ):
'''simple docstring'''
A_ , A_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case , nn.Linear ) )
def SCREAMING_SNAKE_CASE ( self :Optional[int] ):
'''simple docstring'''
A_ , A_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : int = model_class(snake_case )
A_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : Any = [*signature.parameters.keys()]
A_ : Dict = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case )
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE ( self :int ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case )
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE ( self :Optional[int] ):
'''simple docstring'''
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Union[str, Any] = ViTModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def __snake_case ( ) -> Optional[Any]:
A_ : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE ( self :int ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224" ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self :Optional[int] ):
'''simple docstring'''
A_ : Optional[Any] = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224" ).to(snake_case )
A_ : List[Any] = self.default_image_processor
A_ : List[str] = prepare_img()
A_ : Dict = image_processor(images=snake_case , return_tensors="pt" ).to(snake_case )
# forward pass
with torch.no_grad():
A_ : str = model(**snake_case )
# verify the logits
A_ : Optional[Any] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , snake_case )
A_ : Dict = torch.tensor([-0.2744, 0.8215, -0.0836] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
A_ : Optional[Any] = ViTModel.from_pretrained("facebook/dino-vits8" ).to(snake_case )
A_ : List[str] = ViTImageProcessor.from_pretrained("facebook/dino-vits8" , size=480 )
A_ : Dict = prepare_img()
A_ : Dict = image_processor(images=snake_case , return_tensors="pt" )
A_ : List[Any] = inputs.pixel_values.to(snake_case )
# forward pass
with torch.no_grad():
A_ : List[str] = model(snake_case , interpolate_pos_encoding=snake_case )
# verify the logits
A_ : Tuple = torch.Size((1, 3_601, 384) )
self.assertEqual(outputs.last_hidden_state.shape , snake_case )
A_ : Tuple = torch.tensor(
[[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , snake_case , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
A_ : Optional[Any] = ViTModel.from_pretrained("facebook/dino-vits8" , torch_dtype=torch.floataa , device_map="auto" )
A_ : Tuple = self.default_image_processor
A_ : Optional[Any] = prepare_img()
A_ : Optional[int] = image_processor(images=snake_case , return_tensors="pt" )
A_ : List[Any] = inputs.pixel_values.to(snake_case )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A_ : Any = model(snake_case )
| 454 |
def __snake_case ( _lowerCAmelCase : list , _lowerCAmelCase : list , _lowerCAmelCase : int ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than zero." )
if any(p < 0 for p in profit ):
raise ValueError("Profit can not be negative." )
if any(w < 0 for w in weight ):
raise ValueError("Weight can not be negative." )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
A_ : Dict = [p / w for p, w in zip(_lowerCAmelCase , _lowerCAmelCase )]
# Creating a copy of the list and sorting profit/weight in ascending order
A_ : Optional[Any] = sorted(_lowerCAmelCase )
# declaring useful variables
A_ : List[Any] = len(_lowerCAmelCase )
A_ : List[Any] = 0
A_ : Any = 0
A_ : List[Any] = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
A_ : List[Any] = sorted_profit_by_weight[length - i - 1]
A_ : List[Any] = profit_by_weight.index(_lowerCAmelCase )
A_ : List[Any] = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
'''Input profits, weights, and then max_weight (all positive ints) separated by '''
'''spaces.'''
)
_lowerCAmelCase : int = [int(x) for x in input('''Input profits separated by spaces: ''').split()]
_lowerCAmelCase : Dict = [int(x) for x in input('''Input weights separated by spaces: ''').split()]
_lowerCAmelCase : Union[str, Any] = int(input('''Max weight allowed: '''))
# Function Call
calc_profit(profit, weight, max_weight)
| 454 | 1 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase( enum.Enum ):
"""simple docstring"""
a : Dict = 0
a : Optional[int] = 1
a : Union[str, Any] = 2
@add_end_docstrings(__UpperCAmelCase )
class UpperCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
a : Optional[Any] = "\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n "
def __init__( self , *lowerCamelCase , **lowerCamelCase ) -> List[str]:
"""simple docstring"""
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
lowercase__ : Any = None
if self.model.config.prefix is not None:
lowercase__ : List[Any] = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
lowercase__ : Optional[int] = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
lowercase__ , lowercase__ , lowercase__ : Tuple = self._sanitize_parameters(prefix=_lowerCamelCase , **self._forward_params )
lowercase__ : Optional[Any] = {**self._preprocess_params, **preprocess_params}
lowercase__ : List[str] = {**self._forward_params, **forward_params}
def __a ( self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , **lowerCamelCase , ) -> int:
"""simple docstring"""
lowercase__ : Any = {}
if prefix is not None:
lowercase__ : Union[str, Any] = prefix
if prefix:
lowercase__ : Dict = self.tokenizer(
_lowerCamelCase , padding=_lowerCamelCase , add_special_tokens=_lowerCamelCase , return_tensors=self.framework )
lowercase__ : List[Any] = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
" [None, \'hole\']" )
lowercase__ : List[str] = handle_long_generation
preprocess_params.update(_lowerCamelCase )
lowercase__ : Dict = generate_kwargs
lowercase__ : Dict = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
lowercase__ : List[str] = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
lowercase__ : str = ReturnType.TENSORS
if return_type is not None:
lowercase__ : str = return_type
if clean_up_tokenization_spaces is not None:
lowercase__ : Optional[int] = clean_up_tokenization_spaces
if stop_sequence is not None:
lowercase__ : str = self.tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
if len(_lowerCamelCase ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
lowercase__ : Optional[Any] = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __a ( self , *lowerCamelCase , **lowerCamelCase ) -> Optional[int]:
"""simple docstring"""
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*_lowerCamelCase , **_lowerCamelCase )
def __call__( self , lowerCamelCase , **lowerCamelCase ) -> str:
"""simple docstring"""
return super().__call__(_lowerCamelCase , **_lowerCamelCase )
def __a ( self , lowerCamelCase , lowerCamelCase="" , lowerCamelCase=None , **lowerCamelCase ) -> List[Any]:
"""simple docstring"""
lowercase__ : int = self.tokenizer(
prefix + prompt_text , padding=_lowerCamelCase , add_special_tokens=_lowerCamelCase , return_tensors=self.framework )
lowercase__ : Optional[Any] = prompt_text
if handle_long_generation == "hole":
lowercase__ : int = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
lowercase__ : List[str] = generate_kwargs["max_new_tokens"]
else:
lowercase__ : str = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
lowercase__ : Union[str, Any] = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
lowercase__ : Optional[Any] = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
lowercase__ : Union[str, Any] = inputs["attention_mask"][:, -keep_length:]
return inputs
def __a ( self , lowerCamelCase , **lowerCamelCase ) -> Any:
"""simple docstring"""
lowercase__ : Any = model_inputs["input_ids"]
lowercase__ : int = model_inputs.get("attention_mask" , _lowerCamelCase )
# Allow empty prompts
if input_ids.shape[1] == 0:
lowercase__ : Optional[Any] = None
lowercase__ : List[Any] = None
lowercase__ : List[Any] = 1
else:
lowercase__ : Tuple = input_ids.shape[0]
lowercase__ : Union[str, Any] = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
lowercase__ : int = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
lowercase__ : Tuple = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
lowercase__ : List[str] = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
lowercase__ : Dict = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
lowercase__ : Optional[Any] = self.model.generate(input_ids=_lowerCamelCase , attention_mask=_lowerCamelCase , **_lowerCamelCase )
lowercase__ : Union[str, Any] = generated_sequence.shape[0]
if self.framework == "pt":
lowercase__ : Optional[Any] = generated_sequence.reshape(_lowerCamelCase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
lowercase__ : Union[str, Any] = tf.reshape(_lowerCamelCase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def __a ( self , lowerCamelCase , lowerCamelCase=ReturnType.FULL_TEXT , lowerCamelCase=True ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Optional[int] = model_outputs["generated_sequence"][0]
lowercase__ : Dict = model_outputs["input_ids"]
lowercase__ : Any = model_outputs["prompt_text"]
lowercase__ : Tuple = generated_sequence.numpy().tolist()
lowercase__ : Optional[int] = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
lowercase__ : Union[str, Any] = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
lowercase__ : Tuple = self.tokenizer.decode(
_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
lowercase__ : Optional[int] = 0
else:
lowercase__ : List[Any] = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase , ) )
if return_type == ReturnType.FULL_TEXT:
lowercase__ : Union[str, Any] = prompt_text + text[prompt_length:]
else:
lowercase__ : Tuple = text[prompt_length:]
lowercase__ : Union[str, Any] = {"generated_text": all_text}
records.append(_lowerCamelCase )
return records | 701 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : int = logging.get_logger(__name__)
__a : Tuple = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class UpperCAmelCase( snake_case_ ):
"""simple docstring"""
a : Optional[int] = """visual_bert"""
def __init__( self , lowerCamelCase=30522 , lowerCamelCase=768 , lowerCamelCase=512 , lowerCamelCase=12 , lowerCamelCase=12 , lowerCamelCase=3072 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=512 , lowerCamelCase=2 , lowerCamelCase=0.02 , lowerCamelCase=1E-12 , lowerCamelCase=False , lowerCamelCase=True , lowerCamelCase=1 , lowerCamelCase=0 , lowerCamelCase=2 , **lowerCamelCase , ) -> str:
"""simple docstring"""
super().__init__(pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase )
lowercase__ : Optional[Any] = vocab_size
lowercase__ : Any = max_position_embeddings
lowercase__ : str = hidden_size
lowercase__ : Optional[int] = visual_embedding_dim
lowercase__ : Optional[Any] = num_hidden_layers
lowercase__ : Optional[Any] = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : List[str] = hidden_act
lowercase__ : Optional[int] = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : List[str] = initializer_range
lowercase__ : Tuple = type_vocab_size
lowercase__ : int = layer_norm_eps
lowercase__ : Union[str, Any] = bypass_transformer
lowercase__ : Dict = special_visual_initialize | 298 | 0 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase__ = logging.getLogger(__name__)
lowerCAmelCase__ = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
lowerCAmelCase__ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class snake_case__:
"""simple docstring"""
lowercase_ = field(
default=_UpperCamelCase , metadata={
"""help""": (
"""The model checkpoint for weights initialization. Leave None if you want to train a model from"""
""" scratch."""
)
} , )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCamelCase )} , )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class snake_case__:
"""simple docstring"""
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """The input training data file (a text file)."""} )
lowercase_ = field(
default=_UpperCamelCase , metadata={
"""help""": (
"""The input training data files (multiple files in glob format). """
"""Very often splitting large files to smaller files can prevent tokenizer going out of memory"""
)
} , )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """An optional input train ref data file for whole word mask in Chinese."""} , )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """An optional input eval ref data file for whole word mask in Chinese."""} , )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """Whether distinct lines of text in the dataset are to be handled as distinct sequences."""} , )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """Train with masked-language modeling loss instead of language modeling."""} )
lowercase_ = field(default=_UpperCamelCase , metadata={"""help""": """Whether ot not to use whole word mask."""} )
lowercase_ = field(
default=0.15 , metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""} )
lowercase_ = field(
default=1 / 6 , metadata={
"""help""": (
"""Ratio of length of a span of masked tokens to surrounding context length for permutation language"""
""" modeling."""
)
} , )
lowercase_ = field(
default=5 , metadata={"""help""": """Maximum length of a span of masked tokens for permutation language modeling."""} )
lowercase_ = field(
default=-1 , metadata={
"""help""": (
"""Optional input sequence length after tokenization."""
"""The training dataset will be truncated in block of this size for training."""
"""Default to the model max input length for single sentence inputs (take into account special tokens)."""
)
} , )
lowercase_ = field(
default=_UpperCamelCase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False , lowerCamelCase__ = None , ):
"""simple docstring"""
def _dataset(lowerCamelCase__ , lowerCamelCase__=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask" )
return LineByLineWithRefDataset(
tokenizer=lowerCamelCase__ , file_path=lowerCamelCase__ , block_size=args.block_size , ref_path=lowerCamelCase__ , )
return LineByLineTextDataset(tokenizer=lowerCamelCase__ , file_path=lowerCamelCase__ , block_size=args.block_size )
else:
return TextDataset(
tokenizer=lowerCamelCase__ , file_path=lowerCamelCase__ , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=lowerCamelCase__ , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(lowerCamelCase__ ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase__ , lowercase__ , lowercase__ : List[Any] = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
"or remove the --do_eval argument." )
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." )
# 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" , lowerCamelCase__ )
# 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.
if model_args.config_name:
lowercase__ : Any = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
lowercase__ : Dict = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
lowercase__ : Optional[Any] = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.tokenizer_name:
lowercase__ : int = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"
" script, save it,and load it from here, using --tokenizer_name" )
if model_args.model_name_or_path:
lowercase__ : Tuple = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowerCamelCase__ , cache_dir=model_args.cache_dir , )
else:
logger.info("Training new model from scratch" )
lowercase__ : str = AutoModelWithLMHead.from_config(lowerCamelCase__ )
model.resize_token_embeddings(len(lowerCamelCase__ ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
"--mlm flag (masked language modeling)." )
if data_args.block_size <= 0:
lowercase__ : Optional[int] = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
lowercase__ : Optional[int] = min(data_args.block_size , tokenizer.max_len )
# Get datasets
lowercase__ : Optional[int] = (
get_dataset(lowerCamelCase__ , tokenizer=lowerCamelCase__ , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
lowercase__ : List[Any] = (
get_dataset(lowerCamelCase__ , tokenizer=lowerCamelCase__ , evaluate=lowerCamelCase__ , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
lowercase__ : Union[str, Any] = DataCollatorForPermutationLanguageModeling(
tokenizer=lowerCamelCase__ , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
lowercase__ : Optional[Any] = DataCollatorForWholeWordMask(
tokenizer=lowerCamelCase__ , mlm_probability=data_args.mlm_probability )
else:
lowercase__ : str = DataCollatorForLanguageModeling(
tokenizer=lowerCamelCase__ , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
lowercase__ : str = Trainer(
model=lowerCamelCase__ , args=lowerCamelCase__ , data_collator=lowerCamelCase__ , train_dataset=lowerCamelCase__ , eval_dataset=lowerCamelCase__ , prediction_loss_only=lowerCamelCase__ , )
# Training
if training_args.do_train:
lowercase__ : List[Any] = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=lowerCamelCase__ )
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_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ : Union[str, Any] = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
lowercase__ : int = trainer.evaluate()
lowercase__ : str = math.exp(eval_output["eval_loss"] )
lowercase__ : int = {"perplexity": perplexity}
lowercase__ : str = os.path.join(training_args.output_dir , "eval_results_lm.txt" )
if trainer.is_world_master():
with open(lowerCamelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , lowerCamelCase__ , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
results.update(lowerCamelCase__ )
return results
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 496 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowerCAmelCase__ = '''scheduler_config.json'''
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
lowercase_ = 1
lowercase_ = 2
lowercase_ = 3
lowercase_ = 4
lowercase_ = 5
@dataclass
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
lowercase_ = 42
class snake_case__:
"""simple docstring"""
lowercase_ = SCHEDULER_CONFIG_NAME
lowercase_ = ["""dtype"""]
lowercase_ = []
lowercase_ = True
@classmethod
def snake_case ( cls : Optional[Any] , SCREAMING_SNAKE_CASE : Dict[str, Any] = None , SCREAMING_SNAKE_CASE : Optional[str] = None , SCREAMING_SNAKE_CASE : Optional[Any]=False , **SCREAMING_SNAKE_CASE : str , ):
lowercase__ , lowercase__ : Union[str, Any] = cls.load_config(
pretrained_model_name_or_path=SCREAMING_SNAKE_CASE , subfolder=SCREAMING_SNAKE_CASE , return_unused_kwargs=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
lowercase__ , lowercase__ : int = cls.from_config(SCREAMING_SNAKE_CASE , return_unused_kwargs=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
if hasattr(SCREAMING_SNAKE_CASE , "create_state" ) and getattr(SCREAMING_SNAKE_CASE , "has_state" , SCREAMING_SNAKE_CASE ):
lowercase__ : Optional[int] = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def snake_case ( self : Tuple , SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , SCREAMING_SNAKE_CASE : bool = False , **SCREAMING_SNAKE_CASE : Optional[int] ):
self.save_config(save_directory=SCREAMING_SNAKE_CASE , push_to_hub=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@property
def snake_case ( self : int ):
return self._get_compatibles()
@classmethod
def snake_case ( cls : Tuple ):
lowercase__ : Optional[int] = list(set([cls.__name__] + cls._compatibles ) )
lowercase__ : List[str] = importlib.import_module(__name__.split("." )[0] )
lowercase__ : Tuple = [
getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
]
return compatible_classes
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
assert len(lowerCamelCase__ ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase__ ) - x.ndim) ) , lowerCamelCase__ )
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__=0.999 , lowerCamelCase__=jnp.floataa ):
"""simple docstring"""
def alpha_bar(lowerCamelCase__ ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
lowercase__ : Dict = []
for i in range(lowerCamelCase__ ):
lowercase__ : List[str] = i / num_diffusion_timesteps
lowercase__ : Dict = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(lowerCamelCase__ ) / alpha_bar(lowerCamelCase__ ) , lowerCamelCase__ ) )
return jnp.array(lowerCamelCase__ , dtype=lowerCamelCase__ )
@flax.struct.dataclass
class snake_case__:
"""simple docstring"""
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
@classmethod
def snake_case ( cls : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] ):
lowercase__ : List[Any] = scheduler.config
if config.trained_betas is not None:
lowercase__ : List[str] = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
lowercase__ : Optional[int] = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowercase__ : List[str] = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowercase__ : Any = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}""" )
lowercase__ : Dict = 1.0 - betas
lowercase__ : List[Any] = jnp.cumprod(SCREAMING_SNAKE_CASE , axis=0 )
return cls(
alphas=SCREAMING_SNAKE_CASE , betas=SCREAMING_SNAKE_CASE , alphas_cumprod=SCREAMING_SNAKE_CASE , )
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Tuple = state.alphas_cumprod
lowercase__ : int = alphas_cumprod[timesteps] ** 0.5
lowercase__ : Optional[int] = sqrt_alpha_prod.flatten()
lowercase__ : Tuple = broadcast_to_shape_from_left(lowerCamelCase__ , original_samples.shape )
lowercase__ : List[str] = (1 - alphas_cumprod[timesteps]) ** 0.5
lowercase__ : Union[str, Any] = sqrt_one_minus_alpha_prod.flatten()
lowercase__ : Optional[Any] = broadcast_to_shape_from_left(lowerCamelCase__ , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowercase__ , lowercase__ : Dict = get_sqrt_alpha_prod(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowercase__ : int = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowercase__ , lowercase__ : int = get_sqrt_alpha_prod(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowercase__ : Any = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 496 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_A = namedtuple("covid_data", "cases deaths recovered")
def lowercase_ ( A__ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
"""simple docstring"""
snake_case = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(A__ ).content ).xpath(A__ ) )
_A = "Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}"
print(fmt.format(*covid_stats()))
| 721 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class lowerCamelCase ( A_ ):
UpperCAmelCase__ : Dict = "canine"
def __init__(self : List[Any] , _A : Union[str, Any]=7_6_8 , _A : Any=1_2 , _A : List[Any]=1_2 , _A : List[Any]=3_0_7_2 , _A : Dict="gelu" , _A : Optional[Any]=0.1 , _A : Tuple=0.1 , _A : str=1_6_3_8_4 , _A : Union[str, Any]=1_6 , _A : Any=0.02 , _A : List[str]=1E-12 , _A : Union[str, Any]=0 , _A : Dict=0Xe0_00 , _A : List[Any]=0Xe0_01 , _A : int=4 , _A : str=4 , _A : Optional[int]=8 , _A : Optional[Any]=1_6_3_8_4 , _A : Optional[Any]=1_2_8 , **_A : Union[str, Any] , ) -> Union[str, Any]:
super().__init__(pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , **_A )
snake_case = max_position_embeddings
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = initializer_range
snake_case = type_vocab_size
snake_case = layer_norm_eps
# Character config:
snake_case = downsampling_rate
snake_case = upsampling_kernel_size
snake_case = num_hash_functions
snake_case = num_hash_buckets
snake_case = local_transformer_stride
| 294 | 0 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self : Dict , lowercase__ : UNetaDModel , lowercase__ : UNetaDModel , lowercase__ : DDPMScheduler , lowercase__ : Optional[Any] , ):
'''simple docstring'''
super().__init__()
a_ : int = value_function
a_ : Optional[int] = unet
a_ : Dict = scheduler
a_ : Tuple = env
a_ : List[str] = env.get_dataset()
a_ : Dict = {}
for key in self.data.keys():
try:
a_ : Dict = self.data[key].mean()
except: # noqa: E722
pass
a_ : int = {}
for key in self.data.keys():
try:
a_ : Union[str, Any] = self.data[key].std()
except: # noqa: E722
pass
a_ : List[str] = env.observation_space.shape[0]
a_ : Union[str, Any] = env.action_space.shape[0]
def lowercase_ ( self : List[str] , lowercase__ : Tuple , lowercase__ : Dict ):
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def lowercase_ ( self : Dict , lowercase__ : Any , lowercase__ : Any ):
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def lowercase_ ( self : Optional[Any] , lowercase__ : Union[str, Any] ):
'''simple docstring'''
if type(lowercase__ ) is dict:
return {k: self.to_torch(lowercase__ ) for k, v in x_in.items()}
elif torch.is_tensor(lowercase__ ):
return x_in.to(self.unet.device )
return torch.tensor(lowercase__ , device=self.unet.device )
def lowercase_ ( self : Tuple , lowercase__ : Optional[int] , lowercase__ : Any , lowercase__ : Optional[Any] ):
'''simple docstring'''
for key, val in cond.items():
a_ : Optional[int] = val.clone()
return x_in
def lowercase_ ( self : int , lowercase__ : int , lowercase__ : int , lowercase__ : Union[str, Any] , lowercase__ : int ):
'''simple docstring'''
a_ : List[str] = x.shape[0]
a_ : Optional[int] = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
a_ : str = torch.full((batch_size,) , lowercase__ , device=self.unet.device , dtype=torch.long )
for _ in range(lowercase__ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
a_ : Tuple = self.value_function(x.permute(0 , 2 , 1 ) , lowercase__ ).sample
a_ : Optional[Any] = torch.autograd.grad([y.sum()] , [x] )[0]
a_ : Any = self.scheduler._get_variance(lowercase__ )
a_ : Dict = torch.exp(0.5 * posterior_variance )
a_ : str = model_std * grad
a_ : Optional[int] = 0
a_ : Union[str, Any] = x.detach()
a_ : Optional[int] = x + scale * grad
a_ : Optional[Any] = self.reset_xa(lowercase__ , lowercase__ , self.action_dim )
a_ : Any = self.unet(x.permute(0 , 2 , 1 ) , lowercase__ ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
a_ : int = self.scheduler.step(lowercase__ , lowercase__ , lowercase__ , predict_epsilon=lowercase__ )["""prev_sample"""]
# apply conditions to the trajectory (set the initial state)
a_ : Optional[Any] = self.reset_xa(lowercase__ , lowercase__ , self.action_dim )
a_ : Tuple = self.to_torch(lowercase__ )
return x, y
def __call__( self : Optional[Any] , lowercase__ : Dict , lowercase__ : List[str]=64 , lowercase__ : str=32 , lowercase__ : Dict=2 , lowercase__ : Optional[Any]=0.1 ):
'''simple docstring'''
a_ : List[Any] = self.normalize(lowercase__ , """observations""" )
a_ : Dict = obs[None].repeat(lowercase__ , axis=0 )
a_ : str = {0: self.to_torch(lowercase__ )}
a_ : List[str] = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
a_ : Optional[Any] = randn_tensor(lowercase__ , device=self.unet.device )
a_ : int = self.reset_xa(lowercase__ , lowercase__ , self.action_dim )
a_ : int = self.to_torch(lowercase__ )
# run the diffusion process
a_ , a_ : Dict = self.run_diffusion(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# sort output trajectories by value
a_ : List[Any] = y.argsort(0 , descending=lowercase__ ).squeeze()
a_ : Optional[Any] = x[sorted_idx]
a_ : str = sorted_values[:, :, : self.action_dim]
a_ : Any = actions.detach().cpu().numpy()
a_ : int = self.de_normalize(lowercase__ , key="""actions""" )
# select the action with the highest value
if y is not None:
a_ : Optional[int] = 0
else:
# if we didn't run value guiding, select a random action
a_ : Dict = np.random.randint(0 , lowercase__ )
a_ : Union[str, Any] = denorm_actions[selected_index, 0]
return denorm_actions
| 442 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : str = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case_ ):
__magic_name__ : str = '''transfo-xl'''
__magic_name__ : List[str] = ['''mems''']
__magic_name__ : Dict = {
'''n_token''': '''vocab_size''',
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Union[str, Any] , lowercase__ : List[Any]=26_7735 , lowercase__ : Optional[Any]=[2_0000, 4_0000, 20_0000] , lowercase__ : Optional[Any]=1024 , lowercase__ : str=1024 , lowercase__ : Any=16 , lowercase__ : int=64 , lowercase__ : str=4096 , lowercase__ : Union[str, Any]=4 , lowercase__ : List[Any]=False , lowercase__ : List[Any]=18 , lowercase__ : str=1600 , lowercase__ : str=1000 , lowercase__ : Any=True , lowercase__ : Optional[Any]=True , lowercase__ : Union[str, Any]=0 , lowercase__ : str=-1 , lowercase__ : int=True , lowercase__ : str=0.1 , lowercase__ : Optional[Any]=0.0 , lowercase__ : Tuple=True , lowercase__ : Optional[int]="normal" , lowercase__ : str=0.01 , lowercase__ : List[str]=0.01 , lowercase__ : Union[str, Any]=0.02 , lowercase__ : str=1e-5 , lowercase__ : Any=0 , **lowercase__ : List[str] , ):
'''simple docstring'''
a_ : Optional[Any] = vocab_size
a_ : Optional[int] = []
self.cutoffs.extend(lowercase__ )
if proj_share_all_but_first:
a_ : Any = [False] + [True] * len(self.cutoffs )
else:
a_ : Tuple = [False] + [False] * len(self.cutoffs )
a_ : Tuple = d_model
a_ : Optional[int] = d_embed
a_ : List[Any] = d_head
a_ : List[str] = d_inner
a_ : Tuple = div_val
a_ : Dict = pre_lnorm
a_ : Optional[Any] = n_layer
a_ : Dict = n_head
a_ : Any = mem_len
a_ : Union[str, Any] = same_length
a_ : Dict = attn_type
a_ : List[str] = clamp_len
a_ : str = sample_softmax
a_ : Any = adaptive
a_ : List[Any] = dropout
a_ : str = dropatt
a_ : Dict = untie_r
a_ : Tuple = init
a_ : Optional[int] = init_range
a_ : List[Any] = proj_init_std
a_ : Optional[int] = init_std
a_ : int = layer_norm_epsilon
super().__init__(eos_token_id=lowercase__ , **lowercase__ )
@property
def lowercase_ ( self : int ):
'''simple docstring'''
logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def lowercase_ ( self : Optional[int] , lowercase__ : Tuple ):
'''simple docstring'''
raise NotImplementedError(
F"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 442 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
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 MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def UpperCAmelCase__ ( lowercase__ , lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , ) -> Optional[Any]:
if attention_mask is None:
__lowercase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
__lowercase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
__lowercase = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=lowercase__ )
if decoder_head_mask is None:
__lowercase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=lowercase__ )
if cross_attn_head_mask is None:
__lowercase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=lowercase__ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowercase : Optional[Any] , lowercase : Any=13 , lowercase : Union[str, Any]=7 , lowercase : Any=True , lowercase : List[Any]=False , lowercase : List[str]=99 , lowercase : List[Any]=16 , lowercase : int=2 , lowercase : str=4 , lowercase : List[Any]=4 , lowercase : int="relu" , lowercase : Dict=0.1 , lowercase : str=0.1 , lowercase : List[Any]=0.0 , lowercase : Optional[int]=0.0 , lowercase : Tuple=20 , lowercase : str=2 , lowercase : Dict=1 , lowercase : List[Any]=0 , ) -> Tuple:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = max_position_embeddings
__lowercase = eos_token_id
__lowercase = pad_token_id
__lowercase = bos_token_id
def snake_case__ ( self : Optional[int] ) -> str:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = self.eos_token_id # Eos Token
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
__lowercase = input_ids.clamp(self.pad_token_id + 1 )
__lowercase = decoder_input_ids.clamp(self.pad_token_id + 1 )
__lowercase = self.get_config()
__lowercase = prepare_mam_aaa_inputs_dict(lowercase , lowercase , lowercase )
return config, inputs_dict
def snake_case__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def snake_case__ ( self : str ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.prepare_config_and_inputs()
return config, inputs_dict
def snake_case__ ( self : int , lowercase : Optional[int] , lowercase : Any ) -> List[Any]:
"""simple docstring"""
__lowercase = MaMaaaModel(config=lowercase ).get_decoder().to(lowercase ).eval()
__lowercase = inputs_dict["""input_ids"""]
__lowercase = inputs_dict["""attention_mask"""]
__lowercase = inputs_dict["""head_mask"""]
# first forward pass
__lowercase = model(lowercase , attention_mask=lowercase , head_mask=lowercase , use_cache=lowercase )
__lowercase , __lowercase = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
__lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowercase = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
__lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowercase = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
__lowercase = model(lowercase , attention_mask=lowercase )["""last_hidden_state"""]
__lowercase = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[
"""last_hidden_state"""
]
# select random slice
__lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowercase = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowercase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowercase , lowercase , atol=1E-2 ) )
def snake_case__ ( self : Tuple , lowercase : Optional[Any] , lowercase : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = MaMaaaModel(config=lowercase ).to(lowercase ).eval()
__lowercase = model(**lowercase )
__lowercase = outputs.encoder_last_hidden_state
__lowercase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = model.get_encoder()
encoder.save_pretrained(lowercase )
__lowercase = MaMaaaEncoder.from_pretrained(lowercase ).to(lowercase )
__lowercase = encoder(inputs_dict["""input_ids"""] , attention_mask=inputs_dict["""attention_mask"""] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = model.get_decoder()
decoder.save_pretrained(lowercase )
__lowercase = MaMaaaDecoder.from_pretrained(lowercase ).to(lowercase )
__lowercase = decoder(
input_ids=inputs_dict["""decoder_input_ids"""] , attention_mask=inputs_dict["""decoder_attention_mask"""] , encoder_hidden_states=lowercase , encoder_attention_mask=inputs_dict["""attention_mask"""] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class _lowerCAmelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowercase__ : str = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
lowercase__ : Union[str, Any] = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
lowercase__ : Union[str, Any] = (
{
"""conversational""": MaMaaaForConditionalGeneration,
"""feature-extraction""": MaMaaaModel,
"""summarization""": MaMaaaForConditionalGeneration,
"""text2text-generation""": MaMaaaForConditionalGeneration,
"""translation""": MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
lowercase__ : Optional[Any] = True
lowercase__ : List[Any] = True
lowercase__ : Optional[int] = False
lowercase__ : List[Any] = False
def snake_case__ ( self : Tuple , lowercase : List[str] , lowercase : Any , lowercase : Any , lowercase : Dict , lowercase : Tuple ) -> Optional[Any]:
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def snake_case__ ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = MaMaaaModelTester(self )
__lowercase = ConfigTester(self , config_class=lowercase )
def snake_case__ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case__ ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase )
__lowercase , __lowercase = model_class.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertEqual(info["""missing_keys"""] , [] )
def snake_case__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase )
def snake_case__ ( self : Optional[Any] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowercase )
def snake_case__ ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
__lowercase = model_class(lowercase )
model.to(lowercase )
model.eval()
__lowercase = copy.deepcopy(self._prepare_for_class(lowercase , lowercase ) )
if not self.is_encoder_decoder:
__lowercase = inputs["""input_ids"""]
del inputs["input_ids"]
else:
__lowercase = inputs["""input_ids"""]
__lowercase = inputs.get("""decoder_input_ids""" , lowercase )
del inputs["input_ids"]
inputs.pop("""decoder_input_ids""" , lowercase )
__lowercase = model.get_input_embeddings()
if not self.is_encoder_decoder:
__lowercase = wte(lowercase )
else:
__lowercase = wte(lowercase )
__lowercase = wte(lowercase )
with torch.no_grad():
model(**lowercase )[0]
def snake_case__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs()
__lowercase = input_dict["""input_ids"""]
__lowercase = input_ids.ne(1 ).to(lowercase )
__lowercase = MaMaaaForConditionalGeneration(lowercase ).eval().to(lowercase )
if torch_device == "cuda":
model.half()
model.generate(lowercase , attention_mask=lowercase )
model.generate(num_beams=4 , do_sample=lowercase , early_stopping=lowercase , num_return_sequences=3 )
def UpperCAmelCase__ ( lowercase__ ) -> Dict:
return torch.tensor(lowercase__ , dtype=torch.long , device=lowercase__ )
UpperCamelCase__ = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""" )
def snake_case__ ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = MaMaaaModel.from_pretrained("""facebook/m2m100_418M""" ).to(lowercase )
__lowercase = _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
__lowercase = _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
__lowercase = prepare_mam_aaa_inputs_dict(model.config , lowercase , lowercase )
with torch.no_grad():
__lowercase = model(**lowercase )[0]
__lowercase = torch.Size((1, 11, 1_024) )
self.assertEqual(output.shape , lowercase )
# change to expected output here
__lowercase = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=lowercase )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase , atol=lowercase ) )
def snake_case__ ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""" ).to(lowercase )
# change to intended input
__lowercase = _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
__lowercase = _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
__lowercase = prepare_mam_aaa_inputs_dict(model.config , lowercase , lowercase )
with torch.no_grad():
__lowercase = model(**lowercase )[0]
__lowercase = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , lowercase )
# change to expected output here
__lowercase = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=lowercase )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase , atol=lowercase ) )
def snake_case__ ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase = MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""" ).to(lowercase )
__lowercase = MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""" , src_lang="""fr""" , tgt_lang="""en""" )
__lowercase = [
"""L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
"""Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
"""Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
""" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
""" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
__lowercase = tokenizer(lowercase , padding=lowercase , return_tensors="""pt""" )
__lowercase = model.generate(
input_ids=dct["""input_ids"""].to(lowercase ) , attention_mask=dct["""attention_mask"""].to(lowercase ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("""en""" ) , )
__lowercase = [
"""The NSA case highlights the total absence of intelligence debate""",
"""I think there are two levels of response from the French government.""",
"""When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
""" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
""" communications in France.""",
]
__lowercase = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=lowercase , skip_special_tokens=lowercase )
assert generated == expected_en
| 634 |
def UpperCAmelCase__ ( lowercase__ , lowercase__ ) -> bool:
__lowercase = len(lowercase__ )
__lowercase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
__lowercase = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
__lowercase = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
__lowercase = subset[i - 1][j]
if arr[i - 1] <= j:
__lowercase = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 634 | 1 |
'''simple docstring'''
def __UpperCamelCase( _A : float , _A : list[float] ):
'''simple docstring'''
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
UpperCAmelCase__ : str = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_A ) )
return round(_A , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 614 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _lowercase :
'''simple docstring'''
def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=13 ,lowerCamelCase_=7 ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_=99 ,lowerCamelCase_=32 ,lowerCamelCase_=2 ,lowerCamelCase_=4 ,lowerCamelCase_=37 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.1 ,lowerCamelCase_=0.1 ,lowerCamelCase_=512 ,lowerCamelCase_=16 ,lowerCamelCase_=2 ,lowerCamelCase_=0.02 ,lowerCamelCase_=3 ,lowerCamelCase_=4 ,lowerCamelCase_=None ,) -> str:
'''simple docstring'''
UpperCAmelCase__ : Any = parent
UpperCAmelCase__ : str = 13
UpperCAmelCase__ : Any = 7
UpperCAmelCase__ : str = True
UpperCAmelCase__ : List[Any] = True
UpperCAmelCase__ : Tuple = True
UpperCAmelCase__ : List[str] = True
UpperCAmelCase__ : List[Any] = 99
UpperCAmelCase__ : str = 32
UpperCAmelCase__ : Dict = 2
UpperCAmelCase__ : Union[str, Any] = 4
UpperCAmelCase__ : Dict = 37
UpperCAmelCase__ : Dict = '''gelu'''
UpperCAmelCase__ : List[str] = 0.1
UpperCAmelCase__ : List[str] = 0.1
UpperCAmelCase__ : Optional[int] = 512
UpperCAmelCase__ : Any = 16
UpperCAmelCase__ : List[Any] = 2
UpperCAmelCase__ : Optional[Any] = 0.02
UpperCAmelCase__ : Optional[int] = 3
UpperCAmelCase__ : Optional[int] = 4
UpperCAmelCase__ : Optional[int] = None
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_input_mask:
UpperCAmelCase__ : int = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : str = None
if self.use_token_type_ids:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
UpperCAmelCase__ : List[str] = None
UpperCAmelCase__ : Any = None
UpperCAmelCase__ : Dict = None
if self.use_labels:
UpperCAmelCase__ : Optional[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size] ,self.num_choices )
UpperCAmelCase__ : Union[str, Any] = RoFormerConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,return_dict=lowerCamelCase_ ,)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> int:
'''simple docstring'''
UpperCAmelCase__ : List[str] = TFRoFormerModel(config=lowerCamelCase_ )
UpperCAmelCase__ : str = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
UpperCAmelCase__ : str = [input_ids, input_mask]
UpperCAmelCase__ : Any = model(lowerCamelCase_ )
UpperCAmelCase__ : List[Any] = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = True
UpperCAmelCase__ : Any = TFRoFormerForCausalLM(config=lowerCamelCase_ )
UpperCAmelCase__ : List[str] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : int = model(lowerCamelCase_ )['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) ,[self.batch_size, self.seq_length, self.vocab_size] )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = TFRoFormerForMaskedLM(config=lowerCamelCase_ )
UpperCAmelCase__ : Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : Dict = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.num_labels
UpperCAmelCase__ : List[Any] = TFRoFormerForSequenceClassification(config=lowerCamelCase_ )
UpperCAmelCase__ : List[Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : Union[str, Any] = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : int = self.num_choices
UpperCAmelCase__ : Optional[int] = TFRoFormerForMultipleChoice(config=lowerCamelCase_ )
UpperCAmelCase__ : Tuple = tf.tile(tf.expand_dims(lowerCamelCase_ ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase__ : Optional[Any] = tf.tile(tf.expand_dims(lowerCamelCase_ ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase__ : Dict = tf.tile(tf.expand_dims(lowerCamelCase_ ,1 ) ,(1, self.num_choices, 1) )
UpperCAmelCase__ : Optional[int] = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
UpperCAmelCase__ : str = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : Dict = self.num_labels
UpperCAmelCase__ : Optional[Any] = TFRoFormerForTokenClassification(config=lowerCamelCase_ )
UpperCAmelCase__ : Dict = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : Optional[int] = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = TFRoFormerForQuestionAnswering(config=lowerCamelCase_ )
UpperCAmelCase__ : Any = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
UpperCAmelCase__ : int = model(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 lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) : List[Any] = config_and_inputs
UpperCAmelCase__ : Optional[int] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class _lowercase ( lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCAmelCase_ : int = (
{
'''feature-extraction''': TFRoFormerModel,
'''fill-mask''': TFRoFormerForMaskedLM,
'''question-answering''': TFRoFormerForQuestionAnswering,
'''text-classification''': TFRoFormerForSequenceClassification,
'''text-generation''': TFRoFormerForCausalLM,
'''token-classification''': TFRoFormerForTokenClassification,
'''zero-shot''': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCAmelCase_ : Optional[int] = False
UpperCAmelCase_ : Optional[int] = False
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ) -> Any:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = TFRoFormerModelTester(self )
UpperCAmelCase__ : int = ConfigTester(self ,config_class=lowerCamelCase_ ,hidden_size=37 )
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
@slow
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : List[str] = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''' )
self.assertIsNotNone(lowerCamelCase_ )
@require_tf
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
UpperCAmelCase__ : Dict = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ : Union[str, Any] = model(lowerCamelCase_ )[0]
# TODO Replace vocab size
UpperCAmelCase__ : List[str] = 50000
UpperCAmelCase__ : int = [1, 6, vocab_size]
self.assertEqual(output.shape ,lowerCamelCase_ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
UpperCAmelCase__ : Tuple = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] ,lowerCamelCase_ ,atol=1e-4 )
@require_tf
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : int = 1E-4
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = tf.constant([[4, 10]] )
UpperCAmelCase__ : List[str] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 ,embedding_dim=6 )
UpperCAmelCase__ : Optional[Any] = emba(input_ids.shape )
UpperCAmelCase__ : List[str] = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(lowerCamelCase_ ,lowerCamelCase_ ,atol=self.tolerance )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Any = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
UpperCAmelCase__ : Dict = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 ,embedding_dim=512 )
emba([2, 16, 512] )
UpperCAmelCase__ : Optional[int] = emba.weight[:3, :5]
tf.debugging.assert_near(lowerCamelCase_ ,lowerCamelCase_ ,atol=self.tolerance )
@require_tf
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = 1E-4
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = tf.reshape(tf.range(2 * 12 * 16 * 64 ,dtype=tf.floataa ) ,shape=(2, 12, 16, 64) ) / 100
UpperCAmelCase__ : int = -tf.reshape(tf.range(2 * 12 * 16 * 64 ,dtype=tf.floataa ) ,shape=(2, 12, 16, 64) ) / 100
UpperCAmelCase__ : Optional[Any] = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 ,embedding_dim=64 )
UpperCAmelCase__ : int = embed_positions([2, 16, 768] )[None, None, :, :]
UpperCAmelCase__ , UpperCAmelCase__ : Dict = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
UpperCAmelCase__ : Any = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
UpperCAmelCase__ : List[str] = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] ,lowerCamelCase_ ,atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] ,lowerCamelCase_ ,atol=self.tolerance )
| 614 | 1 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCAmelCase ( a_ = "" ):
lowerCamelCase : Optional[int] = url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
lowerCamelCase : int = BeautifulSoup(requests.get(a_ ).text, 'html.parser' )
lowerCamelCase : Tuple = soup.find_all('td', attrs='titleColumn' )
lowerCamelCase : Dict = soup.find_all('td', class_='ratingColumn imdbRating' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(a_, a_ )
}
def UpperCAmelCase ( a_ = "IMDb_Top_250_Movies.csv" ):
lowerCamelCase : Optional[int] = get_imdb_top_aaa_movies()
with open(a_, 'w', newline='' ) as out_file:
lowerCamelCase : List[str] = csv.writer(a_ )
writer.writerow(['Movie title', 'IMDb rating'] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 704 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
# See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class _lowercase ( __UpperCAmelCase ):
lowercase_ = 'donut-swin'
lowercase_ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , UpperCAmelCase_=224 , UpperCAmelCase_=4 , UpperCAmelCase_=3 , UpperCAmelCase_=96 , UpperCAmelCase_=[2, 2, 6, 2] , UpperCAmelCase_=[3, 6, 12, 24] , UpperCAmelCase_=7 , UpperCAmelCase_=4.0 , UpperCAmelCase_=True , UpperCAmelCase_=0.0 , UpperCAmelCase_=0.0 , UpperCAmelCase_=0.1 , UpperCAmelCase_="gelu" , UpperCAmelCase_=False , UpperCAmelCase_=0.02 , UpperCAmelCase_=1E-5 , **UpperCAmelCase_ , ) -> Tuple:
super().__init__(**UpperCAmelCase_ )
lowerCamelCase : Optional[Any] = image_size
lowerCamelCase : List[Any] = patch_size
lowerCamelCase : int = num_channels
lowerCamelCase : str = embed_dim
lowerCamelCase : str = depths
lowerCamelCase : Optional[int] = len(UpperCAmelCase_ )
lowerCamelCase : Optional[Any] = num_heads
lowerCamelCase : List[Any] = window_size
lowerCamelCase : Dict = mlp_ratio
lowerCamelCase : Dict = qkv_bias
lowerCamelCase : int = hidden_dropout_prob
lowerCamelCase : Dict = attention_probs_dropout_prob
lowerCamelCase : List[Any] = drop_path_rate
lowerCamelCase : Optional[Any] = hidden_act
lowerCamelCase : Optional[Any] = use_absolute_embeddings
lowerCamelCase : int = layer_norm_eps
lowerCamelCase : int = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase : Tuple = int(embed_dim * 2 ** (len(UpperCAmelCase_ ) - 1) )
| 133 | 0 |
def a (lowerCAmelCase__ ):
return 10 - x * x
def a (lowerCAmelCase__ , lowerCAmelCase__ ):
# Bolzano theory in order to find if there is a root between a and b
if equation(lowerCAmelCase__ ) * equation(lowerCAmelCase__ ) >= 0:
raise ValueError("""Wrong space!""" )
__a = a
while (b - a) >= 0.0_1:
# Find middle point
__a = (a + b) / 2
# Check if middle point is root
if equation(lowerCAmelCase__ ) == 0.0:
break
# Decide the side to repeat the steps
if equation(lowerCAmelCase__ ) * equation(lowerCAmelCase__ ) < 0:
__a = c
else:
__a = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 99 |
from collections import defaultdict
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : str = first_str.lower().strip()
_snake_case : Dict = second_str.lower().strip()
# Remove whitespace
_snake_case : Dict = first_str.replace(' ' , '' )
_snake_case : Union[str, Any] = second_str.replace(' ' , '' )
# Strings of different lengths are not anagrams
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
return False
# Default values for count should be 0
_snake_case : defaultdict[str, int] = defaultdict(__lowerCAmelCase )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(__lowerCAmelCase ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
lowercase_ : Optional[int] = input('''Enter the first string ''').strip()
lowercase_ : List[str] = input('''Enter the second string ''').strip()
lowercase_ : Any = check_anagrams(input_a, input_b)
print(F'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')
| 304 | 0 |
from __future__ import annotations
import math
class lowercase__ :
def __init__( self : List[str] , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE__ = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE__ = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE__ = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE__ = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A_ ( self : Any , UpperCAmelCase_ : int ):
return idx * 2
def A_ ( self : int , UpperCAmelCase_ : int ):
return idx * 2 + 1
def A_ ( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : list[int] ):
if left_element == right_element:
SCREAMING_SNAKE_CASE__ = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE__ = (left_element + right_element) // 2
self.build(self.left(UpperCAmelCase_ ) , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
self.build(self.right(UpperCAmelCase_ ) , mid + 1 , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = max(
self.segment_tree[self.left(UpperCAmelCase_ )] , self.segment_tree[self.right(UpperCAmelCase_ )] )
def A_ ( self : Dict , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE__ = self.lazy[idx]
SCREAMING_SNAKE_CASE__ = False
if left_element != right_element:
SCREAMING_SNAKE_CASE__ = self.lazy[idx]
SCREAMING_SNAKE_CASE__ = self.lazy[idx]
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE__ = val
if left_element != right_element:
SCREAMING_SNAKE_CASE__ = val
SCREAMING_SNAKE_CASE__ = val
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = True
return True
SCREAMING_SNAKE_CASE__ = (left_element + right_element) // 2
self.update(self.left(UpperCAmelCase_ ) , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
self.update(self.right(UpperCAmelCase_ ) , mid + 1 , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = max(
self.segment_tree[self.left(UpperCAmelCase_ )] , self.segment_tree[self.right(UpperCAmelCase_ )] )
return True
def A_ ( self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE__ = self.lazy[idx]
SCREAMING_SNAKE_CASE__ = False
if left_element != right_element:
SCREAMING_SNAKE_CASE__ = self.lazy[idx]
SCREAMING_SNAKE_CASE__ = self.lazy[idx]
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE__ = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE__ = self.query(self.left(UpperCAmelCase_ ) , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.query(self.right(UpperCAmelCase_ ) , mid + 1 , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return max(UpperCAmelCase_ , UpperCAmelCase_ )
def __str__( self : Tuple ):
return str([self.query(1 , 1 , self.size , UpperCAmelCase_ , UpperCAmelCase_ ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
__snake_case = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
__snake_case = 15
__snake_case = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 1_11)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 2_35)
print(segt)
| 708 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class lowercase__ ( _UpperCAmelCase ):
A__ : Optional[Any] ="""perceiver"""
def __init__( self : Any , UpperCAmelCase_ : List[Any]=256 , UpperCAmelCase_ : Tuple=1280 , UpperCAmelCase_ : Union[str, Any]=768 , UpperCAmelCase_ : Any=1 , UpperCAmelCase_ : Optional[int]=26 , UpperCAmelCase_ : str=8 , UpperCAmelCase_ : Any=8 , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Tuple="kv" , UpperCAmelCase_ : str=1 , UpperCAmelCase_ : int=1 , UpperCAmelCase_ : Optional[int]="gelu" , UpperCAmelCase_ : Union[str, Any]=0.1 , UpperCAmelCase_ : Any=0.02 , UpperCAmelCase_ : Dict=1e-1_2 , UpperCAmelCase_ : Union[str, Any]=True , UpperCAmelCase_ : Dict=262 , UpperCAmelCase_ : int=2048 , UpperCAmelCase_ : Union[str, Any]=56 , UpperCAmelCase_ : Dict=[368, 496] , UpperCAmelCase_ : List[Any]=16 , UpperCAmelCase_ : List[str]=1920 , UpperCAmelCase_ : Tuple=16 , UpperCAmelCase_ : Dict=[1, 16, 224, 224] , **UpperCAmelCase_ : Any , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = num_latents
SCREAMING_SNAKE_CASE__ = d_latents
SCREAMING_SNAKE_CASE__ = d_model
SCREAMING_SNAKE_CASE__ = num_blocks
SCREAMING_SNAKE_CASE__ = num_self_attends_per_block
SCREAMING_SNAKE_CASE__ = num_self_attention_heads
SCREAMING_SNAKE_CASE__ = num_cross_attention_heads
SCREAMING_SNAKE_CASE__ = qk_channels
SCREAMING_SNAKE_CASE__ = v_channels
SCREAMING_SNAKE_CASE__ = cross_attention_shape_for_attention
SCREAMING_SNAKE_CASE__ = self_attention_widening_factor
SCREAMING_SNAKE_CASE__ = cross_attention_widening_factor
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = use_query_residual
# masked language modeling attributes
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = max_position_embeddings
# image classification attributes
SCREAMING_SNAKE_CASE__ = image_size
# flow attributes
SCREAMING_SNAKE_CASE__ = train_size
# multimodal autoencoding attributes
SCREAMING_SNAKE_CASE__ = num_frames
SCREAMING_SNAKE_CASE__ = audio_samples_per_frame
SCREAMING_SNAKE_CASE__ = samples_per_patch
SCREAMING_SNAKE_CASE__ = output_shape
class lowercase__ ( _UpperCAmelCase ):
@property
def A_ ( self : Dict ):
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
SCREAMING_SNAKE_CASE__ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('inputs', dynamic_axis),
('attention_mask', dynamic_axis),
] )
@property
def A_ ( self : List[str] ):
return 1e-4
def A_ ( self : Union[str, Any] , UpperCAmelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[TensorType] = None , UpperCAmelCase_ : int = 3 , UpperCAmelCase_ : int = 40 , UpperCAmelCase_ : int = 40 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ = compute_effective_axis_dimension(
UpperCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ = preprocessor.num_special_tokens_to_add(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = compute_effective_axis_dimension(
UpperCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=UpperCAmelCase_ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE__ = [' '.join(['a'] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE__ = dict(preprocessor(UpperCAmelCase_ , return_tensors=UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = inputs.pop('input_ids' )
return inputs
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ = compute_effective_axis_dimension(UpperCAmelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch )
SCREAMING_SNAKE_CASE__ = self._generate_dummy_images(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = dict(preprocessor(images=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ ) )
SCREAMING_SNAKE_CASE__ = inputs.pop('pixel_values' )
return inputs
else:
raise ValueError(
'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.' )
| 400 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase_ ( yaml.SafeLoader ):
def __UpperCAmelCase ( self , _lowerCAmelCase ):
UpperCAmelCase__ : str = [self.constructed_objects[key_node] for key_node, _ in node.value]
UpperCAmelCase__ : Tuple = [tuple(_snake_case ) if isinstance(_snake_case , _snake_case ) else key for key in keys]
UpperCAmelCase__ : Dict = Counter(_snake_case )
UpperCAmelCase__ : int = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(f"Got duplicate yaml keys: {duplicate_keys}" )
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase=False ):
UpperCAmelCase__ : List[Any] = super().construct_mapping(_snake_case , deep=_snake_case )
self._check_no_duplicates_on_constructed_node(_snake_case )
return mapping
def _lowerCamelCase ( __lowerCamelCase ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Dict = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
UpperCAmelCase__ : Optional[int] = full_content[1:].index("""---""" ) + 1
UpperCAmelCase__ : Tuple = '''\n'''.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(lowerCamelCase_ )
class UpperCAmelCase_ ( a_ ):
# class attributes
__lowerCamelCase = {'train_eval_index'} # train-eval-index in the YAML metadata
@classmethod
def __UpperCAmelCase ( cls , _lowerCAmelCase ):
with open(_snake_case , encoding="""utf-8""" ) as readme_file:
UpperCAmelCase__ : List[Any] = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(_snake_case )
else:
return cls()
def __UpperCAmelCase ( self , _lowerCAmelCase ):
if path.exists():
with open(_snake_case , encoding="""utf-8""" ) as readme_file:
UpperCAmelCase__ : Optional[int] = readme_file.read()
else:
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : List[str] = self._to_readme(_snake_case )
with open(_snake_case , """w""" , encoding="""utf-8""" ) as readme_file:
readme_file.write(_snake_case )
def __UpperCAmelCase ( self , _lowerCAmelCase = None ):
if readme_content is not None:
UpperCAmelCase__ : Optional[Any] = _split_yaml_from_readme(_snake_case )
UpperCAmelCase__ : Union[str, Any] = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
else:
UpperCAmelCase__ : Any = '''---\n''' + self.to_yaml_string() + '''---\n'''
return full_content
@classmethod
def __UpperCAmelCase ( cls , _lowerCAmelCase ):
UpperCAmelCase__ : List[str] = yaml.load(_snake_case , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
UpperCAmelCase__ : List[Any] = {
(key.replace("""-""" , """_""" ) if key.replace("""-""" , """_""" ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**_snake_case )
def __UpperCAmelCase ( self ):
return yaml.safe_dump(
{
(key.replace("""_""" , """-""" ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=_snake_case , allow_unicode=_snake_case , encoding="""utf-8""" , ).decode("""utf-8""" )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""image-classification""": [],
"""translation""": [],
"""image-segmentation""": [],
"""fill-mask""": [],
"""automatic-speech-recognition""": [],
"""token-classification""": [],
"""sentence-similarity""": [],
"""audio-classification""": [],
"""question-answering""": [],
"""summarization""": [],
"""zero-shot-classification""": [],
"""table-to-text""": [],
"""feature-extraction""": [],
"""other""": [],
"""multiple-choice""": [],
"""text-classification""": [],
"""text-to-image""": [],
"""text2text-generation""": [],
"""zero-shot-image-classification""": [],
"""tabular-classification""": [],
"""tabular-regression""": [],
"""image-to-image""": [],
"""tabular-to-text""": [],
"""unconditional-image-generation""": [],
"""text-retrieval""": [],
"""text-to-speech""": [],
"""object-detection""": [],
"""audio-to-audio""": [],
"""text-generation""": [],
"""conversational""": [],
"""table-question-answering""": [],
"""visual-question-answering""": [],
"""image-to-text""": [],
"""reinforcement-learning""": [],
"""voice-activity-detection""": [],
"""time-series-forecasting""": [],
"""document-question-answering""": [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
SCREAMING_SNAKE_CASE__ : Tuple = ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
ap.add_argument("""readme_filepath""")
SCREAMING_SNAKE_CASE__ : Dict = ap.parse_args()
SCREAMING_SNAKE_CASE__ : Optional[int] = Path(args.readme_filepath)
SCREAMING_SNAKE_CASE__ : str = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 79 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Union[str, Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
A : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 | 0 |
from manim import *
class __SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
def __lowerCamelCase ( self : int ) ->List[Any]:
lowerCamelCase__ : List[Any] = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase__ : Union[str, Any] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowerCamelCase__ : Optional[Any] = Rectangle(height=0.25 , width=0.25 )
lowerCamelCase__ : List[Any] = [mem.copy() for i in range(6 )]
lowerCamelCase__ : str = [mem.copy() for i in range(6 )]
lowerCamelCase__ : str = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
lowerCamelCase__ : List[Any] = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
lowerCamelCase__ : Optional[int] = VGroup(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
lowerCamelCase__ : List[str] = Text('''CPU''' , font_size=2_4 )
lowerCamelCase__ : int = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__lowerCAmelCase )
lowerCamelCase__ : str = [mem.copy() for i in range(4 )]
lowerCamelCase__ : Optional[Any] = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
lowerCamelCase__ : Dict = Text('''GPU''' , font_size=2_4 )
lowerCamelCase__ : Tuple = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(__lowerCAmelCase )
lowerCamelCase__ : List[Any] = [mem.copy() for i in range(6 )]
lowerCamelCase__ : Optional[int] = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
lowerCamelCase__ : List[str] = Text('''Model''' , font_size=2_4 )
lowerCamelCase__ : Any = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(__lowerCAmelCase )
lowerCamelCase__ : int = []
lowerCamelCase__ : Union[str, Any] = []
for i, rect in enumerate(__lowerCAmelCase ):
lowerCamelCase__ : Any = fill.copy().set_fill(__lowerCAmelCase , opacity=0.8 )
target.move_to(__lowerCAmelCase )
model_arr.append(__lowerCAmelCase )
lowerCamelCase__ : List[Any] = Rectangle(height=0.46 , width=0.46 ).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 )
lowerCamelCase__ : Dict = [meta_mem.copy() for i in range(6 )]
lowerCamelCase__ : Tuple = [meta_mem.copy() for i in range(6 )]
lowerCamelCase__ : int = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
lowerCamelCase__ : Any = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
lowerCamelCase__ : Dict = VGroup(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
lowerCamelCase__ : str = Text('''Disk''' , font_size=2_4 )
lowerCamelCase__ : Optional[int] = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ : List[str] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowerCamelCase__ : Dict = MarkupText(
F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=1_8 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ : Dict = MarkupText(
F"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=1_8 , )
blue_text.next_to(__lowerCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(__lowerCAmelCase )
lowerCamelCase__ : Any = MarkupText(
F"Now watch as an input is passed through the model\nand how the memory is utilized and handled." , font_size=2_4 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCAmelCase ) )
lowerCamelCase__ : Any = 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=0.02 )
self.play(MoveToTarget(__lowerCAmelCase ) )
self.play(FadeOut(__lowerCAmelCase ) )
lowerCamelCase__ : List[Any] = 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] )
lowerCamelCase__ : Optional[Any] = 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=2_4 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCAmelCase , run_time=3 ) )
lowerCamelCase__ : str = {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}
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] ) )
lowerCamelCase__ : Optional[Any] = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , __lowerCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
lowerCamelCase__ : List[str] = 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:
lowerCamelCase__ : Optional[Any] = 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 + 0.02 , 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] ) )
lowerCamelCase__ : int = a_c
lowerCamelCase__ : Tuple = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(__lowerCAmelCase ) , FadeOut(__lowerCAmelCase , run_time=0.5 ) , )
lowerCamelCase__ : Union[str, Any] = MarkupText(F"Inference on a model too large for GPU memory\nis successfully completed." , font_size=2_4 )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCAmelCase , run_time=3 ) , MoveToTarget(__lowerCAmelCase ) )
self.wait()
| 710 |
from __future__ import annotations
def _a ( UpperCAmelCase ) -> bool:
"""simple docstring"""
lowerCamelCase__ : List[Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
lowerCamelCase__ : Any = [[0 for _ in range(UpperCAmelCase )] for _ in range(UpperCAmelCase )]
lowerCamelCase__ : int = run_maze(UpperCAmelCase , 0 , 0 , UpperCAmelCase )
if solved:
print('''\n'''.join(str(UpperCAmelCase ) for row in solutions ) )
else:
print('''No solution exists!''' )
return solved
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = len(UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
lowerCamelCase__ : str = 1
return True
lowerCamelCase__ : str = (not i < 0) and (not j < 0) # Check lower bounds
lowerCamelCase__ : Optional[int] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
lowerCamelCase__ : List[str] = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
lowerCamelCase__ : Any = 1
# check for directions
if (
run_maze(UpperCAmelCase , i + 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j + 1 , UpperCAmelCase )
or run_maze(UpperCAmelCase , i - 1 , UpperCAmelCase , UpperCAmelCase )
or run_maze(UpperCAmelCase , UpperCAmelCase , j - 1 , UpperCAmelCase )
):
return True
lowerCamelCase__ : Any = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 130 | 0 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase :
'''simple docstring'''
def __init__( self : Optional[Any] , snake_case : int , snake_case : Dict=13 , snake_case : List[Any]=30 , snake_case : int=2 , snake_case : List[Any]=3 , snake_case : Optional[int]=True , snake_case : List[str]=True , snake_case : Optional[Any]=32 , snake_case : Union[str, Any]=5 , snake_case : Tuple=4 , snake_case : Optional[int]=37 , snake_case : List[Any]="gelu" , snake_case : Optional[Any]=0.1 , snake_case : Optional[int]=0.1 , snake_case : Any=10 , snake_case : List[Any]=0.02 , snake_case : List[Any]=None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Dict = batch_size
SCREAMING_SNAKE_CASE : Any = image_size
SCREAMING_SNAKE_CASE : Optional[Any] = patch_size
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : Tuple = is_training
SCREAMING_SNAKE_CASE : Dict = use_labels
SCREAMING_SNAKE_CASE : int = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : int = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Any = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE : Any = initializer_range
SCREAMING_SNAKE_CASE : str = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE : Optional[Any] = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE : Union[str, Any] = num_patches + 1
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Optional[int] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : List[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return ViTMSNConfig(
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 , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self : List[str] , snake_case : int , snake_case : List[str] , snake_case : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = ViTMSNModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self : Dict , snake_case : Any , snake_case : Tuple , snake_case : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[int] = ViTMSNForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(snake_case , labels=snake_case )
print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}' )
print('Labels: {labels}' )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE : Tuple = 1
SCREAMING_SNAKE_CASE : Any = ViTMSNForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Tuple = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = config_and_inputs
SCREAMING_SNAKE_CASE : Optional[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase):
'''simple docstring'''
UpperCAmelCase : str = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
UpperCAmelCase : Union[str, Any] = (
{'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase : Optional[int] = False
UpperCAmelCase : str = False
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : str = False
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = ViTMSNModelTester(self )
SCREAMING_SNAKE_CASE : Dict = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMSN does not use inputs_embeds' )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : str = model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case , nn.Linear ) )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : int = model_class(snake_case )
SCREAMING_SNAKE_CASE : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : Optional[int] = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : Union[str, Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : int = ViTMSNModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def __a ( ) -> Optional[Any]:
SCREAMING_SNAKE_CASE : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('facebook/vit-msn-small' ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(2 )
SCREAMING_SNAKE_CASE : Optional[int] = ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small' ).to(snake_case )
SCREAMING_SNAKE_CASE : List[str] = self.default_image_processor
SCREAMING_SNAKE_CASE : Any = prepare_img()
SCREAMING_SNAKE_CASE : Optional[int] = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(**snake_case )
# verify the logits
SCREAMING_SNAKE_CASE : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
SCREAMING_SNAKE_CASE : Tuple = torch.tensor([-0.0803, -0.4454, -0.2375] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) ) | 352 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
_lowerCamelCase : Optional[Any] = {
"""vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
"""merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
"""tokenizer_config_file""": {
"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
},
}
_lowerCamelCase : Optional[int] = {"""facebook/blenderbot-3B""": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __a ( ) -> int:
SCREAMING_SNAKE_CASE : Dict = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
SCREAMING_SNAKE_CASE : Optional[int] = bs[:]
SCREAMING_SNAKE_CASE : int = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__lowerCAmelCase )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE : Any = [chr(__lowerCAmelCase ) for n in cs]
return dict(zip(__lowerCAmelCase , __lowerCAmelCase ) )
def __a ( __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE : Optional[Any] = set()
SCREAMING_SNAKE_CASE : List[str] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE : Union[str, Any] = char
return pairs
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : Optional[int] = VOCAB_FILES_NAMES
UpperCAmelCase : Any = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Dict = ['input_ids', 'attention_mask']
def __init__( self : Dict , snake_case : Optional[int] , snake_case : Tuple , snake_case : Dict="replace" , snake_case : Optional[Any]="<s>" , snake_case : Dict="</s>" , snake_case : str="</s>" , snake_case : Tuple="<s>" , snake_case : List[Any]="<unk>" , snake_case : Dict="<pad>" , snake_case : int="<mask>" , snake_case : Any=False , **snake_case : str , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else bos_token
SCREAMING_SNAKE_CASE : Union[str, Any] = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else eos_token
SCREAMING_SNAKE_CASE : int = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else sep_token
SCREAMING_SNAKE_CASE : Dict = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else cls_token
SCREAMING_SNAKE_CASE : Optional[Any] = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else unk_token
SCREAMING_SNAKE_CASE : List[Any] = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : str = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else mask_token
super().__init__(
errors=snake_case , bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , sep_token=snake_case , cls_token=snake_case , pad_token=snake_case , mask_token=snake_case , add_prefix_space=snake_case , **snake_case , )
with open(snake_case , encoding='utf-8' ) as vocab_handle:
SCREAMING_SNAKE_CASE : Union[str, Any] = json.load(snake_case )
SCREAMING_SNAKE_CASE : int = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE : Optional[int] = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE : List[Any] = bytes_to_unicode()
SCREAMING_SNAKE_CASE : Union[str, Any] = {v: k for k, v in self.byte_encoder.items()}
with open(snake_case , encoding='utf-8' ) as merges_handle:
SCREAMING_SNAKE_CASE : Union[str, Any] = merges_handle.read().split('\n' )[1:-1]
SCREAMING_SNAKE_CASE : str = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE : List[str] = dict(zip(snake_case , range(len(snake_case ) ) ) )
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : str = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE : List[str] = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return len(self.encoder )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase_ ( self : List[str] , snake_case : int ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE : int = tuple(snake_case )
SCREAMING_SNAKE_CASE : Dict = get_pairs(snake_case )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE : List[str] = min(snake_case , key=lambda snake_case : self.bpe_ranks.get(snake_case , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = bigram
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : Tuple = 0
while i < len(snake_case ):
try:
SCREAMING_SNAKE_CASE : List[str] = word.index(snake_case , snake_case )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE : Dict = j
if word[i] == first and i < len(snake_case ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE : Any = tuple(snake_case )
SCREAMING_SNAKE_CASE : Tuple = new_word
if len(snake_case ) == 1:
break
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = get_pairs(snake_case )
SCREAMING_SNAKE_CASE : List[Any] = ' '.join(snake_case )
SCREAMING_SNAKE_CASE : Dict = word
return word
def lowerCamelCase_ ( self : Any , snake_case : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = []
for token in re.findall(self.pat , snake_case ):
SCREAMING_SNAKE_CASE : Any = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case ).split(' ' ) )
return bpe_tokens
def lowerCamelCase_ ( self : Any , snake_case : int ):
'''simple docstring'''
return self.encoder.get(snake_case , self.encoder.get(self.unk_token ) )
def lowerCamelCase_ ( self : List[Any] , snake_case : Dict ):
'''simple docstring'''
return self.decoder.get(snake_case )
def lowerCamelCase_ ( self : List[Any] , snake_case : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = ''.join(snake_case )
SCREAMING_SNAKE_CASE : Optional[Any] = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def lowerCamelCase_ ( self : Union[str, Any] , snake_case : str , snake_case : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(snake_case ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(snake_case , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=snake_case , ensure_ascii=snake_case ) + '\n' )
SCREAMING_SNAKE_CASE : str = 0
with open(snake_case , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda snake_case : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
SCREAMING_SNAKE_CASE : List[str] = token_index
writer.write(' '.join(snake_case ) + '\n' )
index += 1
return vocab_file, merge_file
def lowerCamelCase_ ( self : Dict , snake_case : List[int] , snake_case : Optional[List[int]] = None , snake_case : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case , token_ids_a=snake_case , already_has_special_tokens=snake_case )
if token_ids_a is None:
return [1] + ([0] * len(snake_case )) + [1]
return [1] + ([0] * len(snake_case )) + [1, 1] + ([0] * len(snake_case )) + [1]
def lowerCamelCase_ ( self : int , snake_case : List[int] , snake_case : Optional[List[int]] = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = [self.sep_token_id]
SCREAMING_SNAKE_CASE : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCamelCase_ ( self : Optional[Any] , snake_case : List[Any] , snake_case : Optional[Any]=False , **snake_case : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(snake_case ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE : Union[str, Any] = ' ' + text
return (text, kwargs)
def lowerCamelCase_ ( self : List[str] , snake_case : List[int] , snake_case : Optional[List[int]] = None ):
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def lowerCamelCase_ ( self : List[str] , snake_case : "Conversation" ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(' ' + text )
else:
# Generated responses should contain them already.
inputs.append(snake_case )
SCREAMING_SNAKE_CASE : Optional[Any] = ' '.join(snake_case )
SCREAMING_SNAKE_CASE : str = self.encode(snake_case )
if len(snake_case ) > self.model_max_length:
SCREAMING_SNAKE_CASE : Optional[Any] = input_ids[-self.model_max_length :]
logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids | 352 | 1 |
def _snake_case ( SCREAMING_SNAKE_CASE ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(SCREAMING_SNAKE_CASE )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=1_0))
| 503 |
from __future__ import annotations
def _snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
print(f'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(SCREAMING_SNAKE_CASE ):
print(f'''{i}\t\t{d}''' )
def _snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
for j in range(SCREAMING_SNAKE_CASE ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Any = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
return True
return False
def _snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list[float]:
"""simple docstring"""
_lowerCAmelCase : Tuple = [float("inf" )] * vertex_count
_lowerCAmelCase : int = 0.0
for _ in range(vertex_count - 1 ):
for j in range(SCREAMING_SNAKE_CASE ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : int = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
_lowerCAmelCase : List[str] = distance[u] + w
_lowerCAmelCase : int = check_negative_cycle(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if negative_cycle_exists:
raise Exception("Negative cycle found" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = int(input('Enter number of vertices: ').strip())
__UpperCAmelCase = int(input('Enter number of edges: ').strip())
__UpperCAmelCase = [{} for _ in range(E)]
for i in range(E):
print('Edge ', i + 1)
__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase = (
int(x)
for x in input('Enter source, destination, weight: ').strip().split(' ')
)
__UpperCAmelCase = {'src': src, 'dst': dest, 'weight': weight}
__UpperCAmelCase = int(input('\nEnter shortest path source:').strip())
__UpperCAmelCase = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 503 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case: List[str] = logging.get_logger(__name__)
def _snake_case ( A_ : Any , A_ : Dict=False ):
"""simple docstring"""
a_ : List[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """vit.embeddings.cls_token"""),
("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
a_ : Tuple = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _snake_case ( A_ : Optional[Any] , A_ : List[str] , A_ : List[str]=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
a_ : List[Any] = """"""
else:
a_ : Union[str, Any] = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
a_ : Tuple = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
a_ : List[str] = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
a_ : List[Any] = in_proj_weight[
: config.hidden_size, :
]
a_ : int = in_proj_bias[: config.hidden_size]
a_ : Tuple = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
a_ : Tuple = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
a_ : List[Any] = in_proj_weight[
-config.hidden_size :, :
]
a_ : int = in_proj_bias[-config.hidden_size :]
def _snake_case ( A_ : Dict ):
"""simple docstring"""
a_ : str = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(A_ , A_ )
def _snake_case ( A_ : str , A_ : List[str] , A_ : List[Any] ):
"""simple docstring"""
a_ : Dict = dct.pop(A_ )
a_ : Dict = val
def _snake_case ( ):
"""simple docstring"""
a_ : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
a_ : Any = Image.open(requests.get(A_ , stream=A_ ).raw )
return im
@torch.no_grad()
def _snake_case ( A_ : Dict , A_ : Any ):
"""simple docstring"""
a_ : List[str] = ViTConfig()
a_ : str = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
a_ : int = True
a_ : int = int(vit_name[-12:-10] )
a_ : Optional[int] = int(vit_name[-9:-6] )
else:
a_ : Union[str, Any] = 1000
a_ : Optional[Any] = """huggingface/label-files"""
a_ : Optional[int] = """imagenet-1k-id2label.json"""
a_ : Tuple = json.load(open(hf_hub_download(A_ , A_ , repo_type="""dataset""" ) , """r""" ) )
a_ : List[str] = {int(A_ ): v for k, v in idalabel.items()}
a_ : int = idalabel
a_ : Any = {v: k for k, v in idalabel.items()}
a_ : Union[str, Any] = int(vit_name[-6:-4] )
a_ : str = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith("""tiny""" ):
a_ : int = 192
a_ : int = 768
a_ : Union[str, Any] = 12
a_ : Dict = 3
elif vit_name[9:].startswith("""small""" ):
a_ : List[str] = 384
a_ : str = 1536
a_ : Optional[Any] = 12
a_ : List[str] = 6
else:
pass
else:
if vit_name[4:].startswith("""small""" ):
a_ : Any = 768
a_ : Union[str, Any] = 2304
a_ : Union[str, Any] = 8
a_ : Optional[int] = 8
elif vit_name[4:].startswith("""base""" ):
pass
elif vit_name[4:].startswith("""large""" ):
a_ : Optional[Any] = 1024
a_ : Tuple = 4096
a_ : Any = 24
a_ : int = 16
elif vit_name[4:].startswith("""huge""" ):
a_ : Optional[int] = 1280
a_ : Optional[int] = 5120
a_ : str = 32
a_ : List[str] = 16
# load original model from timm
a_ : int = timm.create_model(A_ , pretrained=A_ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
a_ : Union[str, Any] = timm_model.state_dict()
if base_model:
remove_classification_head_(A_ )
a_ : Union[str, Any] = create_rename_keys(A_ , A_ )
for src, dest in rename_keys:
rename_key(A_ , A_ , A_ )
read_in_q_k_v(A_ , A_ , A_ )
# load HuggingFace model
if vit_name[-5:] == "in21k":
a_ : Dict = ViTModel(A_ ).eval()
else:
a_ : int = ViTForImageClassification(A_ ).eval()
model.load_state_dict(A_ )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
a_ : List[str] = DeiTImageProcessor(size=config.image_size )
else:
a_ : Optional[Any] = ViTImageProcessor(size=config.image_size )
a_ : Tuple = image_processor(images=prepare_img() , return_tensors="""pt""" )
a_ : Dict = encoding["""pixel_values"""]
a_ : Tuple = model(A_ )
if base_model:
a_ : Dict = timm_model.forward_features(A_ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(A_ , outputs.pooler_output , atol=1E-3 )
else:
a_ : Tuple = timm_model(A_ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(A_ , outputs.logits , atol=1E-3 )
Path(A_ ).mkdir(exist_ok=A_ )
print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(A_ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(A_ )
if __name__ == "__main__":
__snake_case: Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_patch16_224",
type=str,
help="Name of the ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
__snake_case: Optional[Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 577 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case: Optional[Any] = logging.get_logger(__name__)
class _UpperCAmelCase ( lowerCAmelCase__ ):
"""simple docstring"""
a_ = "timm_backbone"
def __init__( self , lowerCAmelCase_=None , lowerCAmelCase_=3 , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=None , **lowerCAmelCase_ , ):
'''simple docstring'''
super().__init__(**lowerCAmelCase_ )
a_ : Optional[Any] = backbone
a_ : Union[str, Any] = num_channels
a_ : str = features_only
a_ : Any = use_pretrained_backbone
a_ : Tuple = True
a_ : Tuple = out_indices if out_indices is not None else (-1,)
| 577 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase ( a , a , unittest.TestCase ):
_lowerCamelCase : int = CycleDiffusionPipeline
_lowerCamelCase : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"""negative_prompt""",
"""height""",
"""width""",
"""negative_prompt_embeds""",
}
_lowerCamelCase : Optional[int] = PipelineTesterMixin.required_optional_params - {"""latents"""}
_lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""} )
_lowerCamelCase : Tuple = IMAGE_TO_IMAGE_IMAGE_PARAMS
_lowerCamelCase : str = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase ( self ):
torch.manual_seed(0 )
lowerCAmelCase : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
lowerCAmelCase : str = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='scaled_linear' , num_train_timesteps=1000 , clip_sample=snake_case__ , set_alpha_to_one=snake_case__ , )
torch.manual_seed(0 )
lowerCAmelCase : List[Any] = 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 )
lowerCAmelCase : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
lowerCAmelCase : Dict = CLIPTextModel(snake_case__ )
lowerCAmelCase : Optional[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowerCAmelCase : List[Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowercase ( self , snake_case__ , snake_case__=0 ):
lowerCAmelCase : List[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
lowerCAmelCase : Dict = image / 2 + 0.5
if str(snake_case__ ).startswith('mps' ):
lowerCAmelCase : Optional[Any] = torch.manual_seed(snake_case__ )
else:
lowerCAmelCase : Dict = torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
lowerCAmelCase : str = {
'prompt': 'An astronaut riding an elephant',
'source_prompt': 'An astronaut riding a horse',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'eta': 0.1,
'strength': 0.8,
'guidance_scale': 3,
'source_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def lowercase ( self ):
lowerCAmelCase : str = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase : Union[str, Any] = self.get_dummy_components()
lowerCAmelCase : Optional[int] = CycleDiffusionPipeline(**snake_case__ )
lowerCAmelCase : str = pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
lowerCAmelCase : Tuple = self.get_dummy_inputs(snake_case__ )
lowerCAmelCase : Optional[Any] = pipe(**snake_case__ )
lowerCAmelCase : Optional[Any] = output.images
lowerCAmelCase : Optional[Any] = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
lowerCAmelCase : Optional[Any] = np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != 'cuda' , 'This test requires a GPU' )
def lowercase ( self ):
lowerCAmelCase : Any = self.get_dummy_components()
for name, module in components.items():
if hasattr(snake_case__ , 'half' ):
lowerCAmelCase : Optional[int] = module.half()
lowerCAmelCase : List[str] = CycleDiffusionPipeline(**snake_case__ )
lowerCAmelCase : List[str] = pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
lowerCAmelCase : Union[str, Any] = self.get_dummy_inputs(snake_case__ )
lowerCAmelCase : List[str] = pipe(**snake_case__ )
lowerCAmelCase : List[Any] = output.images
lowerCAmelCase : Tuple = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
lowerCAmelCase : Tuple = np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def lowercase ( self ):
return super().test_save_load_local()
@unittest.skip('non-deterministic pipeline' )
def lowercase ( self ):
return super().test_inference_batch_single_identical()
@skip_mps
def lowercase ( self ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowercase ( self ):
return super().test_save_load_optional_components()
@skip_mps
def lowercase ( self ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
def lowercase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self ):
lowerCAmelCase : List[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
lowerCAmelCase : Optional[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy' )
lowerCAmelCase : List[Any] = init_image.resize((512, 512) )
lowerCAmelCase : List[str] = 'CompVis/stable-diffusion-v1-4'
lowerCAmelCase : List[Any] = DDIMScheduler.from_pretrained(snake_case__ , subfolder='scheduler' )
lowerCAmelCase : int = CycleDiffusionPipeline.from_pretrained(
snake_case__ , scheduler=snake_case__ , safety_checker=snake_case__ , torch_dtype=torch.floataa , revision='fp16' )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
pipe.enable_attention_slicing()
lowerCAmelCase : Optional[Any] = 'A black colored car'
lowerCAmelCase : Union[str, Any] = 'A blue colored car'
lowerCAmelCase : Dict = torch.manual_seed(0 )
lowerCAmelCase : Any = pipe(
prompt=snake_case__ , source_prompt=snake_case__ , image=snake_case__ , num_inference_steps=100 , eta=0.1 , strength=0.8_5 , guidance_scale=3 , source_guidance_scale=1 , generator=snake_case__ , output_type='np' , )
lowerCAmelCase : Dict = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def lowercase ( self ):
lowerCAmelCase : Optional[int] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
lowerCAmelCase : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy' )
lowerCAmelCase : Union[str, Any] = init_image.resize((512, 512) )
lowerCAmelCase : str = 'CompVis/stable-diffusion-v1-4'
lowerCAmelCase : Optional[int] = DDIMScheduler.from_pretrained(snake_case__ , subfolder='scheduler' )
lowerCAmelCase : Optional[Any] = CycleDiffusionPipeline.from_pretrained(snake_case__ , scheduler=snake_case__ , safety_checker=snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
pipe.enable_attention_slicing()
lowerCAmelCase : Tuple = 'A black colored car'
lowerCAmelCase : List[Any] = 'A blue colored car'
lowerCAmelCase : Union[str, Any] = torch.manual_seed(0 )
lowerCAmelCase : Dict = pipe(
prompt=snake_case__ , source_prompt=snake_case__ , image=snake_case__ , num_inference_steps=100 , eta=0.1 , strength=0.8_5 , guidance_scale=3 , source_guidance_scale=1 , generator=snake_case__ , output_type='np' , )
lowerCAmelCase : Dict = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 717 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCamelCase ( _A : Dict ) -> int:
"""simple docstring"""
lowerCAmelCase : Tuple = []
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
F"stage{idx}.patch_embed.proj.weight",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
F"stage{idx}.patch_embed.proj.bias",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
F"stage{idx}.patch_embed.norm.weight",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
F"stage{idx}.patch_embed.norm.bias",
) )
return embed
def __UpperCamelCase ( _A : List[Any] , _A : Dict ) -> Any:
"""simple docstring"""
lowerCAmelCase : str = []
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
F"stage{idx}.blocks.{cnt}.attn.proj.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
F"stage{idx}.blocks.{cnt}.attn.proj.bias",
) )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias") )
return attention_weights
def __UpperCamelCase ( _A : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase : Optional[int] = []
token.append((F"cvt.encoder.stages.{idx}.cls_token", 'stage2.cls_token') )
return token
def __UpperCamelCase ( ) -> int:
"""simple docstring"""
lowerCAmelCase : List[Any] = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def __UpperCamelCase ( _A : str , _A : Optional[Any] , _A : Dict , _A : str ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase : List[str] = 'imagenet-1k-id2label.json'
lowerCAmelCase : Tuple = 10_00
lowerCAmelCase : str = 'huggingface/label-files'
lowerCAmelCase : List[Any] = num_labels
lowerCAmelCase : Any = json.load(open(cached_download(hf_hub_url(_A , _A , repo_type='dataset' ) ) , 'r' ) )
lowerCAmelCase : List[str] = {int(_A ): v for k, v in idalabel.items()}
lowerCAmelCase : List[str] = idalabel
lowerCAmelCase : str = {v: k for k, v in idalabel.items()}
lowerCAmelCase : int = CvtConfig(num_labels=_A , idalabel=_A , labelaid=_A )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' , 1 )[-1][4:6] == "13":
lowerCAmelCase : List[str] = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' , 1 )[-1][4:6] == "21":
lowerCAmelCase : Tuple = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
lowerCAmelCase : Any = [2, 2, 20]
lowerCAmelCase : List[str] = [3, 12, 16]
lowerCAmelCase : List[Any] = [1_92, 7_68, 10_24]
lowerCAmelCase : Union[str, Any] = CvtForImageClassification(_A )
lowerCAmelCase : str = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
lowerCAmelCase : Optional[Any] = image_size
lowerCAmelCase : List[Any] = torch.load(_A , map_location=torch.device('cpu' ) )
lowerCAmelCase : str = OrderedDict()
lowerCAmelCase : int = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
lowerCAmelCase : List[str] = list_of_state_dict + cls_token(_A )
lowerCAmelCase : Optional[Any] = list_of_state_dict + embeddings(_A )
for cnt in range(config.depth[idx] ):
lowerCAmelCase : List[Any] = list_of_state_dict + attention(_A , _A )
lowerCAmelCase : List[str] = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_A )
for i in range(len(_A ) ):
lowerCAmelCase : Tuple = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_A )
model.save_pretrained(_A )
image_processor.save_pretrained(_A )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
_lowerCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'--cvt_model',
default='cvt-w24',
type=str,
help='Name of the cvt model you\'d like to convert.',
)
parser.add_argument(
'--image_size',
default=384,
type=int,
help='Input Image Size',
)
parser.add_argument(
'--cvt_file_name',
default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
type=str,
help='Input Image Size',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_lowerCAmelCase : str = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 646 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig""",
"""PoolFormerOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = ["""PoolFormerFeatureExtractor"""]
_UpperCAmelCase = ["""PoolFormerImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PoolFormerForImageClassification""",
"""PoolFormerModel""",
"""PoolFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 409 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""UniSpeechForCTC""",
"""UniSpeechForPreTraining""",
"""UniSpeechForSequenceClassification""",
"""UniSpeechModel""",
"""UniSpeechPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 409 | 1 |
import sys
from collections import defaultdict
class snake_case__ :
def __init__( self : List[Any] ):
snake_case__ : Dict = []
def UpperCAmelCase__ ( self : List[str] , _lowerCamelCase : Tuple ):
return self.node_position[vertex]
def UpperCAmelCase__ ( self : Union[str, Any] , _lowerCamelCase : int , _lowerCamelCase : str ):
snake_case__ : Union[str, Any] = pos
def UpperCAmelCase__ ( self : List[str] , _lowerCamelCase : List[Any] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : Optional[Any] ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
snake_case__ : str = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
snake_case__ : Optional[int] = 2 * start + 1
else:
snake_case__ : str = 2 * start + 2
if heap[smallest_child] < heap[start]:
snake_case__ , snake_case__ : int = heap[smallest_child], positions[smallest_child]
snake_case__ , snake_case__ : str = (
heap[start],
positions[start],
)
snake_case__ , snake_case__ : int = temp, tempa
snake_case__ : int = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _lowerCamelCase )
self.top_to_bottom(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def UpperCAmelCase__ ( self : Union[str, Any] , _lowerCamelCase : int , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : List[Any] ):
snake_case__ : Optional[Any] = position[index]
while index != 0:
snake_case__ : Optional[Any] = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
snake_case__ : Optional[Any] = heap[parent]
snake_case__ : Dict = position[parent]
self.set_position(position[parent] , _lowerCamelCase )
else:
snake_case__ : Tuple = val
snake_case__ : Optional[Any] = temp
self.set_position(_lowerCamelCase , _lowerCamelCase )
break
snake_case__ : Optional[int] = parent
else:
snake_case__ : List[str] = val
snake_case__ : List[Any] = temp
self.set_position(_lowerCamelCase , 0 )
def UpperCAmelCase__ ( self : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : Dict ):
snake_case__ : int = len(_lowerCamelCase ) // 2 - 1
for i in range(_lowerCamelCase , -1 , -1 ):
self.top_to_bottom(_lowerCamelCase , _lowerCamelCase , len(_lowerCamelCase ) , _lowerCamelCase )
def UpperCAmelCase__ ( self : Tuple , _lowerCamelCase : Dict , _lowerCamelCase : List[str] ):
snake_case__ : Any = positions[0]
snake_case__ : List[str] = sys.maxsize
self.top_to_bottom(_lowerCamelCase , 0 , len(_lowerCamelCase ) , _lowerCamelCase )
return temp
def lowercase__( A ):
snake_case__ : int = Heap()
snake_case__ : Optional[int] = [0] * len(A )
snake_case__ : Any = [-1] * len(A ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
snake_case__ : Union[str, Any] = [] # Heap of Distance of vertices from their neighboring vertex
snake_case__ : Dict = []
for vertex in range(len(A ) ):
distance_tv.append(sys.maxsize )
positions.append(A )
heap.node_position.append(A )
snake_case__ : Tuple = []
snake_case__ : int = 1
snake_case__ : int = sys.maxsize
for neighbor, distance in adjacency_list[0]:
snake_case__ : Optional[int] = 0
snake_case__ : Optional[int] = distance
heap.heapify(A , A )
for _ in range(1 , len(A ) ):
snake_case__ : Tuple = heap.delete_minimum(A , A )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
snake_case__ : List[str] = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(A )]
):
snake_case__ : Any = distance
heap.bottom_to_top(
A , heap.get_position(A ) , A , A )
snake_case__ : Union[str, Any] = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
lowerCamelCase : Union[str, Any] = int(input('Enter number of edges: ').strip())
lowerCamelCase : str = defaultdict(list)
for _ in range(edges_number):
lowerCamelCase : Any = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 303 |
def lowercase__( A = 1_0 , A = 1_0_0_0 , A = True ):
assert (
isinstance(A , A )
and isinstance(A , A )
and isinstance(A , A )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError('Invalid value for min_val or max_val (min_value < max_value)' )
return min_val if option else max_val
def lowercase__( A , A ):
return int((number_a + number_a) / 2 )
def lowercase__( A , A , A ):
assert (
isinstance(A , A ) and isinstance(A , A ) and isinstance(A , A )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError('argument value for lower and higher must be(lower > higher)' )
if not lower < to_guess < higher:
raise ValueError(
'guess value must be within the range of lower and higher value' )
def answer(A ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print('started...' )
snake_case__ : Dict = lower
snake_case__ : Tuple = higher
snake_case__ : List[str] = []
while True:
snake_case__ : Dict = get_avg(A , A )
last_numbers.append(A )
if answer(A ) == "low":
snake_case__ : Tuple = number
elif answer(A ) == "high":
snake_case__ : Union[str, Any] = number
else:
break
print(f'''guess the number : {last_numbers[-1]}''' )
print(f'''details : {last_numbers!s}''' )
def lowercase__( ):
snake_case__ : Tuple = int(input('Enter lower value : ' ).strip() )
snake_case__ : Optional[Any] = int(input('Enter high value : ' ).strip() )
snake_case__ : Dict = int(input('Enter value to guess : ' ).strip() )
guess_the_number(A , A , A )
if __name__ == "__main__":
main()
| 303 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab.txt'}
_SCREAMING_SNAKE_CASE = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
_SCREAMING_SNAKE_CASE = {
'YituTech/conv-bert-base': 512,
'YituTech/conv-bert-medium-small': 512,
'YituTech/conv-bert-small': 512,
}
_SCREAMING_SNAKE_CASE = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class __magic_name__ ( __snake_case ):
_SCREAMING_SNAKE_CASE : Any = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE : int = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE : List[str] = PRETRAINED_INIT_CONFIGURATION
_SCREAMING_SNAKE_CASE : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE : List[str] = ConvBertTokenizer
def __init__( self : Optional[int] , snake_case_ : List[Any]=None , snake_case_ : List[str]=None , snake_case_ : List[Any]=True , snake_case_ : List[str]="[UNK]" , snake_case_ : Tuple="[SEP]" , snake_case_ : Optional[int]="[PAD]" , snake_case_ : str="[CLS]" , snake_case_ : Optional[Any]="[MASK]" , snake_case_ : Tuple=True , snake_case_ : str=None , **snake_case_ : List[Any] , ):
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , do_lower_case=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , tokenize_chinese_chars=lowercase_ , strip_accents=lowercase_ , **lowercase_ , )
__snake_case = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , lowercase_ ) != do_lower_case
or normalizer_state.get("strip_accents" , lowercase_ ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , lowercase_ ) != tokenize_chinese_chars
):
__snake_case = getattr(lowercase_ , normalizer_state.pop("type" ) )
__snake_case = do_lower_case
__snake_case = strip_accents
__snake_case = tokenize_chinese_chars
__snake_case = normalizer_class(**lowercase_ )
__snake_case = do_lower_case
def lowerCAmelCase ( self : Any , snake_case_ : int , snake_case_ : Any=None ):
__snake_case = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCAmelCase ( self : Dict , snake_case_ : Any , snake_case_ : Dict = None ):
__snake_case = [self.sep_token_id]
__snake_case = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase ( self : List[str] , snake_case_ : int , snake_case_ : Dict = None ):
__snake_case = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
| 163 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )
for i in range(len(__lowercase ) ):
_snake_case : List[str] = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = [
"CUDA out of memory.", # CUDA OOM
"cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.", # CUDNN SNAFU
"DefaultCPUAllocator: can't allocate memory", # CPU OOM
]
if isinstance(__lowercase , __lowercase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def snake_case (__lowercase = None , __lowercase = 128 ) -> Any:
'''simple docstring'''
if function is None:
return functools.partial(__lowercase , starting_batch_size=__lowercase )
_snake_case : List[str] = starting_batch_size
def decorator(*__lowercase , **__lowercase ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
_snake_case : Optional[Any] = list(inspect.signature(__lowercase ).parameters.keys() )
# Guard against user error
if len(__lowercase ) < (len(__lowercase ) + 1):
_snake_case : str = ", ".join([F"""{arg}={value}""" for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
F"""Batch size was passed into `{function.__name__}` as the first argument when called."""
F"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`""" )
while True:
if batch_size == 0:
raise RuntimeError("No executable batch size found, reached zero." )
try:
return function(__lowercase , *__lowercase , **__lowercase )
except Exception as e:
if should_reduce_batch_size(__lowercase ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator | 670 | 0 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str] , lowerCAmelCase : str=2 , lowerCAmelCase : str=3 , lowerCAmelCase : Any=4 , lowerCAmelCase : Union[str, Any]=2 , lowerCAmelCase : Dict=7 , lowerCAmelCase : int=True , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : int=True , lowerCAmelCase : Dict=True , lowerCAmelCase : Tuple=99 , lowerCAmelCase : List[Any]=36 , lowerCAmelCase : List[str]=2 , lowerCAmelCase : List[Any]=4 , lowerCAmelCase : str=37 , lowerCAmelCase : str="gelu" , lowerCAmelCase : Union[str, Any]=0.1 , lowerCAmelCase : Tuple=0.1 , lowerCAmelCase : Dict=5_12 , lowerCAmelCase : str=16 , lowerCAmelCase : Any=2 , lowerCAmelCase : Optional[Any]=0.02 , lowerCAmelCase : Optional[int]=6 , lowerCAmelCase : Optional[int]=6 , lowerCAmelCase : Any=3 , lowerCAmelCase : str=4 , lowerCAmelCase : Union[str, Any]=None , lowerCAmelCase : Dict=10_00 , ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Dict = parent
__lowerCAmelCase : List[Any] = batch_size
__lowerCAmelCase : Dict = num_channels
__lowerCAmelCase : Any = image_size
__lowerCAmelCase : int = patch_size
__lowerCAmelCase : Optional[int] = is_training
__lowerCAmelCase : Optional[int] = use_input_mask
__lowerCAmelCase : Tuple = use_token_type_ids
__lowerCAmelCase : Any = use_labels
__lowerCAmelCase : List[Any] = vocab_size
__lowerCAmelCase : List[Any] = hidden_size
__lowerCAmelCase : List[Any] = num_hidden_layers
__lowerCAmelCase : Union[str, Any] = num_attention_heads
__lowerCAmelCase : Any = intermediate_size
__lowerCAmelCase : Tuple = hidden_act
__lowerCAmelCase : str = hidden_dropout_prob
__lowerCAmelCase : int = attention_probs_dropout_prob
__lowerCAmelCase : int = max_position_embeddings
__lowerCAmelCase : int = type_vocab_size
__lowerCAmelCase : Optional[int] = type_sequence_label_size
__lowerCAmelCase : Optional[int] = initializer_range
__lowerCAmelCase : Tuple = coordinate_size
__lowerCAmelCase : str = shape_size
__lowerCAmelCase : List[str] = num_labels
__lowerCAmelCase : Tuple = num_choices
__lowerCAmelCase : Optional[Any] = scope
__lowerCAmelCase : Tuple = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__lowerCAmelCase : Any = text_seq_length
__lowerCAmelCase : Optional[Any] = (image_size // patch_size) ** 2 + 1
__lowerCAmelCase : List[str] = self.text_seq_length + self.image_seq_length
def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
__lowerCAmelCase : Dict = bbox.numpy()
# 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]:
__lowerCAmelCase : List[str] = bbox[i, j, 3]
__lowerCAmelCase : Union[str, Any] = bbox[i, j, 1]
__lowerCAmelCase : Optional[Any] = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCAmelCase : List[Any] = bbox[i, j, 2]
__lowerCAmelCase : List[str] = bbox[i, j, 0]
__lowerCAmelCase : List[str] = tmp_coordinate
__lowerCAmelCase : int = tf.constant(lowerCAmelCase )
__lowerCAmelCase : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase : Tuple = None
if self.use_input_mask:
__lowerCAmelCase : Dict = random_attention_mask([self.batch_size, self.text_seq_length] )
__lowerCAmelCase : Union[str, Any] = None
if self.use_token_type_ids:
__lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__lowerCAmelCase : Dict = None
__lowerCAmelCase : Tuple = None
if self.use_labels:
__lowerCAmelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase : str = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__lowerCAmelCase : Union[str, Any] = LayoutLMvaConfig(
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 , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase : Tuple , lowerCAmelCase : List[Any] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : List[str] , lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : str = TFLayoutLMvaModel(config=lowerCAmelCase )
# text + image
__lowerCAmelCase : Dict = model(lowerCAmelCase , pixel_values=lowerCAmelCase , training=lowerCAmelCase )
__lowerCAmelCase : int = model(
lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , training=lowerCAmelCase , )
__lowerCAmelCase : Any = model(lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , training=lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__lowerCAmelCase : Optional[int] = model(lowerCAmelCase , training=lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__lowerCAmelCase : Optional[Any] = model({"""pixel_values""": pixel_values} , training=lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase : List[str] , lowerCAmelCase : Dict , lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : Tuple , lowerCAmelCase : List[Any] , lowerCAmelCase : Optional[int] ) -> Any:
"""simple docstring"""
__lowerCAmelCase : str = self.num_labels
__lowerCAmelCase : int = TFLayoutLMvaForSequenceClassification(config=lowerCAmelCase )
__lowerCAmelCase : List[Any] = model(
lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase , training=lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase : Tuple , lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : List[str] , lowerCAmelCase : int , lowerCAmelCase : List[str] , lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = self.num_labels
__lowerCAmelCase : Optional[int] = TFLayoutLMvaForTokenClassification(config=lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = model(
lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase , training=lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : List[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Dict , lowerCAmelCase : str , lowerCAmelCase : Any ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Dict = 2
__lowerCAmelCase : Dict = TFLayoutLMvaForQuestionAnswering(config=lowerCAmelCase )
__lowerCAmelCase : List[str] = model(
lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , start_positions=lowerCAmelCase , end_positions=lowerCAmelCase , training=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 SCREAMING_SNAKE_CASE ( self : Any ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.prepare_config_and_inputs()
((__lowerCAmelCase) ,(__lowerCAmelCase) ,(__lowerCAmelCase) ,(__lowerCAmelCase) ,(__lowerCAmelCase) ,(__lowerCAmelCase) ,(__lowerCAmelCase) ,(__lowerCAmelCase)) : Tuple = config_and_inputs
__lowerCAmelCase : List[str] = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] =(
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCamelCase : List[str] =(
{"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
if is_tf_available()
else {}
)
lowerCamelCase : Optional[int] =False
lowerCamelCase : Tuple =False
lowerCamelCase : Union[str, Any] =False
def SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : Optional[Any] , lowerCAmelCase : str , lowerCAmelCase : Tuple ) -> List[Any]:
"""simple docstring"""
return True
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict , lowerCAmelCase : int=False ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = copy.deepcopy(lowerCAmelCase )
if model_class in get_values(lowerCAmelCase ):
__lowerCAmelCase : List[str] = {
k: tf.tile(tf.expand_dims(lowerCAmelCase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(lowerCAmelCase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase ):
__lowerCAmelCase : List[Any] = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(lowerCAmelCase ):
__lowerCAmelCase : Dict = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
__lowerCAmelCase : List[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(lowerCAmelCase ):
__lowerCAmelCase : str = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(lowerCAmelCase ):
__lowerCAmelCase : Any = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
"""simple docstring"""
__lowerCAmelCase : Any = TFLayoutLMvaModelTester(self )
__lowerCAmelCase : Any = ConfigTester(self , config_class=lowerCAmelCase , hidden_size=37 )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
"""simple docstring"""
__lowerCAmelCase ,__lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase : str = model_class(lowerCAmelCase )
if getattr(lowerCAmelCase , """hf_compute_loss""" , lowerCAmelCase ):
# The number of elements in the loss should be the same as the number of elements in the label
__lowerCAmelCase : Tuple = self._prepare_for_class(inputs_dict.copy() , lowerCAmelCase , return_labels=lowerCAmelCase )
__lowerCAmelCase : Dict = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=lowerCAmelCase )[0]
]
__lowerCAmelCase : Tuple = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
__lowerCAmelCase : Tuple = self._prepare_for_class(inputs_dict.copy() , lowerCAmelCase , return_labels=lowerCAmelCase )
__lowerCAmelCase : List[str] = prepared_for_class.pop("""input_ids""" )
__lowerCAmelCase : List[str] = model(lowerCAmelCase , **lowerCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
__lowerCAmelCase : Optional[int] = self._prepare_for_class(inputs_dict.copy() , lowerCAmelCase , return_labels=lowerCAmelCase )
__lowerCAmelCase : List[str] = prepared_for_class.pop("""input_ids""" )
if "labels" in prepared_for_class:
__lowerCAmelCase : List[Any] = prepared_for_class["""labels"""].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
__lowerCAmelCase : List[Any] = -1_00
__lowerCAmelCase : Tuple = tf.convert_to_tensor(lowerCAmelCase )
__lowerCAmelCase : List[Any] = model(lowerCAmelCase , **lowerCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
__lowerCAmelCase : Tuple = self._prepare_for_class(inputs_dict.copy() , lowerCAmelCase , return_labels=lowerCAmelCase )
__lowerCAmelCase : List[str] = model(lowerCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
__lowerCAmelCase : int = self._prepare_for_class(inputs_dict.copy() , lowerCAmelCase , return_labels=lowerCAmelCase )
# Get keys that were added with the _prepare_for_class function
__lowerCAmelCase : Dict = prepared_for_class.keys() - inputs_dict.keys()
__lowerCAmelCase : Union[str, Any] = inspect.signature(model.call ).parameters
__lowerCAmelCase : List[Any] = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
__lowerCAmelCase : Any = {0: """input_ids"""}
for label_key in label_keys:
__lowerCAmelCase : int = signature_names.index(lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = label_key
__lowerCAmelCase : str = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
__lowerCAmelCase : Optional[Any] = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
__lowerCAmelCase : int = prepared_for_class[value]
__lowerCAmelCase : Tuple = tuple(lowerCAmelCase )
# Send to model
__lowerCAmelCase : List[Any] = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
"""simple docstring"""
(
(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,
) : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
(
(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,
) : str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCAmelCase : Optional[int] = type
self.model_tester.create_and_check_model(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> List[str]:
"""simple docstring"""
(
(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,
) : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str ) -> Tuple:
"""simple docstring"""
(
(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,
) : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
"""simple docstring"""
(
(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,(
__lowerCAmelCase
) ,
) : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
@slow
def SCREAMING_SNAKE_CASE ( self : Any ) -> Tuple:
"""simple docstring"""
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase : str = TFLayoutLMvaModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def snake_case_ () -> Optional[Any]:
__lowerCAmelCase : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )
__lowerCAmelCase : Dict = self.default_image_processor
__lowerCAmelCase : Union[str, Any] = prepare_img()
__lowerCAmelCase : Union[str, Any] = image_processor(images=lowerCAmelCase , return_tensors="""tf""" ).pixel_values
__lowerCAmelCase : List[str] = tf.constant([[1, 2]] )
__lowerCAmelCase : Any = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
__lowerCAmelCase : Optional[int] = model(input_ids=lowerCAmelCase , bbox=lowerCAmelCase , pixel_values=lowerCAmelCase , training=lowerCAmelCase )
# verify the logits
__lowerCAmelCase : Optional[Any] = (1, 1_99, 7_68)
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase , atol=1e-4 ) )
| 701 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=a_ )
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowerCamelCase : str =field(default="audio-classification" , metadata={"include_in_asdict_even_if_is_default": True} )
lowerCamelCase : ClassVar[Features] =Features({"audio": Audio()} )
lowerCamelCase : ClassVar[Features] =Features({"labels": ClassLabel} )
lowerCamelCase : str ="audio"
lowerCamelCase : str ="labels"
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , lowerCAmelCase ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
__lowerCAmelCase : Optional[int] = copy.deepcopy(self )
__lowerCAmelCase : Tuple = self.label_schema.copy()
__lowerCAmelCase : Optional[int] = features[self.label_column]
__lowerCAmelCase : int = label_schema
return task_template
@property
def SCREAMING_SNAKE_CASE ( self : str ) -> Dict[str, str]:
"""simple docstring"""
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 218 | 0 |
import sys
from collections import defaultdict
class _a :
'''simple docstring'''
def __init__( self ):
__A : Optional[Any] = []
def __UpperCAmelCase( self , __UpperCAmelCase ):
return self.node_position[vertex]
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase ):
__A : Any = pos
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
__A : Tuple = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
__A : List[str] = 2 * start + 1
else:
__A : Dict = 2 * start + 2
if heap[smallest_child] < heap[start]:
__A : List[str] = heap[smallest_child], positions[smallest_child]
__A : List[Any] = (
heap[start],
positions[start],
)
__A : Tuple = temp, tempa
__A : Optional[int] = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _a )
self.top_to_bottom(_a , _a , _a , _a )
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__A : List[str] = position[index]
while index != 0:
__A : Tuple = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
__A : List[Any] = heap[parent]
__A : Optional[Any] = position[parent]
self.set_position(position[parent] , _a )
else:
__A : Optional[Any] = val
__A : Tuple = temp
self.set_position(_a , _a )
break
__A : List[Any] = parent
else:
__A : List[str] = val
__A : Union[str, Any] = temp
self.set_position(_a , 0 )
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase ):
__A : List[str] = len(_a ) // 2 - 1
for i in range(_a , -1 , -1 ):
self.top_to_bottom(_a , _a , len(_a ) , _a )
def __UpperCAmelCase( self , __UpperCAmelCase , __UpperCAmelCase ):
__A : Tuple = positions[0]
__A : List[Any] = sys.maxsize
self.top_to_bottom(_a , 0 , len(_a ) , _a )
return temp
def lowerCamelCase_ ( _lowercase ) -> Tuple:
__A : Tuple = Heap()
__A : Any = [0] * len(__a )
__A : Optional[int] = [-1] * len(__a ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
__A : Optional[int] = [] # Heap of Distance of vertices from their neighboring vertex
__A : List[str] = []
for vertex in range(len(__a ) ):
distance_tv.append(sys.maxsize )
positions.append(__a )
heap.node_position.append(__a )
__A : Optional[int] = []
__A : Optional[Any] = 1
__A : int = sys.maxsize
for neighbor, distance in adjacency_list[0]:
__A : Dict = 0
__A : str = distance
heap.heapify(__a , __a )
for _ in range(1 , len(__a ) ):
__A : List[str] = heap.delete_minimum(__a , __a )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
__A : int = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(__a )]
):
__A : Dict = distance
heap.bottom_to_top(
__a , heap.get_position(__a ) , __a , __a )
__A : Union[str, Any] = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCamelCase = int(input('Enter number of edges: ').strip())
UpperCamelCase = defaultdict(list)
for _ in range(edges_number):
UpperCamelCase = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 520 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
__lowerCAmelCase = get_logger(__name__)
class UpperCAmelCase__ :
"""simple docstring"""
__UpperCAmelCase : Optional[int] = '''dummy_data'''
__UpperCAmelCase : List[str] = '''datasets'''
__UpperCAmelCase : int = False
def __init__( self : Optional[Any] ,_a : str ,_a : str ,_a : Union[Version, str] ,_a : Optional[str] = None ,_a : bool = False ,_a : bool = True ,_a : Optional[List[Callable]] = None ,):
'''simple docstring'''
_a : List[Any] = 0
_a : List[Any] = dataset_name
_a : Any = cache_dir
_a : Tuple = use_local_dummy_data
_a : List[Any] = config
# download_callbacks take a single url as input
_a : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_a : Tuple = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_a : Union[str, Any] = str(_a )
# to be downloaded
_a : Optional[Any] = None
_a : str = None
@property
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
if self._dummy_file is None:
_a : List[str] = self.download_dummy_data()
return self._dummy_file
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' ,self.config.name ,self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' ,self.version_name )
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return os.path.join(self.dummy_data_folder ,'dummy_data.zip' )
def __lowercase ( self : Tuple ):
'''simple docstring'''
_a : Optional[Any] = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_a : Tuple = cached_path(
_a ,cache_dir=self.cache_dir ,extract_compressed_file=_a ,force_extract=_a )
return os.path.join(_a ,self.dummy_file_name )
@property
def __lowercase ( self : int ):
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir ,self.dataset_name ,self.dummy_zip_file )
@property
def __lowercase ( self : Tuple ):
'''simple docstring'''
if self._bucket_url is None:
_a : int = hf_github_url(self.dataset_name ,self.dummy_zip_file.replace(os.sep ,'/' ) )
return self._bucket_url
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep ,'/' ).split('/' )[:-1] )
def __lowercase ( self : Optional[int] ,_a : Tuple ,*_a : str ):
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_a : Tuple = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_a : List[str] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(_a ,_a ):
return self.create_dummy_data_dict(_a ,_a )
elif isinstance(_a ,(list, tuple) ):
return self.create_dummy_data_list(_a ,_a )
else:
return self.create_dummy_data_single(_a ,_a )
def __lowercase ( self : Any ,_a : Union[str, Any] ,*_a : List[str] ):
'''simple docstring'''
return self.download_and_extract(_a )
def __lowercase ( self : Any ,_a : Optional[Any] ,_a : int ):
'''simple docstring'''
return self.download_and_extract(_a )
def __lowercase ( self : Optional[int] ,_a : List[str] ,*_a : Union[str, Any] ,**_a : List[str] ):
'''simple docstring'''
return path
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return {}
def __lowercase ( self : Optional[int] ,_a : str ,_a : Union[str, Any] ):
'''simple docstring'''
_a : Tuple = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(_a ,_a ):
for single_url in single_urls:
download_callback(_a )
else:
_a : str = single_urls
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(_a ,_a ):
_a : List[str] = [os.path.join(_a ,urllib.parse.quote_plus(Path(_a ).name ) ) for x in single_urls]
else:
_a : List[Any] = single_urls
_a : Optional[Any] = os.path.join(_a ,urllib.parse.quote_plus(Path(_a ).name ) )
_a : str = value
# make sure that values are unique
if all(isinstance(_a ,_a ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_a : List[str] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def __lowercase ( self : Any ,_a : Tuple ,_a : List[Any] ):
'''simple docstring'''
_a : int = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_a : Tuple = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' ,_a ) ) for url in data_url )
_a : Optional[int] = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_a : List[Any] = [data_url[0]] * len(_a )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_a : str = os.path.join(_a ,urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(_a )
return dummy_data_list
def __lowercase ( self : Optional[Any] ,_a : str ,_a : Optional[int] ):
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_a : Optional[Any] = os.path.join(_a ,urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(_a ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
pass
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
pass
def __lowercase ( self : Optional[int] ,_a : str ):
'''simple docstring'''
def _iter_archive_members(_a : Any ):
# this preserves the order of the members inside the ZIP archive
_a : int = Path(self.dummy_file ).parent
_a : Any = path.relative_to(_a )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_a : Tuple = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(_a )
_a : Optional[Any] = Path(_a )
_a : str = _iter_archive_members(_a ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(_a ).as_posix(), file_path.open('rb' )
def __lowercase ( self : Union[str, Any] ,_a : Tuple ):
'''simple docstring'''
if not isinstance(_a ,_a ):
_a : Optional[int] = [paths]
for path in paths:
if os.path.isfile(_a ):
if os.path.basename(_a ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(_a ):
if os.path.basename(_a ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(_a ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(_a ,_a )
| 229 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowercase ( a , unittest.TestCase ):
lowercase__ : Union[str, Any] = KandinskyVaaPipeline
lowercase__ : str = [
"""image_embeds""",
"""negative_image_embeds""",
]
lowercase__ : Optional[Any] = ["""image_embeds""", """negative_image_embeds"""]
lowercase__ : Tuple = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
lowercase__ : Any = False
@property
def __snake_case( self : Any ) -> List[Any]:
'''simple docstring'''
return 32
@property
def __snake_case( self : List[str] ) -> int:
'''simple docstring'''
return 32
@property
def __snake_case( self : Tuple ) -> Dict:
'''simple docstring'''
return self.time_input_dim
@property
def __snake_case( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def __snake_case( self : Tuple ) -> Tuple:
'''simple docstring'''
return 100
@property
def __snake_case( self : Dict ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
SCREAMING_SNAKE_CASE = UNetaDConditionModel(**_UpperCamelCase )
return model
@property
def __snake_case( self : int ) -> List[Any]:
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __snake_case( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = VQModel(**self.dummy_movq_kwargs )
return model
def __snake_case( self : Optional[int] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.dummy_unet
SCREAMING_SNAKE_CASE = self.dummy_movq
SCREAMING_SNAKE_CASE = DDIMScheduler(
num_train_timesteps=1_000 , beta_schedule="linear" , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , clip_sample=_UpperCamelCase , set_alpha_to_one=_UpperCamelCase , steps_offset=1 , prediction_type="epsilon" , thresholding=_UpperCamelCase , )
SCREAMING_SNAKE_CASE = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def __snake_case( self : Optional[Any] , _UpperCamelCase : Tuple , _UpperCamelCase : int=0 ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
SCREAMING_SNAKE_CASE = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_UpperCamelCase )
if str(_UpperCamelCase ).startswith("mps" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(_UpperCamelCase )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
SCREAMING_SNAKE_CASE = {
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def __snake_case( self : int ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = "cpu"
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**_UpperCamelCase )
SCREAMING_SNAKE_CASE = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs(_UpperCamelCase ) )
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = pipe(
**self.get_dummy_inputs(_UpperCamelCase ) , return_dict=_UpperCamelCase , )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE = np.array(
[0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
def __snake_case( self : str ) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case( self : Any ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy" )
SCREAMING_SNAKE_CASE = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(_UpperCamelCase )
SCREAMING_SNAKE_CASE = KandinskyVaaPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE = pipeline.to(_UpperCamelCase )
pipeline.set_progress_bar_config(disable=_UpperCamelCase )
SCREAMING_SNAKE_CASE = "red cat, 4k photo"
SCREAMING_SNAKE_CASE = torch.Generator(device="cuda" ).manual_seed(0 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = pipe_prior(
_UpperCamelCase , generator=_UpperCamelCase , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
SCREAMING_SNAKE_CASE = torch.Generator(device="cuda" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipeline(
image_embeds=_UpperCamelCase , negative_image_embeds=_UpperCamelCase , generator=_UpperCamelCase , num_inference_steps=100 , output_type="np" , )
SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_UpperCamelCase , _UpperCamelCase )
| 647 | 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 : Any , _UpperCamelCase : Any , _UpperCamelCase : Dict=13 , _UpperCamelCase : List[Any]=64 , _UpperCamelCase : Union[str, Any]=2 , _UpperCamelCase : int=3 , _UpperCamelCase : Union[str, Any]=True , _UpperCamelCase : Optional[int]=True , _UpperCamelCase : Tuple=32 , _UpperCamelCase : str=5 , _UpperCamelCase : Tuple=4 , _UpperCamelCase : Any=37 , _UpperCamelCase : List[str]="gelu" , _UpperCamelCase : int=0.1 , _UpperCamelCase : int=0.1 , _UpperCamelCase : Optional[int]=10 , _UpperCamelCase : Tuple=0.0_2 , _UpperCamelCase : Union[str, Any]=[1, 16, 4, 4] , _UpperCamelCase : Optional[Any]=None , ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = scope
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = (self.image_size // 32) ** 2
SCREAMING_SNAKE_CASE = num_patches + 1
def __snake_case( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def __snake_case( self : Dict ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
"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=_UpperCamelCase , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=_UpperCamelCase , )
def __snake_case( self : Dict , _UpperCamelCase : int , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[int] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ViTHybridModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(_UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __snake_case( self : Any , _UpperCamelCase : List[Any] , _UpperCamelCase : List[Any] , _UpperCamelCase : Tuple ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.type_sequence_label_size
SCREAMING_SNAKE_CASE = ViTHybridForImageClassification(_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
SCREAMING_SNAKE_CASE = model(_UpperCamelCase , labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __snake_case( self : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( a , a , unittest.TestCase ):
lowercase__ : Optional[int] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
lowercase__ : List[Any] = (
{"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : Any = False
lowercase__ : Optional[int] = False
def __snake_case( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ViTHybridModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=_UpperCamelCase , has_text_modality=_UpperCamelCase , hidden_size=37 )
def __snake_case( self : Optional[Any] ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds" )
def __snake_case( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
def __snake_case( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(_UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_UpperCamelCase , nn.Linear ) )
def __snake_case( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(_UpperCamelCase )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _UpperCamelCase )
def __snake_case( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def __snake_case( self : Dict ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase )
def __snake_case( self : Optional[int] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = _config_zero_init(_UpperCamelCase )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(config=_UpperCamelCase )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
SCREAMING_SNAKE_CASE = [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 __snake_case( self : Any ) -> List[Any]:
'''simple docstring'''
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = ViTHybridModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
def __lowerCamelCase ():
SCREAMING_SNAKE_CASE = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
@cached_property
def __snake_case( self : List[Any] ) -> Any:
'''simple docstring'''
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __snake_case( self : Tuple ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
_UpperCamelCase )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=_UpperCamelCase , return_tensors="pt" ).to(_UpperCamelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**_UpperCamelCase )
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _UpperCamelCase )
SCREAMING_SNAKE_CASE = torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(_UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _UpperCamelCase , atol=1e-4 ) )
@slow
@require_accelerate
def __snake_case( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384" )
SCREAMING_SNAKE_CASE = ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384" , device_map="auto" )
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=_UpperCamelCase , return_tensors="pt" )
SCREAMING_SNAKE_CASE = model(**_UpperCamelCase )
SCREAMING_SNAKE_CASE = outputs.logits
# model predicts one of the 1000 ImageNet classes
SCREAMING_SNAKE_CASE = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , "tabby, tabby cat" )
| 647 | 1 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(__lowerCamelCase )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowerCamelCase__ ( ):
assert gamma(0.5 ) == sqrt(__lowerCamelCase )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
a : Optional[int] = 1.0
while num:
a : List[str] = float(input("Gamma of: "))
print(f"""gamma({num}) = {gamma(num)}""")
print("\nEnter 0 to exit...")
| 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(__lowerCamelCase )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowerCamelCase__ ( ):
assert gamma(0.5 ) == sqrt(__lowerCamelCase )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
a : Optional[int] = 1.0
while num:
a : List[str] = float(input("Gamma of: "))
print(f"""gamma({num}) = {gamma(num)}""")
print("\nEnter 0 to exit...")
| 63 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__lowerCAmelCase : List[Any] = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
'bicubic': PIL.Image.Resampling.BICUBIC,
'lanczos': PIL.Image.Resampling.LANCZOS,
'nearest': PIL.Image.Resampling.NEAREST,
}
else:
__lowerCAmelCase : str = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
'nearest': PIL.Image.NEAREST,
}
def a_ (_lowerCAmelCase : Optional[Any] )-> Optional[int]:
snake_case: Dict = (images / 2 + 0.5).clamp(0 , 1 )
snake_case: Optional[int] = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
snake_case: int = numpy_to_pil(_lowerCAmelCase )
return images
def a_ (_lowerCAmelCase : Union[str, Any] )-> Dict:
if images.ndim == 3:
snake_case: List[Any] = images[None, ...]
snake_case: str = (images * 255).round().astype("""uint8""" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
snake_case: int = [Image.fromarray(image.squeeze() , mode="""L""" ) for image in images]
else:
snake_case: Dict = [Image.fromarray(_lowerCAmelCase ) for image in images]
return pil_images
| 164 | import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class lowerCamelCase ( unittest.TestCase ):
def __init__( self , __lowerCamelCase , __lowerCamelCase=2 , __lowerCamelCase=56 , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=99 , __lowerCamelCase=32 , __lowerCamelCase=2 , __lowerCamelCase=2 , __lowerCamelCase=7 , __lowerCamelCase="gelu_new" , __lowerCamelCase=0.1 , __lowerCamelCase=0.1 , __lowerCamelCase=5_12 , __lowerCamelCase=16 , __lowerCamelCase=2 , __lowerCamelCase=0.02 , __lowerCamelCase=4 , __lowerCamelCase="block_sparse" , __lowerCamelCase=True , __lowerCamelCase=False , __lowerCamelCase=2 , __lowerCamelCase=3 , ) -> int:
'''simple docstring'''
snake_case: str = parent
snake_case: Any = batch_size
snake_case: List[str] = seq_length
snake_case: str = is_training
snake_case: List[str] = use_attention_mask
snake_case: Optional[Any] = use_token_type_ids
snake_case: Union[str, Any] = use_labels
snake_case: Dict = vocab_size
snake_case: Dict = hidden_size
snake_case: Optional[Any] = num_hidden_layers
snake_case: int = num_attention_heads
snake_case: List[Any] = intermediate_size
snake_case: Optional[Any] = hidden_act
snake_case: List[str] = hidden_dropout_prob
snake_case: Dict = attention_probs_dropout_prob
snake_case: Optional[Any] = max_position_embeddings
snake_case: str = type_vocab_size
snake_case: Dict = type_sequence_label_size
snake_case: List[Any] = initializer_range
snake_case: str = num_choices
snake_case: Any = rescale_embeddings
snake_case: int = attention_type
snake_case: int = use_bias
snake_case: List[str] = block_size
snake_case: Any = num_random_blocks
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
snake_case: Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case: Tuple = None
if self.use_attention_mask:
snake_case: Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case: Tuple = None
if self.use_token_type_ids:
snake_case: Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case: Any = BigBirdConfig(
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 , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )
return config, input_ids, token_type_ids, attention_mask
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
snake_case: Union[str, Any] = self.prepare_config_and_inputs()
snake_case , snake_case , snake_case , snake_case: List[Any] = config_and_inputs
snake_case: Any = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""attention_mask""": attention_mask,
}
return config, inputs_dict
@require_flax
class lowerCamelCase ( __snake_case , unittest.TestCase ):
__lowerCamelCase = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
__lowerCamelCase = False
__lowerCamelCase = False
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
snake_case: List[Any] = FlaxBigBirdModelTester(self )
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowerCAmelCase_ ( self ) -> int:
'''simple docstring'''
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowerCAmelCase_ ( self ) -> str:
'''simple docstring'''
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_hidden_states_output()
@slow
def lowerCAmelCase_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case: Optional[Any] = model_class_name.from_pretrained("""google/bigbird-roberta-base""" )
self.assertIsNotNone(__lowerCamelCase )
def lowerCAmelCase_ ( self ) -> List[str]:
'''simple docstring'''
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
snake_case , snake_case: Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
snake_case: Optional[Any] = self._prepare_for_class(__lowerCamelCase , __lowerCamelCase )
snake_case: Tuple = model_class(__lowerCamelCase )
@jax.jit
def model_jitted(__lowerCamelCase , __lowerCamelCase=None , **__lowerCamelCase ):
return model(input_ids=__lowerCamelCase , attention_mask=__lowerCamelCase , **__lowerCamelCase )
with self.subTest("""JIT Enabled""" ):
snake_case: Union[str, Any] = model_jitted(**__lowerCamelCase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
snake_case: List[str] = model_jitted(**__lowerCamelCase ).to_tuple()
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
for jitted_output, output in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def lowerCAmelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=1e-5 , __lowerCamelCase="outputs" , __lowerCamelCase=None ) -> List[str]:
'''simple docstring'''
if name.startswith("""outputs.attentions""" ):
return
else:
super().check_pt_flax_outputs(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
| 164 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_a : Any = logging.get_logger(__name__)
class __A (UpperCAmelCase_ ):
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ):
warnings.warn(
"The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use GLPNImageProcessor instead." , lowerCAmelCase__ , )
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
| 168 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ : float):
return 0.0
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
SCREAMING_SNAKE_CASE_: Dict = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[int] = 5_12
SCREAMING_SNAKE_CASE_: str = [1] + [0] * (size - 1)
SCREAMING_SNAKE_CASE_: Dict = [filter_type.process(_UpperCAmelCase ) for item in inputs]
SCREAMING_SNAKE_CASE_: Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
SCREAMING_SNAKE_CASE_: Tuple = np.abs(np.fft.fft(_UpperCAmelCase ) )
SCREAMING_SNAKE_CASE_: Optional[Any] = 20 * np.logaa(_UpperCAmelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
SCREAMING_SNAKE_CASE_: Any = get_bounds(_UpperCAmelCase , _UpperCAmelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(_UpperCAmelCase )
plt.show()
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[int] = 5_12
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] + [0] * (size - 1)
SCREAMING_SNAKE_CASE_: Dict = [filter_type.process(_UpperCAmelCase ) for item in inputs]
SCREAMING_SNAKE_CASE_: int = [0] * (samplerate - size) # zero-padding
outputs += filler
SCREAMING_SNAKE_CASE_: Any = np.angle(np.fft.fft(_UpperCAmelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(_UpperCAmelCase , -2 * pi ) )
plt.show()
| 671 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
lowercase__ = TypeVar("T")
class SCREAMING_SNAKE_CASE__ ( Generic[T] ):
def __init__(self , _lowercase ):
'''simple docstring'''
__a : List[Any] = data
__a : Tuple = self
__a : str = 0
class SCREAMING_SNAKE_CASE__ ( Generic[T] ):
def __init__(self ):
'''simple docstring'''
__a : dict[T, DisjointSetTreeNode[T]] = {}
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
__a : List[Any] = DisjointSetTreeNode(_lowercase )
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
__a : Union[str, Any] = self.map[data]
if elem_ref != elem_ref.parent:
__a : List[str] = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def lowerCAmelCase__(self , _lowercase , _lowercase ):
'''simple docstring'''
if nodea.rank > nodea.rank:
__a : List[str] = nodea
else:
__a : int = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def lowerCAmelCase__(self , _lowercase , _lowercase ):
'''simple docstring'''
self.link(self.find_set(_lowercase ) , self.find_set(_lowercase ) )
class SCREAMING_SNAKE_CASE__ ( Generic[T] ):
def __init__(self ):
'''simple docstring'''
__a : dict[T, dict[T, int]] = {}
def lowerCAmelCase__(self , _lowercase ):
'''simple docstring'''
if node not in self.connections:
__a : List[str] = {}
def lowerCAmelCase__(self , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
self.add_node(_lowercase )
self.add_node(_lowercase )
__a : Optional[Any] = weight
__a : Tuple = weight
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : str = []
__a : Tuple = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda _lowercase : x[2] )
# creating the disjoint set
__a : Union[str, Any] = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(_lowercase )
# MST generation
__a : Optional[int] = 0
__a : Optional[Any] = 0
__a : List[Any] = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__a , __a , __a : Any = edges[index]
index += 1
__a : List[str] = disjoint_set.find_set(_lowercase )
__a : Any = disjoint_set.find_set(_lowercase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(_lowercase , _lowercase , _lowercase )
disjoint_set.union(_lowercase , _lowercase )
return graph
| 63 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def __magic_name__ ( _lowerCamelCase : np.ndarray , _lowerCamelCase : float ):
# For applying gaussian function for each element in matrix.
__a : int = math.sqrt(_lowerCamelCase )
__a : Any = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigma) ** 2) * 0.5 )
def __magic_name__ ( _lowerCamelCase : np.ndarray , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ):
__a : Any = kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def __magic_name__ ( _lowerCamelCase : int , _lowerCamelCase : float ):
# Creates a gaussian kernel of given dimension.
__a : int = np.zeros((kernel_size, kernel_size) )
for i in range(0 , _lowerCamelCase ):
for j in range(0 , _lowerCamelCase ):
__a : Any = math.sqrt(
abs(i - kernel_size // 2 ) ** 2 + abs(j - kernel_size // 2 ) ** 2 )
return vec_gaussian(_lowerCamelCase , _lowerCamelCase )
def __magic_name__ ( _lowerCamelCase : np.ndarray , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : int , ):
__a : Tuple = np.zeros(img.shape )
__a : Optional[int] = get_gauss_kernel(_lowerCamelCase , _lowerCamelCase )
__a , __a : int = img.shape
for i in range(kernel_size // 2 , size_x - kernel_size // 2 ):
for j in range(kernel_size // 2 , size_y - kernel_size // 2 ):
__a : List[str] = get_slice(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__a : Any = img_s - img_s[kernel_size // 2, kernel_size // 2]
__a : Optional[Any] = vec_gaussian(_lowerCamelCase , _lowerCamelCase )
__a : Optional[Any] = np.multiply(_lowerCamelCase , _lowerCamelCase )
__a : Any = np.multiply(_lowerCamelCase , _lowerCamelCase )
__a : Tuple = np.sum(_lowerCamelCase ) / np.sum(_lowerCamelCase )
__a : Optional[Any] = val
return imga
def __magic_name__ ( _lowerCamelCase : list ):
__a : Optional[Any] = args[1] if args[1:] else """../image_data/lena.jpg"""
__a : Union[str, Any] = float(args[2] ) if args[2:] else 1.0
__a : Optional[int] = float(args[3] ) if args[3:] else 1.0
if args[4:]:
__a : Any = int(args[4] )
__a : Any = kernel_size + abs(kernel_size % 2 - 1 )
else:
__a : Optional[int] = 5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
lowercase__ , lowercase__ , lowercase__ , lowercase__ = parse_args(sys.argv)
lowercase__ = cva.imread(filename, 0)
cva.imshow("input image", img)
lowercase__ = img / 255
lowercase__ = out.astype("float32")
lowercase__ = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
lowercase__ = out * 255
lowercase__ = np.uinta(out)
cva.imshow("output image", out)
cva.waitKey(0)
cva.destroyAllWindows()
| 63 | 1 |
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 A_ (a_ ):
def __init__( self , _A = None , _A = None , _A = None , _A = None , _A = False , _A = False , _A = None , **_A , ):
'''simple docstring'''
UpperCAmelCase = path_or_paths
UpperCAmelCase = split if split or isinstance(_A , _A ) else '''train'''
UpperCAmelCase = features
UpperCAmelCase = cache_dir
UpperCAmelCase = keep_in_memory
UpperCAmelCase = streaming
UpperCAmelCase = num_proc
UpperCAmelCase = kwargs
@abstractmethod
def _lowercase ( self ):
'''simple docstring'''
pass
class A_ (a_ ):
def __init__( self , _A = None , _A = None , _A = False , _A = False , _A = None , **_A , ):
'''simple docstring'''
UpperCAmelCase = features
UpperCAmelCase = cache_dir
UpperCAmelCase = keep_in_memory
UpperCAmelCase = streaming
UpperCAmelCase = num_proc
UpperCAmelCase = kwargs
@abstractmethod
def _lowercase ( self ):
'''simple docstring'''
pass
| 130 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ (a_ , unittest.TestCase ):
UpperCAmelCase__ = LEDTokenizer
UpperCAmelCase__ = LEDTokenizerFast
UpperCAmelCase__ = True
def _lowercase ( self ):
'''simple docstring'''
super().setUp()
UpperCAmelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
UpperCAmelCase = dict(zip(_A , range(len(_A ) ) ) )
UpperCAmelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
UpperCAmelCase = {'''unk_token''': '''<unk>'''}
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_A ) )
def _lowercase ( self , **_A ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_A )
def _lowercase ( self , **_A ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **_A )
def _lowercase ( self , _A ):
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def _lowercase ( self ):
'''simple docstring'''
return LEDTokenizer.from_pretrained('''allenai/led-base-16384''' )
@cached_property
def _lowercase ( self ):
'''simple docstring'''
return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''' )
@require_torch
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
UpperCAmelCase = [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase = tokenizer(_A , max_length=len(_A ) , padding=_A , return_tensors='''pt''' )
self.assertIsInstance(_A , _A )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(_A , _A )
@require_torch
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase = tokenizer(_A , padding=_A , return_tensors='''pt''' )
self.assertIn('''input_ids''' , _A )
self.assertIn('''attention_mask''' , _A )
self.assertNotIn('''labels''' , _A )
self.assertNotIn('''decoder_attention_mask''' , _A )
@require_torch
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = [
'''Summary of the text.''',
'''Another summary.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase = tokenizer(text_target=_A , max_length=3_2 , padding='''max_length''' , return_tensors='''pt''' )
self.assertEqual(3_2 , targets['''input_ids'''].shape[1] )
@require_torch
def _lowercase ( self ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase = tokenizer(
['''I am a small frog''' * 1_0_2_4, '''I am a small frog'''] , padding=_A , truncation=_A , return_tensors='''pt''' )
self.assertIsInstance(_A , _A )
self.assertEqual(batch.input_ids.shape , (2, 5_1_2_2) )
@require_torch
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase = ['''A long paragraph for summarization.''']
UpperCAmelCase = [
'''Summary of the text.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase = tokenizer(_A , return_tensors='''pt''' )
UpperCAmelCase = tokenizer(text_target=_A , return_tensors='''pt''' )
UpperCAmelCase = inputs['''input_ids''']
UpperCAmelCase = targets['''input_ids''']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def _lowercase ( self ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
UpperCAmelCase = ['''Summary of the text.''', '''Another summary.''']
UpperCAmelCase = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
UpperCAmelCase = tokenizer(_A , padding=_A )
UpperCAmelCase = [[0] * len(_A ) for x in encoded_output['''input_ids''']]
UpperCAmelCase = tokenizer.pad(_A )
self.assertSequenceEqual(outputs['''global_attention_mask'''] , _A )
def _lowercase ( self ):
'''simple docstring'''
pass
def _lowercase ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(_A , **_A )
UpperCAmelCase = self.tokenizer_class.from_pretrained(_A , **_A )
UpperCAmelCase = '''A, <mask> AllenNLP sentence.'''
UpperCAmelCase = tokenizer_r.encode_plus(_A , add_special_tokens=_A , return_token_type_ids=_A )
UpperCAmelCase = tokenizer_p.encode_plus(_A , add_special_tokens=_A , return_token_type_ids=_A )
self.assertEqual(sum(tokens_r['''token_type_ids'''] ) , sum(tokens_p['''token_type_ids'''] ) )
self.assertEqual(
sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ) , sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ) , )
UpperCAmelCase = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
UpperCAmelCase = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(
_A , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
_A , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
| 130 | 1 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class __lowercase :
def __init__( self : str ,A : Tuple ,A : Optional[int]=100 ,A : Any=13 ,A : Optional[Any]=30 ,A : Dict=2 ,A : Any=3 ,A : Any=True ,A : Any=True ,A : str=32 ,A : str=4 ,A : Tuple=4 ,A : Any=37 ,A : Optional[int]="gelu" ,A : Union[str, Any]=0.1 ,A : Union[str, Any]=0.1 ,A : List[str]=10 ,A : str=0.0_2 ,A : Any=3 ,A : Tuple=None ,A : List[Any]=[0, 1, 2, 3] ,):
'''simple docstring'''
UpperCAmelCase__ : Tuple = parent
UpperCAmelCase__ : List[str] = 100
UpperCAmelCase__ : Any = batch_size
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : Any = patch_size
UpperCAmelCase__ : Optional[Any] = num_channels
UpperCAmelCase__ : List[str] = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : Tuple = hidden_size
UpperCAmelCase__ : Tuple = num_hidden_layers
UpperCAmelCase__ : List[Any] = num_attention_heads
UpperCAmelCase__ : List[str] = intermediate_size
UpperCAmelCase__ : Union[str, Any] = hidden_act
UpperCAmelCase__ : Optional[Any] = hidden_dropout_prob
UpperCAmelCase__ : int = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[Any] = type_sequence_label_size
UpperCAmelCase__ : Optional[Any] = initializer_range
UpperCAmelCase__ : int = scope
UpperCAmelCase__ : str = out_indices
UpperCAmelCase__ : Tuple = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase__ : int = (image_size // patch_size) ** 2
UpperCAmelCase__ : Any = num_patches + 1
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[int] = None
UpperCAmelCase__ : Union[str, Any] = None
if self.use_labels:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
UpperCAmelCase__ : str = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowercase ( self : Any ):
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size ,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=A ,initializer_range=self.initializer_range ,out_indices=self.out_indices ,)
def __lowercase ( self : Optional[Any] ,A : int ,A : Union[str, Any] ,A : Union[str, Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = BeitModel(config=A )
model.to(A )
model.eval()
UpperCAmelCase__ : Optional[int] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase ( self : List[Any] ,A : Dict ,A : List[Any] ,A : str ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = BeitForMaskedImageModeling(config=A )
model.to(A )
model.eval()
UpperCAmelCase__ : Optional[int] = model(A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length - 1, self.vocab_size) )
def __lowercase ( self : Dict ,A : Union[str, Any] ,A : Any ,A : List[str] ,A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.type_sequence_label_size
UpperCAmelCase__ : Any = BeitForImageClassification(A )
model.to(A )
model.eval()
UpperCAmelCase__ : Optional[int] = model(A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase__ : List[Any] = 1
UpperCAmelCase__ : List[str] = BeitForImageClassification(A )
model.to(A )
model.eval()
UpperCAmelCase__ : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : Union[str, Any] = model(A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def __lowercase ( self : List[Any] ,A : str ,A : int ,A : Optional[int] ,A : str ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.num_labels
UpperCAmelCase__ : int = BeitForSemanticSegmentation(A )
model.to(A )
model.eval()
UpperCAmelCase__ : Dict = model(A )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
UpperCAmelCase__ : int = model(A ,labels=A )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Dict = config_and_inputs
UpperCAmelCase__ : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __lowercase ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
snake_case_ = (
{
"""feature-extraction""": BeitModel,
"""image-classification""": BeitForImageClassification,
"""image-segmentation""": BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = BeitModelTester(self )
UpperCAmelCase__ : Optional[Any] = ConfigTester(self ,config_class=A ,has_text_modality=A ,hidden_size=37 )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""BEiT does not use inputs_embeds""" )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
pass
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(A )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
UpperCAmelCase__ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A ,nn.Linear ) )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = model_class(A )
UpperCAmelCase__ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : List[str] = [*signature.parameters.keys()]
UpperCAmelCase__ : Union[str, Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,A )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A )
def __lowercase ( self : str ):
'''simple docstring'''
if not self.model_tester.is_training:
return
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Optional[Any] = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(A ), BeitForMaskedImageModeling]:
continue
UpperCAmelCase__ : str = model_class(A )
model.to(A )
model.train()
UpperCAmelCase__ : Optional[int] = self._prepare_for_class(A ,A ,return_labels=A )
UpperCAmelCase__ : int = model(**A ).loss
loss.backward()
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
UpperCAmelCase__ : Any = False
UpperCAmelCase__ : List[str] = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(A ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
UpperCAmelCase__ : Optional[int] = model_class(A )
model.gradient_checkpointing_enable()
model.to(A )
model.train()
UpperCAmelCase__ : int = self._prepare_for_class(A ,A ,return_labels=A )
UpperCAmelCase__ : Union[str, Any] = model(**A ).loss
loss.backward()
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Union[str, Any] = _config_zero_init(A )
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class(config=A )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() ,[0.0, 1.0] ,msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,)
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : List[Any] = BeitModel.from_pretrained(A )
self.assertIsNotNone(A )
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __lowercase ( unittest.TestCase ):
@cached_property
def __lowercase ( self : Dict ):
'''simple docstring'''
return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""" ) if is_vision_available() else None
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""" ).to(A )
UpperCAmelCase__ : Optional[Any] = self.default_image_processor
UpperCAmelCase__ : List[str] = prepare_img()
UpperCAmelCase__ : List[str] = image_processor(images=A ,return_tensors="""pt""" ).pixel_values.to(A )
# prepare bool_masked_pos
UpperCAmelCase__ : Dict = torch.ones((1, 196) ,dtype=torch.bool ).to(A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Any = model(pixel_values=A ,bool_masked_pos=A )
UpperCAmelCase__ : Optional[int] = outputs.logits
# verify the logits
UpperCAmelCase__ : List[Any] = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape ,A )
UpperCAmelCase__ : int = torch.tensor(
[[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]] ).to(A )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] ,A ,atol=1e-2 ) )
@slow
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""" ).to(A )
UpperCAmelCase__ : Any = self.default_image_processor
UpperCAmelCase__ : List[str] = prepare_img()
UpperCAmelCase__ : Optional[Any] = image_processor(images=A ,return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : int = model(**A )
UpperCAmelCase__ : Tuple = outputs.logits
# verify the logits
UpperCAmelCase__ : Union[str, Any] = torch.Size((1, 1_000) )
self.assertEqual(logits.shape ,A )
UpperCAmelCase__ : Union[str, Any] = torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8] ).to(A )
self.assertTrue(torch.allclose(logits[0, :3] ,A ,atol=1e-4 ) )
UpperCAmelCase__ : Optional[Any] = 281
self.assertEqual(logits.argmax(-1 ).item() ,A )
@slow
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : int = BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""" ).to(
A )
UpperCAmelCase__ : List[str] = self.default_image_processor
UpperCAmelCase__ : List[str] = prepare_img()
UpperCAmelCase__ : Optional[int] = image_processor(images=A ,return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**A )
UpperCAmelCase__ : List[Any] = outputs.logits
# verify the logits
UpperCAmelCase__ : List[str] = torch.Size((1, 21_841) )
self.assertEqual(logits.shape ,A )
UpperCAmelCase__ : Optional[Any] = torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1] ).to(A )
self.assertTrue(torch.allclose(logits[0, :3] ,A ,atol=1e-4 ) )
UpperCAmelCase__ : Tuple = 2_396
self.assertEqual(logits.argmax(-1 ).item() ,A )
@slow
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""" )
UpperCAmelCase__ : Union[str, Any] = model.to(A )
UpperCAmelCase__ : Optional[Any] = BeitImageProcessor(do_resize=A ,size=640 ,do_center_crop=A )
UpperCAmelCase__ : List[Any] = load_dataset("""hf-internal-testing/fixtures_ade20k""" ,split="""test""" )
UpperCAmelCase__ : str = Image.open(ds[0]["""file"""] )
UpperCAmelCase__ : Optional[Any] = image_processor(images=A ,return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**A )
UpperCAmelCase__ : Union[str, Any] = outputs.logits
# verify the logits
UpperCAmelCase__ : Union[str, Any] = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape ,A )
UpperCAmelCase__ : str = version.parse(PIL.__version__ ) < version.parse("""9.0.0""" )
if is_pillow_less_than_a:
UpperCAmelCase__ : Dict = torch.tensor(
[
[[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
[[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
[[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
] ,device=A ,)
else:
UpperCAmelCase__ : List[str] = torch.tensor(
[
[[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
[[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
[[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
] ,device=A ,)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,A ,atol=1e-4 ) )
@slow
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Any = BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""" )
UpperCAmelCase__ : str = model.to(A )
UpperCAmelCase__ : int = BeitImageProcessor(do_resize=A ,size=640 ,do_center_crop=A )
UpperCAmelCase__ : Union[str, Any] = load_dataset("""hf-internal-testing/fixtures_ade20k""" ,split="""test""" )
UpperCAmelCase__ : Dict = Image.open(ds[0]["""file"""] )
UpperCAmelCase__ : Any = image_processor(images=A ,return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Dict = model(**A )
UpperCAmelCase__ : int = outputs.logits.detach().cpu()
UpperCAmelCase__ : List[Any] = image_processor.post_process_semantic_segmentation(outputs=A ,target_sizes=[(500, 300)] )
UpperCAmelCase__ : Dict = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape ,A )
UpperCAmelCase__ : List[str] = image_processor.post_process_semantic_segmentation(outputs=A )
UpperCAmelCase__ : List[str] = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape ,A )
| 194 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
__UpperCAmelCase = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : str = Github(os.environ["""GITHUB_TOKEN"""] )
UpperCAmelCase__ : int = g.get_repo("""huggingface/transformers""" )
UpperCAmelCase__ : Optional[int] = repo.get_issues(state="""open""" )
for issue in open_issues:
UpperCAmelCase__ : Any = sorted([comment for comment in issue.get_comments()] , key=lambda __UpperCamelCase : i.created_at , reverse=__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = comments[0] if len(__UpperCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="""closed""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
if __name__ == "__main__":
main()
| 194 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
class A_ (a_ ):
UpperCAmelCase__ = ['''pixel_values''']
def __init__( self , _A = True , _A = None , _A = None , _A = PILImageResampling.BILINEAR , _A = True , _A = 1 / 2_5_5 , _A = True , _A = None , _A = None , **_A , ):
'''simple docstring'''
super().__init__(**_A )
UpperCAmelCase = size if size is not None else {'''shortest_edge''': 3_8_4}
UpperCAmelCase = get_size_dict(_A , default_to_square=_A )
UpperCAmelCase = do_resize
UpperCAmelCase = size
# Default value set here for backwards compatibility where the value in config is None
UpperCAmelCase = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
UpperCAmelCase = resample
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowercase ( self , _A , _A , _A , _A = PILImageResampling.BICUBIC , _A = None , **_A , ):
'''simple docstring'''
UpperCAmelCase = get_size_dict(_A , default_to_square=_A )
if "shortest_edge" not in size:
raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""" )
UpperCAmelCase = size['''shortest_edge''']
if shortest_edge < 3_8_4:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
UpperCAmelCase = int(shortest_edge / crop_pct )
UpperCAmelCase = get_resize_output_image_size(_A , size=_A , default_to_square=_A )
UpperCAmelCase = resize(image=_A , size=_A , resample=_A , data_format=_A , **_A )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_A , size=(shortest_edge, shortest_edge) , data_format=_A , **_A )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_A , size=(shortest_edge, shortest_edge) , resample=_A , data_format=_A , **_A )
def _lowercase ( self , _A , _A , _A = None , **_A , ):
'''simple docstring'''
return rescale(_A , scale=_A , data_format=_A , **_A )
def _lowercase ( self , _A , _A , _A , _A = None , **_A , ):
'''simple docstring'''
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def _lowercase ( self , _A , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = None , _A = ChannelDimension.FIRST , **_A , ):
'''simple docstring'''
UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase = crop_pct if crop_pct is not None else self.crop_pct
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = size if size is not None else self.size
UpperCAmelCase = get_size_dict(_A , default_to_square=_A )
UpperCAmelCase = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
raise ValueError('''crop_pct must be specified if size < 384.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
UpperCAmelCase = [to_numpy_array(_A ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=_A , size=_A , crop_pct=_A , resample=_A ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(_A , _A ) for image in images]
UpperCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
| 130 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : Optional[Any] = logging.get_logger(__name__)
__A : Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__A : Tuple = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
__A : Dict = {
"gpt-neox-20b": 2_048,
}
class A_ (a_ ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self , _A=None , _A=None , _A=None , _A="<|endoftext|>" , _A="<|endoftext|>" , _A="<|endoftext|>" , _A=False , **_A , ):
'''simple docstring'''
super().__init__(
_A , _A , tokenizer_file=_A , unk_token=_A , bos_token=_A , eos_token=_A , add_prefix_space=_A , **_A , )
UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , _A ) != add_prefix_space:
UpperCAmelCase = getattr(_A , pre_tok_state.pop('''type''' ) )
UpperCAmelCase = add_prefix_space
UpperCAmelCase = pre_tok_class(**_A )
UpperCAmelCase = add_prefix_space
def _lowercase ( self , _A , _A = None ):
'''simple docstring'''
UpperCAmelCase = self._tokenizer.model.save(_A , name=_A )
return tuple(_A )
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_A , add_special_tokens=_A ) + [self.eos_token_id] )
if len(_A ) > self.model_max_length:
UpperCAmelCase = input_ids[-self.model_max_length :]
return input_ids
| 130 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCAmelCase : Any = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : str = [
"LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"LongT5EncoderModel",
"LongT5ForConditionalGeneration",
"LongT5Model",
"LongT5PreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Any = [
"FlaxLongT5ForConditionalGeneration",
"FlaxLongT5Model",
"FlaxLongT5PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
_UpperCAmelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 288 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all BART models at https://huggingface.co/models?filter=bart
_UpperCAmelCase : Dict = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
}
_UpperCAmelCase : Tuple = {
"facebook/bart-base": 1_024,
"facebook/bart-large": 1_024,
"facebook/bart-large-mnli": 1_024,
"facebook/bart-large-cnn": 1_024,
"facebook/bart-large-xsum": 1_024,
"yjernite/bart_eli5": 1_024,
}
@lru_cache()
def lowerCAmelCase_ () -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
lowerCAmelCase__ = bs[:]
lowerCAmelCase__ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase__ )
cs.append(2**8 + n )
n += 1
lowerCAmelCase__ = [chr(lowercase__ ) for n in cs]
return dict(zip(lowercase__ , lowercase__ ) )
def lowerCAmelCase_ (lowercase__ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = set()
lowerCAmelCase__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCAmelCase__ = char
return pairs
class lowerCAmelCase_ ( snake_case__ ):
UpperCamelCase_ :List[str] = VOCAB_FILES_NAMES
UpperCamelCase_ :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ :Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ :str = ['input_ids', 'attention_mask']
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int="replace" , SCREAMING_SNAKE_CASE_ : Tuple="<s>" , SCREAMING_SNAKE_CASE_ : Any="</s>" , SCREAMING_SNAKE_CASE_ : List[Any]="</s>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="<s>" , SCREAMING_SNAKE_CASE_ : Any="<unk>" , SCREAMING_SNAKE_CASE_ : int="<pad>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="<mask>" , SCREAMING_SNAKE_CASE_ : Tuple=False , **SCREAMING_SNAKE_CASE_ : Dict , ):
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else bos_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else sep_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else cls_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase__ = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as vocab_handle:
lowerCAmelCase__ = json.load(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = {v: k for k, v in self.encoder.items()}
lowerCAmelCase__ = errors # how to handle errors in decoding
lowerCAmelCase__ = bytes_to_unicode()
lowerCAmelCase__ = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE_ , encoding='''utf-8''' ) as merges_handle:
lowerCAmelCase__ = merges_handle.read().split('''\n''' )[1:-1]
lowerCAmelCase__ = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase__ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCAmelCase__ = {}
lowerCAmelCase__ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase__ = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def __snake_case ( self : List[str] ):
return len(self.encoder )
def __snake_case ( self : Union[str, Any] ):
return dict(self.encoder , **self.added_tokens_encoder )
def __snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Tuple ):
if token in self.cache:
return self.cache[token]
lowerCAmelCase__ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = get_pairs(SCREAMING_SNAKE_CASE_ )
if not pairs:
return token
while True:
lowerCAmelCase__ = min(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase__ , lowerCAmelCase__ = bigram
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
try:
lowerCAmelCase__ = word.index(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase__ = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase__ = tuple(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = new_word
if len(SCREAMING_SNAKE_CASE_ ) == 1:
break
else:
lowerCAmelCase__ = get_pairs(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = ''' '''.join(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = word
return word
def __snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
lowerCAmelCase__ = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_ ).split(''' ''' ) )
return bpe_tokens
def __snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : List[Any] ):
return self.encoder.get(SCREAMING_SNAKE_CASE_ , self.encoder.get(self.unk_token ) )
def __snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : str ):
return self.decoder.get(SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
lowerCAmelCase__ = ''''''.join(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def __snake_case ( self : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase__ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCAmelCase__ = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ ) + '''\n''' )
lowerCAmelCase__ = 0
with open(SCREAMING_SNAKE_CASE_ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
''' Please check that the tokenizer is not corrupted!''' )
lowerCAmelCase__ = token_index
writer.write(''' '''.join(SCREAMING_SNAKE_CASE_ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def __snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
lowerCAmelCase__ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None , SCREAMING_SNAKE_CASE_ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def __snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ):
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False , **SCREAMING_SNAKE_CASE_ : Optional[int] ):
lowerCAmelCase__ = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_ ) > 0 and not text[0].isspace()):
lowerCAmelCase__ = ''' ''' + text
return (text, kwargs)
| 288 | 1 |
def __snake_case ( __magic_name__ ):
'''simple docstring'''
lowercase = len(__magic_name__ )
for _ in range(__magic_name__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
lowercase , lowercase = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
_snake_case : int = list(range(10, 0, -1))
print(F"Original: {arr}. Sorted: {odd_even_transposition(arr)}")
| 441 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
if fractional_part == 0:
return int(lowercase ), 1
else:
__lowerCAmelCase = len(str(lowercase ).split(""".""" )[1] )
__lowerCAmelCase = int(decimal * (10**number_of_frac_digits) )
__lowerCAmelCase = 10**number_of_frac_digits
__lowerCAmelCase , __lowerCAmelCase = denominator, numerator
while True:
__lowerCAmelCase = dividend % divisor
if remainder == 0:
break
__lowerCAmelCase , __lowerCAmelCase = divisor, remainder
__lowerCAmelCase , __lowerCAmelCase = numerator / divisor, denominator / divisor
return int(lowercase ), int(lowercase )
if __name__ == "__main__":
print(f'{decimal_to_fraction(2) = }')
print(f'{decimal_to_fraction(89.0) = }')
print(f'{decimal_to_fraction("67") = }')
print(f'{decimal_to_fraction("45.0") = }')
print(f'{decimal_to_fraction(1.5) = }')
print(f'{decimal_to_fraction("6.25") = }')
print(f'{decimal_to_fraction("78td") = }')
| 689 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : int = {
"""huggingface/time-series-transformer-tourism-monthly""": (
"""https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"""
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "time_series_transformer"
__UpperCamelCase = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self , _a = None , _a = None , _a = "student_t" , _a = "nll" , _a = 1 , _a = [1, 2, 3, 4, 5, 6, 7] , _a = "mean" , _a = 0 , _a = 0 , _a = 0 , _a = 0 , _a = None , _a = None , _a = 32 , _a = 32 , _a = 2 , _a = 2 , _a = 2 , _a = 2 , _a = True , _a = "gelu" , _a = 64 , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 100 , _a = 0.02 , _a=True , **_a , ):
"""simple docstring"""
lowerCamelCase = prediction_length
lowerCamelCase = context_length or prediction_length
lowerCamelCase = distribution_output
lowerCamelCase = loss
lowerCamelCase = input_size
lowerCamelCase = num_time_features
lowerCamelCase = lags_sequence
lowerCamelCase = scaling
lowerCamelCase = num_dynamic_real_features
lowerCamelCase = num_static_real_features
lowerCamelCase = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
lowerCamelCase = cardinality
else:
lowerCamelCase = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
lowerCamelCase = embedding_dimension
else:
lowerCamelCase = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowerCamelCase = num_parallel_samples
# Transformer architecture configuration
lowerCamelCase = input_size * len(_a ) + self._number_of_features
lowerCamelCase = d_model
lowerCamelCase = encoder_attention_heads
lowerCamelCase = decoder_attention_heads
lowerCamelCase = encoder_ffn_dim
lowerCamelCase = decoder_ffn_dim
lowerCamelCase = encoder_layers
lowerCamelCase = decoder_layers
lowerCamelCase = dropout
lowerCamelCase = attention_dropout
lowerCamelCase = activation_dropout
lowerCamelCase = encoder_layerdrop
lowerCamelCase = decoder_layerdrop
lowerCamelCase = activation_function
lowerCamelCase = init_std
lowerCamelCase = use_cache
super().__init__(is_encoder_decoder=_a , **_a )
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 707 |
"""simple docstring"""
from functools import lru_cache
def a__ ( snake_case__ ) -> set:
lowerCamelCase = 2
lowerCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(snake_case__ )
if n > 1:
factors.add(snake_case__ )
return factors
@lru_cache
def a__ ( snake_case__ ) -> int:
return len(unique_prime_factors(snake_case__ ) )
def a__ ( snake_case__ ) -> bool:
return len(set(snake_case__ ) ) in (0, 1)
def a__ ( snake_case__ ) -> list:
lowerCamelCase = 2
while True:
# Increment each value of a generated range
lowerCamelCase = [base + i for i in range(snake_case__ )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
lowerCamelCase = [upf_len(snake_case__ ) for x in group]
checker.append(snake_case__ )
# If all numbers in the list are equal, return the group variable.
if equality(snake_case__ ):
return group
# Increment our base variable by 1
base += 1
def a__ ( snake_case__ = 4 ) -> int:
lowerCamelCase = run(snake_case__ )
return results[0] if len(snake_case__ ) else None
if __name__ == "__main__":
print(solution())
| 533 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
lowerCamelCase : int
lowerCamelCase : int
class snake_case__ :
'''simple docstring'''
def __init__( self , a__ ) -> Tuple:
'''simple docstring'''
__snake_case :list[list[Edge]] = [[] for _ in range(a__ )]
__snake_case :int = size
def __getitem__( self , a__ ) -> Iterator[Edge]:
'''simple docstring'''
return iter(self._graph[vertex] )
@property
def __lowercase ( self ) -> Dict:
'''simple docstring'''
return self._size
def __lowercase ( self , a__ , a__ , a__ ) -> List[Any]:
'''simple docstring'''
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(a__ , a__ ) )
def __lowercase ( self , a__ , a__ ) -> int | None:
'''simple docstring'''
__snake_case :Dict = deque([start_vertex] )
__snake_case :list[int | None] = [None] * self.size
__snake_case :Tuple = 0
while queue:
__snake_case :Optional[Any] = queue.popleft()
__snake_case :Union[str, Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
__snake_case :Tuple = current_distance + edge.weight
__snake_case :List[str] = distances[edge.destination_vertex]
if (
isinstance(a__ , a__ )
and new_distance >= dest_vertex_distance
):
continue
__snake_case :int = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 455 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class snake_case__ ( lowercase_ , unittest.TestCase):
'''simple docstring'''
lowerCamelCase : Tuple = KandinskyVaaPriorPipeline
lowerCamelCase : Optional[Any] = ["prompt"]
lowerCamelCase : Any = ["prompt", "negative_prompt"]
lowerCamelCase : Tuple = [
"num_images_per_prompt",
"generator",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
lowerCamelCase : Any = False
@property
def __lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
return 32
@property
def __lowercase ( self ) -> Any:
'''simple docstring'''
return 32
@property
def __lowercase ( self ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim
@property
def __lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
return 1_00
@property
def __lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
__snake_case :Tuple = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def __lowercase ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case :Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModelWithProjection(a__ )
@property
def __lowercase ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case :List[Any] = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 12,
"""embedding_dim""": self.text_embedder_hidden_size,
"""num_layers""": 1,
}
__snake_case :Union[str, Any] = PriorTransformer(**a__ )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__snake_case :int = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def __lowercase ( self ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case :List[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=2_24 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
__snake_case :Tuple = CLIPVisionModelWithProjection(a__ )
return model
@property
def __lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
__snake_case :Optional[int] = CLIPImageProcessor(
crop_size=2_24 , do_center_crop=a__ , do_normalize=a__ , do_resize=a__ , image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , resample=3 , size=2_24 , )
return image_processor
def __lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
__snake_case :Optional[Any] = self.dummy_prior
__snake_case :Tuple = self.dummy_image_encoder
__snake_case :List[Any] = self.dummy_text_encoder
__snake_case :List[str] = self.dummy_tokenizer
__snake_case :int = self.dummy_image_processor
__snake_case :Union[str, Any] = UnCLIPScheduler(
variance_type="""fixed_small_log""" , prediction_type="""sample""" , num_train_timesteps=10_00 , clip_sample=a__ , clip_sample_range=10.0 , )
__snake_case :Union[str, Any] = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""scheduler""": scheduler,
"""image_processor""": image_processor,
}
return components
def __lowercase ( self , a__ , a__=0 ) -> Optional[Any]:
'''simple docstring'''
if str(a__ ).startswith("""mps""" ):
__snake_case :Any = torch.manual_seed(a__ )
else:
__snake_case :Any = torch.Generator(device=a__ ).manual_seed(a__ )
__snake_case :Union[str, Any] = {
"""prompt""": """horse""",
"""generator""": generator,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def __lowercase ( self ) -> List[str]:
'''simple docstring'''
__snake_case :str = """cpu"""
__snake_case :Optional[Any] = self.get_dummy_components()
__snake_case :List[Any] = self.pipeline_class(**a__ )
__snake_case :Union[str, Any] = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
__snake_case :List[Any] = pipe(**self.get_dummy_inputs(a__ ) )
__snake_case :Any = output.image_embeds
__snake_case :Any = pipe(
**self.get_dummy_inputs(a__ ) , return_dict=a__ , )[0]
__snake_case :Any = image[0, -10:]
__snake_case :Any = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
__snake_case :List[str] = np.array(
[-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def __lowercase ( self ) -> str:
'''simple docstring'''
__snake_case :List[Any] = torch_device == """cpu"""
__snake_case :List[str] = True
__snake_case :Optional[Any] = False
self._test_inference_batch_single_identical(
test_max_difference=a__ , relax_max_difference=a__ , test_mean_pixel_difference=a__ , )
@skip_mps
def __lowercase ( self ) -> List[Any]:
'''simple docstring'''
__snake_case :Any = torch_device == """cpu"""
__snake_case :str = False
self._test_attention_slicing_forward_pass(
test_max_difference=a__ , test_mean_pixel_difference=a__ , )
| 455 | 1 |
import argparse
import copy
def snake_case__ ( lowerCamelCase_ ):
A : Tuple = {}
with open(SCREAMING_SNAKE_CASE_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
A : Union[str, Any] = []
_list.append([line.split()[1], line.split()[2]] )
A : int = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
A : int = []
_list.append([line.split()[0], line.split()[2]] )
A : int = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
with open(SCREAMING_SNAKE_CASE_ ) as f:
A : Dict = f.read(1 )
A : Any = start_node
A : Any = []
A : str = start_node
A : Union[str, Any] = 0
while visiting not in first_solution:
A : Any = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(SCREAMING_SNAKE_CASE_ ) and k[0] not in first_solution:
A : Tuple = k[1]
A : str = k[0]
first_solution.append(SCREAMING_SNAKE_CASE_ )
A : Optional[int] = distance_of_first_solution + int(SCREAMING_SNAKE_CASE_ )
A : Optional[int] = best_node
first_solution.append(SCREAMING_SNAKE_CASE_ )
A : Optional[Any] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
A : int = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
A : Optional[Any] = []
for n in solution[1:-1]:
A : Optional[int] = solution.index(SCREAMING_SNAKE_CASE_ )
for kn in solution[1:-1]:
A : Dict = solution.index(SCREAMING_SNAKE_CASE_ )
if n == kn:
continue
A : Any = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
A : List[str] = kn
A : Dict = n
A : List[str] = 0
for k in _tmp[:-1]:
A : Any = _tmp[_tmp.index(SCREAMING_SNAKE_CASE_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
A : Any = distance + int(i[1] )
_tmp.append(SCREAMING_SNAKE_CASE_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
A : Tuple = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda lowerCamelCase_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
A : List[str] = 1
A : List[Any] = first_solution
A : Dict = []
A : Optional[Any] = distance_of_first_solution
A : List[str] = solution
while count <= iters:
A : Dict = find_neighborhood(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A : Dict = 0
A : Optional[Any] = neighborhood[index_of_best_solution]
A : Union[str, Any] = len(SCREAMING_SNAKE_CASE_ ) - 1
A : Tuple = False
while not found:
A : Any = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
if best_solution[i] != solution[i]:
A : Union[str, Any] = best_solution[i]
A : Optional[int] = solution[i]
break
A : Union[str, Any] = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
A : int = True
A : int = best_solution[:-1]
A : List[str] = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
A : str = cost
A : List[Any] = solution
else:
A : int = index_of_best_solution + 1
A : Optional[int] = neighborhood[index_of_best_solution]
if len(SCREAMING_SNAKE_CASE_ ) >= size:
tabu_list.pop(0 )
A : Any = count + 1
return best_solution_ever, best_cost
def snake_case__ ( lowerCamelCase_=None ):
A : List[Any] = generate_neighbours(args.File )
A , A : List[str] = generate_first_solution(
args.File , SCREAMING_SNAKE_CASE_ )
A , A : int = tabu_search(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , args.Iterations , args.Size , )
print(F'Best solution: {best_sol}, with total distance: {best_cost}.' )
if __name__ == "__main__":
lowercase : Tuple = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 700 |
# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class __lowercase ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = '''facebook/bart-large-mnli'''
UpperCAmelCase_ : Optional[int] = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
UpperCAmelCase_ : Optional[Any] = '''text_classifier'''
UpperCAmelCase_ : str = AutoTokenizer
UpperCAmelCase_ : int = AutoModelForSequenceClassification
UpperCAmelCase_ : Union[str, Any] = ['''text''', ['''text''']]
UpperCAmelCase_ : Tuple = ['''text''']
def snake_case ( self ) -> List[str]:
super().setup()
A : int = self.model.config
A : List[str] = -1
for idx, label in config.idalabel.items():
if label.lower().startswith('''entail''' ):
A : Dict = int(__UpperCAmelCase )
if self.entailment_id == -1:
raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''' )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
A : List[str] = labels
return self.pre_processor(
[text] * len(__UpperCAmelCase ) , [f'This example is {label}' for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )
def snake_case ( self , __UpperCAmelCase ) -> Tuple:
A : int = outputs.logits
A : int = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 423 | 0 |
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