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"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase ( snake_case_ ):
'''simple docstring'''
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__snake_case , """embed_dim""" ) )
self.parent.assertTrue(hasattr(__snake_case , """num_heads""" ) )
class lowerCamelCase :
'''simple docstring'''
def __init__(self , _lowerCamelCase , _lowerCamelCase=13 , _lowerCamelCase=64 , _lowerCamelCase=3 , _lowerCamelCase=[16, 48, 96] , _lowerCamelCase=[1, 3, 6] , _lowerCamelCase=[1, 2, 10] , _lowerCamelCase=[7, 3, 3] , _lowerCamelCase=[4, 2, 2] , _lowerCamelCase=[2, 1, 1] , _lowerCamelCase=[2, 2, 2] , _lowerCamelCase=[False, False, True] , _lowerCamelCase=[0.0, 0.0, 0.0] , _lowerCamelCase=0.02 , _lowerCamelCase=1e-12 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=2 , ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : Any = image_size
UpperCAmelCase__ : int = patch_sizes
UpperCAmelCase__ : Dict = patch_stride
UpperCAmelCase__ : int = patch_padding
UpperCAmelCase__ : Optional[Any] = is_training
UpperCAmelCase__ : List[str] = use_labels
UpperCAmelCase__ : int = num_labels
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : List[str] = embed_dim
UpperCAmelCase__ : Optional[Any] = num_heads
UpperCAmelCase__ : Tuple = stride_kv
UpperCAmelCase__ : Tuple = depth
UpperCAmelCase__ : Tuple = cls_token
UpperCAmelCase__ : int = attention_drop_rate
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : int = layer_norm_eps
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : int = None
if self.use_labels:
# create a random int32 tensor of given shape
UpperCAmelCase__ : Tuple = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase__ : List[Any] = self.get_config()
return config, pixel_values, labels
def _a (self ):
"""simple docstring"""
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = TFCvtModel(config=__snake_case )
UpperCAmelCase__ : Union[str, Any] = model(__snake_case , training=__snake_case )
UpperCAmelCase__ : Any = (self.image_size, self.image_size)
UpperCAmelCase__ , UpperCAmelCase__ : str = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCAmelCase__ : Any = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCAmelCase__ : int = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : int = self.num_labels
UpperCAmelCase__ : Any = TFCvtForImageClassification(__snake_case )
UpperCAmelCase__ : Tuple = model(__snake_case , labels=__snake_case , training=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : int = config_and_inputs
UpperCAmelCase__ : Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE = (
{'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = TFCvtModelTester(self )
UpperCAmelCase__ : Optional[int] = TFCvtConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case , hidden_size=37 )
def _a (self ):
"""simple docstring"""
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="""Cvt does not output attentions""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def _a (self ):
"""simple docstring"""
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
def _a (self ):
"""simple docstring"""
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
@slow
def _a (self ):
"""simple docstring"""
super().test_keras_fit()
@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8""" )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = tf.keras.mixed_precision.Policy("""mixed_float16""" )
tf.keras.mixed_precision.set_global_policy(__snake_case )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("""float32""" )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Union[str, Any] = model_class(__snake_case )
UpperCAmelCase__ : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : str = [*signature.parameters.keys()]
UpperCAmelCase__ : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
def _a (self ):
"""simple docstring"""
def check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCAmelCase__ : int = model_class(__snake_case )
UpperCAmelCase__ : Tuple = model(**self._prepare_for_class(__snake_case , __snake_case ) )
UpperCAmelCase__ : Dict = outputs.hidden_states
UpperCAmelCase__ : Optional[int] = len(self.model_tester.depth )
self.assertEqual(len(__snake_case ) , __snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = True
check_hidden_states_output(__snake_case , __snake_case , __snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : Union[str, Any] = True
check_hidden_states_output(__snake_case , __snake_case , __snake_case )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__snake_case )
@slow
def _a (self ):
"""simple docstring"""
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Tuple = TFCvtModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase__ : Any = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _a (self ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCAmelCase__ : Any = self.default_image_processor
UpperCAmelCase__ : Optional[int] = prepare_img()
UpperCAmelCase__ : List[Any] = image_processor(images=__snake_case , return_tensors="""tf""" )
# forward pass
UpperCAmelCase__ : str = model(**__snake_case )
# verify the logits
UpperCAmelCase__ : Dict = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __snake_case )
UpperCAmelCase__ : Optional[Any] = tf.constant([0.9_285, 0.9_015, -0.3_150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , __snake_case , atol=1e-4 ) )
| 171 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
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 ( snake_case_ , unittest.TestCase ):
_lowercase: int = KandinskyVaaImgaImgPipeline
_lowercase: List[str] = ['''image_embeds''', '''negative_image_embeds''', '''image''']
_lowercase: Optional[int] = [
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
_lowercase: Tuple = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
_lowercase: List[str] = False
@property
def lowercase__ ( self : str ) -> List[str]:
return 32
@property
def lowercase__ ( self : Optional[int] ) -> List[Any]:
return 32
@property
def lowercase__ ( self : Tuple ) -> str:
return self.time_input_dim
@property
def lowercase__ ( self : Any ) -> Optional[int]:
return self.time_input_dim * 4
@property
def lowercase__ ( self : int ) -> Optional[Any]:
return 1_00
@property
def lowercase__ ( self : int ) -> Dict:
torch.manual_seed(0 )
_lowerCAmelCase = {
"""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,
}
_lowerCAmelCase = UNetaDConditionModel(**__snake_case )
return model
@property
def lowercase__ ( self : Union[str, Any] ) -> Tuple:
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 lowercase__ ( self : Dict ) -> str:
torch.manual_seed(0 )
_lowerCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
_lowerCAmelCase = self.dummy_unet
_lowerCAmelCase = self.dummy_movq
_lowerCAmelCase = {
"""num_train_timesteps""": 10_00,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_00_85,
"""beta_end""": 0.0_12,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
_lowerCAmelCase = DDIMScheduler(**__snake_case )
_lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def lowercase__ ( self : int , __snake_case : List[str] , __snake_case : List[Any]=0 ) -> Union[str, Any]:
_lowerCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__snake_case ) ).to(__snake_case )
_lowerCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__snake_case )
# create init_image
_lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
_lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase = Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
if str(__snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(__snake_case )
else:
_lowerCAmelCase = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
_lowerCAmelCase = {
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def lowercase__ ( self : str ) -> Tuple:
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**__snake_case )
_lowerCAmelCase = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
_lowerCAmelCase = pipe(**self.get_dummy_inputs(__snake_case ) )
_lowerCAmelCase = output.images
_lowerCAmelCase = pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
_lowerCAmelCase = image[0, -3:, -3:, -1]
_lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase = np.array(
[0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63] )
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 ):
def lowercase__ ( self : Any ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : int ) -> Dict:
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_img2img_frog.npy""" )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
_lowerCAmelCase = """A red cartoon frog, 4k"""
_lowerCAmelCase = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
_lowerCAmelCase = KandinskyVaaImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-decoder""" , torch_dtype=torch.floataa )
_lowerCAmelCase = pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
_lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
_lowerCAmelCase , _lowerCAmelCase = pipe_prior(
__snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
_lowerCAmelCase = pipeline(
image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type="""np""" , )
_lowerCAmelCase = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 70 | 0 |
"""simple docstring"""
from math import factorial, radians
def UpperCamelCase_ ( lowerCAmelCase__ : float , lowerCAmelCase__ : int = 18 , lowerCAmelCase__ : int = 10 ) -> float:
"""simple docstring"""
lowerCAmelCase_ : int = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
lowerCAmelCase_ : Optional[int] = radians(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = angle_in_radians
lowerCAmelCase_ : Tuple = 3
lowerCAmelCase_ : str = -1
for _ in range(lowerCAmelCase__ ):
result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 364 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCamelCase__ :
"""simple docstring"""
pass
| 289 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCamelCase__ ):
__lowercase = ["""note_seq"""]
def __init__( self :Optional[Any] , *lowercase_ :List[Any] , **lowercase_ :List[str] )-> int:
requires_backends(self , ["note_seq"] )
@classmethod
def UpperCAmelCase_ ( cls :str , *lowercase_ :Union[str, Any] , **lowercase_ :Any )-> Optional[int]:
requires_backends(cls , ["note_seq"] )
@classmethod
def UpperCAmelCase_ ( cls :Dict , *lowercase_ :Tuple , **lowercase_ :List[Any] )-> Optional[Any]:
requires_backends(cls , ["note_seq"] )
| 237 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCAmelCase : Optional[int] =16
__lowerCAmelCase : Tuple =32
def UpperCamelCase ( _lowerCamelCase : Accelerator , _lowerCamelCase : DatasetDict , _lowerCamelCase : List[int] , _lowerCamelCase : List[int] , _lowerCamelCase : int = 16 ):
A__ = AutoTokenizer.from_pretrained("bert-base-cased" )
A__ = DatasetDict(
{
"train": dataset["train"].select(_lowerCamelCase ),
"validation": dataset["train"].select(_lowerCamelCase ),
"test": dataset["validation"],
} )
def tokenize_function(_lowerCamelCase : Dict ):
# max_length=None => use the model max length (it's actually the default)
A__ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_lowerCamelCase , max_length=_lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A__ = datasets.map(
_lowerCamelCase , batched=_lowerCamelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A__ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(_lowerCamelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A__ = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A__ = 16
elif accelerator.mixed_precision != "no":
A__ = 8
else:
A__ = None
return tokenizer.pad(
_lowerCamelCase , padding="longest" , max_length=_lowerCamelCase , pad_to_multiple_of=_lowerCamelCase , return_tensors="pt" , )
# Instantiate dataloaders.
A__ = DataLoader(
tokenized_datasets["train"] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase )
A__ = DataLoader(
tokenized_datasets["validation"] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase )
A__ = DataLoader(
tokenized_datasets["test"] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase )
return train_dataloader, eval_dataloader, test_dataloader
def UpperCamelCase ( _lowerCamelCase : List[Any] , _lowerCamelCase : str ):
# New Code #
A__ = []
# Download the dataset
A__ = load_dataset("glue" , "mrpc" )
# Create our splits
A__ = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
A__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A__ = config["lr"]
A__ = int(config["num_epochs"] )
A__ = int(config["seed"] )
A__ = int(config["batch_size"] )
A__ = evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
A__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A__ = batch_size // MAX_GPU_BATCH_SIZE
A__ = MAX_GPU_BATCH_SIZE
set_seed(_lowerCamelCase )
# New Code #
# Create our folds:
A__ = kfold.split(np.zeros(datasets["train"].num_rows ) , datasets["train"]["label"] )
A__ = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(_lowerCamelCase ):
A__, A__, A__ = get_fold_dataloaders(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A__ = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=_lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A__ = model.to(accelerator.device )
# Instantiate optimizer
A__ = AdamW(params=model.parameters() , lr=_lowerCamelCase )
# Instantiate scheduler
A__ = get_linear_schedule_with_warmup(
optimizer=_lowerCamelCase , num_warmup_steps=1_00 , num_training_steps=(len(_lowerCamelCase ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A__, A__, A__, A__, A__ = accelerator.prepare(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# Now we train the model
for epoch in range(_lowerCamelCase ):
model.train()
for step, batch in enumerate(_lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A__ = model(**_lowerCamelCase )
A__ = outputs.loss
A__ = loss / gradient_accumulation_steps
accelerator.backward(_lowerCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ = model(**_lowerCamelCase )
A__ = outputs.logits.argmax(dim=-1 )
A__, A__ = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=_lowerCamelCase , references=_lowerCamelCase , )
A__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" , _lowerCamelCase )
# New Code #
# We also run predictions on the test set at the very end
A__ = []
for step, batch in enumerate(_lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ = model(**_lowerCamelCase )
A__ = outputs.logits
A__, A__ = accelerator.gather_for_metrics((predictions, batch["labels"]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(_lowerCamelCase , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
A__ = torch.cat(_lowerCamelCase , dim=0 )
A__ = torch.stack(_lowerCamelCase , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
A__ = metric.compute(predictions=_lowerCamelCase , references=_lowerCamelCase )
accelerator.print("Average test metrics from all folds:" , _lowerCamelCase )
def UpperCamelCase ( ):
A__ = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=_lowerCamelCase , default=_lowerCamelCase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
# New Code #
parser.add_argument("--num_folds" , type=_lowerCamelCase , default=3 , help="The number of splits to perform across the dataset" )
A__ = parser.parse_args()
A__ = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(_lowerCamelCase , _lowerCamelCase )
if __name__ == "__main__":
main()
| 237 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowercase ( UpperCamelCase__ ):
_a = "char"
_a = "bpe"
_a = "wp"
_snake_case = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class lowercase ( UpperCamelCase__ ):
_a = ["image_processor", "char_tokenizer"]
_a = "ViTImageProcessor"
_a = "MgpstrTokenizer"
def __init__( self , _a=None , _a=None , **_a ) -> Dict:
_A : List[str] = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _a , )
_A : List[str] = kwargs.pop("""feature_extractor""" )
_A : Union[str, Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
_A : List[Any] = tokenizer
_A : Union[str, Any] = AutoTokenizer.from_pretrained("""gpt2""" )
_A : str = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(_a , _a )
def __call__( self , _a=None , _a=None , _a=None , **_a ) -> List[Any]:
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
_A : Optional[Any] = self.image_processor(_a , return_tensors=_a , **_a )
if text is not None:
_A : Union[str, Any] = self.char_tokenizer(_a , return_tensors=_a , **_a )
if text is None:
return inputs
elif images is None:
return encodings
else:
_A : Dict = encodings["""input_ids"""]
return inputs
def a__ ( self , _a ) -> Dict:
_A , _A , _A : List[str] = sequences
_A : Union[str, Any] = char_preds.size(0 )
_A , _A : Optional[int] = self._decode_helper(_a , """char""" )
_A , _A : Any = self._decode_helper(_a , """bpe""" )
_A , _A : Optional[int] = self._decode_helper(_a , """wp""" )
_A : str = []
_A : Dict = []
for i in range(_a ):
_A : List[Any] = [char_scores[i], bpe_scores[i], wp_scores[i]]
_A : Any = [char_strs[i], bpe_strs[i], wp_strs[i]]
_A : str = scores.index(max(_a ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
_A : int = {}
_A : str = final_strs
_A : Union[str, Any] = final_scores
_A : Dict = char_strs
_A : List[Any] = bpe_strs
_A : Tuple = wp_strs
return out
def a__ ( self , _a , _a ) -> Union[str, Any]:
if format == DecodeType.CHARACTER:
_A : str = self.char_decode
_A : List[str] = 1
_A : Dict = """[s]"""
elif format == DecodeType.BPE:
_A : Any = self.bpe_decode
_A : Union[str, Any] = 2
_A : Optional[int] = """#"""
elif format == DecodeType.WORDPIECE:
_A : Any = self.wp_decode
_A : Dict = 102
_A : Optional[Any] = """[SEP]"""
else:
raise ValueError(F'''Format {format} is not supported.''' )
_A , _A : List[Any] = [], []
_A : List[str] = pred_logits.size(0 )
_A : List[Any] = pred_logits.size(1 )
_A , _A : str = pred_logits.topk(1 , dim=-1 , largest=_a , sorted=_a )
_A : Optional[Any] = preds_index.view(-1 , _a )[:, 1:]
_A : Any = decoder(_a )
_A , _A : Optional[Any] = torch.nn.functional.softmax(_a , dim=2 ).max(dim=2 )
_A : List[str] = preds_max_prob[:, 1:]
for index in range(_a ):
_A : List[Any] = preds_str[index].find(_a )
_A : Any = preds_str[index][:pred_eos]
_A : Dict = preds_index[index].cpu().tolist()
_A : Optional[int] = pred_index.index(_a ) if eos_token in pred_index else -1
_A : str = preds_max_prob[index][: pred_eos_index + 1]
_A : Tuple = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(_a )
conf_scores.append(_a )
return dec_strs, conf_scores
def a__ ( self , _a ) -> List[Any]:
_A : Optional[int] = [seq.replace(""" """ , """""" ) for seq in self.char_tokenizer.batch_decode(_a )]
return decode_strs
def a__ ( self , _a ) -> List[Any]:
return self.bpe_tokenizer.batch_decode(_a )
def a__ ( self , _a ) -> Dict:
_A : int = [seq.replace(""" """ , """""" ) for seq in self.wp_tokenizer.batch_decode(_a )]
return decode_strs
| 343 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase ( UpperCamelCase__ ):
_a = ["image_processor", "tokenizer"]
_a = "BlipImageProcessor"
_a = ("BertTokenizer", "BertTokenizerFast")
def __init__( self , _a , _a ) -> Any:
_A : List[Any] = False
super().__init__(_a , _a )
_A : Optional[int] = self.image_processor
def __call__( self , _a = None , _a = None , _a = True , _a = False , _a = None , _a = None , _a = 0 , _a = None , _a = None , _a = False , _a = False , _a = False , _a = False , _a = False , _a = True , _a = None , **_a , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None:
_A : Dict = self.tokenizer
_A : Dict = self.tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_token_type_ids=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
return text_encoding
# add pixel_values
_A : int = self.image_processor(_a , return_tensors=_a )
if text is not None:
_A : List[Any] = self.tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_token_type_ids=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
else:
_A : int = None
if text_encoding is not None:
encoding_image_processor.update(_a )
return encoding_image_processor
def a__ ( self , *_a , **_a ) -> Any:
return self.tokenizer.batch_decode(*_a , **_a )
def a__ ( self , *_a , **_a ) -> List[str]:
return self.tokenizer.decode(*_a , **_a )
@property
def a__ ( self ) -> Optional[Any]:
_A : Any = self.tokenizer.model_input_names
_A : List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 343 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
"tokenization_roformer": ["RoFormerTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["RoFormerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRoFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 45 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase : int = 10
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
for i in range(UpperCAmelCase , UpperCAmelCase ):
if array[i] == target:
return i
return -1
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
A = 0
A = len(UpperCAmelCase )
while left <= right:
if right - left < precision:
return lin_search(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
A = (left + right) // 3 + 1
A = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
A = one_third - 1
elif array[two_third] < target:
A = two_third + 1
else:
A = one_third + 1
A = two_third - 1
else:
return -1
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
if left < right:
if right - left < precision:
return lin_search(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
A = (left + right) // 3 + 1
A = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(UpperCAmelCase , one_third - 1 , UpperCAmelCase , UpperCAmelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , UpperCAmelCase , UpperCAmelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCamelCase : str = input('Enter numbers separated by comma:\n').strip()
_lowerCamelCase : str = [int(item.strip()) for item in user_input.split(',')]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_lowerCamelCase : Optional[int] = int(input('Enter the number to be found in the list:\n').strip())
_lowerCamelCase : Union[str, Any] = ite_ternary_search(collection, target)
_lowerCamelCase : Union[str, Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f"Iterative search: {target} found at positions: {resulta}")
print(f"Recursive search: {target} found at positions: {resulta}")
else:
print('Not found')
| 258 | 0 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
def decorator(_UpperCAmelCase : List[str] ):
lowerCAmelCase = getattr(__SCREAMING_SNAKE_CASE , 'handle_key' , [] )
handle += [key]
setattr(__SCREAMING_SNAKE_CASE , 'handle_key' , __SCREAMING_SNAKE_CASE )
return func
return decorator
def _SCREAMING_SNAKE_CASE (*_UpperCAmelCase : List[str] ):
def decorator(_UpperCAmelCase : Dict ):
lowerCAmelCase = getattr(__SCREAMING_SNAKE_CASE , 'handle_key' , [] )
handle += keys
setattr(__SCREAMING_SNAKE_CASE , 'handle_key' , __SCREAMING_SNAKE_CASE )
return func
return decorator
class a ( __UpperCamelCase ):
def __new__( cls , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = super().__new__(cls , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if not hasattr(_lowerCAmelCase , 'key_handler' ):
setattr(_lowerCAmelCase , 'key_handler' , {} )
setattr(_lowerCAmelCase , 'handle_input' , KeyHandler.handle_input )
for value in attrs.values():
lowerCAmelCase = getattr(_lowerCAmelCase , 'handle_key' , [] )
for key in handled_keys:
lowerCAmelCase = value
return new_cls
@staticmethod
def UpperCamelCase__ ( cls ):
"""simple docstring"""
lowerCAmelCase = get_character()
if char != KEYMAP["undefined"]:
lowerCAmelCase = ord(_lowerCAmelCase )
lowerCAmelCase = cls.key_handler.get(_lowerCAmelCase )
if handler:
lowerCAmelCase = char
return handler(cls )
else:
return None
def _SCREAMING_SNAKE_CASE (cls : Optional[int] ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
'''processing_mctct''': ['''MCTCTProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Tuple = [
'''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MCTCTForCTC''',
'''MCTCTModel''',
'''MCTCTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
__UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 309 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
lowerCamelCase_ = key.replace("module.encoder" , "glpn.encoder" )
if key.startswith("module.decoder" ):
lowerCamelCase_ = key.replace("module.decoder" , "decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowerCamelCase_ = key[key.find("patch_embed" ) + len("patch_embed" )]
lowerCamelCase_ = key.replace(F'patch_embed{idx}' , F'patch_embeddings.{int(_A )-1}' )
if "norm" in key:
lowerCamelCase_ = key.replace("norm" , "layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowerCamelCase_ = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
lowerCamelCase_ = key.replace(F'layer_norm{idx}' , F'layer_norm.{int(_A )-1}' )
if "layer_norm1" in key:
lowerCamelCase_ = key.replace("layer_norm1" , "layer_norm_1" )
if "layer_norm2" in key:
lowerCamelCase_ = key.replace("layer_norm2" , "layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
lowerCamelCase_ = key[key.find("block" ) + len("block" )]
lowerCamelCase_ = key.replace(F'block{idx}' , F'block.{int(_A )-1}' )
if "attn.q" in key:
lowerCamelCase_ = key.replace("attn.q" , "attention.self.query" )
if "attn.proj" in key:
lowerCamelCase_ = key.replace("attn.proj" , "attention.output.dense" )
if "attn" in key:
lowerCamelCase_ = key.replace("attn" , "attention.self" )
if "fc1" in key:
lowerCamelCase_ = key.replace("fc1" , "dense1" )
if "fc2" in key:
lowerCamelCase_ = key.replace("fc2" , "dense2" )
if "linear_pred" in key:
lowerCamelCase_ = key.replace("linear_pred" , "classifier" )
if "linear_fuse" in key:
lowerCamelCase_ = key.replace("linear_fuse.conv" , "linear_fuse" )
lowerCamelCase_ = key.replace("linear_fuse.bn" , "batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowerCamelCase_ = key[key.find("linear_c" ) + len("linear_c" )]
lowerCamelCase_ = key.replace(F'linear_c{idx}' , F'linear_c.{int(_A )-1}' )
if "bot_conv" in key:
lowerCamelCase_ = key.replace("bot_conv" , "0.convolution" )
if "skip_conv1" in key:
lowerCamelCase_ = key.replace("skip_conv1" , "1.convolution" )
if "skip_conv2" in key:
lowerCamelCase_ = key.replace("skip_conv2" , "2.convolution" )
if "fusion1" in key:
lowerCamelCase_ = key.replace("fusion1" , "1.fusion" )
if "fusion2" in key:
lowerCamelCase_ = key.replace("fusion2" , "2.fusion" )
if "fusion3" in key:
lowerCamelCase_ = key.replace("fusion3" , "3.fusion" )
if "fusion" in key and "conv" in key:
lowerCamelCase_ = key.replace("conv" , "convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
lowerCamelCase_ = key.replace("module.last_layer_depth" , "head.head" )
lowerCamelCase_ = value
return new_state_dict
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowerCamelCase_ = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
lowerCamelCase_ = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
lowerCamelCase_ = kv_weight[
: config.hidden_sizes[i], :
]
lowerCamelCase_ = kv_bias[: config.hidden_sizes[i]]
lowerCamelCase_ = kv_weight[
config.hidden_sizes[i] :, :
]
lowerCamelCase_ = kv_bias[config.hidden_sizes[i] :]
def lowerCamelCase_ ( ):
lowerCamelCase_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase_ = Image.open(requests.get(_A , stream=_A ).raw )
return image
@torch.no_grad()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False , lowerCamelCase__=None ):
lowerCamelCase_ = GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2] , decoder_hidden_size=6_4 , depths=[3, 8, 2_7, 3] )
# load image processor (only resize + rescale)
lowerCamelCase_ = GLPNImageProcessor()
# prepare image
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = image_processor(images=_A , return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
lowerCamelCase_ = torch.load(_A , map_location=torch.device("cpu" ) )
# rename keys
lowerCamelCase_ = rename_keys(_A )
# key and value matrices need special treatment
read_in_k_v(_A , _A )
# create HuggingFace model and load state dict
lowerCamelCase_ = GLPNForDepthEstimation(_A )
model.load_state_dict(_A )
model.eval()
# forward pass
lowerCamelCase_ = model(_A )
lowerCamelCase_ = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowerCamelCase_ = torch.tensor(
[[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]] )
elif "kitti" in model_name:
lowerCamelCase_ = torch.tensor(
[[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]] )
else:
raise ValueError(F'Unknown model name: {model_name}' )
lowerCamelCase_ = torch.Size([1, 4_8_0, 6_4_0] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , _A , atol=1e-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the hub..." )
model.push_to_hub(
repo_path_or_name=Path(_A , _A ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=_A , )
image_processor.push_to_hub(
repo_path_or_name=Path(_A , _A ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=_A , )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''',
default=None,
type=str,
help='''Path to the original PyTorch checkpoint (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
)
parser.add_argument(
'''--model_name''',
default='''glpn-kitti''',
type=str,
help='''Name of the model in case you\'re pushing to the hub.''',
)
__A =parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 19 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''andreasmadsen/efficient_mlm_m0.40''': (
'''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
),
}
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = "roberta-prelayernorm"
def __init__( self : Optional[Any] , __lowerCamelCase : List[Any]=5_0265 , __lowerCamelCase : str=768 , __lowerCamelCase : str=12 , __lowerCamelCase : Any=12 , __lowerCamelCase : str=3072 , __lowerCamelCase : Dict="gelu" , __lowerCamelCase : Union[str, Any]=0.1 , __lowerCamelCase : Optional[Any]=0.1 , __lowerCamelCase : Dict=512 , __lowerCamelCase : Dict=2 , __lowerCamelCase : Dict=0.02 , __lowerCamelCase : List[Any]=1e-12 , __lowerCamelCase : Union[str, Any]=1 , __lowerCamelCase : Any=0 , __lowerCamelCase : Optional[Any]=2 , __lowerCamelCase : List[str]="absolute" , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Dict=None , **__lowerCamelCase : Optional[int] , ) -> Optional[Any]:
super().__init__(pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = type_vocab_size
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = position_embedding_type
SCREAMING_SNAKE_CASE__ = use_cache
SCREAMING_SNAKE_CASE__ = classifier_dropout
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
@property
def lowercase_ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
SCREAMING_SNAKE_CASE__ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 314 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : Tuple = '''umt5'''
__UpperCamelCase : Tuple = ['''past_key_values''']
def __init__( self : str , lowerCAmelCase_ : Any=2_5_0_1_1_2 , lowerCAmelCase_ : int=5_1_2 , lowerCAmelCase_ : List[Any]=6_4 , lowerCAmelCase_ : List[Any]=1_0_2_4 , lowerCAmelCase_ : int=8 , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Dict=6 , lowerCAmelCase_ : int=3_2 , lowerCAmelCase_ : Any=1_2_8 , lowerCAmelCase_ : Union[str, Any]=0.1 , lowerCAmelCase_ : List[str]=1e-6 , lowerCAmelCase_ : int=1.0 , lowerCAmelCase_ : Dict="gated-gelu" , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[Any]="T5Tokenizer" , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : Optional[Any]=0 , lowerCAmelCase_ : List[Any]=1 , lowerCAmelCase_ : Any=0 , **lowerCAmelCase_ : Dict , ):
"""simple docstring"""
super().__init__(
is_encoder_decoder=lowerCAmelCase_ , tokenizer_class=lowerCAmelCase_ , tie_word_embeddings=lowerCAmelCase_ , pad_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , decoder_start_token_id=lowerCAmelCase_ , **lowerCAmelCase_ , )
_A: List[Any] = vocab_size
_A: Dict = d_model
_A: int = d_kv
_A: List[str] = d_ff
_A: Any = num_layers
_A: List[str] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_A: int = num_heads
_A: str = relative_attention_num_buckets
_A: Optional[Any] = relative_attention_max_distance
_A: Tuple = dropout_rate
_A: Union[str, Any] = layer_norm_epsilon
_A: Optional[int] = initializer_factor
_A: List[Any] = feed_forward_proj
_A: int = use_cache
_A: Tuple = self.feed_forward_proj.split('''-''' )
_A: List[Any] = act_info[-1]
_A: int = act_info[0] == '''gated'''
if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 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":
_A: Tuple = '''gelu_new'''
@property
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
return self.d_model
@property
def __magic_name__ ( self : Dict ):
"""simple docstring"""
return self.num_heads
@property
def __magic_name__ ( self : str ):
"""simple docstring"""
return self.num_layers
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
_A: str = {
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
_A: List[str] = '''past_encoder_sequence + sequence'''
_A: Optional[Any] = {0: '''batch'''}
_A: List[Any] = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
_A: Any = {0: '''batch''', 1: '''decoder_sequence'''}
_A: List[str] = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowerCAmelCase_ , direction='''inputs''' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def __magic_name__ ( self : int ):
"""simple docstring"""
return 1_3
@property
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
return 5e-4
| 301 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCamelCase__ ( a , a=0.999 , a="cosine" , ) -> int:
if alpha_transform_type == "cosine":
def alpha_bar_fn(a ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(a ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_A: Dict = []
for i in range(a ):
_A: Optional[int] = i / num_diffusion_timesteps
_A: Optional[int] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(a ) / alpha_bar_fn(a ) , a ) )
return torch.tensor(a , dtype=torch.floataa )
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = [e.name for e in KarrasDiffusionSchedulers]
__UpperCamelCase : Tuple = 2
@register_to_config
def __init__( self : str , lowerCAmelCase_ : int = 1_0_0_0 , lowerCAmelCase_ : float = 0.00085 , lowerCAmelCase_ : float = 0.012 , lowerCAmelCase_ : str = "linear" , lowerCAmelCase_ : Optional[Union[np.ndarray, List[float]]] = None , lowerCAmelCase_ : str = "epsilon" , lowerCAmelCase_ : Optional[bool] = False , lowerCAmelCase_ : Optional[bool] = False , lowerCAmelCase_ : float = 1.0 , lowerCAmelCase_ : str = "linspace" , lowerCAmelCase_ : int = 0 , ):
"""simple docstring"""
if trained_betas is not None:
_A: Optional[Any] = torch.tensor(lowerCAmelCase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
_A: List[str] = torch.linspace(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_A: Optional[Any] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowerCAmelCase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_A: Tuple = betas_for_alpha_bar(lowerCAmelCase_ , alpha_transform_type='''cosine''' )
elif beta_schedule == "exp":
_A: int = betas_for_alpha_bar(lowerCAmelCase_ , alpha_transform_type='''exp''' )
else:
raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""" )
_A: Union[str, Any] = 1.0 - self.betas
_A: Dict = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
_A: str = use_karras_sigmas
def __magic_name__ ( self : List[str] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int]=None ):
"""simple docstring"""
if schedule_timesteps is None:
_A: List[str] = self.timesteps
_A: int = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_A: Optional[int] = 1 if len(lowerCAmelCase_ ) > 1 else 0
else:
_A: int = timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_ ) else timestep
_A: List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def __magic_name__ ( self : int ):
"""simple docstring"""
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def __magic_name__ ( self : List[str] , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : Union[float, torch.FloatTensor] , ):
"""simple docstring"""
_A: List[str] = self.index_for_timestep(lowerCAmelCase_ )
_A: str = self.sigmas[step_index]
_A: str = sample / ((sigma**2 + 1) ** 0.5)
return sample
def __magic_name__ ( self : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, torch.device] = None , lowerCAmelCase_ : Optional[int] = None , ):
"""simple docstring"""
_A: Union[str, Any] = num_inference_steps
_A: str = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_A: Optional[Any] = np.linspace(0 , num_train_timesteps - 1 , lowerCAmelCase_ , dtype=lowerCAmelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_A: List[Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_A: Dict = (np.arange(0 , lowerCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_A: Union[str, Any] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_A: List[Any] = (np.arange(lowerCAmelCase_ , 0 , -step_ratio )).round().copy().astype(lowerCAmelCase_ )
timesteps -= 1
else:
raise ValueError(
F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_A: Optional[int] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_A: str = np.log(lowerCAmelCase_ )
_A: int = np.interp(lowerCAmelCase_ , np.arange(0 , len(lowerCAmelCase_ ) ) , lowerCAmelCase_ )
if self.config.use_karras_sigmas:
_A: Optional[int] = self._convert_to_karras(in_sigmas=lowerCAmelCase_ , num_inference_steps=self.num_inference_steps )
_A: List[str] = np.array([self._sigma_to_t(lowerCAmelCase_ , lowerCAmelCase_ ) for sigma in sigmas] )
_A: Optional[int] = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_A: Optional[Any] = torch.from_numpy(lowerCAmelCase_ ).to(device=lowerCAmelCase_ )
_A: Tuple = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
_A: str = torch.from_numpy(lowerCAmelCase_ )
_A: str = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(lowerCAmelCase_ ).startswith('''mps''' ):
# mps does not support float64
_A: List[Any] = timesteps.to(lowerCAmelCase_ , dtype=torch.floataa )
else:
_A: Optional[int] = timesteps.to(device=lowerCAmelCase_ )
# empty dt and derivative
_A: Dict = None
_A: List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_A: Dict = defaultdict(lowerCAmelCase_ )
def __magic_name__ ( self : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict ):
"""simple docstring"""
# get log sigma
_A: Tuple = np.log(lowerCAmelCase_ )
# get distribution
_A: List[str] = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
_A: Dict = np.cumsum((dists >= 0) , axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
_A: int = low_idx + 1
_A: Optional[int] = log_sigmas[low_idx]
_A: Dict = log_sigmas[high_idx]
# interpolate sigmas
_A: Union[str, Any] = (low - log_sigma) / (low - high)
_A: Optional[Any] = np.clip(lowerCAmelCase_ , 0 , 1 )
# transform interpolation to time range
_A: Any = (1 - w) * low_idx + w * high_idx
_A: List[Any] = t.reshape(sigma.shape )
return t
def __magic_name__ ( self : Any , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : Optional[Any] ):
"""simple docstring"""
_A: float = in_sigmas[-1].item()
_A: float = in_sigmas[0].item()
_A: Union[str, Any] = 7.0 # 7.0 is the value used in the paper
_A: Optional[Any] = np.linspace(0 , 1 , lowerCAmelCase_ )
_A: Tuple = sigma_min ** (1 / rho)
_A: Optional[Any] = sigma_max ** (1 / rho)
_A: List[str] = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
return self.dt is None
def __magic_name__ ( self : Tuple , lowerCAmelCase_ : Union[torch.FloatTensor, np.ndarray] , lowerCAmelCase_ : Union[float, torch.FloatTensor] , lowerCAmelCase_ : Union[torch.FloatTensor, np.ndarray] , lowerCAmelCase_ : bool = True , ):
"""simple docstring"""
_A: Optional[int] = self.index_for_timestep(lowerCAmelCase_ )
# advance index counter by 1
_A: Union[str, Any] = timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_A: Optional[int] = self.sigmas[step_index]
_A: Union[str, Any] = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
_A: Union[str, Any] = self.sigmas[step_index - 1]
_A: Optional[int] = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_A: List[Any] = 0
_A: Tuple = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_A: Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_next
_A: List[str] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_A: int = sigma_hat if self.state_in_first_order else sigma_next
_A: List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
_A: Optional[int] = model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.config.clip_sample:
_A: Tuple = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_A: Optional[int] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_A: List[Any] = sigma_next - sigma_hat
# store for 2nd order step
_A: str = derivative
_A: Any = dt
_A: Dict = sample
else:
# 2. 2nd order / Heun's method
_A: List[str] = (sample - pred_original_sample) / sigma_next
_A: str = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
_A: Dict = self.dt
_A: int = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
_A: int = None
_A: int = None
_A: Optional[Any] = None
_A: Optional[Any] = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCAmelCase_ )
def __magic_name__ ( self : Any , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : torch.FloatTensor , ):
"""simple docstring"""
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_A: str = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase_ ):
# mps does not support float64
_A: Optional[int] = self.timesteps.to(original_samples.device , dtype=torch.floataa )
_A: Any = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
_A: Union[str, Any] = self.timesteps.to(original_samples.device )
_A: int = timesteps.to(original_samples.device )
_A: str = [self.index_for_timestep(lowerCAmelCase_ , lowerCAmelCase_ ) for t in timesteps]
_A: Optional[Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_A: List[str] = sigma.unsqueeze(-1 )
_A: Any = original_samples + noise * sigma
return noisy_samples
def __len__( self : Dict ):
"""simple docstring"""
return self.config.num_train_timesteps
| 301 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def A ( _UpperCAmelCase : List[Any] ) -> Optional[int]:
'''simple docstring'''
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
_UpperCAmelCase = key.replace('heads.cmd.mim_head.cls.predictions' , 'mmm_image_head' )
_UpperCAmelCase = key.replace('heads.cmd.mlm_head.cls.predictions' , 'mmm_text_head' )
_UpperCAmelCase = key.replace('heads.cmd.itm_head.cls' , 'itm_head' )
_UpperCAmelCase = key.replace('heads.cmd.itm_head.pooler' , 'itm_head.pooler' )
_UpperCAmelCase = key.replace('heads.cmd.clip_head.logit_scale' , 'flava.logit_scale' )
_UpperCAmelCase = key.replace('heads.fairseq_mlm.cls.predictions' , 'mlm_head' )
_UpperCAmelCase = key.replace('heads.imagenet.mim_head.cls.predictions' , 'mim_head' )
_UpperCAmelCase = key.replace('mm_text_projection' , 'flava.text_to_mm_projection' )
_UpperCAmelCase = key.replace('mm_image_projection' , 'flava.image_to_mm_projection' )
_UpperCAmelCase = key.replace('image_encoder.module' , 'flava.image_model' )
_UpperCAmelCase = key.replace('text_encoder.module' , 'flava.text_model' )
_UpperCAmelCase = key.replace('mm_encoder.module.encoder.cls_token' , 'flava.multimodal_model.cls_token' )
_UpperCAmelCase = key.replace('mm_encoder.module' , 'flava.multimodal_model' )
_UpperCAmelCase = key.replace('text_projection' , 'flava.text_projection' )
_UpperCAmelCase = key.replace('image_projection' , 'flava.image_projection' )
_UpperCAmelCase = value.float()
for key, value in codebook_state_dict.items():
_UpperCAmelCase = value
return upgrade
@torch.no_grad()
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : int=None ) -> Tuple:
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = FlavaConfig.from_pretrained(_UpperCAmelCase )
else:
_UpperCAmelCase = FlavaConfig()
_UpperCAmelCase = FlavaForPreTraining(_UpperCAmelCase ).eval()
_UpperCAmelCase = convert_dalle_checkpoint(_UpperCAmelCase , _UpperCAmelCase , save_checkpoint=_UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ):
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
else:
_UpperCAmelCase = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = upgrade_state_dict(_UpperCAmelCase , _UpperCAmelCase )
hf_model.load_state_dict(_UpperCAmelCase )
_UpperCAmelCase = hf_model.state_dict()
_UpperCAmelCase = count_parameters(_UpperCAmelCase )
_UpperCAmelCase = count_parameters(_UpperCAmelCase ) + count_parameters(_UpperCAmelCase )
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 )
hf_model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
UpperCAmelCase__ = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 339 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
_UpperCAmelCase = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class _a ( unittest.TestCase ):
def lowerCamelCase_ ( self: Dict , UpperCamelCase_: int , UpperCamelCase_: int ) -> Any:
"""simple docstring"""
lowercase__ = jnp.ones((batch_size, length) ) / length
return scores
def lowerCamelCase_ ( self: str ) -> str:
"""simple docstring"""
lowercase__ = None
lowercase__ = 20
lowercase__ = self._get_uniform_logits(batch_size=2 , length=UpperCamelCase_ )
# tweak scores to not be uniform anymore
lowercase__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowercase__ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowercase__ = jax.nn.softmax(UpperCamelCase_ , axis=-1 )
lowercase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowercase__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowercase__ = jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase_ , scores.copy() , cur_len=UpperCamelCase_ ) , axis=-1 )
lowercase__ = jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase_ , scores.copy() , cur_len=UpperCamelCase_ ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def lowerCamelCase_ ( self: Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = None
lowercase__ = 10
lowercase__ = 2
# create ramp distribution
lowercase__ = np.broadcast_to(np.arange(UpperCamelCase_ )[None, :] , (batch_size, vocab_size) ).copy()
lowercase__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowercase__ = FlaxTopKLogitsWarper(3 )
lowercase__ = top_k_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowercase__ = 5
lowercase__ = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
lowercase__ = np.broadcast_to(np.arange(UpperCamelCase_ )[None, :] , (batch_size, length) ).copy()
lowercase__ = top_k_warp_safety_check(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def lowerCamelCase_ ( self: Tuple ) -> Any:
"""simple docstring"""
lowercase__ = None
lowercase__ = 10
lowercase__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowercase__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowercase__ = FlaxTopPLogitsWarper(0.8 )
lowercase__ = np.exp(top_p_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowercase__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowercase__ = np.broadcast_to(np.arange(UpperCamelCase_ )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowercase__ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowercase__ = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
lowercase__ = top_p_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def lowerCamelCase_ ( self: List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = 20
lowercase__ = 4
lowercase__ = 0
lowercase__ = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase_ )
# check that min length is applied at length 5
lowercase__ = ids_tensor((batch_size, 20) , vocab_size=20 )
lowercase__ = 5
lowercase__ = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = min_dist_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('''inf''' )] )
# check that min length is not applied anymore at length 15
lowercase__ = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = 15
lowercase__ = min_dist_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
self.assertFalse(jnp.isinf(UpperCamelCase_ ).any() )
def lowerCamelCase_ ( self: Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = 20
lowercase__ = 4
lowercase__ = 0
lowercase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_ )
# check that all scores are -inf except the bos_token_id score
lowercase__ = ids_tensor((batch_size, 1) , vocab_size=20 )
lowercase__ = 1
lowercase__ = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = logits_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowercase__ = 3
lowercase__ = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = logits_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
self.assertFalse(jnp.isinf(UpperCamelCase_ ).any() )
def lowerCamelCase_ ( self: List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = 20
lowercase__ = 4
lowercase__ = 0
lowercase__ = 5
lowercase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_ , eos_token_id=UpperCamelCase_ )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowercase__ = ids_tensor((batch_size, 4) , vocab_size=20 )
lowercase__ = 4
lowercase__ = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = logits_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowercase__ = 3
lowercase__ = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = logits_processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
self.assertFalse(jnp.isinf(UpperCamelCase_ ).any() )
def lowerCamelCase_ ( self: str ) -> List[str]:
"""simple docstring"""
lowercase__ = 4
lowercase__ = 10
lowercase__ = 15
lowercase__ = 2
lowercase__ = 1
lowercase__ = 15
# dummy input_ids and scores
lowercase__ = ids_tensor((batch_size, sequence_length) , UpperCamelCase_ )
lowercase__ = input_ids.copy()
lowercase__ = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = scores.copy()
# instantiate all dist processors
lowercase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowercase__ = FlaxTopKLogitsWarper(3 )
lowercase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowercase__ = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase_ )
lowercase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_ )
lowercase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_ , eos_token_id=UpperCamelCase_ )
lowercase__ = 10
# no processor list
lowercase__ = temp_dist_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = top_k_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = top_p_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = min_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = bos_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = eos_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
# with processor list
lowercase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowercase__ = processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
# scores should be equal
self.assertTrue(jnp.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def lowerCamelCase_ ( self: List[Any] ) -> str:
"""simple docstring"""
lowercase__ = 4
lowercase__ = 10
lowercase__ = 15
lowercase__ = 2
lowercase__ = 1
lowercase__ = 15
# dummy input_ids and scores
lowercase__ = ids_tensor((batch_size, sequence_length) , UpperCamelCase_ )
lowercase__ = input_ids.copy()
lowercase__ = self._get_uniform_logits(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = scores.copy()
# instantiate all dist processors
lowercase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowercase__ = FlaxTopKLogitsWarper(3 )
lowercase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowercase__ = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase_ )
lowercase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_ )
lowercase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_ , eos_token_id=UpperCamelCase_ )
lowercase__ = 10
# no processor list
def run_no_processor_list(UpperCamelCase_: List[str] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: str ):
lowercase__ = temp_dist_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = top_k_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = top_p_warp(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = min_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = bos_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
lowercase__ = eos_dist_proc(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
return scores
# with processor list
def run_processor_list(UpperCamelCase_: Any , UpperCamelCase_: Optional[Any] , UpperCamelCase_: int ):
lowercase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowercase__ = processor(UpperCamelCase_ , UpperCamelCase_ , cur_len=UpperCamelCase_ )
return scores
lowercase__ = jax.jit(UpperCamelCase_ )
lowercase__ = jax.jit(UpperCamelCase_ )
lowercase__ = jitted_run_no_processor_list(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = jitted_run_processor_list(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# scores should be equal
self.assertTrue(jnp.allclose(UpperCamelCase_ , UpperCamelCase_ , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 93 |
lowerCAmelCase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
lowerCAmelCase = [None] * 1000_0000
lowerCAmelCase = True
lowerCAmelCase = False
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
lowercase__ = chain(next_number(SCREAMING_SNAKE_CASE ) )
lowercase__ = number_chain
while number < 10_00_00_00:
lowercase__ = number_chain
number *= 10
return number_chain
def _a ( SCREAMING_SNAKE_CASE = 10_00_00_00 ):
"""simple docstring"""
for i in range(1 , SCREAMING_SNAKE_CASE ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"""{solution() = }""")
| 93 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : list[int | float] , __lowercase : int , __lowercase : int ) -> int | float:
'''simple docstring'''
if len(__lowercase ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(__lowercase )
or left < -len(__lowercase )
or right >= len(__lowercase )
or right < -len(__lowercase )
):
raise IndexError("list index out of range" )
if left == right:
return nums[left]
_UpperCAmelCase = (left + right) >> 1 # the middle
_UpperCAmelCase = find_max(__lowercase , __lowercase , __lowercase ) # find max in range[left, mid]
_UpperCAmelCase = find_max(__lowercase , mid + 1 , __lowercase ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 22 |
'''simple docstring'''
import math
def A_ ( snake_case , snake_case ):
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("In Malus Law, the angle is in the range 0-360 degrees" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(snake_case ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name="malus_law")
| 139 | 0 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowercase :
"""simple docstring"""
@staticmethod
def _snake_case ( *a_ ,**a_ ) -> Optional[Any]:
pass
@is_pipeline_test
@require_vision
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@require_torch
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : List[Any] = pipeline(
model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification""" ,)
_UpperCAmelCase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_UpperCAmelCase : List[Any] = image_classifier(a_ ,candidate_labels=["""a""", """b""", """c"""] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(a_ ) ,[
[{"""score""": 0.333, """label""": """a"""}, {"""score""": 0.333, """label""": """b"""}, {"""score""": 0.333, """label""": """c"""}],
[{"""score""": 0.333, """label""": """a"""}, {"""score""": 0.333, """label""": """c"""}, {"""score""": 0.333, """label""": """b"""}],
] ,)
_UpperCAmelCase : Dict = image_classifier([image] * 5 ,candidate_labels=["""A""", """B""", """C"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(a_ ) ,[
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
] ,)
@require_tf
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : int = pipeline(
model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification""" ,framework="""tf""" )
_UpperCAmelCase : int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_UpperCAmelCase : Any = image_classifier(a_ ,candidate_labels=["""a""", """b""", """c"""] )
self.assertEqual(
nested_simplify(a_ ) ,[{"""score""": 0.333, """label""": """a"""}, {"""score""": 0.333, """label""": """b"""}, {"""score""": 0.333, """label""": """c"""}] ,)
_UpperCAmelCase : List[str] = image_classifier([image] * 5 ,candidate_labels=["""A""", """B""", """C"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(a_ ) ,[
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
[
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
{"""score""": 0.333, """label""": ANY(a_ )},
],
] ,)
@slow
@require_torch
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Union[str, Any] = pipeline(
task="""zero-shot-image-classification""" ,model="""openai/clip-vit-base-patch32""" ,)
# This is an image of 2 cats with remotes and no planes
_UpperCAmelCase : Tuple = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_UpperCAmelCase : Optional[int] = image_classifier(a_ ,candidate_labels=["""cat""", """plane""", """remote"""] )
self.assertEqual(
nested_simplify(a_ ) ,[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
] ,)
_UpperCAmelCase : Optional[int] = image_classifier([image] * 5 ,candidate_labels=["""cat""", """plane""", """remote"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(a_ ) ,[
[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
],
]
* 5 ,)
@slow
@require_tf
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : Any = pipeline(
task="""zero-shot-image-classification""" ,model="""openai/clip-vit-base-patch32""" ,framework="""tf""" )
# This is an image of 2 cats with remotes and no planes
_UpperCAmelCase : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_UpperCAmelCase : List[Any] = image_classifier(a_ ,candidate_labels=["""cat""", """plane""", """remote"""] )
self.assertEqual(
nested_simplify(a_ ) ,[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
] ,)
_UpperCAmelCase : Dict = image_classifier([image] * 5 ,candidate_labels=["""cat""", """plane""", """remote"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(a_ ) ,[
[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
],
]
* 5 ,)
| 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : str = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
A_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 | 1 |
"""simple docstring"""
def UpperCAmelCase ( UpperCAmelCase ) -> int:
snake_case_ = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
snake_case_ = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
__UpperCamelCase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
__UpperCamelCase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 69 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__a :Any = logging.get_logger(__name__)
def __snake_case ( __UpperCamelCase : Optional[int] ):
"""simple docstring"""
A_ = "huggingface/label-files"
A_ = "imagenet-1k-id2label.json"
A_ = json.load(open(hf_hub_download(__UpperCamelCase ,__UpperCamelCase ,repo_type="dataset" ) ,"r" ) )
A_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
A_ = {v: k for k, v in idalabel.items()}
A_ = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
A_ = BitConfig(
conv_layer=__UpperCamelCase ,num_labels=1000 ,idalabel=__UpperCamelCase ,labelaid=__UpperCamelCase ,)
return config
def __snake_case ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
if "stem.conv" in name:
A_ = name.replace("stem.conv" ,"bit.embedder.convolution" )
if "blocks" in name:
A_ = name.replace("blocks" ,"layers" )
if "head.fc" in name:
A_ = name.replace("head.fc" ,"classifier.1" )
if name.startswith("norm" ):
A_ = "bit." + name
if "bit" not in name and "classifier" not in name:
A_ = "bit.encoder." + name
return name
def __snake_case ( ):
"""simple docstring"""
A_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
A_ = Image.open(requests.get(__UpperCamelCase ,stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : Optional[Any] ,__UpperCamelCase : Tuple=False ):
"""simple docstring"""
A_ = get_config(__UpperCamelCase )
# load original model from timm
A_ = create_model(__UpperCamelCase ,pretrained=__UpperCamelCase )
timm_model.eval()
# load state_dict of original model
A_ = timm_model.state_dict()
for key in state_dict.copy().keys():
A_ = state_dict.pop(__UpperCamelCase )
A_ = val.squeeze() if "head" in key else val
# load HuggingFace model
A_ = BitForImageClassification(__UpperCamelCase )
model.eval()
model.load_state_dict(__UpperCamelCase )
# create image processor
A_ = create_transform(**resolve_data_config({} ,model=__UpperCamelCase ) )
A_ = transform.transforms
A_ = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
A_ = BitImageProcessor(
do_resize=__UpperCamelCase ,size={"shortest_edge": timm_transforms[0].size} ,resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,do_center_crop=__UpperCamelCase ,crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} ,do_normalize=__UpperCamelCase ,image_mean=timm_transforms[-1].mean.tolist() ,image_std=timm_transforms[-1].std.tolist() ,)
A_ = prepare_img()
A_ = transform(__UpperCamelCase ).unsqueeze(0 )
A_ = processor(__UpperCamelCase ,return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(__UpperCamelCase ,__UpperCamelCase )
# verify logits
with torch.no_grad():
A_ = model(__UpperCamelCase )
A_ = outputs.logits
print("Logits:" ,logits[0, :3] )
print("Predicted class:" ,model.config.idalabel[logits.argmax(-1 ).item()] )
A_ = timm_model(__UpperCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__UpperCamelCase ,outputs.logits ,atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(__UpperCamelCase )
processor.save_pretrained(__UpperCamelCase )
if push_to_hub:
print(f'''Pushing model {model_name} and processor to the hub''' )
model.push_to_hub(f'''ybelkada/{model_name}''' )
processor.push_to_hub(f'''ybelkada/{model_name}''' )
if __name__ == "__main__":
__a :List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='resnetv2_50x1_bitm',
type=str,
help='Name of the BiT 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.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model to the hub.',
)
__a :str = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 312 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a_ ( __snake_case : str ) -> str:
"""simple docstring"""
return "".join(sorted(__snake_case ) )
def a_ ( __snake_case : str ) -> list[str]:
"""simple docstring"""
return word_by_signature[signature(__snake_case )]
a_ : str = Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
a_ : Union[str, Any] = sorted({word.strip().lower() for word in data.splitlines()})
a_ : List[Any] = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
a_ : str = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("""anagrams.txt""", """w""") as file:
file.write("""all_anagrams = \n """)
file.write(pprint.pformat(all_anagrams))
| 365 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =ShapEImgaImgPipeline
lowercase : Dict =['image']
lowercase : str =['image']
lowercase : int =[
'num_images_per_prompt',
'num_inference_steps',
'generator',
'latents',
'guidance_scale',
'frame_size',
'output_type',
'return_dict',
]
lowercase : int =False
@property
def lowercase__ ( self ):
"""simple docstring"""
return 32
@property
def lowercase__ ( self ):
"""simple docstring"""
return 32
@property
def lowercase__ ( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowercase__ ( self ):
"""simple docstring"""
return 8
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ =CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size, image_size=64, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, num_attention_heads=4, num_channels=3, num_hidden_layers=5, patch_size=1, )
lowerCamelCase_ =CLIPVisionModel(lowerCAmelCase )
return model
@property
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =CLIPImageProcessor(
crop_size=224, do_center_crop=lowerCAmelCase, do_normalize=lowerCAmelCase, do_resize=lowerCAmelCase, image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3], image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1], resample=3, size=224, )
return image_processor
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ ={
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''embedding_proj_norm_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
lowerCamelCase_ =PriorTransformer(**lowerCAmelCase )
return model
@property
def lowercase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase_ ={
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
lowerCamelCase_ =ShapERenderer(**lowerCAmelCase )
return model
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.dummy_prior
lowerCamelCase_ =self.dummy_image_encoder
lowerCamelCase_ =self.dummy_image_processor
lowerCamelCase_ =self.dummy_renderer
lowerCamelCase_ =HeunDiscreteScheduler(
beta_schedule='''exp''', num_train_timesteps=1_024, prediction_type='''sample''', use_karras_sigmas=lowerCAmelCase, clip_sample=lowerCAmelCase, clip_sample_range=1.0, )
lowerCamelCase_ ={
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=0 ):
"""simple docstring"""
lowerCamelCase_ =floats_tensor((1, 3, 64, 64), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
if str(lowerCAmelCase ).startswith('''mps''' ):
lowerCamelCase_ =torch.manual_seed(lowerCAmelCase )
else:
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCamelCase_ ={
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''cpu'''
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =pipe(**self.get_dummy_inputs(lowerCAmelCase ) )
lowerCamelCase_ =output.images[0]
lowerCamelCase_ =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowerCamelCase_ =np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase__ ( self ):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =torch_device == '''cpu'''
lowerCamelCase_ =True
self._test_inference_batch_single_identical(
batch_size=2, test_max_difference=lowerCAmelCase, relax_max_difference=lowerCAmelCase, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.get_dummy_components()
lowerCamelCase_ =self.pipeline_class(**lowerCAmelCase )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =1
lowerCamelCase_ =2
lowerCamelCase_ =self.get_dummy_inputs(lowerCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowerCamelCase_ =batch_size * [inputs[key]]
lowerCamelCase_ =pipe(**lowerCAmelCase, num_images_per_prompt=lowerCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def lowercase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
lowerCamelCase_ =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
lowerCamelCase_ =ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
lowerCamelCase_ =pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowerCamelCase_ =torch.Generator(device=lowerCAmelCase ).manual_seed(0 )
lowerCamelCase_ =pipe(
lowerCAmelCase, generator=lowerCAmelCase, guidance_scale=3.0, num_inference_steps=64, frame_size=64, output_type='''np''', ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowerCAmelCase, lowerCAmelCase )
| 6 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_snake_case = logging.get_logger(__name__)
_snake_case = OrderedDict(
[
# Base model mapping
('''albert''', '''FlaxAlbertModel'''),
('''bart''', '''FlaxBartModel'''),
('''beit''', '''FlaxBeitModel'''),
('''bert''', '''FlaxBertModel'''),
('''big_bird''', '''FlaxBigBirdModel'''),
('''blenderbot''', '''FlaxBlenderbotModel'''),
('''blenderbot-small''', '''FlaxBlenderbotSmallModel'''),
('''clip''', '''FlaxCLIPModel'''),
('''distilbert''', '''FlaxDistilBertModel'''),
('''electra''', '''FlaxElectraModel'''),
('''gpt-sw3''', '''FlaxGPT2Model'''),
('''gpt2''', '''FlaxGPT2Model'''),
('''gpt_neo''', '''FlaxGPTNeoModel'''),
('''gptj''', '''FlaxGPTJModel'''),
('''longt5''', '''FlaxLongT5Model'''),
('''marian''', '''FlaxMarianModel'''),
('''mbart''', '''FlaxMBartModel'''),
('''mt5''', '''FlaxMT5Model'''),
('''opt''', '''FlaxOPTModel'''),
('''pegasus''', '''FlaxPegasusModel'''),
('''regnet''', '''FlaxRegNetModel'''),
('''resnet''', '''FlaxResNetModel'''),
('''roberta''', '''FlaxRobertaModel'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormModel'''),
('''roformer''', '''FlaxRoFormerModel'''),
('''t5''', '''FlaxT5Model'''),
('''vision-text-dual-encoder''', '''FlaxVisionTextDualEncoderModel'''),
('''vit''', '''FlaxViTModel'''),
('''wav2vec2''', '''FlaxWav2Vec2Model'''),
('''whisper''', '''FlaxWhisperModel'''),
('''xglm''', '''FlaxXGLMModel'''),
('''xlm-roberta''', '''FlaxXLMRobertaModel'''),
]
)
_snake_case = OrderedDict(
[
# Model for pre-training mapping
('''albert''', '''FlaxAlbertForPreTraining'''),
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''bert''', '''FlaxBertForPreTraining'''),
('''big_bird''', '''FlaxBigBirdForPreTraining'''),
('''electra''', '''FlaxElectraForPreTraining'''),
('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
('''roberta''', '''FlaxRobertaForMaskedLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
('''roformer''', '''FlaxRoFormerForMaskedLM'''),
('''t5''', '''FlaxT5ForConditionalGeneration'''),
('''wav2vec2''', '''FlaxWav2Vec2ForPreTraining'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
]
)
_snake_case = OrderedDict(
[
# Model for Masked LM mapping
('''albert''', '''FlaxAlbertForMaskedLM'''),
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''bert''', '''FlaxBertForMaskedLM'''),
('''big_bird''', '''FlaxBigBirdForMaskedLM'''),
('''distilbert''', '''FlaxDistilBertForMaskedLM'''),
('''electra''', '''FlaxElectraForMaskedLM'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''roberta''', '''FlaxRobertaForMaskedLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
('''roformer''', '''FlaxRoFormerForMaskedLM'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
]
)
_snake_case = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''blenderbot''', '''FlaxBlenderbotForConditionalGeneration'''),
('''blenderbot-small''', '''FlaxBlenderbotSmallForConditionalGeneration'''),
('''encoder-decoder''', '''FlaxEncoderDecoderModel'''),
('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
('''marian''', '''FlaxMarianMTModel'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
('''pegasus''', '''FlaxPegasusForConditionalGeneration'''),
('''t5''', '''FlaxT5ForConditionalGeneration'''),
]
)
_snake_case = OrderedDict(
[
# Model for Image-classsification
('''beit''', '''FlaxBeitForImageClassification'''),
('''regnet''', '''FlaxRegNetForImageClassification'''),
('''resnet''', '''FlaxResNetForImageClassification'''),
('''vit''', '''FlaxViTForImageClassification'''),
]
)
_snake_case = OrderedDict(
[
('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
]
)
_snake_case = OrderedDict(
[
# Model for Causal LM mapping
('''bart''', '''FlaxBartForCausalLM'''),
('''bert''', '''FlaxBertForCausalLM'''),
('''big_bird''', '''FlaxBigBirdForCausalLM'''),
('''electra''', '''FlaxElectraForCausalLM'''),
('''gpt-sw3''', '''FlaxGPT2LMHeadModel'''),
('''gpt2''', '''FlaxGPT2LMHeadModel'''),
('''gpt_neo''', '''FlaxGPTNeoForCausalLM'''),
('''gptj''', '''FlaxGPTJForCausalLM'''),
('''opt''', '''FlaxOPTForCausalLM'''),
('''roberta''', '''FlaxRobertaForCausalLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForCausalLM'''),
('''xglm''', '''FlaxXGLMForCausalLM'''),
('''xlm-roberta''', '''FlaxXLMRobertaForCausalLM'''),
]
)
_snake_case = OrderedDict(
[
# Model for Sequence Classification mapping
('''albert''', '''FlaxAlbertForSequenceClassification'''),
('''bart''', '''FlaxBartForSequenceClassification'''),
('''bert''', '''FlaxBertForSequenceClassification'''),
('''big_bird''', '''FlaxBigBirdForSequenceClassification'''),
('''distilbert''', '''FlaxDistilBertForSequenceClassification'''),
('''electra''', '''FlaxElectraForSequenceClassification'''),
('''mbart''', '''FlaxMBartForSequenceClassification'''),
('''roberta''', '''FlaxRobertaForSequenceClassification'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForSequenceClassification'''),
('''roformer''', '''FlaxRoFormerForSequenceClassification'''),
('''xlm-roberta''', '''FlaxXLMRobertaForSequenceClassification'''),
]
)
_snake_case = OrderedDict(
[
# Model for Question Answering mapping
('''albert''', '''FlaxAlbertForQuestionAnswering'''),
('''bart''', '''FlaxBartForQuestionAnswering'''),
('''bert''', '''FlaxBertForQuestionAnswering'''),
('''big_bird''', '''FlaxBigBirdForQuestionAnswering'''),
('''distilbert''', '''FlaxDistilBertForQuestionAnswering'''),
('''electra''', '''FlaxElectraForQuestionAnswering'''),
('''mbart''', '''FlaxMBartForQuestionAnswering'''),
('''roberta''', '''FlaxRobertaForQuestionAnswering'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForQuestionAnswering'''),
('''roformer''', '''FlaxRoFormerForQuestionAnswering'''),
('''xlm-roberta''', '''FlaxXLMRobertaForQuestionAnswering'''),
]
)
_snake_case = OrderedDict(
[
# Model for Token Classification mapping
('''albert''', '''FlaxAlbertForTokenClassification'''),
('''bert''', '''FlaxBertForTokenClassification'''),
('''big_bird''', '''FlaxBigBirdForTokenClassification'''),
('''distilbert''', '''FlaxDistilBertForTokenClassification'''),
('''electra''', '''FlaxElectraForTokenClassification'''),
('''roberta''', '''FlaxRobertaForTokenClassification'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForTokenClassification'''),
('''roformer''', '''FlaxRoFormerForTokenClassification'''),
('''xlm-roberta''', '''FlaxXLMRobertaForTokenClassification'''),
]
)
_snake_case = OrderedDict(
[
# Model for Multiple Choice mapping
('''albert''', '''FlaxAlbertForMultipleChoice'''),
('''bert''', '''FlaxBertForMultipleChoice'''),
('''big_bird''', '''FlaxBigBirdForMultipleChoice'''),
('''distilbert''', '''FlaxDistilBertForMultipleChoice'''),
('''electra''', '''FlaxElectraForMultipleChoice'''),
('''roberta''', '''FlaxRobertaForMultipleChoice'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMultipleChoice'''),
('''roformer''', '''FlaxRoFormerForMultipleChoice'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMultipleChoice'''),
]
)
_snake_case = OrderedDict(
[
('''bert''', '''FlaxBertForNextSentencePrediction'''),
]
)
_snake_case = OrderedDict(
[
('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
]
)
_snake_case = OrderedDict(
[
('''whisper''', '''FlaxWhisperForAudioClassification'''),
]
)
_snake_case = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_snake_case = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_snake_case = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_snake_case = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_snake_case = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_snake_case = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: List[Any] = FLAX_MODEL_MAPPING
_snake_case = auto_class_update(FlaxAutoModel)
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Union[str, Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
_snake_case = auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Union[str, Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
_snake_case = auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: str = FLAX_MODEL_FOR_MASKED_LM_MAPPING
_snake_case = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Optional[int] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_snake_case = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Optional[int] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_snake_case = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: str = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
_snake_case = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Union[str, Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
_snake_case = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Optional[Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
_snake_case = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
_snake_case = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_snake_case = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='''image classification'''
)
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: str = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
_snake_case = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')
class _snake_case ( _BaseAutoModelClass ):
lowerCamelCase__: Optional[int] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
_snake_case = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)
| 157 | import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
_snake_case = logging.get_logger(__name__)
def _UpperCamelCase ( snake_case__, snake_case__ ) -> List[str]:
def run_func(snake_case__ ):
@wraps(snake_case__ )
def run_in_eager_mode(*snake_case__, **snake_case__ ):
return func(*snake_case__, **snake_case__ )
@wraps(snake_case__ )
@tf.function(experimental_compile=snake_case__ )
def run_in_graph_mode(*snake_case__, **snake_case__ ):
return func(*snake_case__, **snake_case__ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> ["tf.Tensor"]:
__UpperCAmelCase : str = random.Random()
__UpperCAmelCase : str = [rng.randint(0, vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(snake_case__, shape=(batch_size, sequence_length), dtype=tf.intaa )
class _snake_case ( _lowercase ):
lowerCamelCase__: TensorFlowBenchmarkArguments
lowerCamelCase__: PretrainedConfig
lowerCamelCase__: str = "TensorFlow"
@property
def _lowerCamelCase ( self: int ) -> Any:
return tf.__version__
def _lowerCamelCase ( self: Dict , __lowerCamelCase: str , __lowerCamelCase: int , __lowerCamelCase: int ) -> float:
# initialize GPU on separate process
__UpperCAmelCase : List[Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
__UpperCAmelCase : int = self._prepare_inference_func(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return self._measure_speed(_inference )
def _lowerCamelCase ( self: Tuple , __lowerCamelCase: str , __lowerCamelCase: int , __lowerCamelCase: int ) -> float:
__UpperCAmelCase : Union[str, Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
__UpperCAmelCase : Dict = self._prepare_train_func(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return self._measure_speed(_train )
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: str , __lowerCamelCase: int , __lowerCamelCase: int ) -> [Memory, Optional[MemorySummary]]:
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __lowerCamelCase )
__UpperCAmelCase : List[str] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
__UpperCAmelCase : int = self._prepare_inference_func(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return self._measure_memory(_inference )
def _lowerCamelCase ( self: str , __lowerCamelCase: str , __lowerCamelCase: int , __lowerCamelCase: int ) -> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __lowerCamelCase )
__UpperCAmelCase : int = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
__UpperCAmelCase : int = self._prepare_train_func(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return self._measure_memory(_train )
def _lowerCamelCase ( self: int , __lowerCamelCase: str , __lowerCamelCase: int , __lowerCamelCase: int ) -> Callable[[], None]:
__UpperCAmelCase : Union[str, Any] = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
__UpperCAmelCase : int = (
hasattr(__lowerCamelCase , "architectures" )
and isinstance(config.architectures , __lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
__UpperCAmelCase : int = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
__UpperCAmelCase : Dict = __import__("transformers" , fromlist=[model_class] )
__UpperCAmelCase : str = getattr(__lowerCamelCase , __lowerCamelCase )
__UpperCAmelCase : Optional[Any] = model_cls(__lowerCamelCase )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
__UpperCAmelCase : int = TF_MODEL_MAPPING[config.__class__](__lowerCamelCase )
# encoder-decoder has vocab size saved differently
__UpperCAmelCase : List[str] = config.vocab_size if hasattr(__lowerCamelCase , "vocab_size" ) else config.encoder.vocab_size
__UpperCAmelCase : Dict = random_input_ids(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__lowerCamelCase , decoder_input_ids=__lowerCamelCase , training=__lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__lowerCamelCase , training=__lowerCamelCase )
__UpperCAmelCase : int = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def _lowerCamelCase ( self: List[str] , __lowerCamelCase: str , __lowerCamelCase: int , __lowerCamelCase: int ) -> Callable[[], None]:
__UpperCAmelCase : Any = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
__UpperCAmelCase : Tuple = (
hasattr(__lowerCamelCase , "architectures" )
and isinstance(config.architectures , __lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
__UpperCAmelCase : Dict = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
__UpperCAmelCase : Optional[Any] = __import__("transformers" , fromlist=[model_class] )
__UpperCAmelCase : int = getattr(__lowerCamelCase , __lowerCamelCase )
__UpperCAmelCase : Any = model_cls(__lowerCamelCase )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
__UpperCAmelCase : Union[str, Any] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowerCamelCase )
# encoder-decoder has vocab size saved differently
__UpperCAmelCase : List[Any] = config.vocab_size if hasattr(__lowerCamelCase , "vocab_size" ) else config.encoder.vocab_size
__UpperCAmelCase : Dict = random_input_ids(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
__UpperCAmelCase : List[Any] = model(__lowerCamelCase , decoder_input_ids=__lowerCamelCase , labels=__lowerCamelCase , training=__lowerCamelCase )[0]
__UpperCAmelCase : Optional[Any] = tf.gradients(__lowerCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
__UpperCAmelCase : Optional[Any] = model(__lowerCamelCase , labels=__lowerCamelCase , training=__lowerCamelCase )[0]
__UpperCAmelCase : List[Any] = tf.gradients(__lowerCamelCase , model.trainable_variables )
return gradients
__UpperCAmelCase : Optional[int] = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def _lowerCamelCase ( self: List[Any] , __lowerCamelCase: Any ) -> float:
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(__lowerCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
__UpperCAmelCase : List[str] = timeit.repeat(
__lowerCamelCase , repeat=self.args.repeat , number=10 , )
return min(__lowerCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
def _lowerCamelCase ( self: Optional[int] , __lowerCamelCase: Callable[[], None] ) -> [Memory, MemorySummary]:
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
__UpperCAmelCase : Union[str, Any] = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
__UpperCAmelCase : Union[str, Any] = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
__UpperCAmelCase : str = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
__UpperCAmelCase : List[Any] = nvml.nvmlDeviceGetMemoryInfo(__lowerCamelCase )
__UpperCAmelCase : List[Any] = meminfo.used
__UpperCAmelCase : List[Any] = Memory(__lowerCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
__UpperCAmelCase : Tuple = None
else:
__UpperCAmelCase : str = measure_peak_memory_cpu(__lowerCamelCase )
__UpperCAmelCase : Optional[int] = Memory(__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
__UpperCAmelCase : str = stop_memory_tracing(__lowerCamelCase )
if memory is None:
__UpperCAmelCase : Tuple = summary.total
else:
__UpperCAmelCase : Union[str, Any] = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 157 | 1 |
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 UpperCAmelCase__ ( a__ , a__ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = StableDiffusionPanoramaPipeline
UpperCAmelCase__ : Any = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase__ : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase__ : Any = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase__ : Union[str, Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
def _a ( self ) -> int:
torch.manual_seed(0 )
__UpperCamelCase =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 , )
__UpperCamelCase =DDIMScheduler()
torch.manual_seed(0 )
__UpperCamelCase =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
__UpperCamelCase =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__UpperCamelCase =CLIPTextModel(lowerCAmelCase__ )
__UpperCamelCase =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
__UpperCamelCase ={
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _a ( self , A_ , A_=0 ) -> Optional[Any]:
__UpperCamelCase =torch.manual_seed(lowerCAmelCase__ )
__UpperCamelCase ={
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def _a ( self ) -> int:
__UpperCamelCase ="cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase =self.get_dummy_components()
__UpperCamelCase =StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__UpperCamelCase =sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__UpperCamelCase =self.get_dummy_inputs(lowerCAmelCase__ )
__UpperCamelCase =sd_pipe(**lowerCAmelCase__ ).images
__UpperCamelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCamelCase =np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Optional[int]:
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def _a ( self ) -> Optional[int]:
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def _a ( self ) -> List[Any]:
__UpperCamelCase ="cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase =self.get_dummy_components()
__UpperCamelCase =StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__UpperCamelCase =sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__UpperCamelCase =self.get_dummy_inputs(lowerCAmelCase__ )
__UpperCamelCase ="french fries"
__UpperCamelCase =sd_pipe(**lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ )
__UpperCamelCase =output.images
__UpperCamelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCamelCase =np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Any:
__UpperCamelCase ="cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase =self.get_dummy_components()
__UpperCamelCase =StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__UpperCamelCase =sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__UpperCamelCase =self.get_dummy_inputs(lowerCAmelCase__ )
__UpperCamelCase =sd_pipe(**lowerCAmelCase__ , view_batch_size=2 )
__UpperCamelCase =output.images
__UpperCamelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCamelCase =np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase ="cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase =self.get_dummy_components()
__UpperCamelCase =EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' )
__UpperCamelCase =StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__UpperCamelCase =sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__UpperCamelCase =self.get_dummy_inputs(lowerCAmelCase__ )
__UpperCamelCase =sd_pipe(**lowerCAmelCase__ ).images
__UpperCamelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCamelCase =np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Any:
__UpperCamelCase ="cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase =self.get_dummy_components()
__UpperCamelCase =PNDMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , skip_prk_steps=lowerCAmelCase__ )
__UpperCamelCase =StableDiffusionPanoramaPipeline(**lowerCAmelCase__ )
__UpperCamelCase =sd_pipe.to(lowerCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
__UpperCamelCase =self.get_dummy_inputs(lowerCAmelCase__ )
__UpperCamelCase =sd_pipe(**lowerCAmelCase__ ).images
__UpperCamelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCamelCase =np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self ) -> str:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , A_=0 ) -> int:
__UpperCamelCase =torch.manual_seed(lowerCAmelCase__ )
__UpperCamelCase ={
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def _a ( self ) -> List[str]:
__UpperCamelCase ="stabilityai/stable-diffusion-2-base"
__UpperCamelCase =DDIMScheduler.from_pretrained(lowerCAmelCase__ , subfolder='scheduler' )
__UpperCamelCase =StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__ , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
__UpperCamelCase =self.get_inputs()
__UpperCamelCase =pipe(**lowerCAmelCase__ ).images
__UpperCamelCase =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
__UpperCamelCase =np.array(
[
0.3696_8392,
0.2702_5372,
0.3244_6766,
0.2837_9387,
0.3636_3274,
0.3073_3347,
0.2710_0027,
0.2705_4125,
0.2553_6096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def _a ( self ) -> List[Any]:
__UpperCamelCase =StableDiffusionPanoramaPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-base' , safety_checker=lowerCAmelCase__ )
__UpperCamelCase =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
__UpperCamelCase =self.get_inputs()
__UpperCamelCase =pipe(**lowerCAmelCase__ ).images
__UpperCamelCase =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
__UpperCamelCase =np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def _a ( self ) -> Dict:
__UpperCamelCase =0
def callback_fn(A_ , A_ , A_ ) -> None:
__UpperCamelCase =True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__UpperCamelCase =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
__UpperCamelCase =latents[0, -3:, -3:, -1]
__UpperCamelCase =np.array(
[
0.1868_1869,
0.3390_7816,
0.536_1276,
0.1443_2865,
-0.0285_6611,
-0.7394_1123,
0.2339_7987,
0.4732_2682,
-0.3782_3164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
__UpperCamelCase =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
__UpperCamelCase =latents[0, -3:, -3:, -1]
__UpperCamelCase =np.array(
[
0.1853_9645,
0.3398_7248,
0.537_8559,
0.1443_7142,
-0.0245_5261,
-0.733_8317,
0.2399_0755,
0.4735_6272,
-0.378_6505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
__UpperCamelCase =False
__UpperCamelCase ="stabilityai/stable-diffusion-2-base"
__UpperCamelCase =DDIMScheduler.from_pretrained(lowerCAmelCase__ , subfolder='scheduler' )
__UpperCamelCase =StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__ , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ )
__UpperCamelCase =pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing()
__UpperCamelCase =self.get_inputs()
pipe(**lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _a ( self ) -> int:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__UpperCamelCase ="stabilityai/stable-diffusion-2-base"
__UpperCamelCase =DDIMScheduler.from_pretrained(lowerCAmelCase__ , subfolder='scheduler' )
__UpperCamelCase =StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__ , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ )
__UpperCamelCase =pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__UpperCamelCase =self.get_inputs()
__UpperCamelCase =pipe(**lowerCAmelCase__ )
__UpperCamelCase =torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 363 |
from __future__ import annotations
from typing import Any
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list[Any] ):
create_state_space_tree(SCREAMING_SNAKE_CASE__ , [] , 0 )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list[Any] , SCREAMING_SNAKE_CASE__ : list[Any] , SCREAMING_SNAKE_CASE__ : int ):
if index == len(SCREAMING_SNAKE_CASE__ ):
print(SCREAMING_SNAKE_CASE__ )
return
create_state_space_tree(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
_A = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(['A', 'B', 'C'])
generate_all_subsequences(seq)
| 117 | 0 |
"""simple docstring"""
import numpy as np
from PIL import Image
def lowercase ( __snake_case : np.ndarray , __snake_case : int , __snake_case : int ):
lowercase_ : Tuple = np.array(__snake_case )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
lowercase_ : Dict = 0
lowercase_ : Any = 0
lowercase_ : List[str] = 0
lowercase_ : Union[str, Any] = 0
# compute the shape of the output matrix
lowercase_ : Optional[int] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
lowercase_ : Union[str, Any] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
lowercase_ : Tuple = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
lowercase_ : Any = 0
lowercase_ : Optional[Any] = 0
return updated_arr
def lowercase ( __snake_case : np.ndarray , __snake_case : int , __snake_case : int ):
lowercase_ : int = np.array(__snake_case )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
lowercase_ : int = 0
lowercase_ : Dict = 0
lowercase_ : Tuple = 0
lowercase_ : Tuple = 0
# compute the shape of the output matrix
lowercase_ : List[str] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
lowercase_ : Optional[int] = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
lowercase_ : str = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
lowercase_ : int = 0
lowercase_ : str = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name='''avgpooling''', verbose=True)
# Loading the image
__A : List[Any] = Image.open('''path_to_image''')
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 33 |
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Dict ):
"""simple docstring"""
snake_case_ = {} # Mapping from char to TrieNode
snake_case_ = False
def snake_case__ ( self : Dict , __lowercase : list[str] ):
"""simple docstring"""
for word in words:
self.insert(__lowercase )
def snake_case__ ( self : List[str] , __lowercase : str ):
"""simple docstring"""
snake_case_ = self
for char in word:
if char not in curr.nodes:
snake_case_ = TrieNode()
snake_case_ = curr.nodes[char]
snake_case_ = True
def snake_case__ ( self : List[Any] , __lowercase : str ):
"""simple docstring"""
snake_case_ = self
for char in word:
if char not in curr.nodes:
return False
snake_case_ = curr.nodes[char]
return curr.is_leaf
def snake_case__ ( self : Optional[Any] , __lowercase : str ):
"""simple docstring"""
def _delete(__lowercase : TrieNode , __lowercase : str , __lowercase : int ) -> bool:
if index == len(__lowercase ):
# If word does not exist
if not curr.is_leaf:
return False
snake_case_ = False
return len(curr.nodes ) == 0
snake_case_ = word[index]
snake_case_ = curr.nodes.get(__lowercase )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
snake_case_ = _delete(__lowercase , __lowercase , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , __lowercase , 0 )
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
if node.is_leaf:
print(_A , end=" " )
for key, value in node.nodes.items():
print_words(_A , word + key )
def lowerCamelCase__ ( ):
'''simple docstring'''
snake_case_ = "banana bananas bandana band apple all beast".split()
snake_case_ = TrieNode()
root.insert_many(_A )
# print_words(root, "")
assert all(root.find(_A ) for word in words )
assert root.find("banana" )
assert not root.find("bandanas" )
assert not root.find("apps" )
assert root.find("apple" )
assert root.find("all" )
root.delete("all" )
assert not root.find("all" )
root.delete("banana" )
assert not root.find("banana" )
assert root.find("bananas" )
return True
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
print(str(_A ) , "works!" if passes else "doesn't work :(" )
def lowerCamelCase__ ( ):
'''simple docstring'''
assert test_trie()
def lowerCamelCase__ ( ):
'''simple docstring'''
print_results("Testing trie functionality" , test_trie() )
if __name__ == "__main__":
main()
| 187 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowerCAmelCase__ :Optional[int] = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''
class __a ( unittest.TestCase , UpperCAmelCase ):
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = load_tool('text-question-answering' )
self.tool.setup()
_UpperCAmelCase = load_tool('text-question-answering' , remote=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = self.tool(_SCREAMING_SNAKE_CASE , 'What did Hugging Face do in April 2021?' )
self.assertEqual(_SCREAMING_SNAKE_CASE , 'launched the BigScience Research Workshop' )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.remote_tool(_SCREAMING_SNAKE_CASE , 'What did Hugging Face do in April 2021?' )
self.assertEqual(_SCREAMING_SNAKE_CASE , 'launched the BigScience Research Workshop' )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.tool(text=_SCREAMING_SNAKE_CASE , question='What did Hugging Face do in April 2021?' )
self.assertEqual(_SCREAMING_SNAKE_CASE , 'launched the BigScience Research Workshop' )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.remote_tool(text=_SCREAMING_SNAKE_CASE , question='What did Hugging Face do in April 2021?' )
self.assertEqual(_SCREAMING_SNAKE_CASE , 'launched the BigScience Research Workshop' )
| 185 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def lowerCAmelCase__ ( a__: Sequence[float] , a__: int , a__: int ) -> tuple[int | None, int | None, float]:
'''simple docstring'''
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
_UpperCAmelCase = (low + high) // 2
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_subarray(a__ , a__ , a__ )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_subarray(a__ , mid + 1 , a__ )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_cross_sum(a__ , a__ , a__ , a__ )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def lowerCAmelCase__ ( a__: Sequence[float] , a__: int , a__: int , a__: int ) -> tuple[int, int, float]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = float('-inf' ), -1
_UpperCAmelCase , _UpperCAmelCase = float('-inf' ), -1
_UpperCAmelCase = 0
for i in range(a__ , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
_UpperCAmelCase = summ
_UpperCAmelCase = i
_UpperCAmelCase = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
_UpperCAmelCase = summ
_UpperCAmelCase = i
return max_left, max_right, (left_sum + right_sum)
def lowerCAmelCase__ ( a__: int ) -> float:
'''simple docstring'''
_UpperCAmelCase = [randint(1 , a__ ) for _ in range(a__ )]
_UpperCAmelCase = time.time()
max_subarray(a__ , 0 , input_size - 1 )
_UpperCAmelCase = time.time()
return end - start
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
_UpperCAmelCase = [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
_UpperCAmelCase = [time_max_subarray(a__ ) for input_size in input_sizes]
print('No of Inputs\t\tTime Taken' )
for input_size, runtime in zip(a__ , a__ ):
print(a__ , '\t\t' , a__ )
plt.plot(a__ , a__ )
plt.xlabel('Number of Inputs' )
plt.ylabel('Time taken in seconds' )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 185 | 1 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : str = sorted(zip(UpperCAmelCase_ , UpperCAmelCase_ ) , key=lambda UpperCAmelCase_ : x[0] / x[1] , reverse=UpperCAmelCase_ )
UpperCAmelCase , UpperCAmelCase : List[str] = [i[0] for i in r], [i[1] for i in r]
UpperCAmelCase : Optional[int] = list(accumulate(UpperCAmelCase_ ) )
UpperCAmelCase : int = bisect(UpperCAmelCase_ , UpperCAmelCase_ )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 151 |
'''simple docstring'''
import string
from math import logaa
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = document.translate(
str.maketrans('' , '' , string.punctuation ) ).replace('\n' , '' )
UpperCAmelCase : Optional[Any] = document_without_punctuation.split(' ' ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : List[Any] = corpus.lower().translate(
str.maketrans('' , '' , string.punctuation ) ) # strip all punctuation and replace it with ''
UpperCAmelCase : Tuple = corpus_without_punctuation.split('\n' )
UpperCAmelCase : List[Any] = term.lower()
return (len([doc for doc in docs if term in doc] ), len(UpperCAmelCase_ ))
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=False ):
if smoothing:
if n == 0:
raise ValueError('log10(0) is undefined.' )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError('df must be > 0' )
elif n == 0:
raise ValueError('log10(0) is undefined.' )
return round(logaa(n / df ) , 3 )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return round(tf * idf , 3 )
| 151 | 1 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def a_ ( lowerCamelCase : int ):
lowerCAmelCase = torch.exp(lowerCamelCase )
lowerCAmelCase = torch.sum(lowerCamelCase , dim=1 ) # sum of exp(x_i)
lowerCAmelCase = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(lowerCamelCase ) - B / A
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : int , UpperCAmelCase__ : int ) -> str:
super().__init__()
lowerCAmelCase = config.output_attentions
lowerCAmelCase = config.output_hidden_states
lowerCAmelCase = nn.ModuleList([BertLayer(UpperCAmelCase__ ) for _ in range(config.num_hidden_layers )] )
lowerCAmelCase = nn.ModuleList([BertHighway(UpperCAmelCase__ ) for _ in range(config.num_hidden_layers )] )
lowerCAmelCase = [-1 for _ in range(config.num_hidden_layers )]
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : str ) -> int:
if (type(UpperCAmelCase__ ) is float) or (type(UpperCAmelCase__ ) is int):
for i in range(len(self.early_exit_entropy ) ):
lowerCAmelCase = x
else:
lowerCAmelCase = x
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : List[str] ) -> Optional[Any]:
lowerCAmelCase = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Optional[int]=None , ) -> str:
lowerCAmelCase = ()
lowerCAmelCase = ()
lowerCAmelCase = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
lowerCAmelCase = all_hidden_states + (hidden_states,)
lowerCAmelCase = layer_module(
UpperCAmelCase__ , UpperCAmelCase__ , head_mask[i] , UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = layer_outputs[0]
if self.output_attentions:
lowerCAmelCase = all_attentions + (layer_outputs[1],)
lowerCAmelCase = (hidden_states,)
if self.output_hidden_states:
lowerCAmelCase = current_outputs + (all_hidden_states,)
if self.output_attentions:
lowerCAmelCase = current_outputs + (all_attentions,)
lowerCAmelCase = self.highway[i](UpperCAmelCase__ )
# logits, pooled_output
if not self.training:
lowerCAmelCase = highway_exit[0]
lowerCAmelCase = entropy(UpperCAmelCase__ )
lowerCAmelCase = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
lowerCAmelCase = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
lowerCAmelCase = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(UpperCAmelCase__ , i + 1 )
else:
lowerCAmelCase = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
lowerCAmelCase = all_hidden_states + (hidden_states,)
lowerCAmelCase = (hidden_states,)
if self.output_hidden_states:
lowerCAmelCase = outputs + (all_hidden_states,)
if self.output_attentions:
lowerCAmelCase = outputs + (all_attentions,)
lowerCAmelCase = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
'''The Bert Model transformer with early exiting (DeeBERT). ''' , __lowercase , )
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Optional[int] ) -> str:
super().__init__(UpperCAmelCase__ )
lowerCAmelCase = config
lowerCAmelCase = BertEmbeddings(UpperCAmelCase__ )
lowerCAmelCase = DeeBertEncoder(UpperCAmelCase__ )
lowerCAmelCase = BertPooler(UpperCAmelCase__ )
self.init_weights()
def __UpperCAmelCase ( self : Any ) -> int:
self.encoder.init_highway_pooler(self.pooler )
def __UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
return self.embeddings.word_embeddings
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Dict ) -> List[Any]:
lowerCAmelCase = value
def __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : int ) -> Dict:
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(UpperCAmelCase__ )
@add_start_docstrings_to_model_forward(UpperCAmelCase__ )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : str=None , UpperCAmelCase__ : List[Any]=None , ) -> Optional[int]:
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
lowerCAmelCase = input_ids.size()
elif inputs_embeds is not None:
lowerCAmelCase = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
lowerCAmelCase = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowerCAmelCase = torch.ones(UpperCAmelCase__ , device=UpperCAmelCase__ )
if encoder_attention_mask is None:
lowerCAmelCase = torch.ones(UpperCAmelCase__ , device=UpperCAmelCase__ )
if token_type_ids is None:
lowerCAmelCase = torch.zeros(UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowerCAmelCase = self.get_extended_attention_mask(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
lowerCAmelCase = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
lowerCAmelCase = encoder_attention_mask[:, None, None, :]
lowerCAmelCase = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
lowerCAmelCase = (1.0 - encoder_extended_attention_mask) * -10_000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowerCAmelCase = self.get_head_mask(UpperCAmelCase__ , self.config.num_hidden_layers )
lowerCAmelCase = self.embeddings(
input_ids=UpperCAmelCase__ , position_ids=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , inputs_embeds=UpperCAmelCase__ )
lowerCAmelCase = self.encoder(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , head_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , )
lowerCAmelCase = encoder_outputs[0]
lowerCAmelCase = self.pooler(UpperCAmelCase__ )
lowerCAmelCase = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int ) -> Dict:
lowerCAmelCase = message
lowerCAmelCase = exit_layer # start from 1!
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : List[Any] , UpperCAmelCase__ : Optional[Any] ) -> List[str]:
super().__init__()
lowerCAmelCase = BertPooler(UpperCAmelCase__ )
lowerCAmelCase = nn.Dropout(config.hidden_dropout_prob )
lowerCAmelCase = nn.Linear(config.hidden_size , config.num_labels )
def __UpperCAmelCase ( self : str , UpperCAmelCase__ : Dict ) -> Optional[int]:
# Pooler
lowerCAmelCase = encoder_outputs[0]
lowerCAmelCase = self.pooler(UpperCAmelCase__ )
# "return" pooler_output
# BertModel
lowerCAmelCase = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
lowerCAmelCase = bmodel_output[1]
lowerCAmelCase = self.dropout(UpperCAmelCase__ )
lowerCAmelCase = self.classifier(UpperCAmelCase__ )
return logits, pooled_output
@add_start_docstrings(
'''Bert Model (with early exiting - DeeBERT) with a classifier on top,
also takes care of multi-layer training. ''' , __lowercase , )
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : Dict , UpperCAmelCase__ : Dict ) -> Any:
super().__init__(UpperCAmelCase__ )
lowerCAmelCase = config.num_labels
lowerCAmelCase = config.num_hidden_layers
lowerCAmelCase = DeeBertModel(UpperCAmelCase__ )
lowerCAmelCase = nn.Dropout(config.hidden_dropout_prob )
lowerCAmelCase = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(UpperCAmelCase__ )
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : str=None , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : List[Any]=-1 , UpperCAmelCase__ : Optional[Any]=False , ) -> Dict:
lowerCAmelCase = self.num_layers
try:
lowerCAmelCase = self.bert(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , position_ids=UpperCAmelCase__ , head_mask=UpperCAmelCase__ , inputs_embeds=UpperCAmelCase__ , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
lowerCAmelCase = outputs[1]
lowerCAmelCase = self.dropout(UpperCAmelCase__ )
lowerCAmelCase = self.classifier(UpperCAmelCase__ )
lowerCAmelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
lowerCAmelCase = e.message
lowerCAmelCase = e.exit_layer
lowerCAmelCase = outputs[0]
if not self.training:
lowerCAmelCase = entropy(UpperCAmelCase__ )
lowerCAmelCase = []
lowerCAmelCase = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
lowerCAmelCase = MSELoss()
lowerCAmelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
lowerCAmelCase = CrossEntropyLoss()
lowerCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
lowerCAmelCase = []
for highway_exit in outputs[-1]:
lowerCAmelCase = highway_exit[0]
if not self.training:
highway_logits_all.append(UpperCAmelCase__ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
lowerCAmelCase = MSELoss()
lowerCAmelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
lowerCAmelCase = CrossEntropyLoss()
lowerCAmelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(UpperCAmelCase__ )
if train_highway:
lowerCAmelCase = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
lowerCAmelCase = (loss,) + outputs
if not self.training:
lowerCAmelCase = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
lowerCAmelCase = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 55 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class UpperCAmelCase_ ( __lowercase , __lowercase , __lowercase , unittest.TestCase ):
lowerCamelCase : Any = StableUnCLIPPipeline
lowerCamelCase : int = TEXT_TO_IMAGE_PARAMS
lowerCamelCase : Any = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase : Optional[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase : List[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
lowerCamelCase : Optional[int] = False
def __UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
lowerCAmelCase = 3_2
lowerCAmelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
lowerCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
torch.manual_seed(0 )
lowerCAmelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=UpperCAmelCase__ , projection_dim=UpperCAmelCase__ , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
lowerCAmelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=1_2 , embedding_dim=UpperCAmelCase__ , num_layers=1 , )
torch.manual_seed(0 )
lowerCAmelCase = DDPMScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1_0_0_0 , clip_sample=UpperCAmelCase__ , clip_sample_range=5.0 , beta_schedule='squaredcos_cap_v2' , )
# regular denoising components
torch.manual_seed(0 )
lowerCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=UpperCAmelCase__ )
lowerCAmelCase = DDPMScheduler(beta_schedule='squaredcos_cap_v2' )
torch.manual_seed(0 )
lowerCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
torch.manual_seed(0 )
lowerCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=UpperCAmelCase__ , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
lowerCAmelCase = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type='projection' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=UpperCAmelCase__ , layers_per_block=1 , upcast_attention=UpperCAmelCase__ , use_linear_projection=UpperCAmelCase__ , )
torch.manual_seed(0 )
lowerCAmelCase = DDIMScheduler(
beta_schedule='scaled_linear' , beta_start=0.00_085 , beta_end=0.012 , prediction_type='v_prediction' , set_alpha_to_one=UpperCAmelCase__ , steps_offset=1 , )
torch.manual_seed(0 )
lowerCAmelCase = AutoencoderKL()
lowerCAmelCase = {
# prior components
'prior_tokenizer': prior_tokenizer,
'prior_text_encoder': prior_text_encoder,
'prior': prior,
'prior_scheduler': prior_scheduler,
# image noising components
'image_normalizer': image_normalizer,
'image_noising_scheduler': image_noising_scheduler,
# regular denoising components
'tokenizer': tokenizer,
'text_encoder': text_encoder,
'unet': unet,
'scheduler': scheduler,
'vae': vae,
}
return components
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[Any]=0 ) -> Optional[Any]:
if str(UpperCAmelCase__ ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(UpperCAmelCase__ )
else:
lowerCAmelCase = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
lowerCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'prior_num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def __UpperCAmelCase ( self : Dict ) -> Optional[Any]:
lowerCAmelCase = torch_device == 'cpu'
self._test_attention_slicing_forward_pass(test_max_difference=UpperCAmelCase__ )
def __UpperCAmelCase ( self : int ) -> Union[str, Any]:
lowerCAmelCase = torch_device in ['cpu', 'mps']
self._test_inference_batch_single_identical(test_max_difference=UpperCAmelCase__ )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
def __UpperCAmelCase ( self : Union[str, Any] ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
lowerCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy' )
lowerCAmelCase = StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l' , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowerCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCAmelCase = pipe('anime turle' , generator=UpperCAmelCase__ , output_type='np' )
lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(UpperCAmelCase__ , UpperCAmelCase__ )
def __UpperCAmelCase ( self : Any ) -> Optional[int]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCAmelCase = StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l' , torch_dtype=torch.floataa )
lowerCAmelCase = pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowerCAmelCase = pipe(
'anime turtle' , prior_num_inference_steps=2 , num_inference_steps=2 , output_type='np' , )
lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 55 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self, __a):
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["bs"], model_result["ss"]):
_lowerCAmelCase : Tuple = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = "sshleifer/tiny-gpt2"
_lowerCAmelCase : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], eager_mode=__a, multi_process=__a, )
_lowerCAmelCase : str = TensorFlowBenchmark(__a)
_lowerCAmelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = "sgugger/tiny-distilbert-classification"
_lowerCAmelCase : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], multi_process=__a, only_pretrain_model=__a, )
_lowerCAmelCase : Tuple = TensorFlowBenchmark(__a)
_lowerCAmelCase : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "sshleifer/tiny-gpt2"
_lowerCAmelCase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], multi_process=__a, )
_lowerCAmelCase : List[Any] = TensorFlowBenchmark(__a)
_lowerCAmelCase : Optional[int] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = "sshleifer/tiny-gpt2"
_lowerCAmelCase : Dict = AutoConfig.from_pretrained(__a)
_lowerCAmelCase : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], eager_mode=__a, multi_process=__a, )
_lowerCAmelCase : Any = TensorFlowBenchmark(__a, [config])
_lowerCAmelCase : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "sshleifer/tiny-gpt2"
_lowerCAmelCase : int = AutoConfig.from_pretrained(__a)
_lowerCAmelCase : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], multi_process=__a, )
_lowerCAmelCase : Any = TensorFlowBenchmark(__a, [config])
_lowerCAmelCase : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = "sshleifer/tiny-gpt2"
_lowerCAmelCase : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], multi_process=__a, )
_lowerCAmelCase : List[Any] = TensorFlowBenchmark(__a)
_lowerCAmelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = "sshleifer/tiny-gpt2"
_lowerCAmelCase : Optional[Any] = AutoConfig.from_pretrained(__a)
_lowerCAmelCase : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], multi_process=__a, )
_lowerCAmelCase : str = TensorFlowBenchmark(__a, [config])
_lowerCAmelCase : int = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = "patrickvonplaten/t5-tiny-random"
_lowerCAmelCase : List[str] = AutoConfig.from_pretrained(__a)
_lowerCAmelCase : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], multi_process=__a, )
_lowerCAmelCase : Optional[Any] = TensorFlowBenchmark(__a, configs=[config])
_lowerCAmelCase : str = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU")) == 0, "Cannot do xla on CPU.")
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = "sshleifer/tiny-gpt2"
_lowerCAmelCase : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID], training=__a, inference=__a, sequence_lengths=[8], batch_sizes=[1], use_xla=__a, multi_process=__a, )
_lowerCAmelCase : Tuple = TensorFlowBenchmark(__a)
_lowerCAmelCase : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID], inference=__a, save_to_csv=__a, sequence_lengths=[8], batch_sizes=[1], inference_time_csv_file=os.path.join(__a, "inf_time.csv"), inference_memory_csv_file=os.path.join(__a, "inf_mem.csv"), env_info_csv_file=os.path.join(__a, "env.csv"), multi_process=__a, )
_lowerCAmelCase : List[str] = TensorFlowBenchmark(__a)
benchmark.run()
self.assertTrue(Path(os.path.join(__a, "inf_time.csv")).exists())
self.assertTrue(Path(os.path.join(__a, "inf_mem.csv")).exists())
self.assertTrue(Path(os.path.join(__a, "env.csv")).exists())
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(__a):
self.assertTrue(hasattr(__a, "sequential"))
self.assertTrue(hasattr(__a, "cumulative"))
self.assertTrue(hasattr(__a, "current"))
self.assertTrue(hasattr(__a, "total"))
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID], inference=__a, sequence_lengths=[8], batch_sizes=[1], log_filename=os.path.join(__a, "log.txt"), log_print=__a, trace_memory_line_by_line=__a, eager_mode=__a, multi_process=__a, )
_lowerCAmelCase : List[Any] = TensorFlowBenchmark(__a)
_lowerCAmelCase : Tuple = benchmark.run()
_check_summary_is_not_empty(result.inference_summary)
self.assertTrue(Path(os.path.join(__a, "log.txt")).exists())
| 36 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
_snake_case = get_tests_dir("fixtures")
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = mock.Mock()
_lowerCAmelCase : int = 500
_lowerCAmelCase : Tuple = {}
_lowerCAmelCase : str = HTTPError
_lowerCAmelCase : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=__a) as mock_head:
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")
def snake_case__ ( self):
'''simple docstring'''
with self.assertRaises(__a):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase : int = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
_lowerCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor")
self.assertIsNotNone(__a)
@is_staging_test
class UpperCAmelCase_ ( unittest.TestCase):
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = TOKEN
HfFolder.save_token(__a)
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : str = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="test-image-processor", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="valid_org/test-image-processor-org", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_lowerCAmelCase : List[str] = CustomImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map, {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"}, )
_lowerCAmelCase : Tuple = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=__a)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
| 36 | 1 |
"""simple docstring"""
import operator as op
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = []
UpperCAmelCase_ = lambda lowerCAmelCase__ , lowerCAmelCase__ : int(x / y ) # noqa: E731 integer division operation
UpperCAmelCase_ = {
"^": op.pow,
"*": op.mul,
"/": div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8 ) , "Action".center(12 ) , "Stack" , sep=" | " )
print("-" * (30 + 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(12 ) , ",".join(lowerCAmelCase__ ) , sep=" | " )
else:
UpperCAmelCase_ = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + b + ")").ljust(12 ) , ",".join(lowerCAmelCase__ ) , sep=" | " )
UpperCAmelCase_ = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + a + ")").ljust(12 ) , ",".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(12 ) , ",".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))
| 241 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def lowercase__ ( self : Tuple ) -> Any:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase__ ( self : List[str] ) -> int:
'''simple docstring'''
UpperCAmelCase_ = ort.SessionOptions()
UpperCAmelCase_ = False
return options
def lowercase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
UpperCAmelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy" )
# using the PNDM scheduler by default
UpperCAmelCase_ = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase_ = "A red cat sitting on a park bench"
UpperCAmelCase_ = np.random.RandomState(0 )
UpperCAmelCase_ = pipe(
prompt=_UpperCAmelCase , image=_UpperCAmelCase , mask_image=_UpperCAmelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=15 , generator=_UpperCAmelCase , output_type="np" , )
UpperCAmelCase_ = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1e-2
| 241 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : List[Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizer
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizerFast
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
def A ( self : int ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCamelCase = XLMRobertaTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def A ( self : Dict ):
"""simple docstring"""
UpperCamelCase = '''<pad>'''
UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 1_0_0_2 )
def A ( self : Optional[Any] ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = XLMRobertaTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
UpperCamelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
UpperCamelCase = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
UpperCamelCase = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def A ( self : Optional[Any] ):
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCamelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
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(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
UpperCamelCase = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
UpperCamelCase = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
UpperCamelCase = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
UpperCamelCase = tempfile.mkdtemp()
UpperCamelCase = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCamelCase = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@cached_property
def A ( self : str ):
"""simple docstring"""
return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base' )
def A ( self : List[str] ):
"""simple docstring"""
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SCREAMING_SNAKE_CASE_ , f.name )
UpperCamelCase = XLMRobertaTokenizer(f.name , keep_accents=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = pickle.dumps(SCREAMING_SNAKE_CASE_ )
pickle.loads(SCREAMING_SNAKE_CASE_ )
def A ( self : Union[str, Any] ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = '''I was born in 92000, and this is falsé.'''
UpperCamelCase = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = '''Hello World!'''
UpperCamelCase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE_ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_ ) )
@slow
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
UpperCamelCase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE_ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_ ) )
@slow
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=SCREAMING_SNAKE_CASE_ , model_name='xlm-roberta-base' , revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' , )
| 28 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
UpperCamelCase = logging.getLogger(__name__)
UpperCamelCase = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCamelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCAmelCase_ :
'''simple docstring'''
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCAmelCase_ )} , )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
UpperCamelCase_ : bool = field(
default=UpperCAmelCase_ , 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=UpperCAmelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def _snake_case ( self : Tuple ) -> List[Any]:
'''simple docstring'''
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
'''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path''' )
@dataclass
class lowerCAmelCase_ :
'''simple docstring'''
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
UpperCamelCase_ : Optional[str] = field(default=UpperCAmelCase_ , metadata={"""help""": """The input training data file (a text file)."""} )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""} , )
UpperCamelCase_ : Optional[str] = field(
default=UpperCAmelCase_ , metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""} , )
UpperCamelCase_ : bool = field(
default=UpperCAmelCase_ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
UpperCamelCase_ : Optional[int] = field(
default=5 , metadata={
"""help""": """The percentage of the train set used as validation set in case there's no validation split"""
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCAmelCase_ , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated. Default to the max input length of the model."""
)
} , )
UpperCamelCase_ : Optional[int] = field(
default=UpperCAmelCase_ , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
UpperCamelCase_ : float = field(
default=0.15 , metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""} )
UpperCamelCase_ : bool = field(
default=UpperCAmelCase_ , 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."""
)
} , )
def _snake_case ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
if self.train_file is not None:
A: Tuple = self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
A: str = self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> List[str]:
with open(__lowercase , '''r''' , encoding='''utf-8''' ) as f:
A: List[Any] = [json.loads(__lowercase ) for line in f.read().splitlines() if (len(__lowercase ) > 0 and not line.isspace())]
assert len(__lowercase ) == len(__lowercase )
A: Optional[int] = {c: dataset[c] for c in dataset.column_names}
A: Union[str, Any] = refs
return Dataset.from_dict(__lowercase )
def SCREAMING_SNAKE_CASE( ) -> int:
# 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.
A: int = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
A , A , A: Optional[int] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
A , A , A: List[Any] = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
A: Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A: Any = 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:
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.''' )
# 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 )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}""" )
# 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''' , __lowercase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. 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.
A: Dict = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
A: int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"""train[:{data_args.validation_split_percentage}%]""" , )
A: Dict = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"""train[{data_args.validation_split_percentage}%:]""" , )
else:
A: Any = {}
if data_args.train_file is not None:
A: int = data_args.train_file
if data_args.validation_file is not None:
A: Optional[int] = data_args.validation_file
A: List[str] = data_args.train_file.split('''.''' )[-1]
if extension == "txt":
A: int = '''text'''
A: Any = load_dataset(__lowercase , data_files=__lowercase )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A: Dict = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name:
A: List[Any] = AutoConfig.from_pretrained(model_args.config_name , **__lowercase )
elif model_args.model_name_or_path:
A: int = AutoConfig.from_pretrained(model_args.model_name_or_path , **__lowercase )
else:
A: str = CONFIG_MAPPING[model_args.model_type]()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
A: Tuple = {
'''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,
}
if model_args.tokenizer_name:
A: Optional[int] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **__lowercase )
elif model_args.model_name_or_path:
A: Union[str, Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **__lowercase )
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.''' )
if model_args.model_name_or_path:
A: List[Any] = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
A: List[Any] = AutoModelForMaskedLM.from_config(__lowercase )
model.resize_token_embeddings(len(__lowercase ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
A: int = datasets['''train'''].column_names
else:
A: str = datasets['''validation'''].column_names
A: Tuple = '''text''' if '''text''' in column_names else column_names[0]
A: List[str] = '''max_length''' if data_args.pad_to_max_length else False
def tokenize_function(__lowercase ):
# Remove empty lines
A: int = [line for line in examples['''text'''] if len(__lowercase ) > 0 and not line.isspace()]
return tokenizer(examples['''text'''] , padding=__lowercase , truncation=__lowercase , max_length=data_args.max_seq_length )
A: str = datasets.map(
__lowercase , batched=__lowercase , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
A: List[str] = add_chinese_references(tokenized_datasets['''train'''] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
A: Dict = add_chinese_references(
tokenized_datasets['''validation'''] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
A: Optional[Any] = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
A: List[Any] = False
# Data collator
# This one will take care of randomly masking the tokens.
A: Optional[Any] = DataCollatorForWholeWordMask(tokenizer=__lowercase , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
A: Optional[int] = Trainer(
model=__lowercase , args=__lowercase , train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None , eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None , tokenizer=__lowercase , data_collator=__lowercase , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
A: Optional[int] = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
A: str = model_args.model_name_or_path
else:
A: List[str] = None
A: str = trainer.train(resume_from_checkpoint=__lowercase )
trainer.save_model() # Saves the tokenizer too for easy upload
A: Union[str, Any] = os.path.join(training_args.output_dir , '''train_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowercase , '''w''' ) as writer:
logger.info('''***** Train results *****''' )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , '''trainer_state.json''' ) )
# Evaluation
A: Optional[int] = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
A: Optional[Any] = trainer.evaluate()
A: Union[str, Any] = math.exp(eval_output['''eval_loss'''] )
A: Dict = perplexity
A: Any = os.path.join(training_args.output_dir , '''eval_results_mlm_wwm.txt''' )
if trainer.is_world_process_zero():
with open(__lowercase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in sorted(results.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
return results
def SCREAMING_SNAKE_CASE( __lowercase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 319 | 0 |
"""simple docstring"""
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 UpperCAmelCase_ :
def __init__( self : Dict , A : List[str] , A : Optional[int]=1_3 , A : List[str]=3_0 , A : int=2 , A : Tuple=3 , A : Optional[int]=True , A : Optional[int]=True , A : Optional[Any]=3_2 , A : Tuple=5 , A : Tuple=4 , A : Tuple=3_7 , A : Optional[int]="gelu" , A : Optional[int]=0.1 , A : Optional[int]=0.1 , A : Tuple=1_0 , A : List[str]=0.02 , A : Union[str, Any]=None , ):
_UpperCAmelCase : str = parent
_UpperCAmelCase : str = batch_size
_UpperCAmelCase : Dict = image_size
_UpperCAmelCase : int = patch_size
_UpperCAmelCase : Any = num_channels
_UpperCAmelCase : Optional[Any] = is_training
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = hidden_size
_UpperCAmelCase : Optional[int] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : Optional[int] = hidden_act
_UpperCAmelCase : Dict = hidden_dropout_prob
_UpperCAmelCase : Dict = attention_probs_dropout_prob
_UpperCAmelCase : Tuple = type_sequence_label_size
_UpperCAmelCase : Optional[int] = initializer_range
_UpperCAmelCase : List[str] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase : str = (image_size // patch_size) ** 2
_UpperCAmelCase : Tuple = num_patches + 1
def snake_case_ ( self : Any ):
_UpperCAmelCase : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : int = None
if self.use_labels:
_UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Optional[int] = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self : Optional[int] ):
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 snake_case_ ( self : int , A : int , A : Any , A : List[Any] ):
_UpperCAmelCase : List[str] = ViTMSNModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase : Tuple = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self : Optional[int] , A : str , A : str , A : List[Any] ):
_UpperCAmelCase : int = self.type_sequence_label_size
_UpperCAmelCase : Optional[int] = ViTMSNForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase : Optional[Any] = model(__lowerCAmelCase , labels=__lowerCAmelCase )
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
_UpperCAmelCase : Union[str, Any] = 1
_UpperCAmelCase : int = ViTMSNForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_UpperCAmelCase : List[Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[str] = config_and_inputs
_UpperCAmelCase : List[str] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : int = (
{'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : Optional[Any] = False
__SCREAMING_SNAKE_CASE : int = False
__SCREAMING_SNAKE_CASE : List[str] = False
__SCREAMING_SNAKE_CASE : int = False
def snake_case_ ( self : str ):
_UpperCAmelCase : Optional[int] = ViTMSNModelTester(self )
_UpperCAmelCase : Any = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=3_7 )
def snake_case_ ( self : Any ):
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMSN does not use inputs_embeds" )
def snake_case_ ( self : List[Any] ):
pass
def snake_case_ ( self : str ):
_UpperCAmelCase , _UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Union[str, Any] = model_class(__lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_UpperCAmelCase : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowerCAmelCase , nn.Linear ) )
def snake_case_ ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Dict = model_class(__lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : Any = [*signature.parameters.keys()]
_UpperCAmelCase : int = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def snake_case_ ( self : int ):
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def snake_case_ ( self : Optional[Any] ):
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : int = ViTMSNModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def __snake_case ( ) -> int:
'''simple docstring'''
_UpperCAmelCase : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
@cached_property
def snake_case_ ( self : str ):
return ViTImageProcessor.from_pretrained("facebook/vit-msn-small" ) if is_vision_available() else None
@slow
def snake_case_ ( self : str ):
torch.manual_seed(2 )
_UpperCAmelCase : Tuple = ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small" ).to(__lowerCAmelCase )
_UpperCAmelCase : str = self.default_image_processor
_UpperCAmelCase : str = prepare_img()
_UpperCAmelCase : List[str] = image_processor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(**__lowerCAmelCase )
# verify the logits
_UpperCAmelCase : int = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
_UpperCAmelCase : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1e-4 ) )
| 358 |
"""simple docstring"""
# 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 re
from ..utils import cached_file
# docstyle-ignore
_lowerCAmelCase : Any = "\nHuman: <<task>>\n\nAssistant: "
_lowerCAmelCase : str = "huggingface-tools/default-prompts"
_lowerCAmelCase : Union[str, Any] = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def __snake_case ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : int="run" ) -> int:
'''simple docstring'''
if prompt_or_repo_id is None:
_UpperCAmelCase : Optional[int] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , SCREAMING_SNAKE_CASE__ ) is not None:
return prompt_or_repo_id
_UpperCAmelCase : Dict = cached_file(
SCREAMING_SNAKE_CASE__ , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(SCREAMING_SNAKE_CASE__ , "r" , encoding="utf-8" ) as f:
return f.read()
| 202 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( lowercase__ , lowercase__ ):
snake_case__ : Optional[int] = '''bit'''
snake_case__ : Optional[Any] = ['''preactivation''', '''bottleneck''']
snake_case__ : Tuple = ['''SAME''', '''VALID''']
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any]=3 , SCREAMING_SNAKE_CASE__ : Optional[int]=6_4 , SCREAMING_SNAKE_CASE__ : Optional[int]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , SCREAMING_SNAKE_CASE__ : Optional[Any]=[3, 4, 6, 3] , SCREAMING_SNAKE_CASE__ : Optional[Any]="preactivation" , SCREAMING_SNAKE_CASE__ : Tuple="relu" , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Dict=3_2 , SCREAMING_SNAKE_CASE__ : Tuple=0.0 , SCREAMING_SNAKE_CASE__ : Any=False , SCREAMING_SNAKE_CASE__ : Tuple=3_2 , SCREAMING_SNAKE_CASE__ : str=1 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , **SCREAMING_SNAKE_CASE__ : str , ) -> str:
super().__init__(**SCREAMING_SNAKE_CASE__ )
if layer_type not in self.layer_types:
raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types )}""" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
a_ : Any = global_padding.upper()
else:
raise ValueError(F"""Padding strategy {global_padding} not supported""" )
a_ : Optional[Any] = num_channels
a_ : List[Any] = embedding_size
a_ : Union[str, Any] = hidden_sizes
a_ : List[str] = depths
a_ : Any = layer_type
a_ : Optional[int] = hidden_act
a_ : Tuple = global_padding
a_ : List[Any] = num_groups
a_ : List[str] = drop_path_rate
a_ : List[Any] = embedding_dynamic_padding
a_ : int = output_stride
a_ : str = width_factor
a_ : Dict = ['stem'] + [F"""stage{idx}""" for idx in range(1 , len(SCREAMING_SNAKE_CASE__ ) + 1 )]
a_ , a_ : List[Any] = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE__ , out_indices=SCREAMING_SNAKE_CASE__ , stage_names=self.stage_names )
| 32 |
from __future__ import annotations
lowerCamelCase__ = """#"""
class SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] ):
'''simple docstring'''
__a = {}
def UpperCamelCase_ ( self : Optional[Any] , __lowercase : str ):
'''simple docstring'''
__a = self._trie
for char in text:
if char not in trie:
__a = {}
__a = trie[char]
__a = True
def UpperCamelCase_ ( self : Tuple , __lowercase : str ):
'''simple docstring'''
__a = self._trie
for char in prefix:
if char in trie:
__a = trie[char]
else:
return []
return self._elements(__lowercase )
def UpperCamelCase_ ( self : Optional[int] , __lowercase : dict ):
'''simple docstring'''
__a = []
for c, v in d.items():
__a = [""" """] if c == END else [(c + s) for s in self._elements(__lowercase )]
result.extend(__lowercase )
return tuple(__lowercase )
lowerCamelCase__ = Trie()
lowerCamelCase__ = ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
trie.insert_word(word)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
__a = trie.find_word(_SCREAMING_SNAKE_CASE )
return tuple(string + word for word in suffixes )
def lowerCAmelCase__ ( ):
"""simple docstring"""
print(autocomplete_using_trie("""de""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 302 | 0 |
"""simple docstring"""
from math import ceil, sqrt
def lowercase ( a__ : int = 1000000 ) -> int:
_UpperCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_UpperCamelCase = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
_UpperCamelCase = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 356 | """simple docstring"""
from __future__ import annotations
import math
def lowercase ( a__ : int ) -> list[int]:
if num <= 0:
_UpperCamelCase = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(a__ )
_UpperCamelCase = [True] * (num + 1)
_UpperCamelCase = []
_UpperCamelCase = 2
_UpperCamelCase = int(math.sqrt(a__ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(a__ )
# Set multiples of start be False
for i in range(start * start , num + 1 , a__ ):
if sieve[i] is True:
_UpperCamelCase = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(a__ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 54 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BigBirdPegasusForCausalLM''',
'''BigBirdPegasusForConditionalGeneration''',
'''BigBirdPegasusForQuestionAnswering''',
'''BigBirdPegasusForSequenceClassification''',
'''BigBirdPegasusModel''',
'''BigBirdPegasusPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = checkpoints.load_tax_checkpoint(_A )
lowerCAmelCase_ = flatten_dict(_A )
return flax_params
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = {}
lowerCAmelCase_ = {
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCAmelCase_ = {
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCAmelCase_ = '''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCAmelCase_ = new_key.replace(_A , _A )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCAmelCase_ = new_key.replace(_A , _A )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCAmelCase_ = re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , _A )
lowerCAmelCase_ = new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCAmelCase_ = re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , _A )
lowerCAmelCase_ = flax_dict[key]
lowerCAmelCase_ = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCAmelCase_ = torch.from_numpy(converted_dict[key].T )
else:
lowerCAmelCase_ = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def __UpperCamelCase ( _A , _A , _A=False , _A=False ):
lowerCAmelCase_ = get_flax_param(_A )
if not use_large:
lowerCAmelCase_ = PixaStructVisionConfig()
lowerCAmelCase_ = PixaStructTextConfig()
else:
lowerCAmelCase_ = PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowerCAmelCase_ = PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowerCAmelCase_ = PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=_A )
lowerCAmelCase_ = PixaStructForConditionalGeneration(_A )
lowerCAmelCase_ = rename_and_convert_flax_params(_A )
model.load_state_dict(_A )
lowerCAmelCase_ = AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCAmelCase_ = PixaStructImageProcessor()
lowerCAmelCase_ = PixaStructProcessor(image_processor=_A , tokenizer=_A )
if use_large:
lowerCAmelCase_ = 4096
lowerCAmelCase_ = True
# mkdir if needed
os.makedirs(_A , exist_ok=_A )
model.save_pretrained(_A )
processor.save_pretrained(_A )
print('''Model saved in {}'''.format(_A ) )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
_A = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 278 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
"""simple docstring"""
_a = ViTImageProcessor if is_vision_available() else None
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :str = (3, 32, 128)
UpperCamelCase__ :Dict = tempfile.mkdtemp()
# fmt: off
UpperCamelCase__ :List[Any] = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
UpperCamelCase__ :Any = dict(zip(UpperCamelCase_ , range(len(UpperCamelCase_ ) ) ) )
UpperCamelCase__ :Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(UpperCamelCase_ ) + '''\n''' )
UpperCamelCase__ :Tuple = {
'''do_normalize''': False,
'''do_resize''': True,
'''image_processor_type''': '''ViTImageProcessor''',
'''resample''': 3,
'''size''': {'''height''': 32, '''width''': 128},
}
UpperCamelCase__ :Any = os.path.join(self.tmpdirname , UpperCamelCase_ )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self , **UpperCamelCase_ ):
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def lowerCAmelCase__ ( self , **UpperCamelCase_ ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
UpperCamelCase__ :List[Any] = Image.fromarray(np.moveaxis(UpperCamelCase_ , 0 , -1 ) )
return image_input
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Tuple = self.get_tokenizer()
UpperCamelCase__ :str = self.get_image_processor()
UpperCamelCase__ :Dict = MgpstrProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase__ :int = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCamelCase_ )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , UpperCamelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = self.get_tokenizer()
UpperCamelCase__ :List[str] = self.get_image_processor()
UpperCamelCase__ :int = MgpstrProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase__ :Any = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
UpperCamelCase__ :List[Any] = self.get_image_processor(do_normalize=UpperCamelCase_ , padding_value=1.0 )
UpperCamelCase__ :Union[str, Any] = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=UpperCamelCase_ , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , UpperCamelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :int = self.get_image_processor()
UpperCamelCase__ :Tuple = self.get_tokenizer()
UpperCamelCase__ :List[str] = MgpstrProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
UpperCamelCase__ :List[str] = self.prepare_image_inputs()
UpperCamelCase__ :List[Any] = image_processor(UpperCamelCase_ , return_tensors='''np''' )
UpperCamelCase__ :str = processor(images=UpperCamelCase_ , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :int = self.get_image_processor()
UpperCamelCase__ :List[Any] = self.get_tokenizer()
UpperCamelCase__ :List[Any] = MgpstrProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
UpperCamelCase__ :Any = '''test'''
UpperCamelCase__ :Dict = processor(text=UpperCamelCase_ )
UpperCamelCase__ :List[Any] = tokenizer(UpperCamelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Tuple = self.get_image_processor()
UpperCamelCase__ :Optional[Any] = self.get_tokenizer()
UpperCamelCase__ :Union[str, Any] = MgpstrProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
UpperCamelCase__ :Optional[Any] = '''test'''
UpperCamelCase__ :Tuple = self.prepare_image_inputs()
UpperCamelCase__ :List[str] = processor(text=UpperCamelCase_ , images=UpperCamelCase_ )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''labels'''] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase_ ):
processor()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.get_image_processor()
UpperCamelCase__ :Tuple = self.get_tokenizer()
UpperCamelCase__ :Tuple = MgpstrProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
UpperCamelCase__ :Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase__ :int = processor.char_decode(UpperCamelCase_ )
UpperCamelCase__ :List[str] = tokenizer.batch_decode(UpperCamelCase_ )
UpperCamelCase__ :Tuple = [seq.replace(''' ''' , '''''' ) for seq in decoded_tok]
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = self.get_image_processor()
UpperCamelCase__ :str = self.get_tokenizer()
UpperCamelCase__ :Optional[int] = MgpstrProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
UpperCamelCase__ :List[str] = None
UpperCamelCase__ :Any = self.prepare_image_inputs()
UpperCamelCase__ :int = processor(text=UpperCamelCase_ , images=UpperCamelCase_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = self.get_image_processor()
UpperCamelCase__ :str = self.get_tokenizer()
UpperCamelCase__ :Dict = MgpstrProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
UpperCamelCase__ :Dict = torch.randn(1 , 27 , 38 )
UpperCamelCase__ :Optional[Any] = torch.randn(1 , 27 , 50257 )
UpperCamelCase__ :Dict = torch.randn(1 , 27 , 30522 )
UpperCamelCase__ :Tuple = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds'''] ) | 219 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def a ( __a ) -> None:
'''simple docstring'''
create_state_space_tree(__a , [] , 0 )
def a ( __a , __a , __a ) -> None:
'''simple docstring'''
if index == len(__a ):
print(__a )
return
create_state_space_tree(__a , __a , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(__a , __a , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
__snake_case = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(['''A''', '''B''', '''C'''])
generate_all_subsequences(seq) | 219 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
class __lowerCAmelCase ( a__ ):
"""simple docstring"""
def __init__( self : Optional[Any] , _snake_case : Dict=None , **_snake_case : List[str] ):
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , _snake_case , )
super().__init__(args=_snake_case , **_snake_case )
| 156 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: Dict ) -> List[str]:
'''simple docstring'''
__lowerCamelCase : Tuple = 1
__lowerCamelCase : int = 2
while i * i <= n:
__lowerCamelCase : Tuple = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def lowercase_ ( ) -> str:
'''simple docstring'''
__lowerCamelCase : List[str] = 1
__lowerCamelCase : Dict = 1
while True:
i += 1
t_num += i
if count_divisors(_lowerCamelCase ) > 500:
break
return t_num
if __name__ == "__main__":
print(solution()) | 135 | 0 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int = 10_00 ) -> int:
'''simple docstring'''
_a , _a = 1, 1
_a = []
for i in range(1 , n + 1 ):
_a = prev_numerator + 2 * prev_denominator
_a = prev_numerator + prev_denominator
if len(str(lowerCAmelCase__ ) ) > len(str(lowerCAmelCase__ ) ):
result.append(lowerCAmelCase__ )
_a = numerator
_a = denominator
return len(lowerCAmelCase__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 104 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
a_ : Tuple = get_tests_dir("fixtures")
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> str:
# A mock response for an HTTP head request to emulate server down
_a = mock.Mock()
_a = 5_00
_a = {}
_a = HTTPError
_a = {}
# Download this model to make sure it's in the cache.
_a = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__magic_name__ ) as mock_head:
_a = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit' )
# This check we did call the fake head request
mock_head.assert_called()
def __UpperCAmelCase ( self ) -> Optional[int]:
# This test is for deprecated behavior and can be removed in v5
_a = ViTImageProcessor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json' )
def __UpperCAmelCase ( self ) -> Dict:
with self.assertRaises(__magic_name__ ):
# config is in subfolder, the following should not work without specifying the subfolder
_a = AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants' )
_a = AutoImageProcessor.from_pretrained(
'hf-internal-testing/stable-diffusion-all-variants' , subfolder='feature_extractor' )
self.assertIsNotNone(__magic_name__ )
@is_staging_test
class a ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls ) -> Dict:
_a = TOKEN
HfFolder.save_token(__magic_name__ )
@classmethod
def __UpperCAmelCase ( cls ) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id='test-image-processor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-image-processor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-image-processor' )
except HTTPError:
pass
def __UpperCAmelCase ( self ) -> str:
_a = ViTImageProcessor.from_pretrained(__magic_name__ )
image_processor.push_to_hub('test-image-processor' , use_auth_token=self._token )
_a = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__magic_name__ , getattr(__magic_name__ , __magic_name__ ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-image-processor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__magic_name__ , repo_id='test-image-processor' , push_to_hub=__magic_name__ , use_auth_token=self._token )
_a = ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__magic_name__ , getattr(__magic_name__ , __magic_name__ ) )
def __UpperCAmelCase ( self ) -> Optional[Any]:
_a = ViTImageProcessor.from_pretrained(__magic_name__ )
image_processor.push_to_hub('valid_org/test-image-processor' , use_auth_token=self._token )
_a = ViTImageProcessor.from_pretrained('valid_org/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__magic_name__ , getattr(__magic_name__ , __magic_name__ ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-image-processor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__magic_name__ , repo_id='valid_org/test-image-processor-org' , push_to_hub=__magic_name__ , use_auth_token=self._token )
_a = ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__magic_name__ , getattr(__magic_name__ , __magic_name__ ) )
def __UpperCAmelCase ( self ) -> Any:
CustomImageProcessor.register_for_auto_class()
_a = CustomImageProcessor.from_pretrained(__magic_name__ )
image_processor.push_to_hub('test-dynamic-image-processor' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'} , )
_a = AutoImageProcessor.from_pretrained(
f'{USER}/test-dynamic-image-processor' , trust_remote_code=__magic_name__ )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , 'CustomImageProcessor' )
| 104 | 1 |
"""simple docstring"""
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
__snake_case = logging.get_logger(__name__)
# General docstring
__snake_case = '''MobileNetV1Config'''
# Base docstring
__snake_case = '''google/mobilenet_v1_1.0_224'''
__snake_case = [1, 1024, 7, 7]
# Image classification docstring
__snake_case = '''google/mobilenet_v1_1.0_224'''
__snake_case = '''tabby, tabby cat'''
__snake_case = [
'''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 A_ ( _lowerCAmelCase : Dict, _lowerCAmelCase : Tuple, _lowerCAmelCase : str=None ):
"""simple docstring"""
_a = {}
if isinstance(lowercase__, lowercase__ ):
_a = model.mobilenet_va
else:
_a = model
_a = "MobilenetV1/Conv2d_0/"
_a = backbone.conv_stem.convolution.weight
_a = backbone.conv_stem.normalization.bias
_a = backbone.conv_stem.normalization.weight
_a = backbone.conv_stem.normalization.running_mean
_a = backbone.conv_stem.normalization.running_var
for i in range(13 ):
_a = i + 1
_a = i * 2
_a = backbone.layer[pt_index]
_a = f'MobilenetV1/Conv2d_{tf_index}_depthwise/'
_a = pointer.convolution.weight
_a = pointer.normalization.bias
_a = pointer.normalization.weight
_a = pointer.normalization.running_mean
_a = pointer.normalization.running_var
_a = backbone.layer[pt_index + 1]
_a = f'MobilenetV1/Conv2d_{tf_index}_pointwise/'
_a = pointer.convolution.weight
_a = pointer.normalization.bias
_a = pointer.normalization.weight
_a = pointer.normalization.running_mean
_a = pointer.normalization.running_var
if isinstance(lowercase__, lowercase__ ):
_a = "MobilenetV1/Logits/Conv2d_1c_1x1/"
_a = model.classifier.weight
_a = model.classifier.bias
return tf_to_pt_map
def A_ ( _lowerCAmelCase : List[Any], _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
'''https://www.tensorflow.org/install/ for installation instructions.''' )
raise
# Load weights from TF model
_a = tf.train.list_variables(lowercase__ )
_a = {}
for name, shape in init_vars:
logger.info(f'Loading TF weight {name} with shape {shape}' )
_a = tf.train.load_variable(lowercase__, lowercase__ )
_a = array
# Build TF to PyTorch weights loading map
_a = _build_tf_to_pytorch_map(lowercase__, lowercase__, lowercase__ )
for name, pointer in tf_to_pt_map.items():
logger.info(f'Importing {name}' )
if name not in tf_weights:
logger.info(f'{name} not in tf pre-trained weights, skipping' )
continue
_a = tf_weights[name]
if "depthwise_weights" in name:
logger.info('''Transposing depthwise''' )
_a = np.transpose(lowercase__, (2, 3, 0, 1) )
elif "weights" in name:
logger.info('''Transposing''' )
if len(pointer.shape ) == 2: # copying into linear layer
_a = array.squeeze().transpose()
else:
_a = np.transpose(lowercase__, (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f'Pointer shape {pointer.shape} and array shape {array.shape} mismatched' )
logger.info(f'Initialize PyTorch weight {name} {array.shape}' )
_a = torch.from_numpy(lowercase__ )
tf_weights.pop(lowercase__, lowercase__ )
tf_weights.pop(name + '''/RMSProp''', lowercase__ )
tf_weights.pop(name + '''/RMSProp_1''', lowercase__ )
tf_weights.pop(name + '''/ExponentialMovingAverage''', lowercase__ )
logger.info(f'Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}' )
return model
def A_ ( _lowerCAmelCase : torch.Tensor, _lowerCAmelCase : nn.Convad ):
"""simple docstring"""
_a = features.shape[-2:]
_a = conv_layer.stride
_a = conv_layer.kernel_size
if in_height % stride_height == 0:
_a = max(kernel_height - stride_height, 0 )
else:
_a = max(kernel_height - (in_height % stride_height), 0 )
if in_width % stride_width == 0:
_a = max(kernel_width - stride_width, 0 )
else:
_a = max(kernel_width - (in_width % stride_width), 0 )
_a = pad_along_width // 2
_a = pad_along_width - pad_left
_a = pad_along_height // 2
_a = pad_along_height - pad_top
_a = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(lowercase__, lowercase__, '''constant''', 0.0 )
class __lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 1 , __UpperCAmelCase = 1 , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = True , ) -> int:
super().__init__()
_a = config
if in_channels % groups != 0:
raise ValueError(F'Input channels ({in_channels}) are not divisible by {groups} groups.' )
if out_channels % groups != 0:
raise ValueError(F'Output channels ({out_channels}) are not divisible by {groups} groups.' )
_a = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
_a = nn.Convad(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , kernel_size=__UpperCAmelCase , stride=__UpperCAmelCase , padding=__UpperCAmelCase , groups=__UpperCAmelCase , bias=__UpperCAmelCase , padding_mode='''zeros''' , )
if use_normalization:
_a = nn.BatchNormad(
num_features=__UpperCAmelCase , eps=config.layer_norm_eps , momentum=0.9997 , affine=__UpperCAmelCase , track_running_stats=__UpperCAmelCase , )
else:
_a = None
if use_activation:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
_a = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __UpperCAmelCase ):
_a = ACTaFN[config.hidden_act]
else:
_a = config.hidden_act
else:
_a = None
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Dict:
if self.config.tf_padding:
_a = apply_tf_padding(__UpperCAmelCase , self.convolution )
_a = self.convolution(__UpperCAmelCase )
if self.normalization is not None:
_a = self.normalization(__UpperCAmelCase )
if self.activation is not None:
_a = self.activation(__UpperCAmelCase )
return features
class __lowerCamelCase ( lowerCamelCase_ ):
'''simple docstring'''
A_ : Any = MobileNetVaConfig
A_ : Any = load_tf_weights_in_mobilenet_va
A_ : int = """mobilenet_v1"""
A_ : int = """pixel_values"""
A_ : int = False
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> List[str]:
if isinstance(__UpperCAmelCase , (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(__UpperCAmelCase , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__snake_case = 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'''
__snake_case = 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.' , lowerCamelCase_ , )
class __lowerCamelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase = True ) -> Union[str, Any]:
super().__init__(__UpperCAmelCase )
_a = config
_a = 32
_a = max(int(depth * config.depth_multiplier ) , config.min_depth )
_a = MobileNetVaConvLayer(
__UpperCAmelCase , in_channels=config.num_channels , out_channels=__UpperCAmelCase , kernel_size=3 , stride=2 , )
_a = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
_a = nn.ModuleList()
for i in range(13 ):
_a = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
_a = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
__UpperCAmelCase , in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , kernel_size=3 , stride=strides[i] , groups=__UpperCAmelCase , ) )
self.layer.append(
MobileNetVaConvLayer(
__UpperCAmelCase , in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , kernel_size=1 , ) )
_a = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> str:
raise NotImplementedError
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _UpperCAmelCase ( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , ) -> Optional[Any]:
_a = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_a = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('''You have to specify pixel_values''' )
_a = self.conv_stem(__UpperCAmelCase )
_a = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
_a = layer_module(__UpperCAmelCase )
if output_hidden_states:
_a = all_hidden_states + (hidden_states,)
_a = hidden_states
if self.pooler is not None:
_a = torch.flatten(self.pooler(__UpperCAmelCase ) , start_dim=1 )
else:
_a = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__UpperCAmelCase , pooler_output=__UpperCAmelCase , hidden_states=__UpperCAmelCase , )
@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 ' , lowerCamelCase_ , )
class __lowerCamelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase ) -> Dict:
super().__init__(__UpperCAmelCase )
_a = config.num_labels
_a = MobileNetVaModel(__UpperCAmelCase )
_a = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
_a = nn.Dropout(config.classifier_dropout_prob , inplace=__UpperCAmelCase )
_a = nn.Linear(__UpperCAmelCase , 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(__UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _UpperCAmelCase ( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , ) -> int:
_a = return_dict if return_dict is not None else self.config.use_return_dict
_a = self.mobilenet_va(__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , return_dict=__UpperCAmelCase )
_a = outputs.pooler_output if return_dict else outputs[1]
_a = self.classifier(self.dropout(__UpperCAmelCase ) )
_a = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_a = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_a = "single_label_classification"
else:
_a = "multi_label_classification"
if self.config.problem_type == "regression":
_a = MSELoss()
if self.num_labels == 1:
_a = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_a = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
elif self.config.problem_type == "single_label_classification":
_a = CrossEntropyLoss()
_a = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_a = BCEWithLogitsLoss()
_a = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
if not return_dict:
_a = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__UpperCAmelCase , logits=__UpperCAmelCase , hidden_states=outputs.hidden_states , ) | 320 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
__A = "path-to-your-trained-model"
__A = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
__A = "A photo of sks dog in a bucket"
__A = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
| 148 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__UpperCAmelCase = re.compile(R"\b(a|an|the)\b", re.UNICODE)
__UpperCAmelCase = None
def A__ ( ):
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.''' )
parser.add_argument('''data_file''', metavar='''data.json''', help='''Input data JSON file.''' )
parser.add_argument('''pred_file''', metavar='''pred.json''', help='''Model predictions.''' )
parser.add_argument(
'''--out-file''', '''-o''', metavar='''eval.json''', help='''Write accuracy metrics to file (default is stdout).''' )
parser.add_argument(
'''--na-prob-file''', '''-n''', metavar='''na_prob.json''', help='''Model estimates of probability of no answer.''' )
parser.add_argument(
'''--na-prob-thresh''', '''-t''', type=__lowerCamelCase, default=1.0, help='''Predict "" if no-answer probability exceeds this (default = 1.0).''', )
parser.add_argument(
'''--out-image-dir''', '''-p''', metavar='''out_images''', default=__lowerCamelCase, help='''Save precision-recall curves to directory.''' )
parser.add_argument('''--verbose''', '''-v''', action='''store_true''' )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
SCREAMING_SNAKE_CASE_ = bool(qa['''answers''']['''text'''] )
return qid_to_has_ans
def A__ ( __lowerCamelCase ):
def remove_articles(__lowerCamelCase ):
return ARTICLES_REGEX.sub(''' ''', __lowerCamelCase )
def white_space_fix(__lowerCamelCase ):
return " ".join(text.split() )
def remove_punc(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__lowerCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase ) ) ) )
def A__ ( __lowerCamelCase ):
if not s:
return []
return normalize_answer(__lowerCamelCase ).split()
def A__ ( __lowerCamelCase, __lowerCamelCase ):
return int(normalize_answer(__lowerCamelCase ) == normalize_answer(__lowerCamelCase ) )
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = get_tokens(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = get_tokens(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = collections.Counter(__lowerCamelCase ) & collections.Counter(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = sum(common.values() )
if len(__lowerCamelCase ) == 0 or len(__lowerCamelCase ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE_ = 1.0 * num_same / len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = 1.0 * num_same / len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = (2 * precision * recall) / (precision + recall)
return fa
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
SCREAMING_SNAKE_CASE_ = qa['''id''']
SCREAMING_SNAKE_CASE_ = [t for t in qa['''answers''']['''text'''] if normalize_answer(__lowerCamelCase )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
SCREAMING_SNAKE_CASE_ = ['''''']
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
SCREAMING_SNAKE_CASE_ = preds[qid]
# Take max over all gold answers
SCREAMING_SNAKE_CASE_ = max(compute_exact(__lowerCamelCase, __lowerCamelCase ) for a in gold_answers )
SCREAMING_SNAKE_CASE_ = max(compute_fa(__lowerCamelCase, __lowerCamelCase ) for a in gold_answers )
return exact_scores, fa_scores
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = {}
for qid, s in scores.items():
SCREAMING_SNAKE_CASE_ = na_probs[qid] > na_prob_thresh
if pred_na:
SCREAMING_SNAKE_CASE_ = float(not qid_to_has_ans[qid] )
else:
SCREAMING_SNAKE_CASE_ = s
return new_scores
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase=None ):
if not qid_list:
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
return collections.OrderedDict(
[
('''exact''', 1_00.0 * sum(exact_scores.values() ) / total),
('''f1''', 1_00.0 * sum(fa_scores.values() ) / total),
('''total''', total),
] )
else:
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
return collections.OrderedDict(
[
('''exact''', 1_00.0 * sum(exact_scores[k] for k in qid_list ) / total),
('''f1''', 1_00.0 * sum(fa_scores[k] for k in qid_list ) / total),
('''total''', total),
] )
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
for k in new_eval:
SCREAMING_SNAKE_CASE_ = new_eval[k]
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
plt.step(__lowerCamelCase, __lowerCamelCase, color='''b''', alpha=0.2, where='''post''' )
plt.fill_between(__lowerCamelCase, __lowerCamelCase, step='''post''', alpha=0.2, color='''b''' )
plt.xlabel('''Recall''' )
plt.ylabel('''Precision''' )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(__lowerCamelCase )
plt.savefig(__lowerCamelCase )
plt.clf()
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase=None, __lowerCamelCase=None ):
SCREAMING_SNAKE_CASE_ = sorted(__lowerCamelCase, key=lambda __lowerCamelCase : na_probs[k] )
SCREAMING_SNAKE_CASE_ = 0.0
SCREAMING_SNAKE_CASE_ = 1.0
SCREAMING_SNAKE_CASE_ = 0.0
SCREAMING_SNAKE_CASE_ = [1.0]
SCREAMING_SNAKE_CASE_ = [0.0]
SCREAMING_SNAKE_CASE_ = 0.0
for i, qid in enumerate(__lowerCamelCase ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
SCREAMING_SNAKE_CASE_ = true_pos / float(i + 1 )
SCREAMING_SNAKE_CASE_ = true_pos / float(__lowerCamelCase )
if i == len(__lowerCamelCase ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__lowerCamelCase )
recalls.append(__lowerCamelCase )
if out_image:
plot_pr_curve(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
return {"ap": 1_00.0 * avg_prec}
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if out_image_dir and not os.path.exists(__lowerCamelCase ):
os.makedirs(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
SCREAMING_SNAKE_CASE_ = make_precision_recall_eval(
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, out_image=os.path.join(__lowerCamelCase, '''pr_exact.png''' ), title='''Precision-Recall curve for Exact Match score''', )
SCREAMING_SNAKE_CASE_ = make_precision_recall_eval(
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, out_image=os.path.join(__lowerCamelCase, '''pr_f1.png''' ), title='''Precision-Recall curve for F1 score''', )
SCREAMING_SNAKE_CASE_ = {k: float(__lowerCamelCase ) for k, v in qid_to_has_ans.items()}
SCREAMING_SNAKE_CASE_ = make_precision_recall_eval(
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, out_image=os.path.join(__lowerCamelCase, '''pr_oracle.png''' ), title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)''', )
merge_eval(__lowerCamelCase, __lowerCamelCase, '''pr_exact''' )
merge_eval(__lowerCamelCase, __lowerCamelCase, '''pr_f1''' )
merge_eval(__lowerCamelCase, __lowerCamelCase, '''pr_oracle''' )
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if not qid_list:
return
SCREAMING_SNAKE_CASE_ = [na_probs[k] for k in qid_list]
SCREAMING_SNAKE_CASE_ = np.ones_like(__lowerCamelCase ) / float(len(__lowerCamelCase ) )
plt.hist(__lowerCamelCase, weights=__lowerCamelCase, bins=20, range=(0.0, 1.0) )
plt.xlabel('''Model probability of no-answer''' )
plt.ylabel('''Proportion of dataset''' )
plt.title(F'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(__lowerCamelCase, F'''na_prob_hist_{name}.png''' ) )
plt.clf()
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
SCREAMING_SNAKE_CASE_ = num_no_ans
SCREAMING_SNAKE_CASE_ = cur_score
SCREAMING_SNAKE_CASE_ = 0.0
SCREAMING_SNAKE_CASE_ = sorted(__lowerCamelCase, key=lambda __lowerCamelCase : na_probs[k] )
for i, qid in enumerate(__lowerCamelCase ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
SCREAMING_SNAKE_CASE_ = scores[qid]
else:
if preds[qid]:
SCREAMING_SNAKE_CASE_ = -1
else:
SCREAMING_SNAKE_CASE_ = 0
cur_score += diff
if cur_score > best_score:
SCREAMING_SNAKE_CASE_ = cur_score
SCREAMING_SNAKE_CASE_ = na_probs[qid]
return 1_00.0 * best_score / len(__lowerCamelCase ), best_thresh
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = find_best_thresh(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = find_best_thresh(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = best_exact
SCREAMING_SNAKE_CASE_ = exact_thresh
SCREAMING_SNAKE_CASE_ = best_fa
SCREAMING_SNAKE_CASE_ = fa_thresh
def A__ ( ):
with open(OPTS.data_file ) as f:
SCREAMING_SNAKE_CASE_ = json.load(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = dataset_json['''data''']
with open(OPTS.pred_file ) as f:
SCREAMING_SNAKE_CASE_ = json.load(__lowerCamelCase )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
SCREAMING_SNAKE_CASE_ = json.load(__lowerCamelCase )
else:
SCREAMING_SNAKE_CASE_ = {k: 0.0 for k in preds}
SCREAMING_SNAKE_CASE_ = make_qid_to_has_ans(__lowerCamelCase ) # maps qid to True/False
SCREAMING_SNAKE_CASE_ = [k for k, v in qid_to_has_ans.items() if v]
SCREAMING_SNAKE_CASE_ = [k for k, v in qid_to_has_ans.items() if not v]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = get_raw_scores(__lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = apply_no_ans_threshold(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, OPTS.na_prob_thresh )
SCREAMING_SNAKE_CASE_ = apply_no_ans_threshold(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, OPTS.na_prob_thresh )
SCREAMING_SNAKE_CASE_ = make_eval_dict(__lowerCamelCase, __lowerCamelCase )
if has_ans_qids:
SCREAMING_SNAKE_CASE_ = make_eval_dict(__lowerCamelCase, __lowerCamelCase, qid_list=__lowerCamelCase )
merge_eval(__lowerCamelCase, __lowerCamelCase, '''HasAns''' )
if no_ans_qids:
SCREAMING_SNAKE_CASE_ = make_eval_dict(__lowerCamelCase, __lowerCamelCase, qid_list=__lowerCamelCase )
merge_eval(__lowerCamelCase, __lowerCamelCase, '''NoAns''' )
if OPTS.na_prob_file:
find_all_best_thresh(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, OPTS.out_image_dir )
histogram_na_prob(__lowerCamelCase, __lowerCamelCase, OPTS.out_image_dir, '''hasAns''' )
histogram_na_prob(__lowerCamelCase, __lowerCamelCase, OPTS.out_image_dir, '''noAns''' )
if OPTS.out_file:
with open(OPTS.out_file, '''w''' ) as f:
json.dump(__lowerCamelCase, __lowerCamelCase )
else:
print(json.dumps(__lowerCamelCase, indent=2 ) )
if __name__ == "__main__":
__UpperCAmelCase = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 368 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
# Initialise PyTorch model
SCREAMING_SNAKE_CASE_ = BigBirdConfig.from_json_file(__lowerCamelCase )
print(F'''Building PyTorch model from configuration: {config}''' )
if is_trivia_qa:
SCREAMING_SNAKE_CASE_ = BigBirdForQuestionAnswering(__lowerCamelCase )
else:
SCREAMING_SNAKE_CASE_ = BigBirdForPreTraining(__lowerCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(__lowerCamelCase, __lowerCamelCase, is_trivia_qa=__lowerCamelCase )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--big_bird_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
)
__UpperCAmelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 257 | 0 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def _A ( UpperCamelCase_ : Union[str, Any], UpperCamelCase_ : int, UpperCamelCase_ : Dict) -> List[Any]:
'''simple docstring'''
__lowercase = os.path.abspath(lowerCAmelCase__)
logger.info(F"""Converting TensorFlow checkpoint from {tf_path}""")
# Load weights from TF model
__lowercase = tf.train.list_variables(lowerCAmelCase__)
__lowercase = []
__lowercase = []
__lowercase = []
for full_name, shape in init_vars:
# logger.info(f"Loading TF weight {name} with shape {shape}")
__lowercase = full_name.split("/")
if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
logger.info(F"""Skipping non-model layer {full_name}""")
continue
if "optimizer" in full_name:
logger.info(F"""Skipping optimization layer {full_name}""")
continue
if name[0] == "model":
# ignore initial 'model'
__lowercase = name[1:]
# figure out how many levels deep the name is
__lowercase = 0
for _name in name:
if _name.startswith("layer_with_weights"):
depth += 1
else:
break
layer_depth.append(lowerCAmelCase__)
# read data
__lowercase = tf.train.load_variable(lowerCAmelCase__, lowerCAmelCase__)
names.append("/".join(lowerCAmelCase__))
arrays.append(lowerCAmelCase__)
logger.info(F"""Read a total of {len(lowerCAmelCase__):,} layers""")
# Sanity check
if len(set(lowerCAmelCase__)) != 1:
raise ValueError(F"""Found layer names with different depths (layer depth {list(set(lowerCAmelCase__))})""")
__lowercase = list(set(lowerCAmelCase__))[0]
if layer_depth != 1:
raise ValueError(
"The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
" heads.")
# convert layers
logger.info("Converting weights...")
for full_name, array in zip(lowerCAmelCase__, lowerCAmelCase__):
__lowercase = full_name.split("/")
__lowercase = model
__lowercase = []
for i, m_name in enumerate(lowerCAmelCase__):
if m_name == ".ATTRIBUTES":
# variable names end with .ATTRIBUTES/VARIABLE_VALUE
break
if m_name.startswith("layer_with_weights"):
__lowercase = int(m_name.split("-")[-1])
if layer_num <= 2:
# embedding layers
# layer_num 0: word_embeddings
# layer_num 1: position_embeddings
# layer_num 2: token_type_embeddings
continue
elif layer_num == 3:
# embedding LayerNorm
trace.extend(["embeddings", "LayerNorm"])
__lowercase = getattr(lowerCAmelCase__, "embeddings")
__lowercase = getattr(lowerCAmelCase__, "LayerNorm")
elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
# encoder layers
trace.extend(["encoder", "layer", str(layer_num - 4)])
__lowercase = getattr(lowerCAmelCase__, "encoder")
__lowercase = getattr(lowerCAmelCase__, "layer")
__lowercase = pointer[layer_num - 4]
elif layer_num == config.num_hidden_layers + 4:
# pooler layer
trace.extend(["pooler", "dense"])
__lowercase = getattr(lowerCAmelCase__, "pooler")
__lowercase = getattr(lowerCAmelCase__, "dense")
elif m_name == "embeddings":
trace.append("embeddings")
__lowercase = getattr(lowerCAmelCase__, "embeddings")
if layer_num == 0:
trace.append("word_embeddings")
__lowercase = getattr(lowerCAmelCase__, "word_embeddings")
elif layer_num == 1:
trace.append("position_embeddings")
__lowercase = getattr(lowerCAmelCase__, "position_embeddings")
elif layer_num == 2:
trace.append("token_type_embeddings")
__lowercase = getattr(lowerCAmelCase__, "token_type_embeddings")
else:
raise ValueError(F"""Unknown embedding layer with name {full_name}""")
trace.append("weight")
__lowercase = getattr(lowerCAmelCase__, "weight")
elif m_name == "_attention_layer":
# self-attention layer
trace.extend(["attention", "self"])
__lowercase = getattr(lowerCAmelCase__, "attention")
__lowercase = getattr(lowerCAmelCase__, "self")
elif m_name == "_attention_layer_norm":
# output attention norm
trace.extend(["attention", "output", "LayerNorm"])
__lowercase = getattr(lowerCAmelCase__, "attention")
__lowercase = getattr(lowerCAmelCase__, "output")
__lowercase = getattr(lowerCAmelCase__, "LayerNorm")
elif m_name == "_attention_output_dense":
# output attention dense
trace.extend(["attention", "output", "dense"])
__lowercase = getattr(lowerCAmelCase__, "attention")
__lowercase = getattr(lowerCAmelCase__, "output")
__lowercase = getattr(lowerCAmelCase__, "dense")
elif m_name == "_output_dense":
# output dense
trace.extend(["output", "dense"])
__lowercase = getattr(lowerCAmelCase__, "output")
__lowercase = getattr(lowerCAmelCase__, "dense")
elif m_name == "_output_layer_norm":
# output dense
trace.extend(["output", "LayerNorm"])
__lowercase = getattr(lowerCAmelCase__, "output")
__lowercase = getattr(lowerCAmelCase__, "LayerNorm")
elif m_name == "_key_dense":
# attention key
trace.append("key")
__lowercase = getattr(lowerCAmelCase__, "key")
elif m_name == "_query_dense":
# attention query
trace.append("query")
__lowercase = getattr(lowerCAmelCase__, "query")
elif m_name == "_value_dense":
# attention value
trace.append("value")
__lowercase = getattr(lowerCAmelCase__, "value")
elif m_name == "_intermediate_dense":
# attention intermediate dense
trace.extend(["intermediate", "dense"])
__lowercase = getattr(lowerCAmelCase__, "intermediate")
__lowercase = getattr(lowerCAmelCase__, "dense")
elif m_name == "_output_layer_norm":
# output layer norm
trace.append("output")
__lowercase = getattr(lowerCAmelCase__, "output")
# weights & biases
elif m_name in ["bias", "beta"]:
trace.append("bias")
__lowercase = getattr(lowerCAmelCase__, "bias")
elif m_name in ["kernel", "gamma"]:
trace.append("weight")
__lowercase = getattr(lowerCAmelCase__, "weight")
else:
logger.warning(F"""Ignored {m_name}""")
# for certain layers reshape is necessary
__lowercase = '''.'''.join(lowerCAmelCase__)
if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)", lowerCAmelCase__) or re.match(
r"(\S+)\.attention\.output\.dense\.weight", lowerCAmelCase__):
__lowercase = array.reshape(pointer.data.shape)
if "kernel" in full_name:
__lowercase = array.transpose()
if pointer.shape == array.shape:
__lowercase = torch.from_numpy(lowerCAmelCase__)
else:
raise ValueError(
F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
F""" {array.shape}""")
logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}""")
return model
def _A ( UpperCamelCase_ : Optional[Any], UpperCamelCase_ : Union[str, Any], UpperCamelCase_ : Optional[Any]) -> Optional[int]:
'''simple docstring'''
logger.info(F"""Loading model based on config from {config_path}...""")
__lowercase = BertConfig.from_json_file(lowerCAmelCase__)
__lowercase = BertModel(lowerCAmelCase__)
# Load weights from checkpoint
logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}...""")
load_tfa_weights_in_bert(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__)
# Save pytorch-model
logger.info(F"""Saving PyTorch model to {pytorch_dump_path}...""")
torch.save(model.state_dict(), lowerCAmelCase__)
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
'--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
)
parser.add_argument(
'--bert_config_file',
type=str,
required=True,
help='The config json file corresponding to the BERT model. This specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path',
type=str,
required=True,
help='Path to the output PyTorch model (must include filename).',
)
_a = parser.parse_args()
convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 17 |
from __future__ import annotations
def __UpperCamelCase ( lowerCAmelCase__ : list[float] , lowerCAmelCase__ : list[float] ):
__a : Dict = sorted(numsa + numsa )
__a , __a : Optional[Any] = divmod(len(lowerCAmelCase__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ =[float(x) for x in input('Enter the elements of first array: ').split()]
lowercase__ =[float(x) for x in input('Enter the elements of second array: ').split()]
print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
| 216 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : int = {
'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : Optional[Any] = "bloom"
a__ : List[Any] = ["past_key_values"]
a__ : Optional[Any] = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
}
def __init__( self : Union[str, Any] , _lowercase : Dict=25_08_80 , _lowercase : str=64 , _lowercase : int=2 , _lowercase : Union[str, Any]=8 , _lowercase : Optional[Any]=1E-5 , _lowercase : Dict=0.02 , _lowercase : Optional[int]=True , _lowercase : Any=1 , _lowercase : Dict=2 , _lowercase : Optional[Any]=False , _lowercase : Union[str, Any]=0.0 , _lowercase : str=0.0 , _lowercase : str=1 , _lowercase : int=False , **_lowercase : List[str] , ):
__UpperCAmelCase = vocab_size
# Backward compatibility with n_embed kwarg
__UpperCAmelCase = kwargs.pop('''n_embed''' , _lowercase )
__UpperCAmelCase = hidden_size if n_embed is None else n_embed
__UpperCAmelCase = n_layer
__UpperCAmelCase = n_head
__UpperCAmelCase = layer_norm_epsilon
__UpperCAmelCase = initializer_range
__UpperCAmelCase = use_cache
__UpperCAmelCase = pretraining_tp
__UpperCAmelCase = apply_residual_connection_post_layernorm
__UpperCAmelCase = hidden_dropout
__UpperCAmelCase = attention_dropout
__UpperCAmelCase = bos_token_id
__UpperCAmelCase = eos_token_id
__UpperCAmelCase = slow_but_exact
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : List[str] = version.parse("1.12" )
def __init__( self : Optional[int] , _lowercase : PretrainedConfig , _lowercase : str = "default" , _lowercase : List[PatchingSpec] = None , _lowercase : bool = False , ):
super().__init__(_lowercase , task=_lowercase , patching_specs=_lowercase , use_past=_lowercase )
if not getattr(self._config , '''pad_token_id''' , _lowercase ):
# TODO: how to do that better?
__UpperCAmelCase = 0
@property
def a ( self : Optional[int] ):
__UpperCAmelCase = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(_lowercase , direction='''inputs''' , inverted_values_shape=_lowercase )
__UpperCAmelCase = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
__UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def a ( self : Any ):
return self._config.n_layer
@property
def a ( self : Tuple ):
return self._config.n_head
@property
def a ( self : Dict ):
return 1E-3
def a ( self : List[str] , _lowercase : "PreTrainedTokenizer" , _lowercase : int = -1 , _lowercase : int = -1 , _lowercase : bool = False , _lowercase : Optional["TensorType"] = None , ):
__UpperCAmelCase = super(_lowercase , self ).generate_dummy_inputs(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
# We need to order the input in the way they appears in the forward()
__UpperCAmelCase = 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
__UpperCAmelCase , __UpperCAmelCase = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__UpperCAmelCase = seqlen + 2
__UpperCAmelCase = self._config.hidden_size // self.num_attention_heads
__UpperCAmelCase = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
__UpperCAmelCase = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
__UpperCAmelCase = [
(torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(self.num_layers )
]
__UpperCAmelCase = common_inputs['''attention_mask''']
if self.use_past:
__UpperCAmelCase = ordered_inputs['''attention_mask'''].dtype
__UpperCAmelCase = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(_lowercase , _lowercase , dtype=_lowercase )] , dim=1 )
return ordered_inputs
@property
def a ( self : Any ):
return 13
| 86 |
"""simple docstring"""
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 ):
a__ : str = 0
a__ : List[Any] = 1
a__ : str = 2
@add_end_docstrings(_lowerCAmelCase )
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : Dict = "\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 : Optional[Any] , *_lowercase : Any , **_lowercase : Optional[int] ):
super().__init__(*_lowercase , **_lowercase )
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.
__UpperCAmelCase = None
if self.model.config.prefix is not None:
__UpperCAmelCase = 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.
__UpperCAmelCase = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = self._sanitize_parameters(prefix=_lowercase , **self._forward_params )
__UpperCAmelCase = {**self._preprocess_params, **preprocess_params}
__UpperCAmelCase = {**self._forward_params, **forward_params}
def a ( self : Any , _lowercase : Optional[Any]=None , _lowercase : List[str]=None , _lowercase : int=None , _lowercase : Union[str, Any]=None , _lowercase : Union[str, Any]=None , _lowercase : Union[str, Any]=None , _lowercase : Union[str, Any]=None , _lowercase : List[Any]=None , **_lowercase : str , ):
__UpperCAmelCase = {}
if prefix is not None:
__UpperCAmelCase = prefix
if prefix:
__UpperCAmelCase = self.tokenizer(
_lowercase , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
__UpperCAmelCase = 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\']''' )
__UpperCAmelCase = handle_long_generation
preprocess_params.update(_lowercase )
__UpperCAmelCase = generate_kwargs
__UpperCAmelCase = {}
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`''' )
__UpperCAmelCase = 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`''' )
__UpperCAmelCase = ReturnType.TENSORS
if return_type is not None:
__UpperCAmelCase = return_type
if clean_up_tokenization_spaces is not None:
__UpperCAmelCase = clean_up_tokenization_spaces
if stop_sequence is not None:
__UpperCAmelCase = self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
if len(_lowercase ) > 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.''' )
__UpperCAmelCase = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def a ( self : Optional[int] , *_lowercase : Optional[int] , **_lowercase : Any ):
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({'''add_space_before_punct_symbol''': True} )
return super()._parse_and_tokenize(*_lowercase , **_lowercase )
def __call__( self : List[str] , _lowercase : str , **_lowercase : Optional[Any] ):
return super().__call__(_lowercase , **_lowercase )
def a ( self : Union[str, Any] , _lowercase : Any , _lowercase : Dict="" , _lowercase : Union[str, Any]=None , **_lowercase : Tuple ):
__UpperCAmelCase = self.tokenizer(
prefix + prompt_text , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
__UpperCAmelCase = prompt_text
if handle_long_generation == "hole":
__UpperCAmelCase = inputs['''input_ids'''].shape[-1]
if "max_new_tokens" in generate_kwargs:
__UpperCAmelCase = generate_kwargs['''max_new_tokens''']
else:
__UpperCAmelCase = 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:
__UpperCAmelCase = 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''' )
__UpperCAmelCase = inputs['''input_ids'''][:, -keep_length:]
if "attention_mask" in inputs:
__UpperCAmelCase = inputs['''attention_mask'''][:, -keep_length:]
return inputs
def a ( self : Union[str, Any] , _lowercase : List[str] , **_lowercase : Optional[int] ):
__UpperCAmelCase = model_inputs['''input_ids''']
__UpperCAmelCase = model_inputs.get('''attention_mask''' , _lowercase )
# Allow empty prompts
if input_ids.shape[1] == 0:
__UpperCAmelCase = None
__UpperCAmelCase = None
__UpperCAmelCase = 1
else:
__UpperCAmelCase = input_ids.shape[0]
__UpperCAmelCase = 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.
__UpperCAmelCase = generate_kwargs.pop('''prefix_length''' , 0 )
if prefix_length > 0:
__UpperCAmelCase = '''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:
__UpperCAmelCase = generate_kwargs.get('''max_length''' ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
__UpperCAmelCase = '''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
__UpperCAmelCase = self.model.generate(input_ids=_lowercase , attention_mask=_lowercase , **_lowercase )
__UpperCAmelCase = generated_sequence.shape[0]
if self.framework == "pt":
__UpperCAmelCase = generated_sequence.reshape(_lowercase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
__UpperCAmelCase = tf.reshape(_lowercase , (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 : Optional[int] , _lowercase : Union[str, Any] , _lowercase : Optional[int]=ReturnType.FULL_TEXT , _lowercase : List[str]=True ):
__UpperCAmelCase = model_outputs['''generated_sequence'''][0]
__UpperCAmelCase = model_outputs['''input_ids''']
__UpperCAmelCase = model_outputs['''prompt_text''']
__UpperCAmelCase = generated_sequence.numpy().tolist()
__UpperCAmelCase = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
__UpperCAmelCase = {'''generated_token_ids''': sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
__UpperCAmelCase = self.tokenizer.decode(
_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
__UpperCAmelCase = 0
else:
__UpperCAmelCase = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , ) )
if return_type == ReturnType.FULL_TEXT:
__UpperCAmelCase = prompt_text + text[prompt_length:]
else:
__UpperCAmelCase = text[prompt_length:]
__UpperCAmelCase = {'''generated_text''': all_text}
records.append(_lowercase )
return records
| 86 | 1 |
from collections.abc import Sequence
def _a ( UpperCamelCase_ : Sequence[float] , UpperCamelCase_ : bool = False ) -> float:
"""simple docstring"""
if not arr:
return 0
lowerCAmelCase__ = 0 if allow_empty_subarrays else float("-inf" )
lowerCAmelCase__ = 0.0
for num in arr:
lowerCAmelCase__ = max(0 if allow_empty_subarrays else num , curr_sum + num )
lowerCAmelCase__ = max(UpperCamelCase_ , UpperCamelCase_ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
a_ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F"{max_subarray_sum(nums) = }")
| 340 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}
class lowercase__ ( _UpperCAmelCase ):
a_ ="""xlnet"""
a_ =["""mems"""]
a_ ={
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __UpperCAmelCase=32000 , __UpperCAmelCase=1024 , __UpperCAmelCase=24 , __UpperCAmelCase=16 , __UpperCAmelCase=4096 , __UpperCAmelCase="gelu" , __UpperCAmelCase=True , __UpperCAmelCase="bi" , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=-1 , __UpperCAmelCase=False , __UpperCAmelCase="last" , __UpperCAmelCase=True , __UpperCAmelCase="tanh" , __UpperCAmelCase=0.1 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=1 , __UpperCAmelCase=2 , **__UpperCAmelCase , )-> int:
'''simple docstring'''
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = d_model
lowerCAmelCase__ = n_layer
lowerCAmelCase__ = n_head
if d_model % n_head != 0:
raise ValueError(F"'d_model % n_head' ({d_model % n_head}) should be equal to 0" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" )
lowerCAmelCase__ = d_model // n_head
lowerCAmelCase__ = ff_activation
lowerCAmelCase__ = d_inner
lowerCAmelCase__ = untie_r
lowerCAmelCase__ = attn_type
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = layer_norm_eps
lowerCAmelCase__ = dropout
lowerCAmelCase__ = mem_len
lowerCAmelCase__ = reuse_len
lowerCAmelCase__ = bi_data
lowerCAmelCase__ = clamp_len
lowerCAmelCase__ = same_length
lowerCAmelCase__ = summary_type
lowerCAmelCase__ = summary_use_proj
lowerCAmelCase__ = summary_activation
lowerCAmelCase__ = summary_last_dropout
lowerCAmelCase__ = start_n_top
lowerCAmelCase__ = end_n_top
lowerCAmelCase__ = bos_token_id
lowerCAmelCase__ = pad_token_id
lowerCAmelCase__ = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
" instead." , __UpperCAmelCase , )
lowerCAmelCase__ = kwargs["use_cache"]
lowerCAmelCase__ = use_mems_eval
lowerCAmelCase__ = use_mems_train
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
@property
def UpperCAmelCase ( self )-> Dict:
'''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 UpperCAmelCase ( self , __UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
raise NotImplementedError(
F"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 340 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def snake_case( ):
'''simple docstring'''
lowercase : int = ArgumentParser('''Accelerate CLI tool''' , usage='''accelerate <command> [<args>]''' , allow_abbrev=__a )
lowercase : Optional[int] = parser.add_subparsers(help='''accelerate command helpers''' )
# Register commands
get_config_parser(subparsers=__a )
env_command_parser(subparsers=__a )
launch_command_parser(subparsers=__a )
tpu_command_parser(subparsers=__a )
test_command_parser(subparsers=__a )
# Let's go
lowercase : Dict = parser.parse_args()
if not hasattr(__a , '''func''' ):
parser.print_help()
exit(1 )
# Run
args.func(__a )
if __name__ == "__main__":
main() | 365 |
class _A : # Public class to implement a graph
def __init__( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> None:
"""simple docstring"""
lowercase : Tuple = row
lowercase : Union[str, Any] = col
lowercase : int = graph
def __a ( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> bool:
"""simple docstring"""
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def __a ( self : int , _A : int , _A : int , _A : list[list[bool]] ) -> None:
"""simple docstring"""
lowercase : List[str] = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
lowercase : Dict = [-1, 0, 1, -1, 1, -1, 0, 1]
lowercase : Dict = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , _A ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , _A )
def __a ( self : List[str] ) -> int: # And finally, count all islands.
"""simple docstring"""
lowercase : List[str] = [[False for j in range(self.COL )] for i in range(self.ROW )]
lowercase : Optional[Any] = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(_A , _A , _A )
count += 1
return count | 116 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
A__ : str = TypeVar('KT')
A__ : List[str] = TypeVar('VT')
class lowercase__ ( Generic[KT, VT] ):
def __init__( self : Union[str, Any] , snake_case__ : KT | str = "root" , snake_case__ : VT | None = None ):
lowerCamelCase_ : List[Any] =key
lowerCamelCase_ : Tuple =value
lowerCamelCase_ : list[Node[KT, VT]] =[]
def __repr__( self : Tuple ):
return F"""Node({self.key}: {self.value})"""
@property
def UpperCAmelCase__ ( self : Optional[Any] ):
return len(self.forward )
class lowercase__ ( Generic[KT, VT] ):
def __init__( self : Optional[int] , snake_case__ : float = 0.5 , snake_case__ : int = 16 ):
lowerCamelCase_ : Node[KT, VT] =Node[KT, VT]()
lowerCamelCase_ : Optional[int] =0
lowerCamelCase_ : List[Any] =p
lowerCamelCase_ : Dict =max_level
def __str__( self : Optional[int] ):
lowerCamelCase_ : Optional[Any] =list(self )
if len(snake_case__ ) == 0:
return F"""SkipList(level={self.level})"""
lowerCamelCase_ : Any =max((len(str(snake_case__ ) ) for item in items) , default=4 )
lowerCamelCase_ : Union[str, Any] =max(snake_case__ , 4 ) + 4
lowerCamelCase_ : Union[str, Any] =self.head
lowerCamelCase_ : Tuple =[]
lowerCamelCase_ : Optional[Any] =node.forward.copy()
lines.append(F"""[{node.key}]""".ljust(snake_case__ , "-" ) + "* " * len(snake_case__ ) )
lines.append(" " * label_size + "| " * len(snake_case__ ) )
while len(node.forward ) != 0:
lowerCamelCase_ : List[Any] =node.forward[0]
lines.append(
F"""[{node.key}]""".ljust(snake_case__ , "-" )
+ " ".join(str(n.key ) if n.key == node.key else "|" for n in forwards ) )
lines.append(" " * label_size + "| " * len(snake_case__ ) )
lowerCamelCase_ : Any =node.forward
lines.append("None".ljust(snake_case__ ) + "* " * len(snake_case__ ) )
return F"""SkipList(level={self.level})\n""" + "\n".join(snake_case__ )
def __iter__( self : Union[str, Any] ):
lowerCamelCase_ : Union[str, Any] =self.head
while len(node.forward ) != 0:
yield node.forward[0].key
lowerCamelCase_ : Any =node.forward[0]
def UpperCAmelCase__ ( self : Dict ):
lowerCamelCase_ : List[str] =1
while random() < self.p and level < self.max_level:
level += 1
return level
def UpperCAmelCase__ ( self : List[str] , snake_case__ : Dict ):
lowerCamelCase_ : str =[]
lowerCamelCase_ : Any =self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
lowerCamelCase_ : List[Any] =node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(snake_case__ )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def UpperCAmelCase__ ( self : Dict , snake_case__ : KT ):
lowerCamelCase_ , lowerCamelCase_ : Tuple =self._locate_node(snake_case__ )
if node is not None:
for i, update_node in enumerate(snake_case__ ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
lowerCamelCase_ : Optional[Any] =node.forward[i]
else:
lowerCamelCase_ : List[str] =update_node.forward[:i]
def UpperCAmelCase__ ( self : Tuple , snake_case__ : KT , snake_case__ : VT ):
lowerCamelCase_ , lowerCamelCase_ : List[Any] =self._locate_node(snake_case__ )
if node is not None:
lowerCamelCase_ : List[Any] =value
else:
lowerCamelCase_ : Dict =self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , snake_case__ ):
update_vector.append(self.head )
lowerCamelCase_ : Union[str, Any] =level
lowerCamelCase_ : List[str] =Node(snake_case__ , snake_case__ )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(snake_case__ )
else:
lowerCamelCase_ : List[Any] =new_node
def UpperCAmelCase__ ( self : int , snake_case__ : VT ):
lowerCamelCase_ , lowerCamelCase_ : str =self._locate_node(snake_case__ )
if node is not None:
return node.value
return None
def _snake_case ( ) -> Tuple:
lowerCamelCase_ : Optional[int] =SkipList()
skip_list.insert("Key1" , 3 )
skip_list.insert("Key2" , 12 )
skip_list.insert("Key3" , 41 )
skip_list.insert("Key4" , -19 )
lowerCamelCase_ : str =skip_list.head
lowerCamelCase_ : int ={}
while node.level != 0:
lowerCamelCase_ : List[str] =node.forward[0]
lowerCamelCase_ : Dict =node.value
assert len(lowerCamelCase__ ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def _snake_case ( ) -> Any:
lowerCamelCase_ : int =SkipList()
skip_list.insert("Key1" , 10 )
skip_list.insert("Key1" , 12 )
skip_list.insert("Key5" , 7 )
skip_list.insert("Key7" , 10 )
skip_list.insert("Key10" , 5 )
skip_list.insert("Key7" , 7 )
skip_list.insert("Key5" , 5 )
skip_list.insert("Key10" , 10 )
lowerCamelCase_ : str =skip_list.head
lowerCamelCase_ : Union[str, Any] ={}
while node.level != 0:
lowerCamelCase_ : List[Any] =node.forward[0]
lowerCamelCase_ : List[Any] =node.value
if len(lowerCamelCase__ ) != 4:
print()
assert len(lowerCamelCase__ ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def _snake_case ( ) -> Any:
lowerCamelCase_ : Optional[int] =SkipList()
assert skip_list.find("Some key" ) is None
def _snake_case ( ) -> Optional[int]:
lowerCamelCase_ : Optional[Any] =SkipList()
skip_list.insert("Key2" , 20 )
assert skip_list.find("Key2" ) == 20
skip_list.insert("Some Key" , 10 )
skip_list.insert("Key2" , 8 )
skip_list.insert("V" , 13 )
assert skip_list.find("Y" ) is None
assert skip_list.find("Key2" ) == 8
assert skip_list.find("Some Key" ) == 10
assert skip_list.find("V" ) == 13
def _snake_case ( ) -> List[str]:
lowerCamelCase_ : Any =SkipList()
skip_list.delete("Some key" )
assert len(skip_list.head.forward ) == 0
def _snake_case ( ) -> Any:
lowerCamelCase_ : List[str] =SkipList()
skip_list.insert("Key1" , 12 )
skip_list.insert("V" , 13 )
skip_list.insert("X" , 14 )
skip_list.insert("Key2" , 15 )
skip_list.delete("V" )
skip_list.delete("Key2" )
assert skip_list.find("V" ) is None
assert skip_list.find("Key2" ) is None
def _snake_case ( ) -> List[Any]:
lowerCamelCase_ : Any =SkipList()
skip_list.insert("Key1" , 12 )
skip_list.insert("V" , 13 )
skip_list.insert("X" , 14 )
skip_list.insert("Key2" , 15 )
skip_list.delete("V" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) == 14
assert skip_list.find("Key1" ) == 12
assert skip_list.find("Key2" ) == 15
skip_list.delete("X" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) == 12
assert skip_list.find("Key2" ) == 15
skip_list.delete("Key1" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) is None
assert skip_list.find("Key2" ) == 15
skip_list.delete("Key2" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) is None
assert skip_list.find("Key2" ) is None
def _snake_case ( ) -> List[Any]:
lowerCamelCase_ : str =SkipList()
skip_list.insert("Key1" , 12 )
skip_list.insert("V" , 13 )
skip_list.insert("X" , 142 )
skip_list.insert("Key2" , 15 )
skip_list.delete("X" )
def traverse_keys(lowerCamelCase__ : Tuple ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(lowerCamelCase__ )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def _snake_case ( ) -> Tuple:
def is_sorted(lowerCamelCase__ : Any ):
return all(next_item >= item for item, next_item in zip(lowerCamelCase__ , lst[1:] ) )
lowerCamelCase_ : str =SkipList()
for i in range(10 ):
skip_list.insert(lowerCamelCase__ , lowerCamelCase__ )
assert is_sorted(list(lowerCamelCase__ ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(lowerCamelCase__ ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(lowerCamelCase__ ) )
def _snake_case ( ) -> List[str]:
for _ in range(100 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def _snake_case ( ) -> List[str]:
lowerCamelCase_ : List[str] =SkipList()
skip_list.insert(2 , "2" )
skip_list.insert(4 , "4" )
skip_list.insert(6 , "4" )
skip_list.insert(4 , "5" )
skip_list.insert(8 , "4" )
skip_list.insert(9 , "4" )
skip_list.delete(4 )
print(lowerCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 144 |
"""simple docstring"""
import math
def _snake_case ( lowerCamelCase__ : list , lowerCamelCase__ : int ) -> int:
lowerCamelCase_ : int =len(lowerCamelCase__ )
lowerCamelCase_ : List[Any] =int(math.floor(math.sqrt(lowerCamelCase__ ) ) )
lowerCamelCase_ : List[Any] =0
while arr[min(lowerCamelCase__ , lowerCamelCase__ ) - 1] < x:
lowerCamelCase_ : str =step
step += int(math.floor(math.sqrt(lowerCamelCase__ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
lowerCamelCase_ : Dict =prev + 1
if prev == min(lowerCamelCase__ , lowerCamelCase__ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A__ : List[Any] = input('Enter numbers separated by a comma:\n').strip()
A__ : Optional[Any] = [int(item) for item in user_input.split(',')]
A__ : List[str] = int(input('Enter the number to be searched:\n'))
A__ : Any = jump_search(arr, x)
if res == -1:
print('Number not found!')
else:
print(f'Number {x} is at index {res}')
| 144 | 1 |
'''simple docstring'''
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : int = torch.device('''cpu''')
def _lowerCAmelCase ( ) -> List[str]:
__A : Tuple = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__A : int = Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return im
def _lowerCAmelCase ( __snake_case : Tuple ) -> Dict:
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2] )
def _lowerCAmelCase ( __snake_case : Tuple , __snake_case : List[Any] , __snake_case : Any ) -> Dict:
__A : List[Any] = dct.pop(__snake_case )
__A : int = val
def _lowerCAmelCase ( __snake_case : List[str] ) -> List[Any]:
__A : Any = []
for k in state_dict.keys():
__A : Optional[int] = k
if ".pwconv" in k:
__A : Optional[Any] = k_new.replace('.pwconv' , '.point_wise_conv' )
if ".dwconv" in k:
__A : int = k_new.replace('.dwconv' , '.depth_wise_conv' )
if ".Proj." in k:
__A : List[str] = k_new.replace('.Proj.' , '.proj.' )
if "patch_embed" in k_new:
__A : List[str] = k_new.replace('patch_embed' , 'swiftformer.patch_embed.patch_embedding' )
if "network" in k_new:
__A : Any = k_new.split('.' )
if ls[2].isdigit():
__A : Dict = 'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:] )
else:
__A : Optional[Any] = k_new.replace('network' , 'swiftformer.encoder.network' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def _lowerCAmelCase ( __snake_case : Any , __snake_case : List[str] , __snake_case : Optional[int] ) -> List[str]:
__A : Tuple = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
__A : Optional[int] = 10_00
__A : List[str] = 'huggingface/label-files'
__A : List[Any] = 'imagenet-1k-id2label.json'
__A : Any = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type='dataset' ) , 'r' ) )
__A : int = {int(__snake_case ): v for k, v in idalabel.items()}
__A : str = idalabel
__A : Optional[int] = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
__A : str = [3, 3, 6, 4]
__A : Optional[int] = [48, 56, 1_12, 2_20]
elif swiftformer_name == "swiftformer_s":
__A : List[str] = [3, 3, 9, 6]
__A : Tuple = [48, 64, 1_68, 2_24]
elif swiftformer_name == "swiftformer_l1":
__A : Dict = [4, 3, 10, 5]
__A : Union[str, Any] = [48, 96, 1_92, 3_84]
elif swiftformer_name == "swiftformer_l3":
__A : Optional[Any] = [4, 4, 12, 6]
__A : int = [64, 1_28, 3_20, 5_12]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('https' ):
__A : Any = torch.hub.load_state_dict_from_url(__snake_case , map_location='cpu' , check_hash=__snake_case )
else:
__A : List[str] = torch.load(__snake_case , map_location='cpu' )
__A : Union[str, Any] = checkpoint
__A : str = create_rename_keys(__snake_case )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
# load HuggingFace model
__A : int = SwiftFormerForImageClassification(__snake_case ).eval()
hf_model.load_state_dict(__snake_case )
# prepare test inputs
__A : Dict = prepare_img()
__A : Optional[Any] = ViTImageProcessor.from_pretrained('preprocessor_config' )
__A : Union[str, Any] = processor(images=__snake_case , return_tensors='pt' )
# compare outputs from both models
__A : str = get_expected_output(__snake_case )
__A : Optional[Any] = hf_model(inputs['pixel_values'] ).logits
assert hf_logits.shape == torch.Size([1, 10_00] )
assert torch.allclose(hf_logits[0, 0:5] , __snake_case , atol=1e-3 )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(f'Saving model {swiftformer_name} to {pytorch_dump_folder_path}' )
hf_model.save_pretrained(__snake_case )
if __name__ == "__main__":
lowercase__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swiftformer_name''',
default='''swiftformer_xs''',
choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
type=str,
help='''Name of the SwiftFormer model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''./converted_outputs/''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')
lowercase__ : int = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt) | 190 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : int ) -> bool:
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(__snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowerCAmelCase ( __snake_case : float = 0.1 ) -> int:
__A : Dict = 3
__A : int = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(__snake_case )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod() | 190 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A :
def __init__(self : List[Any] , __UpperCAmelCase : list[tuple[float, float]] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
UpperCAmelCase__ = len(__UpperCAmelCase ) - 1
def lowercase_ (self : int , __UpperCAmelCase : float ) -> list[float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
UpperCAmelCase__ = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , __UpperCAmelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__UpperCAmelCase ) , 5 ) == 1
return output_values
def lowercase_ (self : Dict , __UpperCAmelCase : float ) -> tuple[float, float]:
"""simple docstring"""
assert 0 <= t <= 1, "Time t must be between 0 and 1."
UpperCAmelCase__ = self.basis_function(__UpperCAmelCase )
UpperCAmelCase__ = 0.0
UpperCAmelCase__ = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowercase_ (self : Optional[int] , __UpperCAmelCase : float = 0.01 ) -> Optional[int]:
"""simple docstring"""
from matplotlib import pyplot as plt # type: ignore
UpperCAmelCase__ = [] # x coordinates of points to plot
UpperCAmelCase__ = [] # y coordinates of points to plot
UpperCAmelCase__ = 0.0
while t <= 1:
UpperCAmelCase__ = self.bezier_curve_function(__UpperCAmelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
UpperCAmelCase__ = [i[0] for i in self.list_of_points]
UpperCAmelCase__ = [i[1] for i in self.list_of_points]
plt.plot(
__UpperCAmelCase , __UpperCAmelCase , color="blue" , label="Curve of Degree " + str(self.degree ) , )
plt.scatter(__UpperCAmelCase , __UpperCAmelCase , color="red" , label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 65 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a ( UpperCAmelCase ) -> Dict:
"""simple docstring"""
lowerCamelCase__ : Dict = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def _a ( UpperCAmelCase ) -> Any:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
lowerCamelCase__ : Any = s_dict.pop(UpperCAmelCase )
elif "subsample" in key:
lowerCamelCase__ : Any = s_dict.pop(UpperCAmelCase )
def _a ( UpperCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = emb.weight.shape
lowerCamelCase__ : str = nn.Linear(UpperCAmelCase , UpperCAmelCase , bias=UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = emb.weight.data
return lin_layer
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase__ : List[Any] = torch.load(UpperCAmelCase , map_location='''cpu''' )
lowerCamelCase__ : List[Any] = mam_aaa['''args''']
lowerCamelCase__ : Dict = mam_aaa['''model''']
lowerCamelCase__ : Optional[Any] = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(UpperCAmelCase )
rename_keys(UpperCAmelCase )
lowerCamelCase__ : Tuple = state_dict['''decoder.embed_tokens.weight'''].shape[0]
lowerCamelCase__ : Tuple = args.share_decoder_input_output_embed
lowerCamelCase__ : Dict = [int(UpperCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
lowerCamelCase__ : str = SpeechaTextConfig(
vocab_size=UpperCAmelCase , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(UpperCAmelCase ) , conv_channels=args.conv_channels , conv_kernel_sizes=UpperCAmelCase , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=UpperCAmelCase , num_beams=5 , max_length=200 , use_cache=UpperCAmelCase , decoder_start_token_id=2 , early_stopping=UpperCAmelCase , )
lowerCamelCase__ : Optional[int] = SpeechaTextForConditionalGeneration(UpperCAmelCase )
lowerCamelCase__ , lowerCamelCase__ : Dict = model.model.load_state_dict(UpperCAmelCase , strict=UpperCAmelCase )
if len(UpperCAmelCase ) > 0 and not set(UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f" but all the following weights are missing {missing}" )
if tie_embeds:
lowerCamelCase__ : List[str] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
lowerCamelCase__ : Tuple = lm_head_weights
model.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_A : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_A : str = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 142 | 0 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( lowerCAmelCase_ : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]
def snake_case_ ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[int]="attention" ):
__lowercase : Optional[int] = np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :] )
__lowercase : Dict = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2] )
__lowercase : str = np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :] )
__lowercase : Union[str, Any] = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2] )
__lowercase : Any = np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :] )
__lowercase : Any = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2] )
__lowercase : Any = np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :] )
__lowercase : Optional[Any] = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def snake_case_ ( lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[Any]=False ):
if split_mlp_wi:
__lowercase : Any = params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
__lowercase : List[Any] = params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
__lowercase : Tuple = (wi_a, wi_a)
else:
__lowercase : Any = params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]
__lowercase : str = params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
return wi, wo
def snake_case_ ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[int] ):
return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]
def snake_case_ ( lowerCAmelCase_ : List[str] , *, lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple = False ):
__lowercase : Optional[Any] = traverse_util.flatten_dict(variables["""target"""] )
__lowercase : Tuple = {'/'.join(_UpperCAmelCase ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
__lowercase : Optional[Any] = 'encoder/encoder/mlp/wi_0/kernel' in old
print("""Split MLP:""" , _UpperCAmelCase )
__lowercase : int = collections.OrderedDict()
# Shared embeddings.
__lowercase : List[Any] = old['token_embedder/embedding']
# Encoder.
for i in range(_UpperCAmelCase ):
# Block i, layer 0 (Self Attention).
__lowercase : Dict = tax_layer_norm_lookup(_UpperCAmelCase , _UpperCAmelCase , """encoder""" , """pre_attention_layer_norm""" )
__lowercase : Tuple = tax_attention_lookup(_UpperCAmelCase , _UpperCAmelCase , """encoder""" , """attention""" )
__lowercase : Optional[int] = layer_norm
__lowercase : str = k.T
__lowercase : Union[str, Any] = o.T
__lowercase : Any = q.T
__lowercase : Dict = v.T
# Block i, layer 1 (MLP).
__lowercase : Optional[Any] = tax_layer_norm_lookup(_UpperCAmelCase , _UpperCAmelCase , """encoder""" , """pre_mlp_layer_norm""" )
__lowercase : List[str] = tax_mlp_lookup(_UpperCAmelCase , _UpperCAmelCase , """encoder""" , _UpperCAmelCase )
__lowercase : List[Any] = layer_norm
if split_mlp_wi:
__lowercase : str = wi[0].T
__lowercase : Any = wi[1].T
else:
__lowercase : int = wi.T
__lowercase : str = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
__lowercase : str = tax_relpos_bias_lookup(
_UpperCAmelCase , _UpperCAmelCase , """encoder""" ).T
__lowercase : int = old['encoder/encoder_norm/scale']
if not scalable_attention:
__lowercase : Optional[Any] = tax_relpos_bias_lookup(
_UpperCAmelCase , 0 , """encoder""" ).T
__lowercase : List[str] = tax_relpos_bias_lookup(
_UpperCAmelCase , 0 , """decoder""" ).T
if not is_encoder_only:
# Decoder.
for i in range(_UpperCAmelCase ):
# Block i, layer 0 (Self Attention).
__lowercase : Optional[int] = tax_layer_norm_lookup(_UpperCAmelCase , _UpperCAmelCase , """decoder""" , """pre_self_attention_layer_norm""" )
__lowercase : Tuple = tax_attention_lookup(_UpperCAmelCase , _UpperCAmelCase , """decoder""" , """self_attention""" )
__lowercase : Dict = layer_norm
__lowercase : Any = k.T
__lowercase : str = o.T
__lowercase : List[Any] = q.T
__lowercase : Optional[int] = v.T
# Block i, layer 1 (Cross Attention).
__lowercase : Any = tax_layer_norm_lookup(_UpperCAmelCase , _UpperCAmelCase , """decoder""" , """pre_cross_attention_layer_norm""" )
__lowercase : Dict = tax_attention_lookup(_UpperCAmelCase , _UpperCAmelCase , """decoder""" , """encoder_decoder_attention""" )
__lowercase : Optional[Any] = layer_norm
__lowercase : Union[str, Any] = k.T
__lowercase : List[Any] = o.T
__lowercase : Any = q.T
__lowercase : Dict = v.T
# Block i, layer 2 (MLP).
__lowercase : int = tax_layer_norm_lookup(_UpperCAmelCase , _UpperCAmelCase , """decoder""" , """pre_mlp_layer_norm""" )
__lowercase : Union[str, Any] = tax_mlp_lookup(_UpperCAmelCase , _UpperCAmelCase , """decoder""" , _UpperCAmelCase )
__lowercase : Optional[Any] = layer_norm
if split_mlp_wi:
__lowercase : Optional[int] = wi[0].T
__lowercase : List[str] = wi[1].T
else:
__lowercase : Any = wi.T
__lowercase : str = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
__lowercase : str = tax_relpos_bias_lookup(_UpperCAmelCase , _UpperCAmelCase , """decoder""" ).T
__lowercase : List[Any] = old['decoder/decoder_norm/scale']
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
__lowercase : Union[str, Any] = old['decoder/logits_dense/kernel'].T
return new
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : Any ):
__lowercase : Optional[Any] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
__lowercase : Union[str, Any] = state_dict['shared.weight']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
__lowercase : Optional[Any] = state_dict['shared.weight']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
__lowercase : Optional[Any] = state_dict['shared.weight']
return state_dict
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Any ):
__lowercase : int = checkpoints.load_tax_checkpoint(_UpperCAmelCase )
__lowercase : Tuple = convert_tax_to_pytorch(
_UpperCAmelCase , num_layers=config.num_layers , is_encoder_only=_UpperCAmelCase , scalable_attention=_UpperCAmelCase )
__lowercase : int = make_state_dict(_UpperCAmelCase , _UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
def snake_case_ ( lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str = False , lowerCAmelCase_ : Union[str, Any] = False , ):
__lowercase : str = MTaConfig.from_json_file(_UpperCAmelCase )
print(F"Building PyTorch model from configuration: {config}" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
__lowercase : Optional[int] = UMTaEncoderModel(_UpperCAmelCase )
else:
__lowercase : Union[str, Any] = UMTaForConditionalGeneration(_UpperCAmelCase )
# Load weights from tf checkpoint
load_tax_weights_in_ta(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(_UpperCAmelCase )
# Verify that we can load the checkpoint.
model.from_pretrained(_UpperCAmelCase )
print("""Done""" )
if __name__ == "__main__":
lowerCamelCase : Tuple = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
parser.add_argument(
'''--scalable_attention''',
action='''store_true''',
help='''Whether the model uses scaled attention (umt5 model)''',
default=False,
)
lowerCamelCase : Dict = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
) | 359 |
def snake_case_ ( lowerCAmelCase_ : int = 200 ):
__lowercase : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
__lowercase : List[str] = [0] * (pence + 1)
__lowercase : Optional[Any] = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowerCAmelCase_ , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(2_00) == 7_36_82 | 306 | 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 ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
UpperCAmelCase__ = 384
if "tiny" in model_name:
UpperCAmelCase__ = [3, 3, 9, 3]
UpperCAmelCase__ = [96, 192, 384, 768]
if "small" in model_name:
UpperCAmelCase__ = [3, 3, 27, 3]
UpperCAmelCase__ = [96, 192, 384, 768]
if "base" in model_name:
UpperCAmelCase__ = [3, 3, 27, 3]
UpperCAmelCase__ = [128, 256, 512, 1024]
UpperCAmelCase__ = 512
if "large" in model_name:
UpperCAmelCase__ = [3, 3, 27, 3]
UpperCAmelCase__ = [192, 384, 768, 1536]
UpperCAmelCase__ = 768
if "xlarge" in model_name:
UpperCAmelCase__ = [3, 3, 27, 3]
UpperCAmelCase__ = [256, 512, 1024, 2048]
UpperCAmelCase__ = 1024
# set label information
UpperCAmelCase__ = 150
UpperCAmelCase__ = """huggingface/label-files"""
UpperCAmelCase__ = """ade20k-id2label.json"""
UpperCAmelCase__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type="""dataset""" ) , """r""" ) )
UpperCAmelCase__ = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
UpperCAmelCase__ = {v: k for k, v in idalabel.items()}
UpperCAmelCase__ = ConvNextConfig(
depths=SCREAMING_SNAKE_CASE__ , hidden_sizes=SCREAMING_SNAKE_CASE__ , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
UpperCAmelCase__ = UperNetConfig(
backbone_config=SCREAMING_SNAKE_CASE__ , auxiliary_in_channels=SCREAMING_SNAKE_CASE__ , num_labels=SCREAMING_SNAKE_CASE__ , idalabel=SCREAMING_SNAKE_CASE__ , labelaid=SCREAMING_SNAKE_CASE__ , )
return config
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
UpperCAmelCase__ = []
# 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 ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] ):
'''simple docstring'''
UpperCAmelCase__ = dct.pop(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = val
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] ):
'''simple docstring'''
UpperCAmelCase__ = {
"""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""",
}
UpperCAmelCase__ = model_name_to_url[model_name]
UpperCAmelCase__ = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ , map_location="""cpu""" )["""state_dict"""]
UpperCAmelCase__ = get_upernet_config(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
UpperCAmelCase__ = state_dict.pop(SCREAMING_SNAKE_CASE__ )
if "bn" in key:
UpperCAmelCase__ = key.replace("""bn""" , """batch_norm""" )
UpperCAmelCase__ = val
# rename keys
UpperCAmelCase__ = create_rename_keys(SCREAMING_SNAKE_CASE__ )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
# verify on image
UpperCAmelCase__ = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
UpperCAmelCase__ = Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw ).convert("""RGB""" )
UpperCAmelCase__ = SegformerImageProcessor()
UpperCAmelCase__ = processor(SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
UpperCAmelCase__ = model(SCREAMING_SNAKE_CASE__ )
if model_name == "upernet-convnext-tiny":
UpperCAmelCase__ = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] )
elif model_name == "upernet-convnext-small":
UpperCAmelCase__ = torch.tensor(
[[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]] )
elif model_name == "upernet-convnext-base":
UpperCAmelCase__ = torch.tensor(
[[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]] )
elif model_name == "upernet-convnext-large":
UpperCAmelCase__ = torch.tensor(
[[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]] )
elif model_name == "upernet-convnext-xlarge":
UpperCAmelCase__ = torch.tensor(
[[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , SCREAMING_SNAKE_CASE__ , 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(SCREAMING_SNAKE_CASE__ )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
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__":
UpperCAmelCase_ = 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.'
)
UpperCAmelCase_ = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 346 |
def _UpperCAmelCase ( snake_case = 10_00 ):
"""simple docstring"""
_lowerCAmelCase = -1
_lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_lowerCAmelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
_lowerCAmelCase = n - a - b
if c * c == (a * a + b * b):
_lowerCAmelCase = a * b * c
if candidate >= product:
_lowerCAmelCase = candidate
return product
if __name__ == "__main__":
print(f"{solution() = }")
| 82 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase):
_lowerCAmelCase : Optional[Any] = StableDiffusionInpaintPipeline
_lowerCAmelCase : str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
_lowerCAmelCase : str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_lowerCAmelCase : Optional[Any] = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_lowerCAmelCase : Union[str, Any] = frozenset([])
def _snake_case ( self : Tuple ):
torch.manual_seed(0 )
snake_case_ : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=lowercase_ , )
snake_case_ : Any = PNDMScheduler(skip_prk_steps=lowercase_ )
torch.manual_seed(0 )
snake_case_ : Union[str, 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 , sample_size=128 , )
torch.manual_seed(0 )
snake_case_ : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=512 , )
snake_case_ : Optional[Any] = CLIPTextModel(lowercase_ )
snake_case_ : Dict = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
snake_case_ : Any = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def _snake_case ( self : Optional[int] , lowercase_ : Tuple , lowercase_ : Optional[Any]=0 ):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
snake_case_ : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
snake_case_ : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case_ : Optional[int] = Image.fromarray(np.uinta(lowercase_ ) ).convert('''RGB''' ).resize((64, 64) )
snake_case_ : List[Any] = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
if str(lowercase_ ).startswith('''mps''' ):
snake_case_ : Optional[Any] = torch.manual_seed(lowercase_ )
else:
snake_case_ : str = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
snake_case_ : Dict = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': init_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _snake_case ( self : Dict ):
snake_case_ : Union[str, Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ : Optional[Any] = self.get_dummy_components()
snake_case_ : str = StableDiffusionInpaintPipeline(**lowercase_ )
snake_case_ : str = sd_pipe.to(lowercase_ )
sd_pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ : Union[str, Any] = self.get_dummy_inputs(lowercase_ )
snake_case_ : Dict = sd_pipe(**lowercase_ ).images
snake_case_ : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case_ : int = np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self : Optional[int] ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : str ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : List[Any] ):
snake_case_ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
snake_case_ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
snake_case_ : int = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench.npy''' )
snake_case_ : Tuple = '''stabilityai/stable-diffusion-2-inpainting'''
snake_case_ : Optional[Any] = StableDiffusionInpaintPipeline.from_pretrained(lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ : Union[str, Any] = '''Face of a yellow cat, high resolution, sitting on a park bench'''
snake_case_ : int = torch.manual_seed(0 )
snake_case_ : Optional[Any] = pipe(
prompt=lowercase_ , image=lowercase_ , mask_image=lowercase_ , generator=lowercase_ , output_type='''np''' , )
snake_case_ : str = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9E-3
def _snake_case ( self : List[str] ):
snake_case_ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
snake_case_ : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
snake_case_ : Union[str, Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )
snake_case_ : int = '''stabilityai/stable-diffusion-2-inpainting'''
snake_case_ : str = StableDiffusionInpaintPipeline.from_pretrained(
lowercase_ , torch_dtype=torch.floataa , safety_checker=lowercase_ , )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ : Optional[Any] = '''Face of a yellow cat, high resolution, sitting on a park bench'''
snake_case_ : Tuple = torch.manual_seed(0 )
snake_case_ : List[str] = pipe(
prompt=lowercase_ , image=lowercase_ , mask_image=lowercase_ , generator=lowercase_ , output_type='''np''' , )
snake_case_ : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def _snake_case ( self : Optional[int] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
snake_case_ : Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
snake_case_ : List[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
snake_case_ : str = '''stabilityai/stable-diffusion-2-inpainting'''
snake_case_ : Optional[Any] = PNDMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ : Tuple = StableDiffusionInpaintPipeline.from_pretrained(
lowercase_ , safety_checker=lowercase_ , scheduler=lowercase_ , torch_dtype=torch.floataa , )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
snake_case_ : Any = '''Face of a yellow cat, high resolution, sitting on a park bench'''
snake_case_ : Dict = torch.manual_seed(0 )
snake_case_ : Dict = pipe(
prompt=lowercase_ , image=lowercase_ , mask_image=lowercase_ , generator=lowercase_ , num_inference_steps=2 , output_type='''np''' , )
snake_case_ : int = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 155 |
"""simple docstring"""
def __lowercase ( _a = 4_000_000 ):
snake_case_ : Dict = []
snake_case_, snake_case_ : List[str] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_a )
snake_case_, snake_case_ : str = b, a + b
return sum(_a )
if __name__ == "__main__":
print(f'{solution() = }')
| 155 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
a__ : str = random.Random()
def snake_case ( UpperCAmelCase , UpperCAmelCase=1.0 , UpperCAmelCase=None , UpperCAmelCase=None )-> Optional[Any]:
"""simple docstring"""
if rng is None:
__A = global_rng
__A = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class UpperCamelCase__ ( unittest.TestCase):
def __init__( self :List[str] , _A :Union[str, Any] , _A :Optional[Any]=7 , _A :Optional[Any]=400 , _A :Optional[int]=2_000 , _A :str=1 , _A :Tuple=0.0 , _A :Optional[int]=16_000 , _A :Any=True , _A :int=80 , _A :str=16 , _A :Any=64 , _A :Optional[Any]="hann_window" , _A :int=80 , _A :Optional[int]=7_600 , _A :Tuple=1E-10 , _A :List[str]=True , ) -> Any:
'''simple docstring'''
__A = parent
__A = batch_size
__A = min_seq_length
__A = max_seq_length
__A = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__A = feature_size
__A = padding_value
__A = sampling_rate
__A = do_normalize
__A = num_mel_bins
__A = hop_length
__A = win_length
__A = win_function
__A = fmin
__A = fmax
__A = mel_floor
__A = return_attention_mask
def lowercase_ ( self :int ) -> int:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def lowercase_ ( self :List[Any] , _A :Union[str, Any]=False , _A :Tuple=False ) -> Union[str, Any]:
'''simple docstring'''
def _flatten(_A :Dict ):
return list(itertools.chain(*_A ) )
if equal_length:
__A = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
__A = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__A = [np.asarray(_A ) for x in speech_inputs]
return speech_inputs
def lowercase_ ( self :Optional[Any] , _A :int=False , _A :Optional[int]=False ) -> Tuple:
'''simple docstring'''
if equal_length:
__A = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
__A = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__A = [np.asarray(_A ) for x in speech_inputs]
return speech_inputs
@require_torch
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE , unittest.TestCase):
UpperCAmelCase__ : Optional[Any] = SpeechTaFeatureExtractor
def lowercase_ ( self :List[str] ) -> Optional[Any]:
'''simple docstring'''
__A = SpeechTaFeatureExtractionTester(self )
def lowercase_ ( self :Optional[Any] , _A :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
self.assertTrue(np.all(np.mean(_A , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(_A , axis=0 ) - 1 ) < 1E-3 ) )
def lowercase_ ( self :Union[str, Any] ) -> List[str]:
'''simple docstring'''
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__A = [np.asarray(_A ) for speech_input in speech_inputs]
# Test not batched input
__A = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
__A = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
# Test batched
__A = feat_extract(_A , return_tensors='np' ).input_values
__A = feat_extract(_A , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(_A , _A ):
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
def lowercase_ ( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__A = ['longest', 'max_length', 'do_not_pad']
__A = [None, 1_600, None]
for max_length, padding in zip(_A , _A ):
__A = feat_extract(_A , padding=_A , max_length=_A , return_tensors='np' )
__A = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[1][:1_000] )
self.assertTrue(input_values[0][1_000:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[2][:1_200] )
def lowercase_ ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__A = range(800 , 1_400 , 200 )
__A = [floats_list((1, x) )[0] for x in lengths]
__A = ['longest', 'max_length', 'do_not_pad']
__A = [None, 1_600, None]
for max_length, padding in zip(_A , _A ):
__A = feat_extract(_A , max_length=_A , padding=_A )
__A = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1_000] )
self._check_zero_mean_unit_variance(input_values[2][:1_200] )
def lowercase_ ( self :int ) -> List[Any]:
'''simple docstring'''
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__A = feat_extract(
_A , truncation=_A , max_length=1_000 , padding='max_length' , return_tensors='np' )
__A = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def lowercase_ ( self :Dict ) -> Any:
'''simple docstring'''
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__A = feat_extract(
_A , truncation=_A , max_length=1_000 , padding='longest' , return_tensors='np' )
__A = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1_000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1_000) )
__A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__A = feat_extract(
_A , truncation=_A , max_length=2_000 , padding='longest' , return_tensors='np' )
__A = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1_000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1_200) )
def lowercase_ ( self :Optional[Any] ) -> Any:
'''simple docstring'''
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__A = np.random.rand(100 ).astype(np.floataa )
__A = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__A = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
__A = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def lowercase_ ( self :Optional[Any] ) -> Tuple:
'''simple docstring'''
__A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__A = [np.asarray(_A ) for speech_input in speech_inputs]
# Test feature size
__A = feature_extractor(audio_target=_A , padding=_A , return_tensors='np' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
__A = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_values
__A = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
# Test batched
__A = feature_extractor(_A , return_tensors='np' ).input_values
__A = feature_extractor(_A , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(_A , _A ):
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
__A = [floats_list((1, x) )[0] for x in (800, 800, 800)]
__A = np.asarray(_A )
__A = feature_extractor(_A , return_tensors='np' ).input_values
__A = feature_extractor(_A , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(_A , _A ):
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
def lowercase_ ( self :Dict ) -> str:
'''simple docstring'''
__A = self.feat_extract_tester.prepare_inputs_for_target()
__A = self.feature_extraction_class(**self.feat_extract_dict )
__A = feat_extract.model_input_names[0]
__A = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_A ) == len(_A ) for x, y in zip(_A , processed_features[input_name] ) ) )
__A = self.feat_extract_tester.prepare_inputs_for_target(equal_length=_A )
__A = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
__A = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__A = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def lowercase_ ( self :Optional[Any] ) -> List[str]:
'''simple docstring'''
__A = self.feat_extract_tester.prepare_inputs_for_target(equal_length=_A )
__A = self.feature_extraction_class(**self.feat_extract_dict )
__A = feat_extract.model_input_names[0]
__A = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
__A = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__A = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def lowercase_ ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
__A = self.feature_extraction_class(**self.feat_extract_dict )
__A = self.feat_extract_tester.prepare_inputs_for_target()
__A = feat_extract.model_input_names[0]
__A = BatchFeature({input_name: speech_inputs} )
__A = feat_extract.num_mel_bins # hack!
__A = feat_extract.pad(_A , padding='longest' , return_tensors='np' )[input_name]
__A = feat_extract.pad(_A , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def lowercase_ ( self :Dict ) -> Union[str, Any]:
'''simple docstring'''
__A = self.feat_extract_dict
__A = True
__A = self.feature_extraction_class(**_A )
__A = self.feat_extract_tester.prepare_inputs_for_target()
__A = [len(_A ) for x in speech_inputs]
__A = feat_extract.model_input_names[0]
__A = BatchFeature({input_name: speech_inputs} )
__A = feat_extract.num_mel_bins # hack!
__A = feat_extract.pad(_A , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , _A )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _A )
def lowercase_ ( self :Optional[Any] ) -> Tuple:
'''simple docstring'''
__A = self.feat_extract_dict
__A = True
__A = self.feature_extraction_class(**_A )
__A = self.feat_extract_tester.prepare_inputs_for_target()
__A = [len(_A ) for x in speech_inputs]
__A = feat_extract.model_input_names[0]
__A = BatchFeature({input_name: speech_inputs} )
__A = min(_A )
__A = feat_extract.num_mel_bins # hack!
__A = feat_extract.pad(
_A , padding='max_length' , max_length=_A , truncation=_A , return_tensors='np' )
self.assertIn('attention_mask' , _A )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def lowercase_ ( self :Union[str, Any] , _A :Any ) -> Union[str, Any]:
'''simple docstring'''
from datasets import load_dataset
__A = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
__A = ds.sort('id' ).select(range(_A ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def lowercase_ ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__A = torch.tensor(
[2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03] )
# fmt: on
__A = self._load_datasamples(1 )
__A = SpeechTaFeatureExtractor()
__A = feature_extractor(_A , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 93_680) )
self.assertTrue(torch.allclose(input_values[0, :30] , _A , atol=1E-6 ) )
def lowercase_ ( self :Optional[int] ) -> Tuple:
'''simple docstring'''
__A = torch.tensor(
[-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
-3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
-3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
-3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998] )
# fmt: on
__A = self._load_datasamples(1 )
__A = SpeechTaFeatureExtractor()
__A = feature_extractor(audio_target=_A , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , _A , atol=1E-4 ) )
| 161 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def snake_case ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> np.ndarray:
"""simple docstring"""
__A = cva.getAffineTransform(UpperCAmelCase , UpperCAmelCase )
return cva.warpAffine(UpperCAmelCase , UpperCAmelCase , (rows, cols) )
if __name__ == "__main__":
# read original image
a__ : str = cva.imread(
str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
)
# turn image in gray scale value
a__ : str = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
a__ , a__ : Optional[int] = gray_img.shape
# set different points to rotate image
a__ : List[str] = np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
a__ : Union[str, Any] = np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
a__ : int = np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
a__ : str = np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)
# add all rotated images in a list
a__ : Union[str, Any] = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
a__ : List[str] = plt.figure(1)
a__ : Optional[Any] = ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
plt.title(titles[i])
plt.axis("off")
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show()
| 161 | 1 |
def UpperCamelCase ( __lowercase : str = "The quick brown fox jumps over the lazy dog" ,):
'''simple docstring'''
A_ : Any = set()
# Replace all the whitespace in our sentence
A_ : Dict = input_str.replace(' ' ,'' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(__lowercase ) == 26
def UpperCamelCase ( __lowercase : str = "The quick brown fox jumps over the lazy dog" ,):
'''simple docstring'''
A_ : List[Any] = [False] * 26
for char in input_str:
if char.islower():
A_ : Any = True
elif char.isupper():
A_ : Optional[Any] = True
return all(__lowercase )
def UpperCamelCase ( __lowercase : str = "The quick brown fox jumps over the lazy dog" ,):
'''simple docstring'''
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def UpperCamelCase ( ):
'''simple docstring'''
from timeit import timeit
A_ : List[Any] = 'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
print(timeit('is_pangram()' ,setup=__lowercase ) )
print(timeit('is_pangram_faster()' ,setup=__lowercase ) )
print(timeit('is_pangram_fastest()' ,setup=__lowercase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 192 | from __future__ import annotations
import requests
_UpperCAmelCase = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def UpperCamelCase ( __lowercase : str ,__lowercase : int = 1 ,__lowercase : str = "new" ,__lowercase : list | None = None ):
'''simple docstring'''
A_ : Tuple = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(__lowercase ) - valid_terms ) ):
A_ : int = f'''Invalid search term: {invalid_search_terms}'''
raise ValueError(__lowercase )
A_ : Optional[int] = requests.get(
f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}''' ,headers={'User-agent': 'A random string'} ,)
if response.status_code == 4_29:
raise requests.HTTPError
A_ : Optional[Any] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(__lowercase )}
A_ : Union[str, Any] = {}
for id_ in range(__lowercase ):
A_ : List[str] = {
item: data['data']['children'][id_]['data'][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))
| 192 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
a_ = logging.getLogger(__name__)
@dataclass(frozen=snake_case )
class UpperCAmelCase_ :
UpperCamelCase =42
UpperCamelCase =42
UpperCamelCase =None
UpperCamelCase =None
UpperCamelCase =None
@dataclass(frozen=snake_case )
class UpperCAmelCase_ :
UpperCamelCase =42
UpperCamelCase =None
UpperCamelCase =None
UpperCamelCase =None
UpperCamelCase =None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =42
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_=False , UpperCamelCase_ = False , ) -> Tuple:
__lowercase : str = hans_processors[task]()
__lowercase : Tuple = os.path.join(
UpperCamelCase_ , '''cached_{}_{}_{}_{}'''.format(
'''dev''' if evaluate else '''train''' , tokenizer.__class__.__name__ , str(UpperCamelCase_ ) , UpperCamelCase_ , ) , )
__lowercase : int = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase ,__lowercase : List[str] = label_list[2], label_list[1]
__lowercase : Union[str, Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase : Any = cached_features_file + '''.lock'''
with FileLock(UpperCamelCase_ ):
if os.path.exists(UpperCamelCase_ ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
__lowercase : Union[str, Any] = torch.load(UpperCamelCase_ )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
__lowercase : Tuple = (
processor.get_dev_examples(UpperCamelCase_ ) if evaluate else processor.get_train_examples(UpperCamelCase_ )
)
logger.info('''Training examples: %s''' , len(UpperCamelCase_ ) )
__lowercase : Tuple = hans_convert_examples_to_features(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
logger.info('''Saving features into cached file %s''' , UpperCamelCase_ )
torch.save(self.features , UpperCamelCase_ )
def __len__( self ) -> Union[str, Any]:
return len(self.features )
def __getitem__( self , UpperCamelCase_ ) -> InputFeatures:
return self.features[i]
def _lowerCamelCase ( self ) -> Optional[int]:
return self.label_list
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase_ :
UpperCamelCase =42
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = 1_28 , UpperCamelCase_=False , UpperCamelCase_ = False , ) -> List[Any]:
__lowercase : Dict = hans_processors[task]()
__lowercase : int = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase ,__lowercase : Tuple = label_list[2], label_list[1]
__lowercase : Optional[int] = label_list
__lowercase : Optional[int] = processor.get_dev_examples(UpperCamelCase_ ) if evaluate else processor.get_train_examples(UpperCamelCase_ )
__lowercase : str = hans_convert_examples_to_features(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='''convert examples to features''' ):
if ex_index % 1_00_00 == 0:
logger.info('''Writing example %d of %d''' % (ex_index, len(UpperCamelCase_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
__lowercase : List[Any] = tf.data.Dataset.from_generator(
UpperCamelCase_ , (
{
'''example_id''': tf.intaa,
'''input_ids''': tf.intaa,
'''attention_mask''': tf.intaa,
'''token_type_ids''': tf.intaa,
},
tf.intaa,
) , (
{
'''example_id''': tf.TensorShape([] ),
'''input_ids''': tf.TensorShape([None, None] ),
'''attention_mask''': tf.TensorShape([None, None] ),
'''token_type_ids''': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _lowerCamelCase ( self ) -> Union[str, Any]:
return self.dataset
def __len__( self ) -> Any:
return len(self.features )
def __getitem__( self , UpperCamelCase_ ) -> InputFeatures:
return self.features[i]
def _lowerCamelCase ( self ) -> Optional[Any]:
return self.label_list
class UpperCAmelCase_ ( snake_case ):
def _lowerCamelCase ( self , UpperCamelCase_ ) -> str:
return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase_ , '''heuristics_train_set.txt''' ) ) , '''train''' )
def _lowerCamelCase ( self , UpperCamelCase_ ) -> Tuple:
return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase_ , '''heuristics_evaluation_set.txt''' ) ) , '''dev''' )
def _lowerCamelCase ( self ) -> Union[str, Any]:
return ["contradiction", "entailment", "neutral"]
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
__lowercase : Dict = []
for i, line in enumerate(UpperCamelCase_ ):
if i == 0:
continue
__lowercase : Optional[Any] = '''%s-%s''' % (set_type, line[0])
__lowercase : Optional[Any] = line[5]
__lowercase : Optional[Any] = line[6]
__lowercase : Tuple = line[7][2:] if line[7].startswith('''ex''' ) else line[7]
__lowercase : str = line[0]
examples.append(InputExample(guid=UpperCamelCase_ , text_a=UpperCamelCase_ , text_b=UpperCamelCase_ , label=UpperCamelCase_ , pairID=UpperCamelCase_ ) )
return examples
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
__lowercase : Tuple = {label: i for i, label in enumerate(__UpperCamelCase )}
__lowercase : Optional[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(__UpperCamelCase ) , desc='''convert examples to features''' ):
if ex_index % 1_00_00 == 0:
logger.info('''Writing example %d''' % (ex_index) )
__lowercase : Tuple = tokenizer(
example.text_a , example.text_b , add_special_tokens=__UpperCamelCase , max_length=__UpperCamelCase , padding='''max_length''' , truncation=__UpperCamelCase , return_overflowing_tokens=__UpperCamelCase , )
__lowercase : Dict = label_map[example.label] if example.label in label_map else 0
__lowercase : List[str] = int(example.pairID )
features.append(InputFeatures(**__UpperCamelCase , label=__UpperCamelCase , pairID=__UpperCamelCase ) )
for i, example in enumerate(examples[:5] ):
logger.info('''*** Example ***''' )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
a_ = {
'hans': 3,
}
a_ = {
'hans': HansProcessor,
}
| 249 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase_ ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase =StableDiffusionDiffEditPipeline
UpperCamelCase =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
UpperCamelCase =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
UpperCamelCase =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase =frozenset([] )
def _lowerCamelCase ( self ) -> str:
torch.manual_seed(0 )
__lowercase : Optional[Any] = 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 , attention_head_dim=(2, 4) , use_linear_projection=UpperCamelCase_ , )
__lowercase : Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , clip_sample=UpperCamelCase_ , set_alpha_to_one=UpperCamelCase_ , )
__lowercase : Optional[int] = DDIMInverseScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , clip_sample=UpperCamelCase_ , set_alpha_to_zero=UpperCamelCase_ , )
torch.manual_seed(0 )
__lowercase : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=1_28 , )
torch.manual_seed(0 )
__lowercase : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='''gelu''' , projection_dim=5_12 , )
__lowercase : Optional[int] = CLIPTextModel(UpperCamelCase_ )
__lowercase : List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__lowercase : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''inverse_scheduler''': inverse_scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_=0 ) -> Any:
__lowercase : Any = floats_tensor((1, 16, 16) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__lowercase : Dict = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
if str(UpperCamelCase_ ).startswith('''mps''' ):
__lowercase : List[Any] = torch.manual_seed(UpperCamelCase_ )
else:
__lowercase : Any = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
__lowercase : Any = {
'''prompt''': '''a dog and a newt''',
'''mask_image''': mask,
'''image_latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_=0 ) -> int:
__lowercase : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__lowercase : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowercase : List[Any] = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' )
if str(UpperCamelCase_ ).startswith('''mps''' ):
__lowercase : List[str] = torch.manual_seed(UpperCamelCase_ )
else:
__lowercase : List[Any] = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
__lowercase : int = {
'''image''': image,
'''source_prompt''': '''a cat and a frog''',
'''target_prompt''': '''a dog and a newt''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''num_maps_per_mask''': 2,
'''mask_encode_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_=0 ) -> Union[str, Any]:
__lowercase : Union[str, Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__lowercase : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowercase : Any = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' )
if str(UpperCamelCase_ ).startswith('''mps''' ):
__lowercase : Optional[Any] = torch.manual_seed(UpperCamelCase_ )
else:
__lowercase : int = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
__lowercase : Optional[int] = {
'''image''': image,
'''prompt''': '''a cat and a frog''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''decode_latents''': True,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCamelCase ( self ) -> Optional[Any]:
if not hasattr(self.pipeline_class , '''_optional_components''' ):
return
__lowercase : Optional[int] = self.get_dummy_components()
__lowercase : List[str] = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
__lowercase : Union[str, Any] = self.get_dummy_inputs(UpperCamelCase_ )
__lowercase : Any = pipe(**UpperCamelCase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCamelCase_ )
__lowercase : Tuple = self.pipeline_class.from_pretrained(UpperCamelCase_ )
pipe_loaded.to(UpperCamelCase_ )
pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(UpperCamelCase_ , UpperCamelCase_ ) is None , F"""`{optional_component}` did not stay set to None after loading.""" , )
__lowercase : List[Any] = self.get_dummy_inputs(UpperCamelCase_ )
__lowercase : Any = pipe_loaded(**UpperCamelCase_ )[0]
__lowercase : Any = np.abs(output - output_loaded ).max()
self.assertLess(UpperCamelCase_ , 1E-4 )
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : int = '''cpu'''
__lowercase : Optional[int] = self.get_dummy_components()
__lowercase : Tuple = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : str = self.get_dummy_mask_inputs(UpperCamelCase_ )
__lowercase : int = pipe.generate_mask(**UpperCamelCase_ )
__lowercase : Any = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
__lowercase : List[Any] = np.array([0] * 9 )
__lowercase : str = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase_ , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def _lowerCamelCase ( self ) -> str:
__lowercase : Optional[int] = '''cpu'''
__lowercase : Dict = self.get_dummy_components()
__lowercase : str = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : int = self.get_dummy_inversion_inputs(UpperCamelCase_ )
__lowercase : List[str] = pipe.invert(**UpperCamelCase_ ).images
__lowercase : Any = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__lowercase : Any = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
__lowercase : int = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase_ , 1E-3 )
def _lowerCamelCase ( self ) -> Optional[int]:
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def _lowerCamelCase ( self ) -> str:
__lowercase : Union[str, Any] = '''cpu'''
__lowercase : str = self.get_dummy_components()
__lowercase : Any = {'''beta_start''': 0.0_0_0_8_5, '''beta_end''': 0.0_1_2, '''beta_schedule''': '''scaled_linear'''}
__lowercase : str = DPMSolverMultistepScheduler(**UpperCamelCase_ )
__lowercase : List[str] = DPMSolverMultistepInverseScheduler(**UpperCamelCase_ )
__lowercase : int = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : str = self.get_dummy_inversion_inputs(UpperCamelCase_ )
__lowercase : str = pipe.invert(**UpperCamelCase_ ).images
__lowercase : Any = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__lowercase : Union[str, Any] = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
__lowercase : Tuple = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase_ , 1E-3 )
@require_torch_gpu
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
def _lowerCamelCase ( self ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def _lowerCamelCase ( cls ) -> Optional[Any]:
__lowercase : List[str] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''' )
__lowercase : Optional[int] = raw_image.convert('''RGB''' ).resize((7_68, 7_68) )
__lowercase : Any = raw_image
def _lowerCamelCase ( self ) -> Optional[int]:
__lowercase : str = torch.manual_seed(0 )
__lowercase : Optional[int] = StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''' , safety_checker=UpperCamelCase_ , torch_dtype=torch.floataa )
__lowercase : List[str] = DDIMScheduler.from_config(pipe.scheduler.config )
__lowercase : Dict = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : Tuple = '''a bowl of fruit'''
__lowercase : int = '''a bowl of pears'''
__lowercase : str = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCamelCase_ , target_prompt=UpperCamelCase_ , generator=UpperCamelCase_ , )
__lowercase : Dict = pipe.invert(
prompt=UpperCamelCase_ , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCamelCase_ ).latents
__lowercase : Optional[int] = pipe(
prompt=UpperCamelCase_ , mask_image=UpperCamelCase_ , image_latents=UpperCamelCase_ , generator=UpperCamelCase_ , negative_prompt=UpperCamelCase_ , inpaint_strength=0.7 , output_type='''numpy''' , ).images[0]
__lowercase : int = (
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5E-1
def _lowerCamelCase ( self ) -> Tuple:
__lowercase : Union[str, Any] = torch.manual_seed(0 )
__lowercase : Dict = StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''' , safety_checker=UpperCamelCase_ , torch_dtype=torch.floataa )
__lowercase : int = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__lowercase : str = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : List[str] = '''a bowl of fruit'''
__lowercase : Union[str, Any] = '''a bowl of pears'''
__lowercase : int = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCamelCase_ , target_prompt=UpperCamelCase_ , generator=UpperCamelCase_ , )
__lowercase : List[Any] = pipe.invert(
prompt=UpperCamelCase_ , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCamelCase_ , num_inference_steps=25 , ).latents
__lowercase : Optional[int] = pipe(
prompt=UpperCamelCase_ , mask_image=UpperCamelCase_ , image_latents=UpperCamelCase_ , generator=UpperCamelCase_ , negative_prompt=UpperCamelCase_ , inpaint_strength=0.7 , num_inference_steps=25 , output_type='''numpy''' , ).images[0]
__lowercase : Union[str, Any] = (
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 249 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all LED models at https://huggingface.co/models?filter=LED
_lowerCamelCase : int = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
_lowerCamelCase : Optional[int] = {
"allenai/led-base-16384": 1_6_3_8_4,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def _UpperCAmelCase ():
'''simple docstring'''
_lowerCAmelCase : Tuple = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
_lowerCAmelCase : List[Any] = bs[:]
_lowerCAmelCase : int = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_lowercase )
cs.append(2**8 + n )
n += 1
_lowerCAmelCase : int = [chr(_lowercase ) for n in cs]
return dict(zip(_lowercase , _lowercase ) )
def _UpperCAmelCase (UpperCamelCase_ : int ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = set()
_lowerCAmelCase : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCAmelCase : Tuple = char
return pairs
class __snake_case (_a ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ["input_ids", "attention_mask"]
def __init__( self : List[str] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[Any]="replace" , _UpperCAmelCase : Optional[Any]="<s>" , _UpperCAmelCase : str="</s>" , _UpperCAmelCase : Tuple="</s>" , _UpperCAmelCase : Union[str, Any]="<s>" , _UpperCAmelCase : Optional[int]="<unk>" , _UpperCAmelCase : int="<pad>" , _UpperCAmelCase : List[Any]="<mask>" , _UpperCAmelCase : Optional[Any]=False , **_UpperCAmelCase : Optional[Any] , ) -> List[str]:
'''simple docstring'''
_lowerCAmelCase : List[Any] = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else bos_token
_lowerCAmelCase : Union[str, Any] = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else eos_token
_lowerCAmelCase : List[Any] = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else sep_token
_lowerCAmelCase : str = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else cls_token
_lowerCAmelCase : List[str] = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else unk_token
_lowerCAmelCase : List[str] = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase : Dict = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else mask_token
super().__init__(
errors=__A , bos_token=__A , eos_token=__A , unk_token=__A , sep_token=__A , cls_token=__A , pad_token=__A , mask_token=__A , add_prefix_space=__A , **__A , )
with open(__A , encoding="""utf-8""" ) as vocab_handle:
_lowerCAmelCase : List[Any] = json.load(__A )
_lowerCAmelCase : str = {v: k for k, v in self.encoder.items()}
_lowerCAmelCase : Tuple = errors # how to handle errors in decoding
_lowerCAmelCase : Tuple = bytes_to_unicode()
_lowerCAmelCase : Tuple = {v: k for k, v in self.byte_encoder.items()}
with open(__A , encoding="""utf-8""" ) as merges_handle:
_lowerCAmelCase : str = merges_handle.read().split("""\n""" )[1:-1]
_lowerCAmelCase : Dict = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCAmelCase : Union[str, Any] = dict(zip(__A , range(len(__A ) ) ) )
_lowerCAmelCase : str = {}
_lowerCAmelCase : List[Any] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCAmelCase : Dict = 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.bart.tokenization_bart.BartTokenizer.vocab_size
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
return len(self.encoder )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[str]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , _UpperCAmelCase : List[Any] ) -> Optional[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
_lowerCAmelCase : Optional[int] = tuple(__A )
_lowerCAmelCase : List[Any] = get_pairs(__A )
if not pairs:
return token
while True:
_lowerCAmelCase : Tuple = min(__A , key=lambda _UpperCAmelCase : self.bpe_ranks.get(__A , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCAmelCase , _lowerCAmelCase : Any = bigram
_lowerCAmelCase : Optional[Any] = []
_lowerCAmelCase : Dict = 0
while i < len(__A ):
try:
_lowerCAmelCase : List[str] = word.index(__A , __A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCAmelCase : Optional[Any] = j
if word[i] == first and i < len(__A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCAmelCase : Tuple = tuple(__A )
_lowerCAmelCase : Tuple = new_word
if len(__A ) == 1:
break
else:
_lowerCAmelCase : Optional[Any] = get_pairs(__A )
_lowerCAmelCase : int = """ """.join(__A )
_lowerCAmelCase : Optional[int] = word
return word
def SCREAMING_SNAKE_CASE ( self : Any , _UpperCAmelCase : List[str] ) -> Tuple:
'''simple docstring'''
_lowerCAmelCase : Optional[int] = []
for token in re.findall(self.pat , __A ):
_lowerCAmelCase : Tuple = """""".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(__A ).split(""" """ ) )
return bpe_tokens
def SCREAMING_SNAKE_CASE ( self : List[str] , _UpperCAmelCase : List[Any] ) -> List[str]:
'''simple docstring'''
return self.encoder.get(__A , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self : Tuple , _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return self.decoder.get(__A )
def SCREAMING_SNAKE_CASE ( self : List[Any] , _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
_lowerCAmelCase : Optional[int] = """""".join(__A )
_lowerCAmelCase : Union[str, Any] = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(__A ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
_lowerCAmelCase : Union[str, Any] = os.path.join(
__A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
_lowerCAmelCase : List[str] = os.path.join(
__A , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(__A , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__A , ensure_ascii=__A ) + """\n""" )
_lowerCAmelCase : List[str] = 0
with open(__A , """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 _UpperCAmelCase : 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 : Optional[int] = token_index
writer.write(""" """.join(__A ) + """\n""" )
index += 1
return vocab_file, merge_file
def SCREAMING_SNAKE_CASE ( self : Optional[int] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase : Tuple = [self.cls_token_id]
_lowerCAmelCase : Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self : Tuple , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A )
if token_ids_a is None:
return [1] + ([0] * len(__A )) + [1]
return [1] + ([0] * len(__A )) + [1, 1] + ([0] * len(__A )) + [1]
def SCREAMING_SNAKE_CASE ( self : str , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
_lowerCAmelCase : Tuple = [self.sep_token_id]
_lowerCAmelCase : 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 SCREAMING_SNAKE_CASE ( self : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple=False , **_UpperCAmelCase : Optional[int] ) -> Dict:
'''simple docstring'''
_lowerCAmelCase : int = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__A ) > 0 and not text[0].isspace()):
_lowerCAmelCase : List[str] = """ """ + text
return (text, kwargs)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , _UpperCAmelCase : Union[Dict[str, EncodedInput], BatchEncoding] , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , ) -> dict:
'''simple docstring'''
_lowerCAmelCase : Optional[int] = super()._pad(
encoded_inputs=__A , max_length=__A , padding_strategy=__A , pad_to_multiple_of=__A , return_attention_mask=__A , )
# Load from model defaults
if return_attention_mask is None:
_lowerCAmelCase : List[str] = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCAmelCase : List[Any] = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCAmelCase : Any = len(encoded_inputs["""global_attention_mask"""] ) != len(__A )
if needs_to_be_padded:
_lowerCAmelCase : Tuple = len(__A ) - len(encoded_inputs["""global_attention_mask"""] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_lowerCAmelCase : Any = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCAmelCase : Optional[Any] = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return encoded_inputs
| 359 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
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
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
_lowerCamelCase : Tuple = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCamelCase : List[str] = get_tests_dir("fixtures/vocab.json")
_lowerCamelCase : str = get_tests_dir("fixtures")
class __snake_case (unittest.TestCase ):
lowerCAmelCase__ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Any:
'''simple docstring'''
_lowerCAmelCase : Any = 0
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Any:
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase : List[Any] = WavaVecaConfig()
_lowerCAmelCase : str = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
# save in new folder
model_config.save_pretrained(_UpperCAmelCase )
processor.save_pretrained(_UpperCAmelCase )
_lowerCAmelCase : Any = AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ) -> Union[str, Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )
copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , """vocab.json""" ) )
_lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase : Any = WavaVecaFeatureExtractor()
_lowerCAmelCase : Optional[int] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
_lowerCAmelCase : List[str] = WavaVecaProcessor(_UpperCAmelCase , _UpperCAmelCase )
# save in new folder
processor.save_pretrained(_UpperCAmelCase )
# drop `processor_class` in tokenizer
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """r""" ) as f:
_lowerCAmelCase : Union[str, Any] = json.load(_UpperCAmelCase )
config_dict.pop("""processor_class""" )
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """w""" ) as f:
f.write(json.dumps(_UpperCAmelCase ) )
_lowerCAmelCase : List[Any] = AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase : Dict = WavaVecaFeatureExtractor()
_lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
_lowerCAmelCase : str = WavaVecaProcessor(_UpperCAmelCase , _UpperCAmelCase )
# save in new folder
processor.save_pretrained(_UpperCAmelCase )
# drop `processor_class` in feature extractor
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """r""" ) as f:
_lowerCAmelCase : str = json.load(_UpperCAmelCase )
config_dict.pop("""processor_class""" )
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """w""" ) as f:
f.write(json.dumps(_UpperCAmelCase ) )
_lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase : Tuple = WavaVecaConfig(processor_class="""Wav2Vec2Processor""" )
model_config.save_pretrained(_UpperCAmelCase )
# copy relevant files
copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , """vocab.json""" ) )
# create emtpy sample processor
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , """w""" ) as f:
f.write("""{}""" )
_lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
with self.assertRaises(_UpperCAmelCase ):
_lowerCAmelCase : Any = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_UpperCAmelCase ):
_lowerCAmelCase : List[str] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase )
_lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
_lowerCAmelCase : Optional[int] = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
_lowerCAmelCase : Union[str, Any] = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
# Test we can also load the slow version
_lowerCAmelCase : Optional[int] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase , use_fast=_UpperCAmelCase )
_lowerCAmelCase : List[str] = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , """NewTokenizer""" )
else:
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
def SCREAMING_SNAKE_CASE ( self : str ) -> Union[str, Any]:
'''simple docstring'''
try:
AutoConfig.register("""custom""" , _UpperCAmelCase )
AutoFeatureExtractor.register(_UpperCAmelCase , _UpperCAmelCase )
AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase )
AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_UpperCAmelCase ):
AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCAmelCase : List[str] = CustomFeatureExtractor.from_pretrained(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase : Tuple = os.path.join(_UpperCAmelCase , """vocab.txt""" )
with open(_UpperCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
_lowerCAmelCase : str = CustomTokenizer(_UpperCAmelCase )
_lowerCAmelCase : List[str] = CustomProcessor(_UpperCAmelCase , _UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(_UpperCAmelCase )
_lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
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]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
'''simple docstring'''
class __snake_case (_a ):
lowerCAmelCase__ = False
class __snake_case (_a ):
lowerCAmelCase__ = False
class __snake_case (_a ):
lowerCAmelCase__ = "AutoFeatureExtractor"
lowerCAmelCase__ = "AutoTokenizer"
lowerCAmelCase__ = False
try:
AutoConfig.register("""custom""" , _UpperCAmelCase )
AutoFeatureExtractor.register(_UpperCAmelCase , _UpperCAmelCase )
AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase )
AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
# If remote code is not set, the default is to use local classes.
_lowerCAmelCase : Optional[Any] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
_lowerCAmelCase : str = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
_lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=_UpperCAmelCase )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
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]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
self.assertEqual(processor.__class__.__name__ , """BertTokenizerFast""" )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict:
'''simple docstring'''
_lowerCAmelCase : List[str] = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext""" )
self.assertEqual(processor.__class__.__name__ , """ConvNextImageProcessor""" )
@is_staging_test
class __snake_case (unittest.TestCase ):
lowerCAmelCase__ = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def SCREAMING_SNAKE_CASE ( cls : int ) -> Any:
'''simple docstring'''
_lowerCAmelCase : List[str] = TOKEN
HfFolder.save_token(_UpperCAmelCase )
@classmethod
def SCREAMING_SNAKE_CASE ( cls : Tuple ) -> Optional[int]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="""test-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-processor""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
'''simple docstring'''
_lowerCAmelCase : Optional[int] = WavaVecaProcessor.from_pretrained(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(_UpperCAmelCase , """test-processor""" ) , push_to_hub=_UpperCAmelCase , use_auth_token=self._token )
_lowerCAmelCase : str = WavaVecaProcessor.from_pretrained(f"{USER}/test-processor" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(_UpperCAmelCase , getattr(new_processor.feature_extractor , _UpperCAmelCase ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
'''simple docstring'''
_lowerCAmelCase : int = WavaVecaProcessor.from_pretrained(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(_UpperCAmelCase , """test-processor-org""" ) , push_to_hub=_UpperCAmelCase , use_auth_token=self._token , organization="""valid_org""" , )
_lowerCAmelCase : str = WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(_UpperCAmelCase , getattr(new_processor.feature_extractor , _UpperCAmelCase ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
_lowerCAmelCase : Any = CustomFeatureExtractor.from_pretrained(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase : int = os.path.join(_UpperCAmelCase , """vocab.txt""" )
with open(_UpperCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
_lowerCAmelCase : List[str] = CustomTokenizer(_UpperCAmelCase )
_lowerCAmelCase : List[str] = CustomProcessor(_UpperCAmelCase , _UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(f"{USER}/test-dynamic-processor" , token=self._token )
_lowerCAmelCase : Union[str, Any] = Repository(_UpperCAmelCase , clone_from=f"{USER}/test-dynamic-processor" , token=self._token )
processor.save_pretrained(_UpperCAmelCase )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(_UpperCAmelCase , """tokenizer_config.json""" ) ) as f:
_lowerCAmelCase : str = json.load(_UpperCAmelCase )
self.assertDictEqual(
tokenizer_config["""auto_map"""] , {
"""AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , """custom_feature_extraction.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , """custom_tokenization.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , """custom_processing.py""" ) ) )
repo.push_to_hub()
_lowerCAmelCase : Tuple = AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor" , trust_remote_code=_UpperCAmelCase )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , """CustomProcessor""" )
| 159 | 0 |
from __future__ import annotations
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __A=None ):
"""simple docstring"""
lowerCamelCase : List[str] = data
lowerCamelCase : Any = None
def __repr__( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = []
lowerCamelCase : Optional[Any] = self
while temp:
string_rep.append(F"""{temp.data}""" )
lowerCamelCase : Tuple = temp.next
return "->".join(__A )
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if not elements_list:
raise Exception("The Elements List is empty" )
lowerCamelCase : Optional[Any] = Node(elements_list[0] )
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
lowerCamelCase : int = Node(elements_list[i] )
lowerCamelCase : List[str] = current.next
return head
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if head_node is not None and isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
print_reverse(head_node.next )
print(head_node.data )
def lowercase_( ):
'''simple docstring'''
from doctest import testmod
testmod()
lowerCamelCase : List[str] = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(SCREAMING_SNAKE_CASE_ )
print("Elements in Reverse:" )
print_reverse(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 283 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_snake_case = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
_snake_case = parser.parse_args()
_snake_case = '''cpu'''
_snake_case = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
_snake_case = '''path-to-your-trained-model'''
_snake_case = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
_snake_case = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_snake_case = pipe.to(device)
# to channels last
_snake_case = pipe.unet.to(memory_format=torch.channels_last)
_snake_case = pipe.vae.to(memory_format=torch.channels_last)
_snake_case = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
_snake_case = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
_snake_case = torch.randn(2, 4, 64, 64)
_snake_case = torch.rand(1) * 9_99
_snake_case = torch.randn(2, 77, 7_68)
_snake_case = (sample, timestep, encoder_hidden_status)
try:
_snake_case = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
_snake_case = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
_snake_case = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
_snake_case = 6_66
_snake_case = torch.Generator(device).manual_seed(seed)
_snake_case = {'''generator''': generator}
if args.steps is not None:
_snake_case = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
_snake_case = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 283 | 1 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCAmelCase = logging.get_logger(__name__)
class A ( A_ ):
def __init__(self , *lowerCAmelCase , **lowerCAmelCase ):
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , lowerCAmelCase , )
super().__init__(*lowerCAmelCase , **lowerCAmelCase )
| 368 |
def _lowerCamelCase( lowercase__ = 1_0_0_0 ) -> int:
'''simple docstring'''
__lowercase= 2**power
__lowercase= str(lowercase__ )
__lowercase= list(lowercase__ )
__lowercase= 0
for i in list_num:
sum_of_num += int(lowercase__ )
return sum_of_num
if __name__ == "__main__":
lowerCAmelCase = int(input('''Enter the power of 2: ''').strip())
print('''2 ^ ''', power, ''' = ''', 2**power)
lowerCAmelCase = solution(power)
print('''Sum of the digits is: ''', result)
| 304 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = jnp.ones((batch_size, length) ) / length
return scores
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = None
lowerCamelCase_ = 20
lowerCamelCase_ = self._get_uniform_logits(batch_size=2 , length=UpperCamelCase )
# tweak scores to not be uniform anymore
lowerCamelCase_ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase_ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase_ = jax.nn.softmax(UpperCamelCase , axis=-1 )
lowerCamelCase_ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase_ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase_ = jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase , scores.copy() , cur_len=UpperCamelCase ) , axis=-1 )
lowerCamelCase_ = jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase , scores.copy() , cur_len=UpperCamelCase ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1e-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1e-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = None
lowerCamelCase_ = 10
lowerCamelCase_ = 2
# create ramp distribution
lowerCamelCase_ = np.broadcast_to(np.arange(UpperCamelCase )[None, :] , (batch_size, vocab_size) ).copy()
lowerCamelCase_ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase_ = FlaxTopKLogitsWarper(3 )
lowerCamelCase_ = top_k_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase_ = 5
lowerCamelCase_ = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
lowerCamelCase_ = np.broadcast_to(np.arange(UpperCamelCase )[None, :] , (batch_size, length) ).copy()
lowerCamelCase_ = top_k_warp_safety_check(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = None
lowerCamelCase_ = 10
lowerCamelCase_ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase_ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase_ = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase_ = np.exp(top_p_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase_ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(UpperCamelCase , UpperCamelCase , atol=1e-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase_ = np.broadcast_to(np.arange(UpperCamelCase )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase_ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase_ = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
lowerCamelCase_ = top_p_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = 20
lowerCamelCase_ = 4
lowerCamelCase_ = 0
lowerCamelCase_ = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase )
# check that min length is applied at length 5
lowerCamelCase_ = ids_tensor((batch_size, 20) , vocab_size=20 )
lowerCamelCase_ = 5
lowerCamelCase_ = self._get_uniform_logits(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = min_dist_processor(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float("inf" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase_ = self._get_uniform_logits(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = 15
lowerCamelCase_ = min_dist_processor(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
self.assertFalse(jnp.isinf(UpperCamelCase ).any() )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = 20
lowerCamelCase_ = 4
lowerCamelCase_ = 0
lowerCamelCase_ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase_ = ids_tensor((batch_size, 1) , vocab_size=20 )
lowerCamelCase_ = 1
lowerCamelCase_ = self._get_uniform_logits(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = logits_processor(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase_ = 3
lowerCamelCase_ = self._get_uniform_logits(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = logits_processor(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
self.assertFalse(jnp.isinf(UpperCamelCase ).any() )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = 20
lowerCamelCase_ = 4
lowerCamelCase_ = 0
lowerCamelCase_ = 5
lowerCamelCase_ = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase , eos_token_id=UpperCamelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase_ = ids_tensor((batch_size, 4) , vocab_size=20 )
lowerCamelCase_ = 4
lowerCamelCase_ = self._get_uniform_logits(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = logits_processor(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase_ = 3
lowerCamelCase_ = self._get_uniform_logits(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = logits_processor(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
self.assertFalse(jnp.isinf(UpperCamelCase ).any() )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = 4
lowerCamelCase_ = 10
lowerCamelCase_ = 15
lowerCamelCase_ = 2
lowerCamelCase_ = 1
lowerCamelCase_ = 15
# dummy input_ids and scores
lowerCamelCase_ = ids_tensor((batch_size, sequence_length) , UpperCamelCase )
lowerCamelCase_ = input_ids.copy()
lowerCamelCase_ = self._get_uniform_logits(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = scores.copy()
# instantiate all dist processors
lowerCamelCase_ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase_ = FlaxTopKLogitsWarper(3 )
lowerCamelCase_ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase_ = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase )
lowerCamelCase_ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase )
lowerCamelCase_ = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase , eos_token_id=UpperCamelCase )
lowerCamelCase_ = 10
# no processor list
lowerCamelCase_ = temp_dist_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = top_k_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = top_p_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = min_dist_proc(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = bos_dist_proc(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = eos_dist_proc(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
# with processor list
lowerCamelCase_ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase_ = processor(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
# scores should be equal
self.assertTrue(jnp.allclose(UpperCamelCase , UpperCamelCase , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = 4
lowerCamelCase_ = 10
lowerCamelCase_ = 15
lowerCamelCase_ = 2
lowerCamelCase_ = 1
lowerCamelCase_ = 15
# dummy input_ids and scores
lowerCamelCase_ = ids_tensor((batch_size, sequence_length) , UpperCamelCase )
lowerCamelCase_ = input_ids.copy()
lowerCamelCase_ = self._get_uniform_logits(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = scores.copy()
# instantiate all dist processors
lowerCamelCase_ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase_ = FlaxTopKLogitsWarper(3 )
lowerCamelCase_ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase_ = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase )
lowerCamelCase_ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase )
lowerCamelCase_ = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase , eos_token_id=UpperCamelCase )
lowerCamelCase_ = 10
# no processor list
def run_no_processor_list(UpperCamelCase , UpperCamelCase , UpperCamelCase ):
lowerCamelCase_ = temp_dist_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = top_k_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = top_p_warp(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = min_dist_proc(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = bos_dist_proc(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
lowerCamelCase_ = eos_dist_proc(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
return scores
# with processor list
def run_processor_list(UpperCamelCase , UpperCamelCase , UpperCamelCase ):
lowerCamelCase_ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase_ = processor(UpperCamelCase , UpperCamelCase , cur_len=UpperCamelCase )
return scores
lowerCamelCase_ = jax.jit(UpperCamelCase )
lowerCamelCase_ = jax.jit(UpperCamelCase )
lowerCamelCase_ = jitted_run_no_processor_list(UpperCamelCase , UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = jitted_run_processor_list(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# scores should be equal
self.assertTrue(jnp.allclose(UpperCamelCase , UpperCamelCase , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 55 | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
lowercase__ : Dict = logging.get_logger(__name__)
@add_end_docstrings(
UpperCamelCase_ , r"""
top_k (`int`, defaults to 5):
The number of predictions to return.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).
""" , )
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->np.ndarray:
if self.framework == "tf":
lowerCAmelCase = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
lowerCAmelCase = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=__SCREAMING_SNAKE_CASE )
else:
raise ValueError('''Unsupported framework''' )
return masked_index
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->np.ndarray:
lowerCAmelCase = self.get_masked_index(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
'''fill-mask''' , self.model.base_model_prefix , F"No mask_token ({self.tokenizer.mask_token}) found on the input" , )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->str:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , **__SCREAMING_SNAKE_CASE ) ->Dict[str, GenericTensor]:
if return_tensors is None:
lowerCAmelCase = self.framework
lowerCAmelCase = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=__SCREAMING_SNAKE_CASE )
self.ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->Tuple:
lowerCAmelCase = self.model(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = model_inputs['''input_ids''']
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=5 , __SCREAMING_SNAKE_CASE=None ) ->str:
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
lowerCAmelCase = target_ids.shape[0]
lowerCAmelCase = model_outputs['''input_ids'''][0]
lowerCAmelCase = model_outputs['''logits''']
if self.framework == "tf":
lowerCAmelCase = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
lowerCAmelCase = outputs.numpy()
lowerCAmelCase = outputs[0, masked_index, :]
lowerCAmelCase = stable_softmax(__SCREAMING_SNAKE_CASE , axis=-1 )
if target_ids is not None:
lowerCAmelCase = tf.gather_nd(tf.squeeze(__SCREAMING_SNAKE_CASE , 0 ) , target_ids.reshape(-1 , 1 ) )
lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE , 0 )
lowerCAmelCase = tf.math.top_k(__SCREAMING_SNAKE_CASE , k=__SCREAMING_SNAKE_CASE )
lowerCAmelCase , lowerCAmelCase = topk.values.numpy(), topk.indices.numpy()
else:
lowerCAmelCase = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=__SCREAMING_SNAKE_CASE ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
lowerCAmelCase = outputs[0, masked_index, :]
lowerCAmelCase = logits.softmax(dim=-1 )
if target_ids is not None:
lowerCAmelCase = probs[..., target_ids]
lowerCAmelCase , lowerCAmelCase = probs.topk(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = []
lowerCAmelCase = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
lowerCAmelCase = []
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
lowerCAmelCase = input_ids.numpy().copy()
if target_ids is not None:
lowerCAmelCase = target_ids[p].tolist()
lowerCAmelCase = p
# Filter padding out:
lowerCAmelCase = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
lowerCAmelCase = self.tokenizer.decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE )
lowerCAmelCase = {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p] ), '''sequence''': sequence}
row.append(__SCREAMING_SNAKE_CASE )
result.append(__SCREAMING_SNAKE_CASE )
if single_mask:
return result[0]
return result
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None ) ->Optional[Any]:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowerCAmelCase = [targets]
try:
lowerCAmelCase = self.tokenizer.get_vocab()
except Exception:
lowerCAmelCase = {}
lowerCAmelCase = []
for target in targets:
lowerCAmelCase = vocab.get(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if id_ is None:
lowerCAmelCase = self.tokenizer(
__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE , max_length=1 , truncation=__SCREAMING_SNAKE_CASE , )['''input_ids''']
if len(__SCREAMING_SNAKE_CASE ) == 0:
logger.warning(
F"The specified target token `{target}` does not exist in the model vocabulary. "
'''We cannot replace it with anything meaningful, ignoring it''' )
continue
lowerCAmelCase = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
F"The specified target token `{target}` does not exist in the model vocabulary. "
F"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`." )
target_ids.append(id_ )
lowerCAmelCase = list(set(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) == 0:
raise ValueError('''At least one target must be provided when passed.''' )
lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
return target_ids
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ) ->Dict:
lowerCAmelCase = {}
if targets is not None:
lowerCAmelCase = self.get_target_ids(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowerCAmelCase = target_ids
if top_k is not None:
lowerCAmelCase = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'''fill-mask''' , self.model.base_model_prefix , '''The tokenizer does not define a `mask_token`.''' )
return {}, {}, postprocess_params
def __call__( self , __SCREAMING_SNAKE_CASE , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ) ->List[Any]:
lowerCAmelCase = super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) and len(__SCREAMING_SNAKE_CASE ) == 1:
return outputs[0]
return outputs
| 338 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : Tuple = (CMStochasticIterativeScheduler,)
lowerCAmelCase_ : List[Any] = 10
def lowercase__ ( self : Optional[Any] , **UpperCAmelCase_ : Optional[Any] ):
lowerCAmelCase : Dict = {
'num_train_timesteps': 201,
'sigma_min': 0.0_02,
'sigma_max': 80.0,
}
config.update(**UpperCAmelCase_ )
return config
def lowercase__ ( self : Tuple ):
lowerCAmelCase : Any = 10
lowerCAmelCase : List[Any] = self.get_scheduler_config()
lowerCAmelCase : Union[str, Any] = self.scheduler_classes[0](**UpperCAmelCase_ )
scheduler.set_timesteps(UpperCAmelCase_ )
lowerCAmelCase : Optional[Any] = scheduler.timesteps[0]
lowerCAmelCase : List[str] = scheduler.timesteps[1]
lowerCAmelCase : Union[str, Any] = self.dummy_sample
lowerCAmelCase : List[Any] = 0.1 * sample
lowerCAmelCase : str = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ).prev_sample
lowerCAmelCase : Any = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowercase__ ( self : Any ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )
def lowercase__ ( self : str ):
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=UpperCAmelCase_ )
def lowercase__ ( self : List[Any] ):
lowerCAmelCase : List[str] = self.scheduler_classes[0]
lowerCAmelCase : Union[str, Any] = self.get_scheduler_config()
lowerCAmelCase : List[Any] = scheduler_class(**UpperCAmelCase_ )
lowerCAmelCase : Tuple = 1
scheduler.set_timesteps(UpperCAmelCase_ )
lowerCAmelCase : Dict = scheduler.timesteps
lowerCAmelCase : Any = torch.manual_seed(0 )
lowerCAmelCase : Dict = self.dummy_model()
lowerCAmelCase : Dict = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(UpperCAmelCase_ ):
# 1. scale model input
lowerCAmelCase : str = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict noise residual
lowerCAmelCase : List[Any] = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 3. predict previous sample x_t-1
lowerCAmelCase : List[Any] = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
lowerCAmelCase : List[Any] = pred_prev_sample
lowerCAmelCase : Dict = torch.sum(torch.abs(UpperCAmelCase_ ) )
lowerCAmelCase : Any = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 1_92.76_14 ) < 1E-2
assert abs(result_mean.item() - 0.25_10 ) < 1E-3
def lowercase__ ( self : Any ):
lowerCAmelCase : Tuple = self.scheduler_classes[0]
lowerCAmelCase : int = self.get_scheduler_config()
lowerCAmelCase : Any = scheduler_class(**UpperCAmelCase_ )
lowerCAmelCase : Union[str, Any] = [106, 0]
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
lowerCAmelCase : Any = scheduler.timesteps
lowerCAmelCase : int = torch.manual_seed(0 )
lowerCAmelCase : List[str] = self.dummy_model()
lowerCAmelCase : Tuple = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
lowerCAmelCase : Optional[Any] = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
# 2. predict noise residual
lowerCAmelCase : Dict = model(UpperCAmelCase_ , UpperCAmelCase_ )
# 3. predict previous sample x_t-1
lowerCAmelCase : str = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ ).prev_sample
lowerCAmelCase : List[str] = pred_prev_sample
lowerCAmelCase : List[Any] = torch.sum(torch.abs(UpperCAmelCase_ ) )
lowerCAmelCase : List[str] = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 3_47.63_57 ) < 1E-2
assert abs(result_mean.item() - 0.45_27 ) < 1E-3
def lowercase__ ( self : Optional[int] ):
lowerCAmelCase : List[str] = self.scheduler_classes[0]
lowerCAmelCase : Dict = self.get_scheduler_config()
lowerCAmelCase : int = scheduler_class(**UpperCAmelCase_ )
lowerCAmelCase : Union[str, Any] = [39, 30, 12, 15, 0]
with self.assertRaises(UpperCAmelCase_ , msg='`timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
def lowercase__ ( self : int ):
lowerCAmelCase : str = self.scheduler_classes[0]
lowerCAmelCase : int = self.get_scheduler_config()
lowerCAmelCase : Union[str, Any] = scheduler_class(**UpperCAmelCase_ )
lowerCAmelCase : Dict = [39, 30, 12, 1, 0]
lowerCAmelCase : str = len(UpperCAmelCase_ )
with self.assertRaises(UpperCAmelCase_ , msg='Can only pass one of `num_inference_steps` or `timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_ , timesteps=UpperCAmelCase_ )
def lowercase__ ( self : Optional[int] ):
lowerCAmelCase : Union[str, Any] = self.scheduler_classes[0]
lowerCAmelCase : Optional[Any] = self.get_scheduler_config()
lowerCAmelCase : List[Any] = scheduler_class(**UpperCAmelCase_ )
lowerCAmelCase : Dict = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCAmelCase_ , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_ )
| 323 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__A : List[Any] = logging.get_logger(__name__)
__A : List[Any] = {
'''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
'''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
'''microsoft/deberta-v2-xlarge-mnli''': (
'''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
),
'''microsoft/deberta-v2-xxlarge-mnli''': (
'''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
),
}
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : Union[str, Any] = "deberta-v2"
def __init__( self : int , UpperCAmelCase_ : Dict=128100 , UpperCAmelCase_ : Optional[int]=1536 , UpperCAmelCase_ : Tuple=24 , UpperCAmelCase_ : Any=24 , UpperCAmelCase_ : Any=6144 , UpperCAmelCase_ : Tuple="gelu" , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : List[Any]=512 , UpperCAmelCase_ : List[str]=0 , UpperCAmelCase_ : List[Any]=0.02 , UpperCAmelCase_ : Optional[int]=1E-7 , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : Dict=-1 , UpperCAmelCase_ : Tuple=0 , UpperCAmelCase_ : int=True , UpperCAmelCase_ : int=None , UpperCAmelCase_ : List[Any]=0 , UpperCAmelCase_ : int="gelu" , **UpperCAmelCase_ : int , ):
super().__init__(**UpperCAmelCase_ )
lowerCAmelCase : Dict = hidden_size
lowerCAmelCase : List[Any] = num_hidden_layers
lowerCAmelCase : str = num_attention_heads
lowerCAmelCase : List[str] = intermediate_size
lowerCAmelCase : List[Any] = hidden_act
lowerCAmelCase : int = hidden_dropout_prob
lowerCAmelCase : int = attention_probs_dropout_prob
lowerCAmelCase : Any = max_position_embeddings
lowerCAmelCase : str = type_vocab_size
lowerCAmelCase : Union[str, Any] = initializer_range
lowerCAmelCase : Union[str, Any] = relative_attention
lowerCAmelCase : List[Any] = max_relative_positions
lowerCAmelCase : List[Any] = pad_token_id
lowerCAmelCase : Optional[Any] = position_biased_input
# Backwards compatibility
if type(UpperCAmelCase_ ) == str:
lowerCAmelCase : Tuple = [x.strip() for x in pos_att_type.lower().split('|' )]
lowerCAmelCase : str = pos_att_type
lowerCAmelCase : Dict = vocab_size
lowerCAmelCase : Optional[Any] = layer_norm_eps
lowerCAmelCase : str = kwargs.get('pooler_hidden_size' , UpperCAmelCase_ )
lowerCAmelCase : Tuple = pooler_dropout
lowerCAmelCase : Union[str, Any] = pooler_hidden_act
class __A ( lowerCAmelCase ):
@property
def lowercase__ ( self : Optional[Any] ):
if self.task == "multiple-choice":
lowerCAmelCase : Tuple = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase : Union[str, Any] = {0: 'batch', 1: 'sequence'}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
else:
return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )
@property
def lowercase__ ( self : int ):
return 12
def lowercase__ ( 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 , UpperCAmelCase_ : "PreTrainedTokenizerBase" = None , ):
lowerCAmelCase : List[str] = super().generate_dummy_inputs(preprocessor=UpperCAmelCase_ , framework=UpperCAmelCase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 323 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__snake_case = TypeVar("""KEY""")
__snake_case = TypeVar("""VAL""")
@dataclass(frozen=_SCREAMING_SNAKE_CASE, slots=_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( Generic[KEY, VAL] ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] =42
UpperCamelCase_ : Tuple =42
class UpperCAmelCase_ ( _Item ):
"""simple docstring"""
def __init__( self ) -> Tuple:
super().__init__(_lowerCAmelCase , _lowerCAmelCase )
def __bool__( self ) -> Dict:
return False
__snake_case = _DeletedItem()
class UpperCAmelCase_ ( MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ = 8 , SCREAMING_SNAKE_CASE_ = 0.75 ) -> str:
UpperCamelCase :Dict = initial_block_size
UpperCamelCase :Dict = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
UpperCamelCase :List[Any] = capacity_factor
UpperCamelCase :Union[str, Any] = 0
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
return hash(_lowerCAmelCase ) % len(self._buckets )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
return (ind + 1) % len(self._buckets )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
UpperCamelCase :List[Any] = self._buckets[ind]
if not stored:
UpperCamelCase :Tuple = _Item(_lowerCAmelCase , _lowerCAmelCase )
self._len += 1
return True
elif stored.key == key:
UpperCamelCase :Dict = _Item(_lowerCAmelCase , _lowerCAmelCase )
return True
else:
return False
def UpperCAmelCase ( self ) -> str:
UpperCamelCase :List[Any] = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_lowerCAmelCase )
def UpperCAmelCase ( self ) -> Optional[Any]:
if len(self._buckets ) <= self._initial_block_size:
return False
UpperCamelCase :Any = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
UpperCamelCase :Tuple = self._buckets
UpperCamelCase :Any = [None] * new_size
UpperCamelCase :Optional[int] = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def UpperCAmelCase ( self ) -> List[str]:
self._resize(len(self._buckets ) * 2 )
def UpperCAmelCase ( self ) -> List[str]:
self._resize(len(self._buckets ) // 2 )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ ) -> Any:
UpperCamelCase :Optional[Any] = self._get_bucket_index(_lowerCAmelCase )
for _ in range(len(self._buckets ) ):
yield ind
UpperCamelCase :List[str] = self._get_next_ind(_lowerCAmelCase )
def UpperCAmelCase ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
for ind in self._iterate_buckets(_lowerCAmelCase ):
if self._try_set(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
break
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
if self._is_full():
self._size_up()
self._add_item(_lowerCAmelCase , _lowerCAmelCase )
def __delitem__( self , SCREAMING_SNAKE_CASE_ ) -> Tuple:
for ind in self._iterate_buckets(_lowerCAmelCase ):
UpperCamelCase :List[Any] = self._buckets[ind]
if item is None:
raise KeyError(_lowerCAmelCase )
if item is _deleted:
continue
if item.key == key:
UpperCamelCase :str = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
for ind in self._iterate_buckets(_lowerCAmelCase ):
UpperCamelCase :str = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_lowerCAmelCase )
def __len__( self ) -> Optional[int]:
return self._len
def __iter__( self ) -> Tuple:
yield from (item.key for item in self._buckets if item)
def __repr__( self ) -> int:
UpperCamelCase :Optional[Any] = ''' ,'''.join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 259 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( ) -> str:
SCREAMING_SNAKE_CASE_ = 10
SCREAMING_SNAKE_CASE_ = datasets.Features(
{
'tokens': datasets.Sequence(datasets.Value('string' ) ),
'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive'] ) ),
'answers': datasets.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
'id': datasets.Value('int64' ),
} )
SCREAMING_SNAKE_CASE_ = datasets.Dataset.from_dict(
{
'tokens': [['foo'] * 5] * n,
'labels': [[1] * 5] * n,
'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
'id': list(range(__UpperCAmelCase ) ),
} , features=__UpperCAmelCase , )
return dataset
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Tuple , __UpperCAmelCase : str ) -> int:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'file.arrow' )
dataset.map(cache_file_name=__UpperCAmelCase )
return filename
# FILE_CONTENT + files
lowerCamelCase__ : List[Any] = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Any ) -> List[str]:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.txt'
SCREAMING_SNAKE_CASE_ = FILE_CONTENT
with open(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase )
return filename
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Dict ) -> List[str]:
import bza
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.txt.bz2'
SCREAMING_SNAKE_CASE_ = bytes(__UpperCAmelCase , 'utf-8' )
with bza.open(__UpperCAmelCase , 'wb' ) as f:
f.write(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[Any] ) -> Any:
import gzip
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'file.txt.gz' )
SCREAMING_SNAKE_CASE_ = bytes(__UpperCAmelCase , 'utf-8' )
with gzip.open(__UpperCAmelCase , 'wb' ) as f:
f.write(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] ) -> int:
if datasets.config.LZ4_AVAILABLE:
import lza.frame
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.txt.lz4'
SCREAMING_SNAKE_CASE_ = bytes(__UpperCAmelCase , 'utf-8' )
with lza.frame.open(__UpperCAmelCase , 'wb' ) as f:
f.write(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[Any] , __UpperCAmelCase : Union[str, Any] ) -> Any:
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.txt.7z'
with pyazr.SevenZipFile(__UpperCAmelCase , 'w' ) as archive:
archive.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Dict , __UpperCAmelCase : str ) -> str:
import tarfile
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.txt.tar'
with tarfile.TarFile(__UpperCAmelCase , 'w' ) as f:
f.add(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> List[Any]:
import lzma
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.txt.xz'
SCREAMING_SNAKE_CASE_ = bytes(__UpperCAmelCase , 'utf-8' )
with lzma.open(__UpperCAmelCase , 'wb' ) as f:
f.write(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ) -> str:
import zipfile
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.txt.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Dict ) -> Any:
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.txt.zst'
SCREAMING_SNAKE_CASE_ = bytes(__UpperCAmelCase , 'utf-8' )
with zstd.open(__UpperCAmelCase , 'wb' ) as f:
f.write(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'file.xml'
SCREAMING_SNAKE_CASE_ = textwrap.dedent(
'\\n <?xml version="1.0" encoding="UTF-8" ?>\n <tmx version="1.4">\n <header segtype="sentence" srclang="ca" />\n <body>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>' )
with open(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase )
return filename
lowerCamelCase__ : Optional[Any] = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
lowerCamelCase__ : Dict = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
lowerCamelCase__ : Optional[int] = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
lowerCamelCase__ : List[Any] = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
lowerCamelCase__ : List[Any] = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( ) -> Tuple:
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = datasets.Dataset.from_dict(__UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.arrow' )
dataset.map(cache_file_name=__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.sqlite' )
with contextlib.closing(sqlitea.connect(__UpperCAmelCase ) ) as con:
SCREAMING_SNAKE_CASE_ = con.cursor()
cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)' )
for item in DATA:
cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)' , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.csv' )
with open(__UpperCAmelCase , 'w' , newline='' ) as f:
SCREAMING_SNAKE_CASE_ = csv.DictWriter(__UpperCAmelCase , fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset2.csv' )
with open(__UpperCAmelCase , 'w' , newline='' ) as f:
SCREAMING_SNAKE_CASE_ = csv.DictWriter(__UpperCAmelCase , fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : str , __UpperCAmelCase : Tuple ) -> str:
import bza
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.csv.bz2'
with open(__UpperCAmelCase , 'rb' ) as f:
SCREAMING_SNAKE_CASE_ = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__UpperCAmelCase , 'wb' ) as f:
f.write(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Dict , __UpperCAmelCase : List[str] , __UpperCAmelCase : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[Any] , __UpperCAmelCase : int , __UpperCAmelCase : str ) -> Any:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.basename(csv_path.replace('.csv' , '.CSV' ) ) )
f.write(__UpperCAmelCase , arcname=os.path.basename(csva_path.replace('.csv' , '.CSV' ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.csv.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(__UpperCAmelCase ) ) )
f.write(__UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(__UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.parquet' )
SCREAMING_SNAKE_CASE_ = pa.schema(
{
'col_1': pa.string(),
'col_2': pa.intaa(),
'col_3': pa.floataa(),
} )
with open(__UpperCAmelCase , 'wb' ) as f:
SCREAMING_SNAKE_CASE_ = pq.ParquetWriter(__UpperCAmelCase , schema=__UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__UpperCAmelCase ) )] for k in DATA[0]} , schema=__UpperCAmelCase )
writer.write_table(__UpperCAmelCase )
writer.close()
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
SCREAMING_SNAKE_CASE_ = {'data': DATA}
with open(__UpperCAmelCase , 'w' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
SCREAMING_SNAKE_CASE_ = {'data': DATA_DICT_OF_LISTS}
with open(__UpperCAmelCase , 'w' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl' )
with open(__UpperCAmelCase , 'w' ) as f:
for item in DATA:
f.write(json.dumps(__UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset2.jsonl' )
with open(__UpperCAmelCase , 'w' ) as f:
for item in DATA:
f.write(json.dumps(__UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset_312.jsonl' )
with open(__UpperCAmelCase , 'w' ) as f:
for item in DATA_312:
f.write(json.dumps(__UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset-str.jsonl' )
with open(__UpperCAmelCase , 'w' ) as f:
for item in DATA_STR:
f.write(json.dumps(__UpperCAmelCase ) + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[str] ) -> Union[str, Any]:
import gzip
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt.gz' )
with open(__UpperCAmelCase , 'rb' ) as orig_file:
with gzip.open(__UpperCAmelCase , 'wb' ) as zipped_file:
zipped_file.writelines(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : str , __UpperCAmelCase : Optional[Any] ) -> List[str]:
import gzip
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.gz' )
with open(__UpperCAmelCase , 'rb' ) as orig_file:
with gzip.open(__UpperCAmelCase , 'wb' ) as zipped_file:
zipped_file.writelines(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Dict ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Any , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.join('nested' , os.path.basename(__UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : str ) -> Any:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.jsonl.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(__UpperCAmelCase ) ) )
f.write(__UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(__UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple ) -> Dict:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.tar'
with tarfile.TarFile(__UpperCAmelCase , 'w' ) as f:
f.add(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
f.add(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.tar'
with tarfile.TarFile(__UpperCAmelCase , 'w' ) as f:
f.add(__UpperCAmelCase , arcname=os.path.join('nested' , os.path.basename(__UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = ['0', '1', '2', '3']
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt' )
with open(__UpperCAmelCase , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Dict ) -> Any:
SCREAMING_SNAKE_CASE_ = ['0', '1', '2', '3']
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset2.txt' )
with open(__UpperCAmelCase , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : List[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = ['0', '1', '2', '3']
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.abc'
with open(__UpperCAmelCase , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.text.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Tuple , __UpperCAmelCase : int , __UpperCAmelCase : Optional[int] ) -> Any:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.text.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(__UpperCAmelCase ) ) )
f.write(__UpperCAmelCase , arcname=os.path.join('main_dir' , os.path.basename(__UpperCAmelCase ) ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Any , __UpperCAmelCase : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.ext.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.basename('unsupported.ext' ) )
f.write(__UpperCAmelCase , arcname=os.path.basename('unsupported_2.ext' ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE_ = '\n'.join(['First', 'Second\u2029with Unicode new line', 'Third'] )
SCREAMING_SNAKE_CASE_ = str(tmp_path_factory.mktemp('data' ) / 'dataset_with_unicode_new_lines.txt' )
with open(__UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(__UpperCAmelCase )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( ) -> List[Any]:
return os.path.join('tests' , 'features' , 'data' , 'test_image_rgb.jpg' )
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( ) -> Tuple:
return os.path.join('tests' , 'features' , 'data' , 'test_audio_44100.wav' )
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ) -> int:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data' ) / 'dataset.img.zip'
with zipfile.ZipFile(__UpperCAmelCase , 'w' ) as f:
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ) )
f.write(__UpperCAmelCase , arcname=os.path.basename(__UpperCAmelCase ).replace('.jpg' , '2.jpg' ) )
return path
@pytest.fixture(scope='session' )
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> Dict:
SCREAMING_SNAKE_CASE_ = tmp_path_factory.mktemp('data_dir' )
(data_dir / "subdir").mkdir()
with open(data_dir / 'subdir' / 'train.txt' , 'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / 'subdir' / 'test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
# hidden file
with open(data_dir / 'subdir' / '.test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '.subdir' / 'train.txt' , 'w' ) as f:
f.write('foo\n' * 10 )
with open(data_dir / '.subdir' / 'test.txt' , 'w' ) as f:
f.write('bar\n' * 10 )
return data_dir | 225 | 0 |
def SCREAMING_SNAKE_CASE( __lowercase ) -> list:
if len(__lowercase ) <= 1:
return [tuple(__lowercase )]
A: Any = []
def generate(__lowercase , __lowercase ):
A: Union[str, Any] = [0] * n
res.append(tuple(__lowercase ) )
A: Optional[int] = 0
while i < n:
if c[i] < i:
if i % 2 == 0:
A: Union[str, Any] = arr[i], arr[0]
else:
A: List[str] = arr[i], arr[c[i]]
res.append(tuple(__lowercase ) )
c[i] += 1
A: Dict = 0
else:
A: str = 0
i += 1
generate(len(__lowercase ) , __lowercase )
return res
if __name__ == "__main__":
UpperCamelCase = input('''Enter numbers separated by a comma:\n''').strip()
UpperCamelCase = [int(item) for item in user_input.split(''',''')]
print(heaps(arr))
| 351 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
UpperCamelCase = None
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
UpperCamelCase = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
},
}
UpperCamelCase = {
'''camembert-base''': 512,
}
UpperCamelCase = '''▁'''
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES
UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : str = ["""input_ids""", """attention_mask"""]
UpperCamelCase_ : int = CamembertTokenizer
def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : int=None , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : str="<s>" , SCREAMING_SNAKE_CASE_ : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE_ : List[Any]="</s>" , SCREAMING_SNAKE_CASE_ : int="<s>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="<unk>" , SCREAMING_SNAKE_CASE_ : str="<pad>" , SCREAMING_SNAKE_CASE_ : List[str]="<mask>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , **SCREAMING_SNAKE_CASE_ : Any , ) -> Any:
'''simple docstring'''
A: Tuple = 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__(
SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
A: Any = vocab_file
A: Any = False if not self.vocab_file else True
def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A: List[str] = [self.cls_token_id]
A: List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A: List[str] = [self.sep_token_id]
A: Optional[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 _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
A: Dict = 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_ ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
| 334 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
A__ : Optional[int] = data_utils.TransfoXLTokenizer
A__ : Dict = data_utils.TransfoXLCorpus
A__ : str = data_utils
A__ : Union[str, Any] = data_utils
def UpperCamelCase( __UpperCamelCase : List[Any] ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Dict ,__UpperCamelCase : Dict ):
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(lowercase_ ,'''rb''' ) as fp:
lowerCAmelCase_ : Optional[int] = pickle.load(lowercase_ ,encoding='''latin1''' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
lowerCAmelCase_ : Optional[Any] = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
print(f"""Save vocabulary to {pytorch_vocab_dump_path}""" )
lowerCAmelCase_ : Any = corpus.vocab.__dict__
torch.save(lowercase_ ,lowercase_ )
lowerCAmelCase_ : int = corpus.__dict__
corpus_dict_no_vocab.pop('''vocab''' ,lowercase_ )
lowerCAmelCase_ : List[Any] = pytorch_dump_folder_path + '''/''' + CORPUS_NAME
print(f"""Save dataset to {pytorch_dataset_dump_path}""" )
torch.save(lowercase_ ,lowercase_ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
lowerCAmelCase_ : int = os.path.abspath(lowercase_ )
lowerCAmelCase_ : List[Any] = os.path.abspath(lowercase_ )
print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
# Initialise PyTorch model
if transfo_xl_config_file == "":
lowerCAmelCase_ : str = TransfoXLConfig()
else:
lowerCAmelCase_ : Optional[Any] = TransfoXLConfig.from_json_file(lowercase_ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase_ : int = TransfoXLLMHeadModel(lowercase_ )
lowerCAmelCase_ : Dict = load_tf_weights_in_transfo_xl(lowercase_ ,lowercase_ ,lowercase_ )
# Save pytorch-model
lowerCAmelCase_ : str = os.path.join(lowercase_ ,lowercase_ )
lowerCAmelCase_ : Union[str, Any] = os.path.join(lowercase_ ,lowercase_ )
print(f"""Save PyTorch model to {os.path.abspath(lowercase_ )}""" )
torch.save(model.state_dict() ,lowercase_ )
print(f"""Save configuration file to {os.path.abspath(lowercase_ )}""" )
with open(lowercase_ ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A__ : Any = argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--tf_checkpoint_path''',
default='''''',
type=str,
help='''An optional path to a TensorFlow checkpoint path to be converted.''',
)
parser.add_argument(
'''--transfo_xl_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--transfo_xl_dataset_file''',
default='''''',
type=str,
help='''An optional dataset file to be converted in a vocabulary.''',
)
A__ : Optional[Any] = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 103 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class A_ :
"""simple docstring"""
def UpperCAmelCase__ ( self :Any ) -> List[str]:
torch.manual_seed(0 )
UpperCAmelCase = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
UpperCAmelCase = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
UpperCAmelCase = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] , mid_block_type='UNetMidBlock2DSimpleCrossAttn' , up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='text' , addition_embed_type_num_heads=2 , cross_attention_norm='group_norm' , resnet_time_scale_shift='scale_shift' , act_fn='gelu' , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
UpperCAmelCase = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , thresholding=lowercase_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
UpperCAmelCase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase__ ( self :List[Any] ) -> Any:
torch.manual_seed(0 )
UpperCAmelCase = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
UpperCAmelCase = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
UpperCAmelCase = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] , mid_block_type='UNetMidBlock2DSimpleCrossAttn' , up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='text' , addition_embed_type_num_heads=2 , cross_attention_norm='group_norm' , resnet_time_scale_shift='scale_shift' , act_fn='gelu' , class_embed_type='timestep' , mid_block_scale_factor=1.414 , time_embedding_act_fn='gelu' , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
UpperCAmelCase = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , thresholding=lowercase_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
UpperCAmelCase = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
UpperCAmelCase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase__ ( self :List[str] ) -> str:
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase = self.get_dummy_inputs(lowercase_ )
UpperCAmelCase = inputs['prompt']
UpperCAmelCase = inputs['generator']
UpperCAmelCase = inputs['num_inference_steps']
UpperCAmelCase = inputs['output_type']
if "image" in inputs:
UpperCAmelCase = inputs['image']
else:
UpperCAmelCase = None
if "mask_image" in inputs:
UpperCAmelCase = inputs['mask_image']
else:
UpperCAmelCase = None
if "original_image" in inputs:
UpperCAmelCase = inputs['original_image']
else:
UpperCAmelCase = None
UpperCAmelCase , UpperCAmelCase = pipe.encode_prompt(lowercase_ )
# inputs with prompt converted to embeddings
UpperCAmelCase = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
UpperCAmelCase = image
if mask_image is not None:
UpperCAmelCase = mask_image
if original_image is not None:
UpperCAmelCase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase = pipe(**lowercase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowercase_ )
UpperCAmelCase = self.pipeline_class.from_pretrained(lowercase_ )
pipe_loaded.to(lowercase_ )
pipe_loaded.set_progress_bar_config(disable=lowercase_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowercase_ , lowercase_ ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
UpperCAmelCase = self.get_dummy_inputs(lowercase_ )
UpperCAmelCase = inputs['generator']
UpperCAmelCase = inputs['num_inference_steps']
UpperCAmelCase = inputs['output_type']
# inputs with prompt converted to embeddings
UpperCAmelCase = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
UpperCAmelCase = image
if mask_image is not None:
UpperCAmelCase = mask_image
if original_image is not None:
UpperCAmelCase = original_image
UpperCAmelCase = pipe_loaded(**lowercase_ )[0]
UpperCAmelCase = np.abs(to_np(lowercase_ ) - to_np(lowercase_ ) ).max()
self.assertLess(lowercase_ , 1E-4 )
def UpperCAmelCase__ ( self :List[Any] ) -> str:
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase = self.get_dummy_inputs(lowercase_ )
UpperCAmelCase = pipe(**lowercase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowercase_ )
UpperCAmelCase = self.pipeline_class.from_pretrained(lowercase_ )
pipe_loaded.to(lowercase_ )
pipe_loaded.set_progress_bar_config(disable=lowercase_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
UpperCAmelCase = self.get_dummy_inputs(lowercase_ )
UpperCAmelCase = pipe_loaded(**lowercase_ )[0]
UpperCAmelCase = np.abs(to_np(lowercase_ ) - to_np(lowercase_ ) ).max()
self.assertLess(lowercase_ , 1E-4 )
| 78 | 0 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = [
"""word_embeddings_layernorm.weight""",
"""word_embeddings_layernorm.bias""",
"""input_layernorm.weight""",
"""input_layernorm.bias""",
"""post_attention_layernorm.weight""",
"""post_attention_layernorm.bias""",
"""self_attention.dense.bias""",
"""mlp.dense_4h_to_h.bias""",
"""ln_f.weight""",
"""ln_f.bias""",
]
lowerCamelCase_ = [
"""mlp.dense_4h_to_h.weight""",
"""self_attention.dense.weight""",
]
def UpperCamelCase( lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = {
"""word_embeddings.weight""": """word_embeddings.weight""",
"""word_embeddings.norm.weight""": """word_embeddings_layernorm.weight""",
"""word_embeddings.norm.bias""": """word_embeddings_layernorm.bias""",
"""weight""": """ln_f.weight""",
"""bias""": """ln_f.bias""",
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
snake_case_ = int(re.match(r""".*layer_(\d*).*""" , lowercase_ )[1] )
layer_number -= 3
return f'''h.{layer_number}.''' + key
def UpperCamelCase( lowercase_ ) -> Optional[int]:
'''simple docstring'''
if dtype == torch.bool:
return 1 / 8
snake_case_ = re.search(r"""[^\d](\d+)$""" , str(lowercase_ ) )
if bit_search is None:
raise ValueError(f'''`dtype` is not a valid dtype: {dtype}.''' )
snake_case_ = int(bit_search.groups()[0] )
return bit_size // 8
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
if bloom_config_file == "":
snake_case_ = BloomConfig()
else:
snake_case_ = BloomConfig.from_json_file(lowercase_ )
if shard_model:
snake_case_ = os.listdir(lowercase_ )
snake_case_ = sorted(filter(lambda lowercase_ : s.startswith("""layer""" ) and "model_00" in s , lowercase_ ) )
snake_case_ = {"""weight_map""": {}, """metadata""": {}}
snake_case_ = 0
snake_case_ = None
snake_case_ = BloomConfig()
for j, file in enumerate(lowercase_ ):
print("""Processing file: {}""".format(lowercase_ ) )
snake_case_ = None
for i in range(lowercase_ ):
# load all TP files
snake_case_ = file.replace("""model_00""" , f'''model_0{i}''' )
snake_case_ = torch.load(os.path.join(lowercase_ , lowercase_ ) , map_location="""cpu""" )
# Rename keys in the transformers names
snake_case_ = list(temp.keys() )
for key in keys:
snake_case_ = temp.pop(lowercase_ )
if tensors is None:
snake_case_ = temp
else:
for key in tensors.keys():
if any(key.endswith(lowercase_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ = torch.cat([tensors[key], temp[key]] , dim=lowercase_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(lowercase_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ = tensors[key] / pretraining_tp
torch.save(
lowercase_ , os.path.join(
lowercase_ , """pytorch_model_{}-of-{}.bin""".format(str(j + 1 ).zfill(5 ) , str(len(lowercase_ ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
snake_case_ = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
snake_case_ = """pytorch_model_{}-of-{}.bin""".format(
str(j + 1 ).zfill(5 ) , str(len(lowercase_ ) ).zfill(5 ) )
snake_case_ = BloomConfig()
snake_case_ = pytorch_dump_folder_path + """/""" + CONFIG_NAME
snake_case_ = total_size
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
with open(os.path.join(lowercase_ , WEIGHTS_NAME + """.index.json""" ) , """w""" , encoding="""utf-8""" ) as f:
snake_case_ = json.dumps(lowercase_ , indent=2 , sort_keys=lowercase_ ) + """\n"""
f.write(lowercase_ )
else:
snake_case_ = BloomModel(lowercase_ )
snake_case_ = os.listdir(lowercase_ )
snake_case_ = sorted(filter(lambda lowercase_ : s.startswith("""layer""" ) and "model_00" in s , lowercase_ ) )
snake_case_ = None
for i, file in enumerate(lowercase_ ):
snake_case_ = None
for i in range(lowercase_ ):
# load all TP files
snake_case_ = file.replace("""model_00""" , f'''model_0{i}''' )
snake_case_ = torch.load(os.path.join(lowercase_ , lowercase_ ) , map_location="""cpu""" )
# Rename keys in the transformers names
snake_case_ = list(temp.keys() )
for key in keys:
snake_case_ = temp.pop(lowercase_ )
if tensors is None:
snake_case_ = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(lowercase_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
snake_case_ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
snake_case_ = torch.cat([tensors[key], temp[key]] , dim=lowercase_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(lowercase_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
snake_case_ = tensors[key] / pretraining_tp
snake_case_ = model.load_state_dict(lowercase_ , strict=lowercase_ )
assert not other_keys.unexpected_keys, f'''The keys {other_keys.unexpected_keys} are unexpected'''
if missing_keys is None:
snake_case_ = set(other_keys.missing_keys )
else:
snake_case_ = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f'''The keys {missing_keys} are missing'''
# Save pytorch-model
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
snake_case_ = pytorch_dump_folder_path + """/""" + CONFIG_NAME
print(f'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}''' )
if config.torch_dtype is not None:
snake_case_ = model.to(config.torch_dtype )
torch.save(model.state_dict() , lowercase_ )
print(f'''Save configuration file to {pytorch_config_dump_path}''' )
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
lowerCamelCase_ = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
) | 363 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCamelCase_ = '''
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save("cat.png")
```
'''
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_=8 ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
snake_case_ = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class __lowerCamelCase ( __snake_case ):
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ) -> Optional[int]:
super().__init__()
self.register_modules(
text_encoder=lowerCamelCase , tokenizer=lowerCamelCase , unet=lowerCamelCase , scheduler=lowerCamelCase , movq=lowerCamelCase , )
snake_case_ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCAmelCase_ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> List[str]:
if latents is None:
snake_case_ = randn_tensor(lowerCamelCase , generator=lowerCamelCase , device=lowerCamelCase , dtype=lowerCamelCase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
snake_case_ = latents.to(lowerCamelCase )
snake_case_ = latents * scheduler.init_noise_sigma
return latents
def lowerCAmelCase_ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=None , ) -> Any:
snake_case_ = len(lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else 1
# get prompt text embeddings
snake_case_ = self.tokenizer(
lowerCamelCase , padding="""max_length""" , truncation=lowerCamelCase , max_length=77 , return_attention_mask=lowerCamelCase , add_special_tokens=lowerCamelCase , return_tensors="""pt""" , )
snake_case_ = text_inputs.input_ids
snake_case_ = self.tokenizer(lowerCamelCase , padding="""longest""" , return_tensors="""pt""" ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCamelCase , lowerCamelCase ):
snake_case_ = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
snake_case_ = text_input_ids.to(lowerCamelCase )
snake_case_ = text_inputs.attention_mask.to(lowerCamelCase )
snake_case_ , snake_case_ = self.text_encoder(
input_ids=lowerCamelCase , attention_mask=lowerCamelCase )
snake_case_ = prompt_embeds.repeat_interleave(lowerCamelCase , dim=0 )
snake_case_ = text_encoder_hidden_states.repeat_interleave(lowerCamelCase , dim=0 )
snake_case_ = text_mask.repeat_interleave(lowerCamelCase , dim=0 )
if do_classifier_free_guidance:
snake_case_ = 42
if negative_prompt is None:
snake_case_ = [""""""] * batch_size
elif type(lowerCamelCase ) is not type(lowerCamelCase ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase )} !='''
f''' {type(lowerCamelCase )}.''' )
elif isinstance(lowerCamelCase , lowerCamelCase ):
snake_case_ = [negative_prompt]
elif batch_size != len(lowerCamelCase ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
""" the batch size of `prompt`.""" )
else:
snake_case_ = negative_prompt
snake_case_ = self.tokenizer(
lowerCamelCase , padding="""max_length""" , max_length=77 , truncation=lowerCamelCase , return_attention_mask=lowerCamelCase , add_special_tokens=lowerCamelCase , return_tensors="""pt""" , )
snake_case_ = uncond_input.input_ids.to(lowerCamelCase )
snake_case_ = uncond_input.attention_mask.to(lowerCamelCase )
snake_case_ , snake_case_ = self.text_encoder(
input_ids=lowerCamelCase , attention_mask=lowerCamelCase )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case_ = negative_prompt_embeds.shape[1]
snake_case_ = negative_prompt_embeds.repeat(1 , lowerCamelCase )
snake_case_ = negative_prompt_embeds.view(batch_size * num_images_per_prompt , lowerCamelCase )
snake_case_ = uncond_text_encoder_hidden_states.shape[1]
snake_case_ = uncond_text_encoder_hidden_states.repeat(1 , lowerCamelCase , 1 )
snake_case_ = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , lowerCamelCase , -1 )
snake_case_ = uncond_text_mask.repeat_interleave(lowerCamelCase , dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case_ = torch.cat([negative_prompt_embeds, prompt_embeds] )
snake_case_ = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
snake_case_ = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def lowerCAmelCase_ ( self , lowerCamelCase=0 ) -> List[Any]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
snake_case_ = torch.device(f'''cuda:{gpu_id}''' )
snake_case_ = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase , lowerCamelCase )
def lowerCAmelCase_ ( self , lowerCamelCase=0 ) -> int:
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
snake_case_ = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=lowerCamelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
snake_case_ = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
snake_case_ , snake_case_ = cpu_offload_with_hook(lowerCamelCase , lowerCamelCase , prev_module_hook=lowerCamelCase )
if self.safety_checker is not None:
snake_case_ , snake_case_ = cpu_offload_with_hook(self.safety_checker , lowerCamelCase , prev_module_hook=lowerCamelCase )
# We'll offload the last model manually.
snake_case_ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCAmelCase_ ( self ) -> List[Any]:
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCamelCase )
def __call__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = 512 , lowerCamelCase = 512 , lowerCamelCase = 100 , lowerCamelCase = 4.0 , lowerCamelCase = 1 , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = "pil" , lowerCamelCase = True , ) -> Union[str, Any]:
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case_ = 1
elif isinstance(lowerCamelCase , lowerCamelCase ):
snake_case_ = len(lowerCamelCase )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase )}''' )
snake_case_ = self._execution_device
snake_case_ = batch_size * num_images_per_prompt
snake_case_ = guidance_scale > 1.0
snake_case_ , snake_case_ , snake_case_ = self._encode_prompt(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case_ = torch.cat(lowerCamelCase , dim=0 )
if isinstance(lowerCamelCase , lowerCamelCase ):
snake_case_ = torch.cat(lowerCamelCase , dim=0 )
if do_classifier_free_guidance:
snake_case_ = image_embeds.repeat_interleave(lowerCamelCase , dim=0 )
snake_case_ = negative_image_embeds.repeat_interleave(lowerCamelCase , dim=0 )
snake_case_ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=lowerCamelCase )
self.scheduler.set_timesteps(lowerCamelCase , device=lowerCamelCase )
snake_case_ = self.scheduler.timesteps
snake_case_ = self.unet.config.in_channels
snake_case_ , snake_case_ = get_new_h_w(lowerCamelCase , lowerCamelCase , self.movq_scale_factor )
# create initial latent
snake_case_ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , lowerCamelCase , lowerCamelCase , lowerCamelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
snake_case_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
snake_case_ = {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
snake_case_ = self.unet(
sample=lowerCamelCase , timestep=lowerCamelCase , encoder_hidden_states=lowerCamelCase , added_cond_kwargs=lowerCamelCase , return_dict=lowerCamelCase , )[0]
if do_classifier_free_guidance:
snake_case_ , snake_case_ = noise_pred.split(latents.shape[1] , dim=1 )
snake_case_ , snake_case_ = noise_pred.chunk(2 )
snake_case_ , snake_case_ = variance_pred.chunk(2 )
snake_case_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
snake_case_ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
snake_case_ , snake_case_ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ = self.scheduler.step(
lowerCamelCase , lowerCamelCase , lowerCamelCase , generator=lowerCamelCase , ).prev_sample
# post-processing
snake_case_ = self.movq.decode(lowerCamelCase , force_not_quantize=lowerCamelCase )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
snake_case_ = image * 0.5 + 0.5
snake_case_ = image.clamp(0 , 1 )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case_ = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase ) | 34 | 0 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
__snake_case = """bart"""
__snake_case = True
@st.cache(allow_output_mutation=__A )
def __lowerCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
if LOAD_DENSE_INDEX:
snake_case : int = AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased" )
snake_case : Any = AutoModel.from_pretrained("yjernite/retribert-base-uncased" ).to("cuda:0" )
snake_case : Optional[int] = qar_model.eval()
else:
snake_case ,snake_case : str = (None, None)
if MODEL_TYPE == "bart":
snake_case : int = AutoTokenizer.from_pretrained("yjernite/bart_eli5" )
snake_case : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5" ).to("cuda:0" )
snake_case : Optional[int] = torch.load("seq2seq_models/eli5_bart_model_blm_2.pth" )
sas_model.load_state_dict(save_dict["model"] )
snake_case : Dict = sas_model.eval()
else:
snake_case ,snake_case : Any = make_qa_sas_model(
model_name="t5-small" , from_file="seq2seq_models/eli5_t5_model_1024_4.pth" , device="cuda:0" )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=__A )
def __lowerCAmelCase ( ) -> Dict:
"""simple docstring"""
if LOAD_DENSE_INDEX:
snake_case : Dict = faiss.StandardGpuResources()
snake_case : Optional[int] = datasets.load_dataset(path="wiki_snippets" , name="wiki40b_en_100_0" )["train"]
snake_case : Optional[Any] = np.memmap(
"wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat" , dtype="float32" , mode="r" , shape=(wikiaab_passages.num_rows, 128) , )
snake_case : Union[str, Any] = faiss.IndexFlatIP(128 )
snake_case : List[Any] = faiss.index_cpu_to_gpu(__A , 1 , __A )
wikiaab_gpu_index_flat.add(__A ) # TODO fix for larger GPU
else:
snake_case ,snake_case : int = (None, None)
snake_case : int = Elasticsearch([{"host": "localhost", "port": "9200"}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=__A )
def __lowerCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case : List[Any] = datasets.load_dataset("eli5" , name="LFQA_reddit" )
snake_case : Tuple = elia["train_eli5"]
snake_case : str = np.memmap(
"eli5_questions_reps.dat" , dtype="float32" , mode="r" , shape=(elia_train.num_rows, 128) )
snake_case : List[Any] = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(__A )
return (elia_train, eli5_train_q_index)
__snake_case , __snake_case , __snake_case = load_indexes()
__snake_case , __snake_case , __snake_case , __snake_case = load_models()
__snake_case , __snake_case = load_train_data()
def __lowerCAmelCase ( lowercase : Optional[Any] , lowercase : Optional[Any]=10 ) -> Optional[int]:
"""simple docstring"""
snake_case : str = embed_questions_for_retrieval([question] , __A , __A )
snake_case ,snake_case : List[Any] = eli5_train_q_index.search(__A , __A )
snake_case : Optional[int] = [elia_train[int(__A )] for i in I[0]]
return nn_examples
def __lowerCAmelCase ( lowercase : List[Any] , lowercase : Union[str, Any]="wiki40b" , lowercase : Union[str, Any]="dense" , lowercase : str=10 ) -> Optional[Any]:
"""simple docstring"""
if source == "none":
snake_case ,snake_case : Optional[int] = (" <P> ".join(["" for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
snake_case ,snake_case : Union[str, Any] = query_qa_dense_index(
__A , __A , __A , __A , __A , __A )
else:
snake_case ,snake_case : Any = query_es_index(
__A , __A , index_name="english_wiki40b_snippets_100w" , n_results=__A , )
snake_case : Optional[Any] = [
(res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
]
snake_case : int = "question: {} context: {}".format(__A , __A )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda lowercase : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowercase : None),
} )
def __lowerCAmelCase ( lowercase : Optional[int] , lowercase : List[Any] , lowercase : int , lowercase : List[Any]=64 , lowercase : Any=256 , lowercase : str=False , lowercase : Optional[Any]=2 , lowercase : Dict=0.95 , lowercase : int=0.8 ) -> Optional[Any]:
"""simple docstring"""
with torch.no_grad():
snake_case : Any = qa_sas_generate(
__A , __A , __A , num_answers=1 , num_beams=__A , min_len=__A , max_len=__A , do_sample=__A , temp=__A , top_p=__A , top_k=__A , max_input_length=1024 , device="cuda:0" , )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
__snake_case = """<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>"""
__snake_case = """\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class=\"img-container\"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
__snake_case = """\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"""
st.sidebar.markdown(description, unsafe_allow_html=True)
__snake_case = [
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
__snake_case = st.sidebar.checkbox("""Demo options""")
if demo_options:
__snake_case = st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
__snake_case = action_list.index(action_st)
__snake_case = st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
__snake_case = show_type == """Show full text of passages"""
else:
__snake_case = 3
__snake_case = True
__snake_case = st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
__snake_case = """\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n """
st.sidebar.markdown(retriever_info)
__snake_case = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
__snake_case = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
__snake_case = """wiki40b"""
__snake_case = """dense"""
__snake_case = """beam"""
__snake_case = 2
__snake_case = 64
__snake_case = 256
__snake_case = None
__snake_case = None
__snake_case = st.sidebar.checkbox("""Generation options""")
if generate_options:
__snake_case = """\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder\'s output probabilities.\n """
st.sidebar.markdown(generate_info)
__snake_case = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
__snake_case = st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
__snake_case = st.sidebar.slider(
"""Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
__snake_case = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
__snake_case = st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
__snake_case = st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
__snake_case = None
# start main text
__snake_case = [
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What\'s the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What\'s the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
__snake_case = st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
__snake_case = st.text_input("""Enter your question here:""", """""")
else:
__snake_case = question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
__snake_case , __snake_case = make_support(question, source=wiki_source, method="""dense""", n_results=10)
__snake_case , __snake_case = make_support(question, source=wiki_source, method="""sparse""", n_results=10)
__snake_case = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
__snake_case = support_list[:10]
__snake_case = """<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
__snake_case , __snake_case = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
__snake_case , __snake_case = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
__snake_case = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
__snake_case = res[1].strip()
if sec_titles == "":
__snake_case = """[{}]({})""".format(res[0], wiki_url)
else:
__snake_case = sec_titles.split(""" & """)
__snake_case = """ & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
__snake_case = find_nearest_training(question)
__snake_case = nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
__snake_case = [
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
__snake_case = """\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 203 | from manim import *
class A ( UpperCAmelCase_ ):
def lowercase_ (self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase__ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCAmelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperCAmelCase__ = [mem.copy() for i in range(6 )]
UpperCAmelCase__ = [mem.copy() for i in range(6 )]
UpperCAmelCase__ = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0 )
UpperCAmelCase__ = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0 )
UpperCAmelCase__ = VGroup(__UpperCAmelCase , __UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0 )
UpperCAmelCase__ = Text("CPU" , font_size=2_4 )
UpperCAmelCase__ = Group(__UpperCAmelCase , __UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0.5 , aligned_edge=__UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__UpperCAmelCase )
UpperCAmelCase__ = [mem.copy() for i in range(4 )]
UpperCAmelCase__ = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0 )
UpperCAmelCase__ = Text("GPU" , font_size=2_4 )
UpperCAmelCase__ = Group(__UpperCAmelCase , __UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0.5 , aligned_edge=__UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(__UpperCAmelCase )
UpperCAmelCase__ = [mem.copy() for i in range(6 )]
UpperCAmelCase__ = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0 )
UpperCAmelCase__ = Text("Model" , font_size=2_4 )
UpperCAmelCase__ = Group(__UpperCAmelCase , __UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0.5 , aligned_edge=__UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(__UpperCAmelCase )
UpperCAmelCase__ = []
UpperCAmelCase__ = []
for i, rect in enumerate(__UpperCAmelCase ):
UpperCAmelCase__ = fill.copy().set_fill(__UpperCAmelCase , opacity=0.8 )
target.move_to(__UpperCAmelCase )
model_arr.append(__UpperCAmelCase )
UpperCAmelCase__ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(__UpperCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(__UpperCAmelCase )
self.add(*__UpperCAmelCase , *__UpperCAmelCase )
UpperCAmelCase__ = [meta_mem.copy() for i in range(6 )]
UpperCAmelCase__ = [meta_mem.copy() for i in range(6 )]
UpperCAmelCase__ = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0 )
UpperCAmelCase__ = VGroup(*__UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0 )
UpperCAmelCase__ = VGroup(__UpperCAmelCase , __UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0 )
UpperCAmelCase__ = Text("Disk" , font_size=2_4 )
UpperCAmelCase__ = Group(__UpperCAmelCase , __UpperCAmelCase ).arrange(__UpperCAmelCase , buff=0.5 , aligned_edge=__UpperCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase__ = 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(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=1_8 , )
blue_text.next_to(__UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(__UpperCAmelCase )
UpperCAmelCase__ = 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(__UpperCAmelCase ) )
UpperCAmelCase__ = Square(0.3 )
input.set_fill(__UpperCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , __UpperCAmelCase , buff=0.5 )
self.play(Write(__UpperCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=__UpperCAmelCase , buff=0.02 )
self.play(MoveToTarget(__UpperCAmelCase ) )
self.play(FadeOut(__UpperCAmelCase ) )
UpperCAmelCase__ = Arrow(start=__UpperCAmelCase , end=__UpperCAmelCase , color=__UpperCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , __UpperCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
UpperCAmelCase__ = 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(__UpperCAmelCase , run_time=3 ) )
UpperCAmelCase__ = {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}
self.play(
Write(__UpperCAmelCase ) , Circumscribe(model_arr[0] , color=__UpperCAmelCase , **__UpperCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=__UpperCAmelCase , **__UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=__UpperCAmelCase , **__UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
UpperCAmelCase__ = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , __UpperCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
UpperCAmelCase__ = AnimationGroup(
FadeOut(__UpperCAmelCase , run_time=0.5 ) , MoveToTarget(__UpperCAmelCase , run_time=0.5 ) , FadeIn(__UpperCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(__UpperCAmelCase )
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:
UpperCAmelCase__ = 0.7
self.play(
Circumscribe(model_arr[i] , **__UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **__UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=__UpperCAmelCase , **__UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=__UpperCAmelCase , **__UpperCAmelCase ) , Circumscribe(model_arr[i + 1] , color=__UpperCAmelCase , **__UpperCAmelCase ) , )
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=__UpperCAmelCase , **__UpperCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=__UpperCAmelCase , **__UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=__UpperCAmelCase , **__UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
UpperCAmelCase__ = a_c
UpperCAmelCase__ = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(__UpperCAmelCase ) , FadeOut(__UpperCAmelCase , run_time=0.5 ) , )
UpperCAmelCase__ = 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(__UpperCAmelCase , run_time=3 ) , MoveToTarget(__UpperCAmelCase ) )
self.wait()
| 65 | 0 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowercase__ : str = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase) -> None:
a = nn.ModuleList([src_layers[i] for i in layers_to_copy])
assert len(__UpperCamelCase) == len(__UpperCamelCase), f'''{len(__UpperCamelCase)} != {len(__UpperCamelCase)}'''
dest_layers.load_state_dict(layers_to_copy.state_dict())
lowercase__ : Union[str, Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowercase__ : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase) -> Tuple:
try:
a = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
f'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
f''' {n_student}''')
return list(range(__UpperCamelCase))
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase) -> List[int]:
if n_student > n_teacher:
raise ValueError(f'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''')
elif n_teacher == n_student:
return list(range(__UpperCamelCase))
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase = "student" , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase=False , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , ) -> Tuple[PreTrainedModel, List[int], List[int]]:
a = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(__UpperCamelCase , __UpperCamelCase):
AutoTokenizer.from_pretrained(__UpperCamelCase).save_pretrained(__UpperCamelCase) # purely for convenience
a = AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase).eval()
else:
assert isinstance(__UpperCamelCase , __UpperCamelCase), f'''teacher must be a model or string got type {type(__UpperCamelCase)}'''
a = teacher.config.to_diff_dict()
try:
a , a = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
a = teacher_e
if d is None:
a = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers"):
a , a = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
a , a = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
a = teacher_e
if d is None:
a = teacher_d
if hasattr(teacher.config , "num_encoder_layers"):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d})
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d})
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(__UpperCamelCase)
# Copy weights
a = teacher.config_class(**__UpperCamelCase)
a = AutoModelForSeqaSeqLM.from_config(__UpperCamelCase)
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
a = student.load_state_dict(teacher.state_dict() , strict=__UpperCamelCase)
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
a , a = list(range(__UpperCamelCase)), list(range(__UpperCamelCase))
logger.info(
f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
f''' {save_path}''')
student.save_pretrained(__UpperCamelCase)
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
a = pick_layers_to_copy(__UpperCamelCase , __UpperCamelCase)
if d_layers_to_copy is None:
a = pick_layers_to_copy(__UpperCamelCase , __UpperCamelCase)
try:
if hasattr(
__UpperCamelCase , "prophetnet"): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , __UpperCamelCase)
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , __UpperCamelCase)
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , __UpperCamelCase)
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , __UpperCamelCase)
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , __UpperCamelCase)
copy_layers(teacher.decoder.block , student.decoder.block , __UpperCamelCase)
logger.info(
f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''')
a = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(__UpperCamelCase)
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 362 |
from manim import *
class a__ ( UpperCamelCase__ ):
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
a = Rectangle(height=0.5 , width=0.5 )
a = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
a = Rectangle(height=0.2_5 , width=0.2_5 )
a = [mem.copy() for i in range(6 )]
a = [mem.copy() for i in range(6 )]
a = VGroup(*A ).arrange(A , buff=0 )
a = VGroup(*A ).arrange(A , buff=0 )
a = VGroup(A , A ).arrange(A , buff=0 )
a = Text("CPU" , font_size=24 )
a = Group(A , A ).arrange(A , buff=0.5 , aligned_edge=A )
cpu.move_to([-2.5, -0.5, 0] )
self.add(A )
a = [mem.copy() for i in range(4 )]
a = VGroup(*A ).arrange(A , buff=0 )
a = Text("GPU" , font_size=24 )
a = Group(A , A ).arrange(A , buff=0.5 , aligned_edge=A )
gpu.move_to([-1, -1, 0] )
self.add(A )
a = [mem.copy() for i in range(6 )]
a = VGroup(*A ).arrange(A , buff=0 )
a = Text("Model" , font_size=24 )
a = Group(A , A ).arrange(A , buff=0.5 , aligned_edge=A )
model.move_to([3, -1.0, 0] )
self.add(A )
a = []
a = []
for i, rect in enumerate(A ):
a = fill.copy().set_fill(A , opacity=0.8 )
target.move_to(A )
model_arr.append(A )
a = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0.0 ).set_fill(A , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(A )
self.add(*A , *A )
a = [meta_mem.copy() for i in range(6 )]
a = [meta_mem.copy() for i in range(6 )]
a = VGroup(*A ).arrange(A , buff=0 )
a = VGroup(*A ).arrange(A , buff=0 )
a = VGroup(A , A ).arrange(A , buff=0 )
a = Text("Disk" , font_size=24 )
a = Group(A , A ).arrange(A , buff=0.5 , aligned_edge=A )
disk.move_to([-4, -1.2_5, 0] )
self.add(A , A )
a = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
a = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(A , A )
a = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(A , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(A )
a = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(A ) )
a = Square(0.3 )
input.set_fill(A , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , A , buff=0.5 )
self.play(Write(A ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=A , buff=0.0_2 )
self.play(MoveToTarget(A ) )
self.play(FadeOut(A ) )
a = Arrow(start=A , end=A , color=A , buff=0.5 )
a.next_to(model_arr[0].get_left() , A , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
a = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(A , run_time=3 ) )
a = {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.0_2}
self.play(
Write(A ) , Circumscribe(model_arr[0] , color=A , **A ) , Circumscribe(model_cpu_arr[0] , color=A , **A ) , Circumscribe(gpu_rect[0] , color=A , **A ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
a = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.0_2 , A , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.0_2 )
a = AnimationGroup(
FadeOut(A , run_time=0.5 ) , MoveToTarget(A , run_time=0.5 ) , FadeIn(A , run_time=0.5 ) , lag_ratio=0.2 )
self.play(A )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
a = 0.7
self.play(
Circumscribe(model_arr[i] , **A ) , Circumscribe(cpu_left_col_base[i] , **A ) , Circumscribe(cpu_left_col_base[i + 1] , color=A , **A ) , Circumscribe(gpu_rect[0] , color=A , **A ) , Circumscribe(model_arr[i + 1] , color=A , **A ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.0_2 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=A , **A ) , Circumscribe(cpu_left_col_base[-1] , color=A , **A ) , Circumscribe(gpu_rect[0] , color=A , **A ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
a = a_c
a = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.0_2 , buff=0.5 )
self.play(
FadeOut(A ) , FadeOut(A , run_time=0.5 ) , )
a = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(A , run_time=3 ) , MoveToTarget(A ) )
self.wait()
| 180 | 0 |
"""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 SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowercase__ = LEDConfig
lowercase__ = {}
lowercase__ = "gelu"
def __init__( self : Any ,lowercase_ : Optional[Any] ,lowercase_ : Optional[int]=1_3 ,lowercase_ : List[str]=7 ,lowercase_ : Any=True ,lowercase_ : Tuple=False ,lowercase_ : Optional[int]=9_9 ,lowercase_ : str=3_2 ,lowercase_ : Dict=2 ,lowercase_ : Dict=4 ,lowercase_ : int=3_7 ,lowercase_ : Optional[int]=0.1 ,lowercase_ : Tuple=0.1 ,lowercase_ : str=2_0 ,lowercase_ : Tuple=2 ,lowercase_ : Dict=1 ,lowercase_ : List[Any]=0 ,lowercase_ : List[str]=4 ,):
lowerCAmelCase__ : Tuple = parent
lowerCAmelCase__ : int = batch_size
lowerCAmelCase__ : str = seq_length
lowerCAmelCase__ : List[str] = is_training
lowerCAmelCase__ : List[str] = use_labels
lowerCAmelCase__ : str = vocab_size
lowerCAmelCase__ : Tuple = hidden_size
lowerCAmelCase__ : Dict = num_hidden_layers
lowerCAmelCase__ : List[str] = num_attention_heads
lowerCAmelCase__ : Any = intermediate_size
lowerCAmelCase__ : Any = hidden_dropout_prob
lowerCAmelCase__ : List[str] = attention_probs_dropout_prob
lowerCAmelCase__ : Optional[Any] = max_position_embeddings
lowerCAmelCase__ : List[Any] = eos_token_id
lowerCAmelCase__ : Optional[int] = pad_token_id
lowerCAmelCase__ : Any = bos_token_id
lowerCAmelCase__ : Any = 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
lowerCAmelCase__ : 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
lowerCAmelCase__ : Optional[Any] = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowerCAmelCase ( self : int ):
lowerCAmelCase__ : str = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
lowerCAmelCase__ : Any = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
lowerCAmelCase__ : Dict = tf.concat([input_ids, eos_tensor] ,axis=1 )
lowerCAmelCase__ : int = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCAmelCase__ : int = 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 ,)
lowerCAmelCase__ : List[Any] = prepare_led_inputs_dict(lowercase_ ,lowercase_ ,lowercase_ )
lowerCAmelCase__ : Any = tf.concat(
[tf.zeros_like(lowercase_ )[:, :-1], tf.ones_like(lowercase_ )[:, -1:]] ,axis=-1 ,)
lowerCAmelCase__ : Optional[int] = global_attention_mask
return config, inputs_dict
def __lowerCAmelCase ( self : Any ,lowercase_ : str ,lowercase_ : Any ):
lowerCAmelCase__ : Optional[int] = TFLEDModel(config=lowercase_ ).get_decoder()
lowerCAmelCase__ : Tuple = inputs_dict['''input_ids''']
lowerCAmelCase__ : Optional[int] = input_ids[:1, :]
lowerCAmelCase__ : Optional[Any] = inputs_dict['''attention_mask'''][:1, :]
lowerCAmelCase__ : str = 1
# first forward pass
lowerCAmelCase__ : List[Any] = model(lowercase_ ,attention_mask=lowercase_ ,use_cache=lowercase_ )
lowerCAmelCase__ ,lowerCAmelCase__ : Tuple = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCAmelCase__ : int = ids_tensor((self.batch_size, 3) ,config.vocab_size )
lowerCAmelCase__ : Union[str, Any] = tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )
# append to next input_ids and
lowerCAmelCase__ : List[str] = tf.concat([input_ids, next_tokens] ,axis=-1 )
lowerCAmelCase__ : Optional[int] = tf.concat([attention_mask, next_attn_mask] ,axis=-1 )
lowerCAmelCase__ : int = model(lowercase_ ,attention_mask=lowercase_ )[0]
lowerCAmelCase__ : int = model(lowercase_ ,attention_mask=lowercase_ ,past_key_values=lowercase_ )[0]
self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )
# select random slice
lowerCAmelCase__ : Union[str, Any] = int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
lowerCAmelCase__ : List[Any] = output_from_no_past[:, -3:, random_slice_idx]
lowerCAmelCase__ : Any = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase_ ,lowercase_ ,rtol=1E-3 )
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_=None , A_=None , A_=None , A_=None , ):
if attention_mask is None:
lowerCAmelCase__ : Optional[Any] = tf.cast(tf.math.not_equal(A_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowerCAmelCase__ : Optional[int] = 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:
lowerCAmelCase__ : int = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCAmelCase__ : Optional[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 SCREAMING_SNAKE_CASE ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
lowercase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
lowercase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
lowercase__ = (
{
"conversational": TFLEDForConditionalGeneration,
"feature-extraction": TFLEDModel,
"summarization": TFLEDForConditionalGeneration,
"text2text-generation": TFLEDForConditionalGeneration,
"translation": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def __lowerCAmelCase ( self : List[str] ):
lowerCAmelCase__ : int = TFLEDModelTester(self )
lowerCAmelCase__ : Union[str, Any] = ConfigTester(self ,config_class=lowercase_ )
def __lowerCAmelCase ( self : Dict ):
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : str ):
lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase_ )
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ ,lowerCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : Any = tf.zeros_like(inputs_dict['''attention_mask'''] )
lowerCAmelCase__ : str = 2
lowerCAmelCase__ : Dict = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices ,1 ,inputs_dict['''global_attention_mask'''] ,)
lowerCAmelCase__ : Union[str, Any] = True
lowerCAmelCase__ : Dict = self.model_tester.seq_length
lowerCAmelCase__ : List[Any] = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(lowercase_ : List[str] ):
lowerCAmelCase__ : str = outputs.decoder_attentions
self.assertEqual(len(lowercase_ ) ,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(lowercase_ : Optional[Any] ):
lowerCAmelCase__ : Optional[Any] = [t.numpy() for t in outputs.encoder_attentions]
lowerCAmelCase__ : str = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(lowercase_ ) ,self.model_tester.num_hidden_layers )
self.assertEqual(len(lowercase_ ) ,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:
lowerCAmelCase__ : Union[str, Any] = True
lowerCAmelCase__ : Dict = False
lowerCAmelCase__ : Optional[Any] = False
lowerCAmelCase__ : Tuple = model_class(lowercase_ )
lowerCAmelCase__ : List[str] = model(self._prepare_for_class(lowercase_ ,lowercase_ ) )
lowerCAmelCase__ : Dict = len(lowercase_ )
self.assertEqual(config.output_hidden_states ,lowercase_ )
check_encoder_attentions_output(lowercase_ )
if self.is_encoder_decoder:
lowerCAmelCase__ : Tuple = model_class(lowercase_ )
lowerCAmelCase__ : Any = model(self._prepare_for_class(lowercase_ ,lowercase_ ) )
self.assertEqual(config.output_hidden_states ,lowercase_ )
check_decoder_attentions_output(lowercase_ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
lowerCAmelCase__ : List[Any] = True
lowerCAmelCase__ : Optional[int] = model_class(lowercase_ )
lowerCAmelCase__ : List[Any] = model(self._prepare_for_class(lowercase_ ,lowercase_ ) )
self.assertEqual(config.output_hidden_states ,lowercase_ )
check_encoder_attentions_output(lowercase_ )
# Check attention is always last and order is fine
lowerCAmelCase__ : Any = True
lowerCAmelCase__ : Union[str, Any] = True
lowerCAmelCase__ : str = model_class(lowercase_ )
lowerCAmelCase__ : str = model(self._prepare_for_class(lowercase_ ,lowercase_ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) ,len(lowercase_ ) )
self.assertEqual(model.config.output_hidden_states ,lowercase_ )
check_encoder_attentions_output(lowercase_ )
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
def __lowerCAmelCase ( self : Dict ):
pass
def __lowerCAmelCase ( self : Dict ):
# TODO: Head-masking not yet implement
pass
def __SCREAMING_SNAKE_CASE ( A_ ):
return tf.constant(A_ , dtype=tf.intaa )
__UpperCamelCase : Any = 1e-4
@slow
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ):
lowerCAmelCase__ : Tuple = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led
# change to intended input here
lowerCAmelCase__ : Tuple = _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowerCAmelCase__ : Any = _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowerCAmelCase__ : str = prepare_led_inputs_dict(model.config ,lowercase_ ,lowercase_ )
lowerCAmelCase__ : str = model(**lowercase_ )[0]
lowerCAmelCase__ : str = (1, 1_0_2_4, 7_6_8)
self.assertEqual(output.shape ,lowercase_ )
# change to expected output here
lowerCAmelCase__ : List[str] = 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] ,lowercase_ ,atol=1E-3 )
def __lowerCAmelCase ( self : int ):
lowerCAmelCase__ : Any = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )
# change to intended input here
lowerCAmelCase__ : List[str] = _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowerCAmelCase__ : Dict = _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] )
lowerCAmelCase__ : Any = prepare_led_inputs_dict(model.config ,lowercase_ ,lowercase_ )
lowerCAmelCase__ : List[str] = model(**lowercase_ )[0]
lowerCAmelCase__ : Any = (1, 1_0_2_4, model.config.vocab_size)
self.assertEqual(output.shape ,lowercase_ )
# change to expected output here
lowerCAmelCase__ : int = 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] ,lowercase_ ,atol=1E-3 ,rtol=1E-3 )
| 106 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , *lowercase , lowercase=None , lowercase=None , **lowercase ) -> List[str]:
super().__init__(*lowercase , **lowercase )
lowerCamelCase_ = eval_examples
lowerCamelCase_ = post_process_function
def SCREAMING_SNAKE_CASE_( self , lowercase = None , lowercase=None , lowercase = None , lowercase = "eval" , **lowercase , ) -> Dict[str, float]:
lowerCamelCase_ = gen_kwargs.copy()
lowerCamelCase_ = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
lowerCamelCase_ = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
lowerCamelCase_ = gen_kwargs
lowerCamelCase_ = self.eval_dataset if eval_dataset is None else eval_dataset
lowerCamelCase_ = self.get_eval_dataloader(lowercase )
lowerCamelCase_ = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowerCamelCase_ = self.compute_metrics
lowerCamelCase_ = None
lowerCamelCase_ = time.time()
lowerCamelCase_ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCamelCase_ = eval_loop(
lowercase , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowercase , metric_key_prefix=lowercase , )
finally:
lowerCamelCase_ = compute_metrics
lowerCamelCase_ = self.args.eval_batch_size * self.args.world_size
if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
output.metrics.update(
speed_metrics(
lowercase , lowercase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
lowerCamelCase_ = self.post_process_function(lowercase , lowercase , lowercase )
lowerCamelCase_ = self.compute_metrics(lowercase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'{metric_key_prefix}_' ):
lowerCamelCase_ = metrics.pop(lowercase )
metrics.update(output.metrics )
else:
lowerCamelCase_ = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(lowercase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
lowerCamelCase_ = self.callback_handler.on_evaluate(self.args , self.state , self.control , lowercase )
return metrics
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase=None , lowercase = "test" , **lowercase ) -> Union[str, Any]:
lowerCamelCase_ = gen_kwargs.copy()
lowerCamelCase_ = self.get_test_dataloader(lowercase )
# Temporarily disable metric computation, we will do it in the loop here.
lowerCamelCase_ = self.compute_metrics
lowerCamelCase_ = None
lowerCamelCase_ = time.time()
lowerCamelCase_ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCamelCase_ = eval_loop(
lowercase , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowercase , metric_key_prefix=lowercase , )
finally:
lowerCamelCase_ = compute_metrics
lowerCamelCase_ = self.args.eval_batch_size * self.args.world_size
if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
output.metrics.update(
speed_metrics(
lowercase , lowercase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
lowerCamelCase_ = self.post_process_function(lowercase , lowercase , lowercase , "predict" )
lowerCamelCase_ = self.compute_metrics(lowercase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'{metric_key_prefix}_' ):
lowerCamelCase_ = metrics.pop(lowercase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowercase )
| 19 | 0 |
import logging
import os
from .state import PartialState
class SCREAMING_SNAKE_CASE__ ( logging.LoggerAdapter ):
@staticmethod
def snake_case__ ( _lowerCAmelCase : Union[str, Any] ):
__snake_case : Dict = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def snake_case__ ( self : Any , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , *_lowerCAmelCase : str , **_lowerCAmelCase : str ):
if PartialState._shared_state == {}:
raise RuntimeError(
"""You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.""" )
__snake_case : Dict = kwargs.pop("""main_process_only""" , _lowerCAmelCase )
__snake_case : List[str] = kwargs.pop("""in_order""" , _lowerCAmelCase )
if self.isEnabledFor(_lowerCAmelCase ):
if self._should_log(_lowerCAmelCase ):
__snake_case , __snake_case : List[str] = self.process(_lowerCAmelCase , _lowerCAmelCase )
self.logger.log(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
elif in_order:
__snake_case : List[str] = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
__snake_case , __snake_case : Dict = self.process(_lowerCAmelCase , _lowerCAmelCase )
self.logger.log(_lowerCAmelCase , _lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
state.wait_for_everyone()
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str = None ):
'''simple docstring'''
if log_level is None:
__snake_case : Dict = os.environ.get("""ACCELERATE_LOG_LEVEL""" , __SCREAMING_SNAKE_CASE )
__snake_case : Union[str, Any] = logging.getLogger(__SCREAMING_SNAKE_CASE )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__SCREAMING_SNAKE_CASE , {} )
| 20 | from __future__ import annotations
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : list , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__snake_case : str = []
__snake_case , __snake_case : List[str] = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
__snake_case : List[Any] = result + left + right
return input_list
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(__SCREAMING_SNAKE_CASE ) <= 1:
return input_list
__snake_case : Union[str, Any] = list(__SCREAMING_SNAKE_CASE )
# iteration for two-way merging
__snake_case : Tuple = 2
while p <= len(__SCREAMING_SNAKE_CASE ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE ):
__snake_case : List[str] = i
__snake_case : str = i + p - 1
__snake_case : Optional[Any] = (low + high + 1) // 2
__snake_case : str = merge(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# final merge of last two parts
if p * 2 >= len(__SCREAMING_SNAKE_CASE ):
__snake_case : List[str] = i
__snake_case : str = merge(__SCREAMING_SNAKE_CASE , 0 , __SCREAMING_SNAKE_CASE , len(__SCREAMING_SNAKE_CASE ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by a comma:\n").strip()
if user_input == "":
lowercase_ = []
else:
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
print(iter_merge_sort(unsorted))
| 20 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
_lowercase : Optional[Any] = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_lowercase : Dict = BASE_URL + "/user"
# https://github.com/settings/tokens
_lowercase : List[str] = os.environ.get("USER_TOKEN", "")
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] ={
'''Authorization''': f'''token {auth_token}''',
'''Accept''': '''application/vnd.github.v3+json''',
}
return requests.get(__lowerCamelCase , headers=__lowerCamelCase ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f'{key}: {value}')
else:
raise ValueError("'USER_TOKEN' field cannot be empty.")
| 238 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
_lowercase : Union[str, Any] = ["text", "image", "audio"]
def snake_case__ ( __lowerCamelCase : List[str] ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] =[]
for input_type in input_types:
if input_type == "text":
inputs.append('''Text input''' )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png''' ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
inputs.append(create_inputs(__lowerCamelCase ) )
else:
raise ValueError(f'''Invalid type requested: {input_type}''' )
return inputs
def snake_case__ ( __lowerCamelCase : List ):
"""simple docstring"""
lowerCamelCase__ : Tuple =[]
for output in outputs:
if isinstance(__lowerCamelCase , (str, AgentText) ):
output_types.append('''text''' )
elif isinstance(__lowerCamelCase , (Image.Image, AgentImage) ):
output_types.append('''image''' )
elif isinstance(__lowerCamelCase , (torch.Tensor, AgentAudio) ):
output_types.append('''audio''' )
else:
raise ValueError(f'''Invalid output: {output}''' )
return output_types
@is_tool_test
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def snake_case ( self : Any )-> Optional[Any]:
self.assertTrue(hasattr(self.tool, '''inputs''' ) )
self.assertTrue(hasattr(self.tool, '''outputs''' ) )
lowerCamelCase__ : Tuple =self.tool.inputs
for _input in inputs:
if isinstance(_input, lowerCamelCase ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
lowerCamelCase__ : Optional[Any] =self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def snake_case ( self : Optional[int] )-> Union[str, Any]:
lowerCamelCase__ : Optional[int] =create_inputs(self.tool.inputs )
lowerCamelCase__ : List[Any] =self.tool(*lowerCamelCase )
# There is a single output
if len(self.tool.outputs ) == 1:
lowerCamelCase__ : Optional[int] =[outputs]
self.assertListEqual(output_types(lowerCamelCase ), self.tool.outputs )
def snake_case ( self : Union[str, Any] )-> List[str]:
self.assertTrue(hasattr(self.tool, '''description''' ) )
self.assertTrue(hasattr(self.tool, '''default_checkpoint''' ) )
self.assertTrue(self.tool.description.startswith('''This is a tool that''' ) )
def snake_case ( self : Union[str, Any] )-> str:
lowerCamelCase__ : List[str] =create_inputs(self.tool.inputs )
lowerCamelCase__ : Optional[Any] =self.tool(*lowerCamelCase )
if not isinstance(lowerCamelCase, lowerCamelCase ):
lowerCamelCase__ : Any =[outputs]
self.assertEqual(len(lowerCamelCase ), len(self.tool.outputs ) )
for output, output_type in zip(lowerCamelCase, self.tool.outputs ):
lowerCamelCase__ : List[Any] =AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(lowerCamelCase, lowerCamelCase ) )
def snake_case ( self : Optional[Any] )-> List[Any]:
lowerCamelCase__ : Optional[Any] =create_inputs(self.tool.inputs )
lowerCamelCase__ : List[str] =[]
for _input, input_type in zip(lowerCamelCase, self.tool.inputs ):
if isinstance(lowerCamelCase, lowerCamelCase ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
lowerCamelCase__ : Any =self.tool(*lowerCamelCase )
if not isinstance(lowerCamelCase, lowerCamelCase ):
lowerCamelCase__ : Optional[int] =[outputs]
self.assertEqual(len(lowerCamelCase ), len(self.tool.outputs ) )
| 238 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegaForCausalLM''',
'''MegaForMaskedLM''',
'''MegaForMultipleChoice''',
'''MegaForQuestionAnswering''',
'''MegaForSequenceClassification''',
'''MegaForTokenClassification''',
'''MegaModel''',
'''MegaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 178 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 178 | 1 |
from collections import defaultdict
from math import gcd
def _a ( lowerCamelCase = 150_0000 ):
lowerCamelCase : defaultdict = defaultdict(lowerCamelCase )
lowerCamelCase : str = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1, lowerCamelCase, 2 ):
if gcd(lowerCamelCase, lowerCamelCase ) > 1:
continue
lowerCamelCase : Dict = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(lowerCamelCase, limit + 1, lowerCamelCase ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 287 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
_lowerCamelCase =get_logger(__name__)
class A__ :
def __init__( self , __magic_name__ = None ):
lowerCamelCase : Dict = (
os.path.join(__magic_name__ , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
lowerCamelCase : List[str] = Extractor
def UpperCamelCase__ ( self , __magic_name__ ):
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
lowerCamelCase : int = os.path.abspath(__magic_name__ )
return os.path.join(self.extract_dir , hash_url_to_filename(__magic_name__ ) )
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ ):
return force_extract or (
not os.path.isfile(__magic_name__ ) and not (os.path.isdir(__magic_name__ ) and os.listdir(__magic_name__ ))
)
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ = False ):
lowerCamelCase : Union[str, Any] = self.extractor.infer_extractor_format(__magic_name__ )
if not extractor_format:
return input_path
lowerCamelCase : int = self._get_output_path(__magic_name__ )
if self._do_extract(__magic_name__ , __magic_name__ ):
self.extractor.extract(__magic_name__ , __magic_name__ , __magic_name__ )
return output_path
class A__ ( __SCREAMING_SNAKE_CASE):
@classmethod
@abstractmethod
def UpperCamelCase__ ( cls , __magic_name__ , **__magic_name__ ):
...
@staticmethod
@abstractmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
...
class A__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[bytes] = []
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with open(__magic_name__ , """rb""" ) as f:
return f.read(__magic_name__ )
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = b"" ):
if not magic_number:
lowerCamelCase : Optional[Any] = max(len(__magic_name__ ) for cls_magic_number in cls.magic_numbers )
try:
lowerCamelCase : Tuple = cls.read_magic_number(__magic_name__ , __magic_name__ )
except OSError:
return False
return any(magic_number.startswith(__magic_name__ ) for cls_magic_number in cls.magic_numbers )
class A__ ( __SCREAMING_SNAKE_CASE):
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , **__magic_name__ ):
return tarfile.is_tarfile(__magic_name__ )
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
def resolved(__magic_name__ ) -> str:
return os.path.realpath(os.path.abspath(__magic_name__ ) )
def badpath(__magic_name__ , __magic_name__ ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(__magic_name__ , __magic_name__ ) ).startswith(__magic_name__ )
def badlink(__magic_name__ , __magic_name__ ) -> bool:
# Links are interpreted relative to the directory containing the link
lowerCamelCase : List[str] = resolved(os.path.join(__magic_name__ , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=__magic_name__ )
lowerCamelCase : Optional[Any] = resolved(__magic_name__ )
for finfo in members:
if badpath(finfo.name , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked (illegal path)''' )
elif finfo.issym() and badlink(__magic_name__ , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}''' )
elif finfo.islnk() and badlink(__magic_name__ , __magic_name__ ):
logger.error(F'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}''' )
else:
yield finfo
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowerCamelCase : Dict = tarfile.open(__magic_name__ )
tar_file.extractall(__magic_name__ , members=TarExtractor.safemembers(__magic_name__ , __magic_name__ ) )
tar_file.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = [B"""\x1F\x8B"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with gzip.open(__magic_name__ , """rb""" ) as gzip_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Optional[int] = [
B"""PK\x03\x04""",
B"""PK\x05\x06""", # empty archive
B"""PK\x07\x08""", # spanned archive
]
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = b"" ):
if super().is_extractable(__magic_name__ , magic_number=__magic_name__ ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(__magic_name__ , """rb""" ) as fp:
lowerCamelCase : List[str] = _EndRecData(__magic_name__ )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
lowerCamelCase : List[Any] = fp.read(__magic_name__ ) # CD is where we expect it to be
if len(__magic_name__ ) == sizeCentralDir:
lowerCamelCase : str = struct.unpack(__magic_name__ , __magic_name__ ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
with zipfile.ZipFile(__magic_name__ , """r""" ) as zip_file:
zip_file.extractall(__magic_name__ )
zip_file.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[str] = [B"""\xFD\x37\x7A\x58\x5A\x00"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with lzma.open(__magic_name__ ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any = [B"""Rar!\x1a\x07\x00""", B"""Rar!\x1a\x07\x01\x00"""] # RAR_ID # RAR5_ID
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.RARFILE_AVAILABLE:
raise ImportError("""Please pip install rarfile""" )
import rarfile
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowerCamelCase : Union[str, Any] = rarfile.RarFile(__magic_name__ )
rf.extractall(__magic_name__ )
rf.close()
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Tuple = [B"""\x28\xb5\x2F\xFD"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("""Please pip install zstandard""" )
import zstandard as zstd
lowerCamelCase : int = zstd.ZstdDecompressor()
with open(__magic_name__ , """rb""" ) as ifh, open(__magic_name__ , """wb""" ) as ofh:
dctx.copy_stream(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any = [B"""\x42\x5A\x68"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
with bza.open(__magic_name__ , """rb""" ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[Any] = [B"""\x37\x7A\xBC\xAF\x27\x1C"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.PY7ZR_AVAILABLE:
raise ImportError("""Please pip install py7zr""" )
import pyazr
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
with pyazr.SevenZipFile(__magic_name__ , """r""" ) as archive:
archive.extractall(__magic_name__ )
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : List[Any] = [B"""\x04\x22\x4D\x18"""]
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
if not config.LZ4_AVAILABLE:
raise ImportError("""Please pip install lz4""" )
import lza.frame
with lza.frame.open(__magic_name__ , """rb""" ) as compressed_file:
with open(__magic_name__ , """wb""" ) as extracted_file:
shutil.copyfileobj(__magic_name__ , __magic_name__ )
class A__ :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
_UpperCAmelCase : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def UpperCamelCase__ ( cls ):
return max(
len(__magic_name__ )
for extractor in cls.extractors.values()
if issubclass(__magic_name__ , __magic_name__ )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def UpperCamelCase__ ( __magic_name__ , __magic_name__ ):
try:
return MagicNumberBaseExtractor.read_magic_number(__magic_name__ , magic_number_length=__magic_name__ )
except OSError:
return b""
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ = False ):
warnings.warn(
"""Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'infer_extractor_format' instead.""" , category=__magic_name__ , )
lowerCamelCase : int = cls.infer_extractor_format(__magic_name__ )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ ): # <Added version="2.4.0"/>
lowerCamelCase : Dict = cls._get_magic_number_max_length()
lowerCamelCase : Optional[Any] = cls._read_magic_number(__magic_name__ , __magic_name__ )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(__magic_name__ , magic_number=__magic_name__ ):
return extractor_format
@classmethod
def UpperCamelCase__ ( cls , __magic_name__ , __magic_name__ , __magic_name__ = None , __magic_name__ = "deprecated" , ):
os.makedirs(os.path.dirname(__magic_name__ ) , exist_ok=__magic_name__ )
# Prevent parallel extractions
lowerCamelCase : Tuple = str(Path(__magic_name__ ).with_suffix(""".lock""" ) )
with FileLock(__magic_name__ ):
shutil.rmtree(__magic_name__ , ignore_errors=__magic_name__ )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(__magic_name__ , __magic_name__ ): # passed as positional arg
warnings.warn(
"""Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'extractor_format' instead.""" , category=__magic_name__ , )
lowerCamelCase : int = extractor if extractor != """deprecated""" else extractor_format
else:
lowerCamelCase : Optional[int] = cls.extractors[extractor_format]
return extractor.extract(__magic_name__ , __magic_name__ )
else:
warnings.warn(
"""Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an """
"""exception in 3.0.0.""" , category=__magic_name__ , )
for extractor in cls.extractors.values():
if extractor.is_extractable(__magic_name__ ):
return extractor.extract(__magic_name__ , __magic_name__ )
| 287 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Tuple = ["pixel_values"]
def __init__( self : Any ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Dict[str, int] = None ,lowerCamelCase__ : PILImageResampling = PILImageResampling.BICUBIC ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Dict[str, int] = None ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Union[int, float] = 1 / 255 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Optional[Union[float, List[float]]] = None ,lowerCamelCase__ : Optional[Union[float, List[float]]] = None ,lowerCamelCase__ : bool = True ,**lowerCamelCase__ : int ,) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = size if size is not None else {"""shortest_edge""": 224}
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,default_to_square=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,default_to_square=lowerCamelCase__ ,param_name="""crop_size""" )
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = resample
SCREAMING_SNAKE_CASE = do_center_crop
SCREAMING_SNAKE_CASE = crop_size
SCREAMING_SNAKE_CASE = do_rescale
SCREAMING_SNAKE_CASE = rescale_factor
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
SCREAMING_SNAKE_CASE = image_std if image_std is not None else OPENAI_CLIP_STD
SCREAMING_SNAKE_CASE = do_convert_rgb
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Dict[str, int] ,lowerCamelCase__ : PILImageResampling = PILImageResampling.BICUBIC ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Optional[Any] ,) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,default_to_square=lowerCamelCase__ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
SCREAMING_SNAKE_CASE = get_resize_output_image_size(lowerCamelCase__ ,size=size["""shortest_edge"""] ,default_to_square=lowerCamelCase__ )
return resize(lowerCamelCase__ ,size=lowerCamelCase__ ,resample=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Dict[str, int] ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Optional[int] ,) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(lowerCamelCase__ ,size=(size["""height"""], size["""width"""]) ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Union[int, float] ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Optional[int] ,) -> Any:
'''simple docstring'''
return rescale(lowerCamelCase__ ,scale=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Union[float, List[float]] ,lowerCamelCase__ : Union[float, List[float]] ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Optional[int] ,) -> np.ndarray:
'''simple docstring'''
return normalize(lowerCamelCase__ ,mean=lowerCamelCase__ ,std=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : ImageInput ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Dict[str, int] = None ,lowerCamelCase__ : PILImageResampling = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : int = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : float = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Optional[Union[float, List[float]]] = None ,lowerCamelCase__ : Optional[Union[float, List[float]]] = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Optional[Union[str, TensorType]] = None ,lowerCamelCase__ : Optional[ChannelDimension] = ChannelDimension.FIRST ,**lowerCamelCase__ : str ,) -> PIL.Image.Image:
'''simple docstring'''
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(lowerCamelCase__ ,param_name="""size""" ,default_to_square=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,param_name="""crop_size""" ,default_to_square=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
SCREAMING_SNAKE_CASE = 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:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
SCREAMING_SNAKE_CASE = [convert_to_rgb(lowerCamelCase__ ) for image in images]
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE = [to_numpy_array(lowerCamelCase__ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE = [self.resize(image=lowerCamelCase__ ,size=lowerCamelCase__ ,resample=lowerCamelCase__ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE = [self.center_crop(image=lowerCamelCase__ ,size=lowerCamelCase__ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE = [self.rescale(image=lowerCamelCase__ ,scale=lowerCamelCase__ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE = [self.normalize(image=lowerCamelCase__ ,mean=lowerCamelCase__ ,std=lowerCamelCase__ ) for image in images]
SCREAMING_SNAKE_CASE = [to_channel_dimension_format(lowerCamelCase__ ,lowerCamelCase__ ) for image in images]
SCREAMING_SNAKE_CASE = {"""pixel_values""": images}
return BatchFeature(data=lowerCamelCase__ ,tensor_type=lowerCamelCase__ )
| 369 |
from __future__ import annotations
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> list[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_SCREAMING_SNAKE_CASE )
if n > 1:
factors.append(_SCREAMING_SNAKE_CASE )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 193 | 0 |
'''simple docstring'''
def a ( __a , __a ) -> List[Any]:
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def a ( __a , __a=0 ) -> List[str]:
'''simple docstring'''
return sorted(lowercase__ , key=lambda __a : x[column] )
def a ( __a , __a , __a=float('''inf''' ) ) -> Optional[Any]:
'''simple docstring'''
for i in range(points_counts - 1 ):
for j in range(i + 1 , lowercase__ ):
UpperCamelCase__ :str = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
UpperCamelCase__ :Optional[int] = current_dis
return min_dis
def a ( __a , __a , __a=float('''inf''' ) ) -> Union[str, Any]:
'''simple docstring'''
for i in range(min(6 , points_counts - 1 ) , lowercase__ ):
for j in range(max(0 , i - 6 ) , lowercase__ ):
UpperCamelCase__ :Dict = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
UpperCamelCase__ :Dict = current_dis
return min_dis
def a ( __a , __a , __a ) -> Any:
'''simple docstring'''
if points_counts <= 3:
return dis_between_closest_pair(lowercase__ , lowercase__ )
# recursion
UpperCamelCase__ :List[Any] = points_counts // 2
UpperCamelCase__ :Optional[Any] = closest_pair_of_points_sqr(
lowercase__ , points_sorted_on_y[:mid] , lowercase__ )
UpperCamelCase__ :Optional[int] = closest_pair_of_points_sqr(
lowercase__ , points_sorted_on_y[mid:] , points_counts - mid )
UpperCamelCase__ :str = min(lowercase__ , lowercase__ )
UpperCamelCase__ :int = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(lowercase__ )
UpperCamelCase__ :Tuple = dis_between_closest_in_strip(
lowercase__ , len(lowercase__ ) , lowercase__ )
return min(lowercase__ , lowercase__ )
def a ( __a , __a ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = column_based_sort(lowercase__ , column=0 )
UpperCamelCase__ :Optional[Any] = column_based_sort(lowercase__ , column=1 )
return (
closest_pair_of_points_sqr(
lowercase__ , lowercase__ , lowercase__ )
) ** 0.5
if __name__ == "__main__":
__snake_case = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print('''Distance:''', closest_pair_of_points(points, len(points))) | 97 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class A ( A_ , unittest.TestCase ):
UpperCamelCase_ : Any =PriorTransformer
UpperCamelCase_ : List[str] ='''hidden_states'''
@property
def _A (self ):
__lowercase= 4
__lowercase= 8
__lowercase= 7
__lowercase= floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase )
__lowercase= floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase )
__lowercase= floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def _A (self , lowerCAmelCase=0 ):
torch.manual_seed(lowerCAmelCase )
__lowercase= 4
__lowercase= 8
__lowercase= 7
__lowercase= torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase )
__lowercase= torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase )
__lowercase= torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def _A (self ):
return (4, 8)
@property
def _A (self ):
return (4, 8)
def _A (self ):
__lowercase= {
'num_attention_heads': 2,
'attention_head_dim': 4,
'num_layers': 2,
'embedding_dim': 8,
'num_embeddings': 7,
'additional_embeddings': 4,
}
__lowercase= self.dummy_input
return init_dict, inputs_dict
def _A (self ):
__lowercase, __lowercase= PriorTransformer.from_pretrained(
'hf-internal-testing/prior-dummy' , output_loading_info=lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(lowerCAmelCase )
__lowercase= model(**self.dummy_input )[0]
assert hidden_states is not None, "Make sure output is not None"
def _A (self ):
__lowercase, __lowercase= self.prepare_init_args_and_inputs_for_common()
__lowercase= self.model_class(**lowerCAmelCase )
__lowercase= inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase= [*signature.parameters.keys()]
__lowercase= ['hidden_states', 'timestep']
self.assertListEqual(arg_names[:2] , lowerCAmelCase )
def _A (self ):
__lowercase= PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy' )
__lowercase= model.to(lowerCAmelCase )
if hasattr(lowerCAmelCase , 'set_default_attn_processor' ):
model.set_default_attn_processor()
__lowercase= self.get_dummy_seed_input()
with torch.no_grad():
__lowercase= model(**lowerCAmelCase )[0]
__lowercase= output[0, :5].flatten().cpu()
print(lowerCAmelCase )
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
__lowercase= torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39] )
self.assertTrue(torch_all_close(lowerCAmelCase , lowerCAmelCase , rtol=1E-2 ) )
@slow
class A ( unittest.TestCase ):
def _A (self , lowerCAmelCase=1 , lowerCAmelCase=7_6_8 , lowerCAmelCase=7_7 , lowerCAmelCase=0 ):
torch.manual_seed(lowerCAmelCase )
__lowercase= batch_size
__lowercase= embedding_dim
__lowercase= num_embeddings
__lowercase= torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase )
__lowercase= torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase )
__lowercase= torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def _A (self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[1_3, [-0.58_61, 0.12_83, -0.09_31, 0.08_82, 0.44_76, 0.13_29, -0.04_98, 0.06_40]],
[3_7, [-0.49_13, 0.01_10, -0.04_83, 0.05_41, 0.49_54, -0.01_70, 0.03_54, 0.16_51]],
# fmt: on
] )
def _A (self , lowerCAmelCase , lowerCAmelCase ):
__lowercase= PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior' , subfolder='prior' )
model.to(lowerCAmelCase )
__lowercase= self.get_dummy_seed_input(seed=lowerCAmelCase )
with torch.no_grad():
__lowercase= model(**lowerCAmelCase )[0]
assert list(sample.shape ) == [1, 7_6_8]
__lowercase= sample[0, :8].flatten().cpu()
print(lowerCAmelCase )
__lowercase= torch.tensor(lowerCAmelCase )
assert torch_all_close(lowerCAmelCase , lowerCAmelCase , atol=1E-3 )
| 295 | 0 |
"""simple docstring"""
__A : Any = [
(10_00, 'M'),
(9_00, 'CM'),
(5_00, 'D'),
(4_00, 'CD'),
(1_00, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def __UpperCamelCase ( _A : int ) ->int:
"""simple docstring"""
lowerCamelCase_ ={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
lowerCamelCase_ =0
lowerCamelCase_ =0
while place < len(_lowerCAmelCase ):
if (place + 1 < len(_lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def __UpperCamelCase ( _A : Union[str, Any] ) ->str:
"""simple docstring"""
lowerCamelCase_ =[]
for arabic, roman in ROMAN:
(lowerCamelCase_) =divmod(_lowerCAmelCase , _lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(_lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 371 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , )-> Optional[int]:
super().__init__(
_SCREAMING_SNAKE_CASE , split=_SCREAMING_SNAKE_CASE , features=_SCREAMING_SNAKE_CASE , cache_dir=_SCREAMING_SNAKE_CASE , keep_in_memory=_SCREAMING_SNAKE_CASE , streaming=_SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
lowerCamelCase_ =path_or_paths if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else {self.split: path_or_paths}
lowerCamelCase_ =Text(
cache_dir=_SCREAMING_SNAKE_CASE , data_files=_SCREAMING_SNAKE_CASE , features=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
def _snake_case ( self )-> List[str]:
# Build iterable dataset
if self.streaming:
lowerCamelCase_ =self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
lowerCamelCase_ =None
lowerCamelCase_ =None
lowerCamelCase_ =None
lowerCamelCase_ =None
self.builder.download_and_prepare(
download_config=_SCREAMING_SNAKE_CASE , download_mode=_SCREAMING_SNAKE_CASE , verification_mode=_SCREAMING_SNAKE_CASE , base_path=_SCREAMING_SNAKE_CASE , num_proc=self.num_proc , )
lowerCamelCase_ =self.builder.as_dataset(
split=self.split , verification_mode=_SCREAMING_SNAKE_CASE , in_memory=self.keep_in_memory )
return dataset
| 49 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = set(_SCREAMING_SNAKE_CASE ), [start]
while stack:
UpperCamelCase = stack.pop()
explored.add(_SCREAMING_SNAKE_CASE )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(_SCREAMING_SNAKE_CASE )
return explored
lowerCAmelCase__ = {
'''A''': ['''B''', '''C''', '''D'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F'''],
'''D''': ['''B''', '''D'''],
'''E''': ['''B''', '''F'''],
'''F''': ['''C''', '''E''', '''G'''],
'''G''': ['''F'''],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, '''A'''))
| 153 |
"""simple docstring"""
import re
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('''+918827897895'''))
| 153 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''GroupViTTextConfig''',
'''GroupViTVisionConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GroupViTModel''',
'''GroupViTPreTrainedModel''',
'''GroupViTTextModel''',
'''GroupViTVisionModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFGroupViTModel''',
'''TFGroupViTPreTrainedModel''',
'''TFGroupViTTextModel''',
'''TFGroupViTVisionModel''',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 34 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[Any] = 'mobilenet_v2'
def __init__( self , lowerCamelCase=3 , lowerCamelCase=224 , lowerCamelCase=1.0 , lowerCamelCase=8 , lowerCamelCase=8 , lowerCamelCase=6 , lowerCamelCase=32 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase="relu6" , lowerCamelCase=True , lowerCamelCase=0.8 , lowerCamelCase=0.02 , lowerCamelCase=0.001 , lowerCamelCase=255 , **lowerCamelCase , ) -> Union[str, Any]:
super().__init__(**lowerCamelCase )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case_ = num_channels
snake_case_ = image_size
snake_case_ = depth_multiplier
snake_case_ = depth_divisible_by
snake_case_ = min_depth
snake_case_ = expand_ratio
snake_case_ = output_stride
snake_case_ = first_layer_is_expansion
snake_case_ = finegrained_output
snake_case_ = hidden_act
snake_case_ = tf_padding
snake_case_ = classifier_dropout_prob
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = semantic_loss_ignore_index
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Dict = version.parse('1.11' )
@property
def lowerCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def lowerCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def lowerCAmelCase_ ( self ) -> float:
return 1e-4 | 34 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowerCAmelCase ( __UpperCamelCase ):
@slow
@require_torch
def A_ ( self : Tuple ) -> str:
lowerCamelCase__ : Optional[int] = EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny' , 'prajjwal1/bert-tiny' )
lowerCamelCase__ : List[Any] = BertTokenizer.from_pretrained('bert-base-uncased' )
lowerCamelCase__ : Tuple = bertabert.config.encoder.vocab_size
lowerCamelCase__ : Optional[int] = tokenizer.sep_token_id
lowerCamelCase__ : List[Any] = tokenizer.cls_token_id
lowerCamelCase__ : str = 128
lowerCamelCase__ : Optional[Any] = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='train[:1%]' )
lowerCamelCase__ : Any = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='validation[:1%]' )
lowerCamelCase__ : List[Any] = train_dataset.select(range(32 ) )
lowerCamelCase__ : Dict = val_dataset.select(range(16 ) )
lowerCamelCase__ : str = 4
def _map_to_encoder_decoder_inputs(UpperCAmelCase : int ):
# Tokenizer will automatically set [BOS] <text> [EOS]
lowerCamelCase__ : List[str] = tokenizer(batch['article'] , padding='max_length' , truncation=UpperCAmelCase , max_length=512 )
lowerCamelCase__ : Optional[Any] = tokenizer(batch['highlights'] , padding='max_length' , truncation=UpperCAmelCase , max_length=128 )
lowerCamelCase__ : int = inputs.input_ids
lowerCamelCase__ : List[str] = inputs.attention_mask
lowerCamelCase__ : Optional[int] = outputs.input_ids
lowerCamelCase__ : Dict = outputs.input_ids.copy()
lowerCamelCase__ : Union[str, Any] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
]
lowerCamelCase__ : Union[str, Any] = outputs.attention_mask
assert all(len(UpperCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(UpperCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(UpperCAmelCase : Optional[int] ):
lowerCamelCase__ : int = pred.label_ids
lowerCamelCase__ : Optional[int] = pred.predictions
# all unnecessary tokens are removed
lowerCamelCase__ : Optional[int] = tokenizer.batch_decode(UpperCAmelCase , skip_special_tokens=UpperCAmelCase )
lowerCamelCase__ : List[str] = tokenizer.batch_decode(UpperCAmelCase , skip_special_tokens=UpperCAmelCase )
lowerCamelCase__ : Any = sum([int(pred_str[i] == label_str[i] ) for i in range(len(UpperCAmelCase ) )] ) / len(UpperCAmelCase )
return {"accuracy": accuracy}
# map train dataset
lowerCamelCase__ : int = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=UpperCAmelCase , batch_size=UpperCAmelCase , remove_columns=['article', 'highlights'] , )
train_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
# same for validation dataset
lowerCamelCase__ : Optional[int] = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=UpperCAmelCase , batch_size=UpperCAmelCase , remove_columns=['article', 'highlights'] , )
val_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
lowerCamelCase__ : Dict = self.get_auto_remove_tmp_dir()
lowerCamelCase__ : List[str] = SeqaSeqTrainingArguments(
output_dir=UpperCAmelCase , per_device_train_batch_size=UpperCAmelCase , per_device_eval_batch_size=UpperCAmelCase , predict_with_generate=UpperCAmelCase , evaluation_strategy='steps' , do_train=UpperCAmelCase , do_eval=UpperCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
lowerCamelCase__ : List[str] = SeqaSeqTrainer(
model=UpperCAmelCase , args=UpperCAmelCase , compute_metrics=_compute_metrics , train_dataset=UpperCAmelCase , eval_dataset=UpperCAmelCase , tokenizer=UpperCAmelCase , )
# start training
trainer.train()
| 50 |
def lowerCAmelCase_ ( __lowerCamelCase ):
if edge <= 0 or not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("Length must be a positive." )
return 3 * ((2_5 + 1_0 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def lowerCAmelCase_ ( __lowerCamelCase ):
if edge <= 0 or not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("Length must be a positive." )
return ((1_5 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 123 | 0 |
# 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
a__ = get_logger(__name__)
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : List[str] = "dummy_data"
UpperCAmelCase__ : Dict = "datasets"
UpperCAmelCase__ : Any = False
def __init__( self , _a , _a , _a , _a = None , _a = False , _a = True , _a = None , ) -> Optional[Any]:
_a : Optional[int] = 0
_a : int = dataset_name
_a : Optional[int] = cache_dir
_a : Optional[Any] = use_local_dummy_data
_a : List[str] = 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 : int = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_a : Optional[int] = str(_a )
# to be downloaded
_a : str = None
_a : int = None
@property
def __lowercase ( self ) -> Dict:
if self._dummy_file is None:
_a : Any = self.download_dummy_data()
return self._dummy_file
@property
def __lowercase ( self ) -> Optional[Any]:
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 ) -> Any:
return os.path.join(self.dummy_data_folder , '''dummy_data.zip''' )
def __lowercase ( self ) -> Tuple:
_a : Tuple = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_a : Dict = 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:
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def __lowercase ( self ) -> Dict:
if self._bucket_url is None:
_a : Dict = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '''/''' ) )
return self._bucket_url
@property
def __lowercase ( 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 __lowercase ( self , _a , *_a ) -> str:
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_a : str = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_a : Optional[int] = 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 , _a , *_a ) -> Optional[Any]:
return self.download_and_extract(_a )
def __lowercase ( self , _a , _a ) -> List[str]:
return self.download_and_extract(_a )
def __lowercase ( self , _a , *_a , **_a ) -> List[Any]:
return path
def __lowercase ( self ) -> str:
return {}
def __lowercase ( self , _a , _a ) -> str:
_a : Dict = {}
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 : Dict = 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 : Optional[int] = [os.path.join(_a , urllib.parse.quote_plus(Path(_a ).name ) ) for x in single_urls]
else:
_a : Tuple = single_urls
_a : Optional[int] = os.path.join(_a , urllib.parse.quote_plus(Path(_a ).name ) )
_a : Union[str, Any] = 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 : Any = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def __lowercase ( self , _a , _a ) -> List[str]:
_a : int = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_a : Any = all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}''' , _a ) ) for url in data_url )
_a : List[str] = 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 : 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 : Optional[int] = os.path.join(_a , urllib.parse.quote_plus(single_url.split('''/''' )[-1] ) )
dummy_data_list.append(_a )
return dummy_data_list
def __lowercase ( self , _a , _a ) -> Dict:
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 : int = 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 ) -> List[Any]:
pass
def __lowercase ( self ) -> Dict:
pass
def __lowercase ( self , _a ) -> List[Any]:
def _iter_archive_members(_a ):
# this preserves the order of the members inside the ZIP archive
_a : Optional[Any] = Path(self.dummy_file ).parent
_a : Tuple = path.relative_to(_a )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_a : List[Any] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(_a )
_a : List[Any] = Path(_a )
_a : int = _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 , _a ) -> List[Any]:
if not isinstance(_a , _a ):
_a : str = [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 )
| 15 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
a__ = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
- name: "Dataset Card for X" # First-level markdown heading
allow_empty: false
allow_empty_text: true
subsections:
- name: "Table of Contents"
allow_empty: false
allow_empty_text: false
subsections: null
- name: "Dataset Description"
allow_empty: false
allow_empty_text: false
subsections:
- name: "Dataset Summary"
allow_empty: false
allow_empty_text: false
subsections: null
- name: "Supported Tasks and Leaderboards"
allow_empty: true
allow_empty_text: true
subsections: null
- name: Languages
allow_empty: false
allow_empty_text: true
subsections: null
'''
)
a__ = {
'''name''': '''root''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{
'''name''': '''Dataset Card for My Dataset''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
{
'''name''': '''Dataset Description''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Dataset Summary''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [],
},
{
'''name''': '''Supported Tasks and Leaderboards''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
},
{'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
],
},
],
}
],
}
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = {
'''name''': '''root''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{
'''name''': '''Dataset Card for My Dataset''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [
{'''name''': '''Table of Contents''', '''text''': '''Some text here.''', '''is_empty_text''': False, '''subsections''': []},
{
'''name''': '''Dataset Description''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Dataset Summary''',
'''text''': '''Some text here.''',
'''is_empty_text''': False,
'''subsections''': [
{
'''name''': '''Extra Ignored Subsection''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
}
],
},
{
'''name''': '''Supported Tasks and Leaderboards''',
'''text''': '''''',
'''is_empty_text''': True,
'''subsections''': [],
},
{'''name''': '''Languages''', '''text''': '''Language Text''', '''is_empty_text''': False, '''subsections''': []},
],
},
],
}
],
}
a__ = '''\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = (
'''The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'''
)
a__ = '''\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = (
'''The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'''
)
a__ = '''\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored).'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found \'None\'.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty.'''
a__ = '''\
---
language:
- zh
- en
---
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'''
a__ = ''''''
a__ = '''The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README.'''
a__ = '''\
---
language:
- zh
- en
---
# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
'''
a__ = '''The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections.'''
@pytest.mark.parametrize(
'''readme_md, expected_dict''' ,[
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] ,)
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : List[str] ) -> Optional[int]:
"""simple docstring"""
assert ReadMe.from_string(__a ,__a ).to_dict() == expected_dict
@pytest.mark.parametrize(
'''readme_md, expected_error''' ,[
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] ,)
def __UpperCAmelCase ( __a : List[str] ,__a : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
with pytest.raises(__a ,match=re.escape(expected_error.format(path='''root''' ) ) ):
_a : List[Any] = ReadMe.from_string(__a ,__a )
readme.validate()
@pytest.mark.parametrize(
'''readme_md, expected_error''' ,[
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] ,)
def __UpperCAmelCase ( __a : Dict ,__a : Dict ) -> Tuple:
"""simple docstring"""
with pytest.raises(__a ,match=re.escape(expected_error.format(path='''root''' ) ) ):
ReadMe.from_string(__a ,__a )
@pytest.mark.parametrize(
'''readme_md,''' ,[
(README_MULTIPLE_SAME_HEADING_1),
] ,)
def __UpperCAmelCase ( __a : Optional[Any] ) -> Tuple:
"""simple docstring"""
ReadMe.from_string(__a ,__a ,suppress_parsing_errors=__a )
@pytest.mark.parametrize(
'''readme_md, expected_dict''' ,[
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] ,)
def __UpperCAmelCase ( __a : Union[str, Any] ,__a : Any ) -> Optional[int]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a : Tuple = Path(__a ) / '''README.md'''
with open(__a ,'''w+''' ) as readme_file:
readme_file.write(__a )
_a : Optional[Any] = ReadMe.from_readme(__a ,__a ).to_dict()
assert out["name"] == path
assert out["text"] == ""
assert out["is_empty_text"]
assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
'''readme_md, expected_error''' ,[
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] ,)
def __UpperCAmelCase ( __a : List[Any] ,__a : List[Any] ) -> int:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a : int = Path(__a ) / '''README.md'''
with open(__a ,'''w+''' ) as readme_file:
readme_file.write(__a )
_a : Optional[int] = expected_error.format(path=__a )
with pytest.raises(__a ,match=re.escape(__a ) ):
_a : Any = ReadMe.from_readme(__a ,__a )
readme.validate()
@pytest.mark.parametrize(
'''readme_md, expected_error''' ,[
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] ,)
def __UpperCAmelCase ( __a : str ,__a : Union[str, Any] ) -> Dict:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a : Optional[Any] = Path(__a ) / '''README.md'''
with open(__a ,'''w+''' ) as readme_file:
readme_file.write(__a )
_a : str = expected_error.format(path=__a )
with pytest.raises(__a ,match=re.escape(__a ) ):
ReadMe.from_readme(__a ,__a )
@pytest.mark.parametrize(
'''readme_md,''' ,[
(README_MULTIPLE_SAME_HEADING_1),
] ,)
def __UpperCAmelCase ( __a : Optional[Any] ) -> str:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a : int = Path(__a ) / '''README.md'''
with open(__a ,'''w+''' ) as readme_file:
readme_file.write(__a )
ReadMe.from_readme(__a ,__a ,suppress_parsing_errors=__a )
| 15 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
UpperCAmelCase_ : Optional[int] = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wikisql-supervised""": (
"""https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-tabfact""": (
"""https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
),
}
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "tapas"
def __init__( self : List[str] , lowercase_ : Tuple=30522 , lowercase_ : str=768 , lowercase_ : List[str]=12 , lowercase_ : str=12 , lowercase_ : str=3072 , lowercase_ : str="gelu" , lowercase_ : Optional[int]=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : Tuple=1024 , lowercase_ : Union[str, Any]=[3, 256, 256, 2, 256, 256, 10] , lowercase_ : Union[str, Any]=0.02 , lowercase_ : List[Any]=1e-12 , lowercase_ : Dict=0 , lowercase_ : Tuple=10.0 , lowercase_ : Optional[int]=0 , lowercase_ : Optional[int]=1.0 , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=1.0 , lowercase_ : int=False , lowercase_ : Any=None , lowercase_ : List[Any]=1.0 , lowercase_ : List[Any]=1.0 , lowercase_ : Any=False , lowercase_ : Optional[int]=False , lowercase_ : Dict="ratio" , lowercase_ : Tuple=None , lowercase_ : Optional[int]=None , lowercase_ : List[str]=64 , lowercase_ : Tuple=32 , lowercase_ : Optional[int]=False , lowercase_ : int=True , lowercase_ : Any=False , lowercase_ : Optional[int]=False , lowercase_ : str=True , lowercase_ : Optional[Any]=False , lowercase_ : str=None , lowercase_ : str=None , **lowercase_ : List[str] , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase_ , **lowercase_)
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
SCREAMING_SNAKE_CASE_ : Dict = vocab_size
SCREAMING_SNAKE_CASE_ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE_ : int = num_hidden_layers
SCREAMING_SNAKE_CASE_ : str = num_attention_heads
SCREAMING_SNAKE_CASE_ : List[Any] = hidden_act
SCREAMING_SNAKE_CASE_ : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE_ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : str = max_position_embeddings
SCREAMING_SNAKE_CASE_ : Dict = type_vocab_sizes
SCREAMING_SNAKE_CASE_ : List[Any] = initializer_range
SCREAMING_SNAKE_CASE_ : List[Any] = layer_norm_eps
# Fine-tuning task hyperparameters
SCREAMING_SNAKE_CASE_ : Dict = positive_label_weight
SCREAMING_SNAKE_CASE_ : Dict = num_aggregation_labels
SCREAMING_SNAKE_CASE_ : Optional[Any] = aggregation_loss_weight
SCREAMING_SNAKE_CASE_ : List[str] = use_answer_as_supervision
SCREAMING_SNAKE_CASE_ : Tuple = answer_loss_importance
SCREAMING_SNAKE_CASE_ : str = use_normalized_answer_loss
SCREAMING_SNAKE_CASE_ : str = huber_loss_delta
SCREAMING_SNAKE_CASE_ : List[str] = temperature
SCREAMING_SNAKE_CASE_ : Optional[Any] = aggregation_temperature
SCREAMING_SNAKE_CASE_ : Union[str, Any] = use_gumbel_for_cells
SCREAMING_SNAKE_CASE_ : List[str] = use_gumbel_for_aggregation
SCREAMING_SNAKE_CASE_ : Tuple = average_approximation_function
SCREAMING_SNAKE_CASE_ : Any = cell_selection_preference
SCREAMING_SNAKE_CASE_ : Tuple = answer_loss_cutoff
SCREAMING_SNAKE_CASE_ : str = max_num_rows
SCREAMING_SNAKE_CASE_ : Any = max_num_columns
SCREAMING_SNAKE_CASE_ : int = average_logits_per_cell
SCREAMING_SNAKE_CASE_ : Dict = select_one_column
SCREAMING_SNAKE_CASE_ : Union[str, Any] = allow_empty_column_selection
SCREAMING_SNAKE_CASE_ : int = init_cell_selection_weights_to_zero
SCREAMING_SNAKE_CASE_ : Union[str, Any] = reset_position_index_per_cell
SCREAMING_SNAKE_CASE_ : Union[str, Any] = disable_per_token_loss
# Aggregation hyperparameters
SCREAMING_SNAKE_CASE_ : List[str] = aggregation_labels
SCREAMING_SNAKE_CASE_ : Any = no_aggregation_label_index
if isinstance(self.aggregation_labels , lowercase_):
SCREAMING_SNAKE_CASE_ : Dict = {int(lowercase_): v for k, v in aggregation_labels.items()}
| 91 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCAmelCase ( A : int , A : int , A : bool , A : list[int] , A : float ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1 , node_index * 2 , A , A , A ) , minimax(depth + 1 , node_index * 2 + 1 , A , A , A ) , )
if is_max
else min(
minimax(depth + 1 , node_index * 2 , A , A , A ) , minimax(depth + 1 , node_index * 2 + 1 , A , A , A ) , )
)
def __UpperCAmelCase ( ) -> None:
UpperCAmelCase_ : List[str] = [9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
UpperCAmelCase_ : List[Any] = math.log(len(A ) , 2 )
print(F"Optimal value : {minimax(0 , 0 , A , A , A )}" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 304 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
def __magic_name__ ( self : str ):
UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
UpperCAmelCase : Dict = BlipImageProcessor()
UpperCAmelCase : Dict = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
UpperCAmelCase : Any = BlipaProcessor(__A, __A )
processor.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Dict, **__A : str ):
return AutoProcessor.from_pretrained(self.tmpdirname, **__A ).tokenizer
def __magic_name__ ( self : Tuple, **__A : int ):
return AutoProcessor.from_pretrained(self.tmpdirname, **__A ).image_processor
def __magic_name__ ( self : int ):
shutil.rmtree(self.tmpdirname )
def __magic_name__ ( self : int ):
UpperCAmelCase : List[Any] = [np.random.randint(2_5_5, size=(3, 3_0, 4_0_0), dtype=np.uinta )]
UpperCAmelCase : Union[str, Any] = [Image.fromarray(np.moveaxis(__A, 0, -1 ) ) for x in image_inputs]
return image_inputs
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : List[Any] = BlipaProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase : List[Any] = self.get_tokenizer(bos_token='''(BOS)''', eos_token='''(EOS)''' )
UpperCAmelCase : Union[str, Any] = self.get_image_processor(do_normalize=__A, padding_value=1.0 )
UpperCAmelCase : Dict = BlipaProcessor.from_pretrained(
self.tmpdirname, bos_token='''(BOS)''', eos_token='''(EOS)''', do_normalize=__A, padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, __A )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, __A )
def __magic_name__ ( self : Dict ):
UpperCAmelCase : Tuple = self.get_image_processor()
UpperCAmelCase : List[str] = self.get_tokenizer()
UpperCAmelCase : Any = BlipaProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Tuple = self.prepare_image_inputs()
UpperCAmelCase : str = image_processor(__A, return_tensors='''np''' )
UpperCAmelCase : Any = processor(images=__A, return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1E-2 )
def __magic_name__ ( self : Any ):
UpperCAmelCase : List[Any] = self.get_image_processor()
UpperCAmelCase : str = self.get_tokenizer()
UpperCAmelCase : Union[str, Any] = BlipaProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Optional[int] = '''lower newer'''
UpperCAmelCase : Any = processor(text=__A )
UpperCAmelCase : List[Any] = tokenizer(__A, return_token_type_ids=__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : int = self.get_image_processor()
UpperCAmelCase : Optional[int] = self.get_tokenizer()
UpperCAmelCase : Optional[Any] = BlipaProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : str = '''lower newer'''
UpperCAmelCase : Optional[int] = self.prepare_image_inputs()
UpperCAmelCase : Any = processor(text=__A, images=__A )
self.assertListEqual(list(inputs.keys() ), ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
# test if it raises when no input is passed
with pytest.raises(__A ):
processor()
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : Tuple = self.get_image_processor()
UpperCAmelCase : Any = self.get_tokenizer()
UpperCAmelCase : List[str] = BlipaProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase : Any = processor.batch_decode(__A )
UpperCAmelCase : Tuple = tokenizer.batch_decode(__A )
self.assertListEqual(__A, __A )
def __magic_name__ ( self : Optional[Any] ):
UpperCAmelCase : Dict = self.get_image_processor()
UpperCAmelCase : List[str] = self.get_tokenizer()
UpperCAmelCase : Dict = BlipaProcessor(tokenizer=__A, image_processor=__A )
UpperCAmelCase : List[str] = '''lower newer'''
UpperCAmelCase : str = self.prepare_image_inputs()
UpperCAmelCase : Tuple = processor(text=__A, images=__A )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ), ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
| 99 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCamelCase : List[str] = True
from torch.cuda.amp import autocast
_lowerCamelCase : Any = logging.getLogger(__name__)
@dataclass
class __UpperCAmelCase :
UpperCamelCase = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """Whether to log verbose messages or not."""} , )
UpperCamelCase = field(
default=2.0 , metadata={"""help""": """Maximum temperature for gumbel softmax."""} )
UpperCamelCase = field(
default=0.5 , metadata={"""help""": """Minimum temperature for gumbel softmax."""} )
UpperCamelCase = field(
default=0.9_9_9_9_9_5 , metadata={"""help""": """Decay of gumbel temperature during training."""} )
def a__ ( UpperCAmelCase : ModelArguments , UpperCAmelCase : TrainingArguments ) -> Any:
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
UpperCAmelCase : Any = logging.WARNING
if model_args.verbose_logging:
UpperCAmelCase : Any = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
UpperCAmelCase : Any = logging.INFO
logger.setLevel(UpperCAmelCase )
@dataclass
class __UpperCAmelCase :
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
UpperCamelCase = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase = field(
default="""file""" , metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""} , )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
UpperCamelCase = field(
default=1 , metadata={
"""help""": """The percentage of the train set used as validation set in case there's no validation split"""
} , )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
UpperCamelCase = field(
default=2_0.0 , metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""} )
@dataclass
class __UpperCAmelCase :
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = "longest"
UpperCamelCase = None
UpperCamelCase = None
def __call__( self : int, __A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# reformat list to dict and set to pytorch format
UpperCAmelCase : List[Any] = self.feature_extractor.pad(
__A, max_length=self.max_length, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors='''pt''', )
UpperCAmelCase : int = self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1] )
UpperCAmelCase : Tuple = batch['''input_values'''].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
UpperCAmelCase : Tuple = self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1 ) ).to(
torch.long )
UpperCAmelCase : Dict = torch.zeros(
(batch_size, mask_indices_seq_length), dtype=torch.long, device=batch['''input_values'''].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
UpperCAmelCase : Tuple = 1
UpperCAmelCase : int = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
UpperCAmelCase : Dict = _compute_mask_indices(
(batch_size, mask_indices_seq_length), self.model.config.mask_time_prob, self.model.config.mask_time_length, attention_mask=__A, min_masks=2, )
return batch
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Union[str, Any], *__A : int, __A : Dict=1, __A : Any=0, __A : Optional[Any]=1.0, **__A : Any ):
super().__init__(*__A, **__A )
UpperCAmelCase : Any = 0
UpperCAmelCase : Any = max_gumbel_temp
UpperCAmelCase : Optional[Any] = min_gumbel_temp
UpperCAmelCase : str = gumbel_temp_decay
def __magic_name__ ( self : Dict, __A : nn.Module, __A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
UpperCAmelCase : List[Any] = self._prepare_inputs(__A )
if self.use_amp:
with autocast():
UpperCAmelCase : Optional[Any] = self.compute_loss(__A, __A )
else:
UpperCAmelCase : Optional[int] = self.compute_loss(__A, __A )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
UpperCAmelCase : Optional[Any] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
UpperCAmelCase : str = loss.sum() / (inputs['''mask_time_indices''']).sum()
else:
raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']''' )
if self.args.gradient_accumulation_steps > 1:
UpperCAmelCase : Any = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(__A ).backward()
elif self.use_apex:
with amp.scale_loss(__A, self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(__A )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp ) )
return loss.detach()
def a__ ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCAmelCase : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = parser.parse_args_into_dataclasses()
configure_logger(UpperCAmelCase , UpperCAmelCase )
# Downloading and loading a dataset from the hub.
UpperCAmelCase : int = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
UpperCAmelCase : Union[str, Any] = DatasetDict()
UpperCAmelCase : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]''' , cache_dir=model_args.cache_dir , )
UpperCAmelCase : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]''' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
UpperCAmelCase : Optional[Any] = DatasetDict()
UpperCAmelCase : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split='''validation''' , cache_dir=model_args.cache_dir , )
UpperCAmelCase : int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}''' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
UpperCAmelCase : Optional[Any] = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=UpperCAmelCase )
def prepare_dataset(UpperCAmelCase : Dict ):
# check that all files have the correct sampling rate
UpperCAmelCase , UpperCAmelCase : Optional[Any] = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
UpperCAmelCase : str = datasets.map(
UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets['''train'''].column_names )
# filter audio files that are too long
UpperCAmelCase : int = vectorized_datasets.filter(
lambda UpperCAmelCase : len(data['''speech'''] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(UpperCAmelCase : Dict ):
return feature_extractor(batch['''speech'''] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
UpperCAmelCase : Any = vectorized_datasets.map(
UpperCAmelCase , batched=UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets['''train'''].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
UpperCAmelCase : Optional[int] = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
'''PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'''
''' ``config.feat_extract_norm=\'layer\'''' )
UpperCAmelCase : Any = WavaVecaForPreTraining(UpperCAmelCase )
UpperCAmelCase : int = DataCollatorForWavaVecaPretraining(model=UpperCAmelCase , feature_extractor=UpperCAmelCase )
UpperCAmelCase : Any = WavaVecaPreTrainer(
model=UpperCAmelCase , data_collator=UpperCAmelCase , args=UpperCAmelCase , train_dataset=vectorized_datasets['''train'''] , eval_dataset=vectorized_datasets['''validation'''] , tokenizer=UpperCAmelCase , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 99 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase ( unittest.TestCase ):
@property
def _lowercase ( self : Optional[int] ) -> Optional[Any]:
torch.manual_seed(0 )
_a : Tuple = 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 _lowercase ( self : Tuple ) -> Optional[Any]:
_a : int = self.dummy_uncond_unet
_a : List[str] = KarrasVeScheduler()
_a : Union[str, Any] = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : Tuple = torch.manual_seed(0 )
_a : Optional[Any] = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : Union[str, Any] = torch.manual_seed(0 )
_a : List[Any] = pipe(num_inference_steps=2 , generator=UpperCAmelCase__ , output_type="""numpy""" , return_dict=UpperCAmelCase__ )[0]
_a : List[Any] = image[0, -3:, -3:, -1]
_a : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a : List[str] = 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 ):
def _lowercase ( self : List[Any] ) -> Tuple:
_a : int = """google/ncsnpp-celebahq-256"""
_a : Optional[Any] = UNetaDModel.from_pretrained(UpperCAmelCase__ )
_a : str = KarrasVeScheduler()
_a : Optional[int] = KarrasVePipeline(unet=UpperCAmelCase__ , scheduler=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
_a : Optional[Any] = torch.manual_seed(0 )
_a : Optional[int] = pipe(num_inference_steps=20 , generator=UpperCAmelCase__ , output_type="""numpy""" ).images
_a : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_a : Dict = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 294 |
"""simple docstring"""
_snake_case = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_snake_case = [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 294 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __a :
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : List[Any]=14 , UpperCAmelCase : str=7 , UpperCAmelCase : str=True , UpperCAmelCase : int=True , UpperCAmelCase : List[Any]=False , UpperCAmelCase : Any=True , UpperCAmelCase : Any=99 , UpperCAmelCase : Any=32 , UpperCAmelCase : Any=4 , UpperCAmelCase : int=4 , UpperCAmelCase : str=4 , UpperCAmelCase : Tuple=37 , UpperCAmelCase : Dict="gelu" , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : Union[str, Any]=0.1 , UpperCAmelCase : Optional[Any]=5_12 , UpperCAmelCase : List[str]=0.02 , ):
lowerCAmelCase_ : List[Any] = parent
lowerCAmelCase_ : Union[str, Any] = batch_size
lowerCAmelCase_ : Dict = seq_length
lowerCAmelCase_ : Optional[Any] = is_training
lowerCAmelCase_ : Optional[int] = use_input_mask
lowerCAmelCase_ : Optional[Any] = use_token_type_ids
lowerCAmelCase_ : Optional[Any] = use_labels
lowerCAmelCase_ : Any = vocab_size
lowerCAmelCase_ : Tuple = hidden_size
lowerCAmelCase_ : Any = rotary_dim
lowerCAmelCase_ : str = num_hidden_layers
lowerCAmelCase_ : int = num_attention_heads
lowerCAmelCase_ : Any = intermediate_size
lowerCAmelCase_ : Dict = hidden_act
lowerCAmelCase_ : Optional[Any] = hidden_dropout_prob
lowerCAmelCase_ : Optional[int] = attention_probs_dropout_prob
lowerCAmelCase_ : Optional[Any] = max_position_embeddings
lowerCAmelCase_ : Union[str, Any] = initializer_range
lowerCAmelCase_ : int = None
lowerCAmelCase_ : Union[str, Any] = vocab_size - 1
lowerCAmelCase_ : str = vocab_size - 1
lowerCAmelCase_ : Optional[int] = vocab_size - 1
def A ( self : List[Any] ):
lowerCAmelCase_ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ : Optional[int] = None
if self.use_input_mask:
lowerCAmelCase_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ : Optional[int] = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=UpperCAmelCase , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def A ( self : str ):
lowerCAmelCase_ : Optional[int] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = config_and_inputs
lowerCAmelCase_ : int = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def A ( self : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : Tuple ):
lowerCAmelCase_ : str = 20
lowerCAmelCase_ : Dict = model_class_name(UpperCAmelCase )
lowerCAmelCase_ : Optional[int] = model.init_cache(input_ids.shape[0] , UpperCAmelCase )
lowerCAmelCase_ : Dict = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
lowerCAmelCase_ : Tuple = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
lowerCAmelCase_ : Dict = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase , past_key_values=UpperCAmelCase , position_ids=UpperCAmelCase , )
lowerCAmelCase_ : Union[str, Any] = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
lowerCAmelCase_ : List[str] = model(
input_ids[:, -1:] , attention_mask=UpperCAmelCase , past_key_values=outputs_cache.past_key_values , position_ids=UpperCAmelCase , )
lowerCAmelCase_ : Any = model(UpperCAmelCase )
lowerCAmelCase_ : Tuple = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F'Max diff is {diff}' )
def A ( self : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Any ):
lowerCAmelCase_ : int = 20
lowerCAmelCase_ : List[Any] = model_class_name(UpperCAmelCase )
lowerCAmelCase_ : Tuple = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
lowerCAmelCase_ : Optional[int] = model.init_cache(input_ids.shape[0] , UpperCAmelCase )
lowerCAmelCase_ : Dict = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
lowerCAmelCase_ : Tuple = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase , past_key_values=UpperCAmelCase , position_ids=UpperCAmelCase , )
lowerCAmelCase_ : List[str] = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
lowerCAmelCase_ : Tuple = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=UpperCAmelCase , position_ids=UpperCAmelCase , )
lowerCAmelCase_ : Union[str, Any] = model(UpperCAmelCase , attention_mask=UpperCAmelCase )
lowerCAmelCase_ : str = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F'Max diff is {diff}' )
@require_flax
class __a ( __UpperCamelCase ,__UpperCamelCase ,unittest.TestCase ):
__snake_case : Union[str, Any] = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__snake_case : Any = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def A ( self : Any ):
lowerCAmelCase_ : List[str] = FlaxGPTJModelTester(self )
def A ( self : Union[str, Any] ):
for model_class_name in self.all_model_classes:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def A ( self : Tuple ):
for model_class_name in self.all_model_classes:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@tooslow
def A ( self : int ):
lowerCAmelCase_ : Optional[int] = GPTaTokenizer.from_pretrained("""gpt2""" , pad_token="""<|endoftext|>""" , padding_side="""left""" )
lowerCAmelCase_ : Tuple = tokenizer(["""Hello this is a long string""", """Hey"""] , return_tensors="""np""" , padding=UpperCAmelCase , truncation=UpperCAmelCase )
lowerCAmelCase_ : Optional[Any] = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
lowerCAmelCase_ : List[str] = False
lowerCAmelCase_ : Optional[Any] = model.config.eos_token_id
lowerCAmelCase_ : List[Any] = jax.jit(model.generate )
lowerCAmelCase_ : Any = jit_generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , pad_token_id=tokenizer.pad_token_id ).sequences
lowerCAmelCase_ : str = tokenizer.batch_decode(UpperCAmelCase , skip_special_tokens=UpperCAmelCase )
lowerCAmelCase_ : Optional[int] = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
@is_pt_flax_cross_test
def A ( self : Optional[Any] ):
lowerCAmelCase_ , lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
lowerCAmelCase_ : int = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
lowerCAmelCase_ : List[Any] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
lowerCAmelCase_ : List[str] = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowerCAmelCase_ : Dict = getattr(UpperCAmelCase , UpperCAmelCase )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = pt_inputs["""input_ids"""].shape
lowerCAmelCase_ : str = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCAmelCase ):
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Any = 1
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Tuple = pt_model_class(UpperCAmelCase ).eval()
lowerCAmelCase_ : List[str] = model_class(UpperCAmelCase , dtype=jnp.floataa )
lowerCAmelCase_ : List[str] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , UpperCAmelCase )
lowerCAmelCase_ : List[str] = fx_state
with torch.no_grad():
lowerCAmelCase_ : List[str] = pt_model(**UpperCAmelCase ).to_tuple()
lowerCAmelCase_ : int = fx_model(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(UpperCAmelCase )
lowerCAmelCase_ : Optional[int] = model_class.from_pretrained(UpperCAmelCase , from_pt=UpperCAmelCase )
lowerCAmelCase_ : Union[str, Any] = fx_model_loaded(**UpperCAmelCase ).to_tuple()
self.assertEqual(
len(UpperCAmelCase ) , len(UpperCAmelCase ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def A ( self : Optional[Any] ):
lowerCAmelCase_ , lowerCAmelCase_ : 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__ ):
# prepare inputs
lowerCAmelCase_ : str = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
lowerCAmelCase_ : int = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
lowerCAmelCase_ : Optional[int] = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowerCAmelCase_ : Any = getattr(UpperCAmelCase , UpperCAmelCase )
lowerCAmelCase_ : str = pt_model_class(UpperCAmelCase ).eval()
lowerCAmelCase_ : Any = model_class(UpperCAmelCase , dtype=jnp.floataa )
lowerCAmelCase_ : Union[str, Any] = load_flax_weights_in_pytorch_model(UpperCAmelCase , fx_model.params )
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = pt_inputs["""input_ids"""].shape
lowerCAmelCase_ : str = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCAmelCase ):
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : Optional[int] = 1
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : str = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
lowerCAmelCase_ : List[str] = pt_model(**UpperCAmelCase ).to_tuple()
lowerCAmelCase_ : Tuple = fx_model(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(UpperCAmelCase )
lowerCAmelCase_ : Optional[Any] = pt_model_class.from_pretrained(UpperCAmelCase , from_flax=UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase_ : Dict = pt_model_loaded(**UpperCAmelCase ).to_tuple()
self.assertEqual(
len(UpperCAmelCase ) , len(UpperCAmelCase ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def A ( self : str ):
for model_class_name in self.all_model_classes:
lowerCAmelCase_ : Optional[Any] = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
lowerCAmelCase_ : Optional[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase )
| 28 |
def __UpperCamelCase ( lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Any = 0
for ch in input_str:
lowerCAmelCase_ : Any = ord(lowercase__ )
lowerCAmelCase_ : Dict = pow(2 , lowercase__ )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 28 | 1 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
__snake_case = parse(importlib.metadata.version('''torch'''))
def a ( __a , __a , __a ) -> Dict:
'''simple docstring'''
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f'''`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}''' )
UpperCamelCase__ :str = STR_OPERATION_TO_FUNC[operation]
if isinstance(__a , __a ):
UpperCamelCase__ :Dict = parse(importlib.metadata.version(__a ) )
return operation(__a , parse(__a ) )
def a ( __a , __a ) -> str:
'''simple docstring'''
return compare_versions(__a , __a , __a ) | 97 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a ( __a ) -> int:
'''simple docstring'''
for param in module.parameters():
UpperCamelCase__ :Dict = False
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :List[Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase__ :Optional[int] = '''mps'''
if device == "mps":
print(
'''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
''' with generations.''' )
return device
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Dict = plt.imshow(__a )
fig.axes.get_xaxis().set_visible(__a )
fig.axes.get_yaxis().set_visible(__a )
plt.show()
def a ( ) -> str:
'''simple docstring'''
UpperCamelCase__ :int = datetime.now()
UpperCamelCase__ :str = current_time.strftime('''%H:%M:%S''' )
return timestamp | 97 | 1 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_lowercase : List[Any] = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
"""
_lowercase : List[Any] = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""
_lowercase : Any = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the CUAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'exact_match': Exact match (the normalized answer exactly match the gold answer)
'f1': The F-score of predicted tokens versus the gold answer
'aupr': Area Under the Precision-Recall curve
'prec_at_80_recall': Precision at 80% recall
'prec_at_90_recall': Precision at 90% recall
Examples:
>>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> cuad_metric = datasets.load_metric(\"cuad\")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def snake_case ( self : str )-> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'''predictions''': {
'''id''': datasets.Value('''string''' ),
'''prediction_text''': datasets.features.Sequence(datasets.Value('''string''' ) ),
},
'''references''': {
'''id''': datasets.Value('''string''' ),
'''answers''': datasets.features.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
},
} ), codebase_urls=['''https://www.atticusprojectai.org/cuad'''], reference_urls=['''https://www.atticusprojectai.org/cuad'''], )
def snake_case ( self : Tuple, lowerCamelCase : Tuple, lowerCamelCase : Optional[Any] )-> int:
lowerCamelCase__ : Any ={prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
lowerCamelCase__ : str =[
{
'''paragraphs''': [
{
'''qas''': [
{
'''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
'''id''': ref['''id'''],
}
for ref in references
]
}
]
}
]
lowerCamelCase__ : str =evaluate(dataset=lowerCamelCase, predictions=lowerCamelCase )
return score
| 356 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : List[Any], lowerCamelCase : Dict="", lowerCamelCase : Tuple="train" )-> Dict:
assert os.path.isdir(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =[]
lowerCamelCase__ : Dict =os.listdir(lowerCamelCase )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
lowerCamelCase__ : Optional[int] =os.path.join(lowerCamelCase, lowerCamelCase )
if not os.path.isfile(lowerCamelCase ):
continue
self.documents.append(lowerCamelCase )
def __len__( self : Optional[Any] )-> List[str]:
return len(self.documents )
def __getitem__( self : List[str], lowerCamelCase : Dict )-> str:
lowerCamelCase__ : int =self.documents[idx]
lowerCamelCase__ : List[Any] =document_path.split('''/''' )[-1]
with open(lowerCamelCase, encoding='''utf-8''' ) as source:
lowerCamelCase__ : Optional[int] =source.read()
lowerCamelCase__ , lowerCamelCase__ : List[Any] =process_story(lowerCamelCase )
return document_name, story_lines, summary_lines
def snake_case__ ( __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : List[str] =list(filter(lambda __lowerCamelCase : len(__lowerCamelCase ) != 0 , [line.strip() for line in raw_story.split('''\n''' )] ) )
# for some unknown reason some lines miss a period, add it
lowerCamelCase__ : Dict =[_add_missing_period(__lowerCamelCase ) for line in nonempty_lines]
# gather article lines
lowerCamelCase__ : Union[str, Any] =[]
lowerCamelCase__ : Optional[Any] =deque(__lowerCamelCase )
while True:
try:
lowerCamelCase__ : Tuple =lines.popleft()
if element.startswith('''@highlight''' ):
break
story_lines.append(__lowerCamelCase )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
lowerCamelCase__ : Dict =list(filter(lambda __lowerCamelCase : not t.startswith('''@highlight''' ) , __lowerCamelCase ) )
return story_lines, summary_lines
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
lowerCamelCase__ : Any =['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
if line.startswith('''@highlight''' ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def snake_case__ ( __lowerCamelCase : Dict , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any] ):
"""simple docstring"""
if len(__lowerCamelCase ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(__lowerCamelCase )) )
return sequence
def snake_case__ ( __lowerCamelCase : Tuple , __lowerCamelCase : str ):
"""simple docstring"""
lowerCamelCase__ : int =torch.ones_like(__lowerCamelCase )
lowerCamelCase__ : Any =sequence == pad_token_id
lowerCamelCase__ : List[str] =0
return mask
def snake_case__ ( __lowerCamelCase : str , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ : Dict =[tokenizer.encode(__lowerCamelCase ) for line in story_lines]
lowerCamelCase__ : List[Any] =[token for sentence in story_lines_token_ids for token in sentence]
lowerCamelCase__ : List[Any] =[tokenizer.encode(__lowerCamelCase ) for line in summary_lines]
lowerCamelCase__ : Optional[int] =[token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def snake_case__ ( __lowerCamelCase : Tuple , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
lowerCamelCase__ : Any =[]
for sequence in batch:
lowerCamelCase__ : Optional[int] =-1
lowerCamelCase__ : List[str] =[]
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(__lowerCamelCase )
return torch.tensor(__lowerCamelCase )
| 272 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class A :
"""simple docstring"""
def __init__( self : str,lowercase_ : Any,lowercase_ : Tuple=1_3,lowercase_ : str=7,lowercase_ : Tuple=True,lowercase_ : int=True,lowercase_ : List[Any]=True,lowercase_ : List[str]=True,lowercase_ : List[str]=9_9,lowercase_ : List[Any]=6_4,lowercase_ : List[str]=5,lowercase_ : Optional[Any]=4,lowercase_ : Optional[Any]=3_7,lowercase_ : Optional[Any]="gelu",lowercase_ : int=0.1,lowercase_ : str=0.1,lowercase_ : Optional[Any]=5_1_2,lowercase_ : int=1_6,lowercase_ : List[Any]=2,lowercase_ : Union[str, Any]=0.02,lowercase_ : Tuple=3,lowercase_ : List[Any]=4,lowercase_ : str=None,)-> Union[str, Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_input_mask
A__ = use_token_type_ids
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = type_sequence_label_size
A__ = initializer_range
A__ = num_labels
A__ = num_choices
A__ = scope
A__ = vocab_size - 1
def snake_case__ ( self : str )-> Optional[Any]:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length],self.vocab_size )
A__ = None
if self.use_input_mask:
A__ = random_attention_mask([self.batch_size, self.seq_length] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size, self.seq_length],self.num_labels )
A__ = self.get_config()
return config, input_ids, input_mask, token_labels
def snake_case__ ( self : List[Any] )-> Tuple:
'''simple docstring'''
return GPTNeoXConfig(
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=lowercase_,initializer_range=self.initializer_range,pad_token_id=self.pad_token_id,)
def snake_case__ ( self : Optional[int] )-> Union[str, Any]:
'''simple docstring'''
A__ , A__ , A__ , A__ = self.prepare_config_and_inputs()
A__ = True
return config, input_ids, input_mask, token_labels
def snake_case__ ( self : Any,lowercase_ : List[Any],lowercase_ : List[Any],lowercase_ : str )-> Any:
'''simple docstring'''
A__ = GPTNeoXModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_,attention_mask=lowercase_ )
A__ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self : Union[str, Any],lowercase_ : List[str],lowercase_ : Dict,lowercase_ : Optional[Any] )-> Tuple:
'''simple docstring'''
A__ = True
A__ = GPTNeoXModel(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_,attention_mask=lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self : Union[str, Any],lowercase_ : str,lowercase_ : Union[str, Any],lowercase_ : Union[str, Any],lowercase_ : List[str] )-> List[str]:
'''simple docstring'''
A__ = GPTNeoXForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_,attention_mask=lowercase_,labels=lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size) )
def snake_case__ ( self : Optional[int],lowercase_ : Optional[int],lowercase_ : Optional[int],lowercase_ : Dict,lowercase_ : Any )-> int:
'''simple docstring'''
A__ = self.num_labels
A__ = GPTNeoXForQuestionAnswering(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_,attention_mask=lowercase_ )
self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length) )
def snake_case__ ( self : List[str],lowercase_ : List[str],lowercase_ : int,lowercase_ : Union[str, Any],lowercase_ : Optional[int] )-> str:
'''simple docstring'''
A__ = self.num_labels
A__ = GPTNeoXForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = ids_tensor([self.batch_size],self.type_sequence_label_size )
A__ = model(lowercase_,attention_mask=lowercase_,labels=lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )
def snake_case__ ( self : Any,lowercase_ : Union[str, Any],lowercase_ : List[Any],lowercase_ : Optional[Any],lowercase_ : int )-> Union[str, Any]:
'''simple docstring'''
A__ = self.num_labels
A__ = GPTNeoXForTokenClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_,attention_mask=lowercase_,labels=lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels) )
def snake_case__ ( self : int,lowercase_ : str,lowercase_ : int,lowercase_ : Union[str, Any] )-> List[Any]:
'''simple docstring'''
A__ = True
A__ = GPTNeoXForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
# first forward pass
A__ = model(lowercase_,attention_mask=lowercase_,use_cache=lowercase_ )
A__ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
A__ = ids_tensor((self.batch_size, 3),config.vocab_size )
A__ = ids_tensor((self.batch_size, 3),vocab_size=2 )
# append to next input_ids and
A__ = torch.cat([input_ids, next_tokens],dim=-1 )
A__ = torch.cat([input_mask, next_mask],dim=-1 )
A__ = model(lowercase_,attention_mask=lowercase_,output_hidden_states=lowercase_ )
A__ = output_from_no_past['hidden_states'][0]
A__ = model(
lowercase_,attention_mask=lowercase_,past_key_values=lowercase_,output_hidden_states=lowercase_,)['hidden_states'][0]
# select random slice
A__ = ids_tensor((1,),output_from_past.shape[-1] ).item()
A__ = output_from_no_past[:, -3:, random_slice_idx].detach()
A__ = 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-3 ) )
def snake_case__ ( self : str )-> Union[str, Any]:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ , A__ = config_and_inputs
A__ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
def snake_case__ ( self : str )-> Tuple:
'''simple docstring'''
A__ = GPTNeoXModelTester(self )
A__ = ConfigTester(self,config_class=lowercase_,hidden_size=6_4,num_attention_heads=8 )
def snake_case__ ( self : Optional[Any] )-> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self : Union[str, Any] )-> Union[str, Any]:
'''simple docstring'''
A__ , A__ , A__ , A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowercase_,lowercase_,lowercase_ )
def snake_case__ ( self : Dict )-> List[Any]:
'''simple docstring'''
A__ , A__ , A__ , A__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowercase_,lowercase_,lowercase_ )
def snake_case__ ( self : List[str] )-> Any:
'''simple docstring'''
A__ , A__ , A__ , A__ = self.model_tester.prepare_config_and_inputs_for_decoder()
A__ = None
self.model_tester.create_and_check_model_as_decoder(lowercase_,lowercase_,lowercase_ )
def snake_case__ ( self : Optional[Any] )-> str:
'''simple docstring'''
A__ , A__ , A__ , A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase_,lowercase_,lowercase_ )
def snake_case__ ( self : Dict )-> Dict:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowercase_ )
def snake_case__ ( self : Tuple )-> List[Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase_ )
def snake_case__ ( self : Any )-> List[str]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase_ )
def snake_case__ ( self : str )-> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase_ )
@unittest.skip(reason='Feed forward chunking is not implemented' )
def snake_case__ ( self : Union[str, Any] )-> Optional[Any]:
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def snake_case__ ( self : List[str],lowercase_ : Any )-> List[str]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = ids_tensor([1, 1_0],config.vocab_size )
A__ = ids_tensor([1, int(config.max_position_embeddings * 1.5 )],config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
A__ = GPTNeoXModel(lowercase_ )
original_model.to(lowercase_ )
original_model.eval()
A__ = original_model(lowercase_ ).last_hidden_state
A__ = original_model(lowercase_ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
A__ = {'type': scaling_type, 'factor': 10.0}
A__ = GPTNeoXModel(lowercase_ )
scaled_model.to(lowercase_ )
scaled_model.eval()
A__ = scaled_model(lowercase_ ).last_hidden_state
A__ = scaled_model(lowercase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowercase_,lowercase_,atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(lowercase_,lowercase_,atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowercase_,lowercase_,atol=1E-5 ) )
@require_torch
class A ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case__ ( self : Tuple )-> Union[str, Any]:
'''simple docstring'''
A__ = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' )
for checkpointing in [True, False]:
A__ = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowercase_ )
A__ = tokenizer('My favorite food is',return_tensors='pt' ).to(lowercase_ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
A__ = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
A__ = model.generate(**lowercase_,do_sample=lowercase_,max_new_tokens=2_0 )
A__ = tokenizer.batch_decode(lowercase_ )[0]
self.assertEqual(lowercase_,lowercase_ )
| 7 | """simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a)
class UpperCAmelCase_ ( _a):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
lowerCamelCase__ : str = field(default="text-classification" , metadata={"include_in_asdict_even_if_is_default": True})
lowerCamelCase__ : ClassVar[Features] = Features({"text": Value("string")})
lowerCamelCase__ : ClassVar[Features] = Features({"labels": ClassLabel})
lowerCamelCase__ : str = "text"
lowerCamelCase__ : str = "labels"
def _UpperCAmelCase ( self , a ) -> Tuple:
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] , a ):
raise ValueError(f"""Column {self.label_column} is not a ClassLabel.""" )
lowercase__ : Optional[Any] = copy.deepcopy(self )
lowercase__ : Optional[Any] = self.label_schema.copy()
lowercase__ : Any = features[self.label_column]
lowercase__ : Optional[Any] = label_schema
return task_template
@property
def _UpperCAmelCase ( self ) -> Dict[str, str]:
return {
self.text_column: "text",
self.label_column: "labels",
}
| 77 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import PoolFormerImageProcessor
class __lowerCAmelCase ( unittest.TestCase):
def __init__( self: int , _lowerCAmelCase: Tuple , _lowerCAmelCase: Optional[int]=7 , _lowerCAmelCase: Dict=3 , _lowerCAmelCase: Optional[int]=30 , _lowerCAmelCase: Optional[int]=4_00 , _lowerCAmelCase: List[Any]=True , _lowerCAmelCase: Optional[int]=None , _lowerCAmelCase: int=0.9 , _lowerCAmelCase: Any=None , _lowerCAmelCase: str=True , _lowerCAmelCase: List[str]=[0.5, 0.5, 0.5] , _lowerCAmelCase: Any=[0.5, 0.5, 0.5] , ):
lowercase :int = size if size is not None else {"shortest_edge": 30}
lowercase :int = crop_size if crop_size is not None else {"height": 30, "width": 30}
lowercase :List[str] = parent
lowercase :Optional[Any] = batch_size
lowercase :Any = num_channels
lowercase :List[Any] = min_resolution
lowercase :Dict = max_resolution
lowercase :Optional[Any] = do_resize_and_center_crop
lowercase :Any = size
lowercase :Tuple = crop_pct
lowercase :Optional[int] = crop_size
lowercase :int = do_normalize
lowercase :Tuple = image_mean
lowercase :List[Any] = image_std
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __lowerCAmelCase ( lowerCAmelCase , unittest.TestCase):
_a = PoolFormerImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :Optional[int] = PoolFormerImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE ( self: Tuple ):
lowercase :Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCAmelCase , "do_resize_and_center_crop" ) )
self.assertTrue(hasattr(_lowerCAmelCase , "size" ) )
self.assertTrue(hasattr(_lowerCAmelCase , "crop_pct" ) )
self.assertTrue(hasattr(_lowerCAmelCase , "do_normalize" ) )
self.assertTrue(hasattr(_lowerCAmelCase , "image_mean" ) )
self.assertTrue(hasattr(_lowerCAmelCase , "image_std" ) )
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
lowercase :Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 30} )
self.assertEqual(image_processor.crop_size , {"height": 30, "width": 30} )
lowercase :Any = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
pass
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
# Initialize image_processing
lowercase :Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase :Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , Image.Image )
# Test not batched input
lowercase :Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowercase :Optional[Any] = image_processing(_lowerCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def SCREAMING_SNAKE_CASE ( self: Dict ):
# Initialize image_processing
lowercase :int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase :Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase , numpify=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , np.ndarray )
# Test not batched input
lowercase :Dict = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowercase :Union[str, Any] = image_processing(_lowerCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
# Initialize image_processing
lowercase :Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase :List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCAmelCase , torchify=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase , torch.Tensor )
# Test not batched input
lowercase :List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowercase :int = image_processing(_lowerCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 158 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: Any , _lowerCAmelCase: int , _lowerCAmelCase: str , _lowerCAmelCase: Union[str, Any] ):
lowercase :List[str] = dataset
lowercase :Optional[int] = process
lowercase :Union[str, Any] = params
def __len__( self: str ):
return len(self.dataset )
def __getitem__( self: int , _lowerCAmelCase: Dict ):
lowercase :Union[str, Any] = self.dataset[i]
lowercase :Optional[int] = self.process(_lowerCAmelCase , **self.params )
return processed
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: int , _lowerCAmelCase: Tuple , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: int , _lowerCAmelCase: Optional[int]=None ):
lowercase :Optional[Any] = loader
lowercase :int = infer
lowercase :Dict = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
lowercase :Union[str, Any] = None
lowercase :Any = loader_batch_size
# Internal bookkeeping
lowercase :Optional[Any] = None
lowercase :Dict = None
def __len__( self: Tuple ):
return len(self.loader )
def __iter__( self: List[str] ):
lowercase :Dict = iter(self.loader )
return self
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
lowercase :Optional[int] = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
lowercase :str = {}
for k, element in self._loader_batch_data.items():
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
# Convert ModelOutput to tuple first
lowercase :Dict = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
lowercase :int = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
lowercase :List[str] = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_lowerCAmelCase , _lowerCAmelCase ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
lowercase :Union[str, Any] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
lowercase :List[Any] = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
lowercase :Optional[int] = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
lowercase :Optional[Any] = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
lowercase :Any = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
lowercase :List[Any] = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
lowercase :List[Any] = self._loader_batch_data.__class__(_lowerCAmelCase )
self._loader_batch_index += 1
return result
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
lowercase :Tuple = next(self.iterator )
lowercase :Dict = self.infer(_lowerCAmelCase , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(_lowerCAmelCase , torch.Tensor ):
lowercase :List[str] = processed
else:
lowercase :Tuple = list(processed.keys() )[0]
lowercase :Optional[Any] = processed[key]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
lowercase :Optional[int] = len(_lowerCAmelCase )
else:
lowercase :Dict = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
lowercase :Tuple = observed_batch_size
# Setting internal index to unwrap the batch
lowercase :int = processed
lowercase :Optional[Any] = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: Union[str, Any] , _lowerCAmelCase: Tuple , _lowerCAmelCase: str , _lowerCAmelCase: str , _lowerCAmelCase: Optional[Any]=None ):
super().__init__(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def __iter__( self: Tuple ):
lowercase :List[str] = iter(self.loader )
lowercase :str = None
return self
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
if self.subiterator is None:
lowercase :List[Any] = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
lowercase :str = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
lowercase :Tuple = self.infer(next(self.iterator ) , **self.params )
lowercase :Dict = next(self.subiterator )
return processed
class __lowerCAmelCase ( lowerCAmelCase):
def __iter__( self: str ):
lowercase :List[Any] = iter(self.loader )
return self
def SCREAMING_SNAKE_CASE ( self: str ):
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
lowercase :str = False
lowercase :int = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
lowercase :str = self.loader_batch_item()
lowercase :int = item.pop("is_last" )
accumulator.append(_lowerCAmelCase )
if is_last:
return accumulator
while not is_last:
lowercase :str = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(_lowerCAmelCase , torch.Tensor ):
lowercase :Tuple = processed
else:
lowercase :Union[str, Any] = list(processed.keys() )[0]
lowercase :Any = processed[key]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
lowercase :Dict = len(_lowerCAmelCase )
else:
lowercase :List[str] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
lowercase :Union[str, Any] = observed_batch_size
lowercase :str = processed
lowercase :Optional[int] = 0
while self._loader_batch_index < self.loader_batch_size:
lowercase :Any = self.loader_batch_item()
lowercase :int = item.pop("is_last" )
accumulator.append(_lowerCAmelCase )
if is_last:
return accumulator
else:
lowercase :Optional[Any] = processed
lowercase :str = item.pop("is_last" )
accumulator.append(_lowerCAmelCase )
return accumulator
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: Union[str, Any] , _lowerCAmelCase: Dataset , _lowerCAmelCase: str ):
lowercase :Tuple = dataset
lowercase :Dict = key
def __len__( self: Any ):
return len(self.dataset )
def __getitem__( self: int , _lowerCAmelCase: int ):
return self.dataset[i][self.key]
class __lowerCAmelCase ( lowerCAmelCase):
def __init__( self: List[Any] , _lowerCAmelCase: Dataset , _lowerCAmelCase: str , _lowerCAmelCase: str ):
lowercase :Union[str, Any] = dataset
lowercase :Optional[int] = keya
lowercase :str = keya
def __len__( self: Optional[Any] ):
return len(self.dataset )
def __getitem__( self: Optional[Any] , _lowerCAmelCase: int ):
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 158 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCamelCase_( snake_case : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def UpperCamelCase_( snake_case : np.ndarray , snake_case : np.ndarray , snake_case : np.ndarray ):
'''simple docstring'''
snake_case_ = XGBRegressor(verbosity=0 , random_state=4_2 )
xgb.fit(lowercase_ , lowercase_ )
# Predict target for test data
snake_case_ = xgb.predict(lowercase_ )
snake_case_ = predictions.reshape(len(lowercase_ ) , 1 )
return predictions
def UpperCamelCase_( ):
'''simple docstring'''
snake_case_ = fetch_california_housing()
snake_case_ = data_handling(lowercase_ )
snake_case_ = train_test_split(
lowercase_ , lowercase_ , test_size=0.25 , random_state=1 )
snake_case_ = xgboost(lowercase_ , lowercase_ , lowercase_ )
# Error printing
print(f'Mean Absolute Error : {mean_absolute_error(lowercase_ , lowercase_ )}' )
print(f'Mean Square Error : {mean_squared_error(lowercase_ , lowercase_ )}' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 85 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : Optional[Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: List[Any] = '''mgp-str'''
def __init__( self , A__=[32, 128] , A__=4 , A__=3 , A__=27 , A__=38 , A__=5_0257 , A__=3_0522 , A__=768 , A__=12 , A__=12 , A__=4.0 , A__=True , A__=False , A__=1e-5 , A__=0.0 , A__=0.0 , A__=0.0 , A__=False , A__=0.0_2 , **A__ , ):
super().__init__(**A__ )
A__ : Dict = image_size
A__ : int = patch_size
A__ : Dict = num_channels
A__ : List[Any] = max_token_length
A__ : str = num_character_labels
A__ : Tuple = num_bpe_labels
A__ : Optional[Any] = num_wordpiece_labels
A__ : Optional[int] = hidden_size
A__ : Tuple = num_hidden_layers
A__ : Any = num_attention_heads
A__ : List[Any] = mlp_ratio
A__ : Tuple = distilled
A__ : Union[str, Any] = layer_norm_eps
A__ : Tuple = drop_rate
A__ : List[str] = qkv_bias
A__ : Optional[Any] = attn_drop_rate
A__ : Union[str, Any] = drop_path_rate
A__ : Optional[Any] = output_aa_attentions
A__ : Optional[int] = initializer_range
| 192 | 0 |
"""simple docstring"""
import torch
def lowerCamelCase_( ) -> List[str]:
'''simple docstring'''
if torch.cuda.is_available():
_lowerCamelCase : Any = torch.cuda.device_count()
else:
_lowerCamelCase : List[str] = 0
print(F"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main() | 340 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
class A_ ( _a ):
lowerCAmelCase__ = 'masked_bert'
def __init__( self: Union[str, Any] ,__lowerCAmelCase: Dict=30_522 ,__lowerCAmelCase: Optional[int]=768 ,__lowerCAmelCase: Dict=12 ,__lowerCAmelCase: List[Any]=12 ,__lowerCAmelCase: List[Any]=3_072 ,__lowerCAmelCase: List[Any]="gelu" ,__lowerCAmelCase: Union[str, Any]=0.1 ,__lowerCAmelCase: List[str]=0.1 ,__lowerCAmelCase: Tuple=512 ,__lowerCAmelCase: str=2 ,__lowerCAmelCase: Tuple=0.02 ,__lowerCAmelCase: Union[str, Any]=1e-12 ,__lowerCAmelCase: Union[str, Any]=0 ,__lowerCAmelCase: List[Any]="topK" ,__lowerCAmelCase: Optional[Any]="constant" ,__lowerCAmelCase: Optional[Any]=0.0 ,**__lowerCAmelCase: str ,):
'''simple docstring'''
super().__init__(pad_token_id=__lowerCAmelCase ,**__lowerCAmelCase )
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : Optional[Any] = hidden_size
_lowerCamelCase : Tuple = num_hidden_layers
_lowerCamelCase : Tuple = num_attention_heads
_lowerCamelCase : Optional[Any] = hidden_act
_lowerCamelCase : Optional[Any] = intermediate_size
_lowerCamelCase : str = hidden_dropout_prob
_lowerCamelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCamelCase : str = max_position_embeddings
_lowerCamelCase : List[str] = type_vocab_size
_lowerCamelCase : Optional[int] = initializer_range
_lowerCamelCase : List[Any] = layer_norm_eps
_lowerCamelCase : int = pruning_method
_lowerCamelCase : str = mask_init
_lowerCamelCase : List[Any] = mask_scale | 340 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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 (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class snake_case__ :
def __init__( self , lowerCamelCase , lowerCamelCase = 13 , lowerCamelCase = 64 , lowerCamelCase = 2 , lowerCamelCase = 3 , lowerCamelCase = 3 , lowerCamelCase = True , lowerCamelCase = True , lowerCamelCase = 128 , lowerCamelCase=[16, 32, 64, 128] , lowerCamelCase = 7 , lowerCamelCase = 4 , lowerCamelCase = 37 , lowerCamelCase = "gelu" , lowerCamelCase = 0.1 , lowerCamelCase = 0.1 , lowerCamelCase = 10 , lowerCamelCase = 0.02 , lowerCamelCase = 2 , lowerCamelCase = 1 , lowerCamelCase = 128 , lowerCamelCase = [2, 2, 2, 2] , lowerCamelCase = 2 , lowerCamelCase = 2 , ):
__a = parent
__a = batch_size
__a = image_size
__a = patch_size
__a = num_channels
__a = is_training
__a = use_labels
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = type_sequence_label_size
__a = initializer_range
__a = encoder_stride
__a = num_attention_outputs
__a = embed_dim
__a = embed_dim + 1
__a = resolution
__a = depths
__a = hidden_sizes
__a = dim
__a = mlp_expansion_ratio
def a__ ( self ):
__a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a = self.get_config()
return config, pixel_values, labels
def a__ ( self ):
return EfficientFormerConfig(
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 , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__a = TFEfficientFormerModel(config=lowerCamelCase )
__a = model(lowerCamelCase , training=lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__a = self.type_sequence_label_size
__a = TFEfficientFormerForImageClassification(lowerCamelCase )
__a = model(lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__a = 1
__a = TFEfficientFormerForImageClassification(lowerCamelCase )
__a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__a = model(lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a__ ( self ):
__a = self.prepare_config_and_inputs()
__a , __a , __a = config_and_inputs
__a = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class snake_case__ ( snake_case_, snake_case_, unittest.TestCase ):
_snake_case : Tuple = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
_snake_case : int = (
{
"""feature-extraction""": TFEfficientFormerModel,
"""image-classification""": (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
_snake_case : List[Any] = False
_snake_case : Tuple = False
_snake_case : Optional[int] = False
_snake_case : List[Any] = False
_snake_case : Tuple = False
def a__ ( self ):
__a = TFEfficientFormerModelTester(self )
__a = ConfigTester(
self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase , hidden_size=37 )
def a__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="EfficientFormer does not use inputs_embeds" )
def a__ ( self ):
pass
@unittest.skip(reason="EfficientFormer does not support input and output embeddings" )
def a__ ( self ):
pass
def a__ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(lowerCamelCase )
__a = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a = [*signature.parameters.keys()]
__a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def a__ ( self ):
def check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__a = model_class(lowerCamelCase )
__a = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) , training=lowerCamelCase )
__a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__a = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(lowerCamelCase ) , lowerCamelCase )
if hasattr(self.model_tester , "encoder_seq_length" ):
__a = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , "chunk_length" ) and self.model_tester.chunk_length > 1:
__a = seq_length * self.model_tester.chunk_length
else:
__a = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
__a = outputs.decoder_hidden_states
self.asseretIsInstance(lowerCamelCase , (list, tuple) )
self.assertEqual(len(lowerCamelCase ) , lowerCamelCase )
__a = getattr(self.model_tester , "seq_length" , lowerCamelCase )
__a = getattr(self.model_tester , "decoder_seq_length" , lowerCamelCase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__a = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase=False ):
__a = super()._prepare_for_class(lowerCamelCase , lowerCamelCase , return_labels=lowerCamelCase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def a__ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet" )
def a__ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase )
def a__ ( self ):
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )
@slow
def a__ ( self ):
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = TFEfficientFormerModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def a__ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = True
__a = getattr(self.model_tester , "seq_length" , lowerCamelCase )
__a = getattr(self.model_tester , "encoder_seq_length" , lowerCamelCase )
__a = getattr(self.model_tester , "key_length" , lowerCamelCase )
__a = getattr(self.model_tester , "chunk_length" , lowerCamelCase )
if chunk_length is not None and hasattr(self.model_tester , "num_hashes" ):
__a = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
__a = True
__a = False
__a = True
__a = model_class(lowerCamelCase )
__a = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) , training=lowerCamelCase )
__a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__a = True
__a = model_class(lowerCamelCase )
__a = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) , training=lowerCamelCase )
__a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def a__ ( self ):
# We use a simplified version of this test for EfficientFormer because it requires training=False
# and Keras refuses to let us force that during functional construction
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
__a = model_class(lowerCamelCase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
__a = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=lowerCamelCase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
__a = model(lowerCamelCase )
self.assertTrue(outputs_dict is not None )
def _lowerCamelCase( ):
__a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class snake_case__ ( unittest.TestCase ):
@cached_property
def a__ ( self ):
return (
EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300" )
if is_vision_available()
else None
)
@slow
def a__ ( self ):
__a = TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300" )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(images=lowerCamelCase , return_tensors="tf" )
# forward pass
__a = model(**lowerCamelCase , training=lowerCamelCase )
# verify the logits
__a = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
__a = tf.constant([-0.0555, 0.4825, -0.0852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , lowerCamelCase , atol=1E-4 ) )
@slow
def a__ ( self ):
__a = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"snap-research/efficientformer-l1-300" )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(images=lowerCamelCase , return_tensors="tf" )
# forward pass
__a = model(**lowerCamelCase , training=lowerCamelCase )
# verify the logits
__a = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
__a = tf.constant([-0.1312, 0.4353, -1.0499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , lowerCamelCase , atol=1E-4 ) )
| 261 | """simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def _lowerCamelCase( a , a , a , a , a=True , a="pt" ):
__a = {"add_prefix_space": True} if isinstance(a , a ) and not line.startswith(" " ) else {}
__a = padding_side
return tokenizer(
[line] , max_length=a , padding="max_length" if pad_to_max_length else None , truncation=a , return_tensors=a , add_special_tokens=a , **a , )
def _lowerCamelCase( a , a , a=None , ):
__a = input_ids.ne(a ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case__ ( snake_case_ ):
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase="train" , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase="" , ):
super().__init__()
__a = Path(lowerCamelCase ).joinpath(type_path + ".source" )
__a = Path(lowerCamelCase ).joinpath(type_path + ".target" )
__a = self.get_char_lens(self.src_file )
__a = max_source_length
__a = max_target_length
assert min(self.src_lens ) > 0, F"found empty line in {self.src_file}"
__a = tokenizer
__a = prefix
if n_obs is not None:
__a = self.src_lens[:n_obs]
__a = src_lang
__a = tgt_lang
def __len__( self ):
return len(self.src_lens )
def __getitem__( self , lowerCamelCase ):
__a = index + 1 # linecache starts at 1
__a = self.prefix + linecache.getline(str(self.src_file ) , lowerCamelCase ).rstrip("\n" )
__a = linecache.getline(str(self.tgt_file ) , lowerCamelCase ).rstrip("\n" )
assert source_line, F"empty source line for index {index}"
assert tgt_line, F"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__a = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCamelCase ) else self.tokenizer
)
__a = self.tokenizer.generator if isinstance(self.tokenizer , lowerCamelCase ) else self.tokenizer
__a = encode_line(lowerCamelCase , lowerCamelCase , self.max_source_length , "right" )
__a = encode_line(lowerCamelCase , lowerCamelCase , self.max_target_length , "right" )
__a = source_inputs["input_ids"].squeeze()
__a = target_inputs["input_ids"].squeeze()
__a = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def a__ ( lowerCamelCase ):
return [len(lowerCamelCase ) for x in Path(lowerCamelCase ).open().readlines()]
def a__ ( self , lowerCamelCase ):
__a = torch.stack([x["input_ids"] for x in batch] )
__a = torch.stack([x["attention_mask"] for x in batch] )
__a = torch.stack([x["decoder_input_ids"] for x in batch] )
__a = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase )
else self.tokenizer.pad_token_id
)
__a = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase )
else self.tokenizer.pad_token_id
)
__a = trim_batch(lowerCamelCase , lowerCamelCase )
__a , __a = trim_batch(lowerCamelCase , lowerCamelCase , attention_mask=lowerCamelCase )
__a = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
SCREAMING_SNAKE_CASE__:Tuple = getLogger(__name__)
def _lowerCamelCase( a ):
return list(itertools.chain.from_iterable(a ) )
def _lowerCamelCase( a ):
__a = get_git_info()
save_json(a , os.path.join(a , "git_log.json" ) )
def _lowerCamelCase( a , a , a=4 , **a ):
with open(a , "w" ) as f:
json.dump(a , a , indent=a , **a )
def _lowerCamelCase( a ):
with open(a ) as f:
return json.load(a )
def _lowerCamelCase( ):
__a = git.Repo(search_parent_directories=a )
__a = {
"repo_id": str(a ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def _lowerCamelCase( a , a ):
return list(map(a , a ) )
def _lowerCamelCase( a , a ):
with open(a , "wb" ) as f:
return pickle.dump(a , a )
def _lowerCamelCase( a ):
def remove_articles(a ):
return re.sub(R"\b(a|an|the)\b" , " " , a )
def white_space_fix(a ):
return " ".join(text.split() )
def remove_punc(a ):
__a = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(a ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(a ) ) ) )
def _lowerCamelCase( a , a ):
__a = normalize_answer(a ).split()
__a = normalize_answer(a ).split()
__a = Counter(a ) & Counter(a )
__a = sum(common.values() )
if num_same == 0:
return 0
__a = 1.0 * num_same / len(a )
__a = 1.0 * num_same / len(a )
__a = (2 * precision * recall) / (precision + recall)
return fa
def _lowerCamelCase( a , a ):
return normalize_answer(a ) == normalize_answer(a )
def _lowerCamelCase( a , a ):
assert len(a ) == len(a )
__a = 0
for hypo, pred in zip(a , a ):
em += exact_match_score(a , a )
if len(a ) > 0:
em /= len(a )
return {"em": em}
def _lowerCamelCase( a ):
return model_prefix.startswith("rag" )
def _lowerCamelCase( a , a , a ):
__a = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__a = "dropout_rate"
for p in extra_params:
if getattr(a , a , a ):
if not hasattr(a , a ) and not hasattr(a , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(a ) )
delattr(a , a )
continue
__a = p if hasattr(a , a ) else equivalent_param[p]
setattr(a , a , getattr(a , a ) )
delattr(a , a )
return hparams, config
| 261 | 1 |
"""simple docstring"""
from collections import defaultdict
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[Any], lowerCamelCase : Dict, lowerCamelCase : List[Any] )-> List[str]:
lowerCamelCase__ : Optional[int] =total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCamelCase__ : List[Any] =[
[-1 for i in range(total + 1 )] for j in range(2 ** len(SCREAMING_SNAKE_CASE_ ) )
]
lowerCamelCase__ : Union[str, Any] =defaultdict(SCREAMING_SNAKE_CASE_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
lowerCamelCase__ : List[Any] =(1 << len(SCREAMING_SNAKE_CASE_ )) - 1
def snake_case ( self : Optional[Any], lowerCamelCase : int, lowerCamelCase : str )-> Union[str, Any]:
# if mask == self.finalmask all persons are distributed tasks, return 1
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
lowerCamelCase__ : List[Any] =self.count_ways_until(SCREAMING_SNAKE_CASE_, task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p), task_no + 1 )
# save the value.
lowerCamelCase__ : List[Any] =total_ways_util
return self.dp[mask][task_no]
def snake_case ( self : Optional[Any], lowerCamelCase : Tuple )-> str:
# Store the list of persons for each task
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
for j in task_performed[i]:
self.task[j].append(SCREAMING_SNAKE_CASE_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0, 1 )
if __name__ == "__main__":
_lowercase : Optional[Any] = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
_lowercase : Optional[Any] = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 366 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
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 (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict, lowerCamelCase : str, lowerCamelCase : Dict=13, lowerCamelCase : Optional[Any]=7, lowerCamelCase : List[Any]=True, lowerCamelCase : Dict=True, lowerCamelCase : List[Any]=True, lowerCamelCase : Optional[int]=True, lowerCamelCase : int=99, lowerCamelCase : Optional[int]=[1, 1, 2], lowerCamelCase : str=1, lowerCamelCase : List[Any]=32, lowerCamelCase : str=4, lowerCamelCase : Dict=8, lowerCamelCase : List[Any]=37, lowerCamelCase : Optional[int]="gelu_new", lowerCamelCase : Union[str, Any]=0.1, lowerCamelCase : List[Any]=0.1, lowerCamelCase : List[Any]=0.0, lowerCamelCase : Dict=512, lowerCamelCase : Dict=3, lowerCamelCase : str=0.02, lowerCamelCase : str=3, lowerCamelCase : Optional[int]=4, lowerCamelCase : List[str]=None, lowerCamelCase : Tuple=False, )-> Union[str, Any]:
lowerCamelCase__ : int =parent
lowerCamelCase__ : Dict =batch_size
lowerCamelCase__ : Dict =seq_length
lowerCamelCase__ : Any =is_training
lowerCamelCase__ : int =use_input_mask
lowerCamelCase__ : Tuple =use_token_type_ids
lowerCamelCase__ : int =use_labels
lowerCamelCase__ : Tuple =vocab_size
lowerCamelCase__ : Union[str, Any] =block_sizes
lowerCamelCase__ : Any =num_decoder_layers
lowerCamelCase__ : Optional[Any] =d_model
lowerCamelCase__ : List[str] =n_head
lowerCamelCase__ : List[Any] =d_head
lowerCamelCase__ : Dict =d_inner
lowerCamelCase__ : Dict =hidden_act
lowerCamelCase__ : List[str] =hidden_dropout
lowerCamelCase__ : Union[str, Any] =attention_dropout
lowerCamelCase__ : Union[str, Any] =activation_dropout
lowerCamelCase__ : Dict =max_position_embeddings
lowerCamelCase__ : Dict =type_vocab_size
lowerCamelCase__ : Union[str, Any] =2
lowerCamelCase__ : Optional[int] =num_labels
lowerCamelCase__ : List[str] =num_choices
lowerCamelCase__ : Tuple =scope
lowerCamelCase__ : Optional[int] =initializer_std
# Used in the tests to check the size of the first attention layer
lowerCamelCase__ : List[str] =n_head
# Used in the tests to check the size of the first hidden state
lowerCamelCase__ : Tuple =self.d_model
# Used in the tests to check the number of output hidden states/attentions
lowerCamelCase__ : List[Any] =sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
lowerCamelCase__ : Union[str, Any] =self.num_hidden_layers + 2
def snake_case ( self : int )-> List[Any]:
lowerCamelCase__ : Dict =ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Union[str, Any] =None
if self.use_input_mask:
lowerCamelCase__ : Any =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : int =None
if self.use_token_type_ids:
lowerCamelCase__ : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase__ : List[str] =None
lowerCamelCase__ : Union[str, Any] =None
lowerCamelCase__ : List[str] =None
if self.use_labels:
lowerCamelCase__ : List[Any] =ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : Optional[Any] =ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : Union[str, Any] =ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase__ : Optional[int] =FunnelConfig(
vocab_size=self.vocab_size, block_sizes=self.block_sizes, num_decoder_layers=self.num_decoder_layers, d_model=self.d_model, n_head=self.n_head, d_head=self.d_head, d_inner=self.d_inner, hidden_act=self.hidden_act, hidden_dropout=self.hidden_dropout, attention_dropout=self.attention_dropout, activation_dropout=self.activation_dropout, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_std=self.initializer_std, )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def snake_case ( self : List[Any], lowerCamelCase : Optional[Any], lowerCamelCase : Optional[int], lowerCamelCase : int, lowerCamelCase : int, lowerCamelCase : str, lowerCamelCase : List[str], lowerCamelCase : Dict, )-> Union[str, Any]:
lowerCamelCase__ : Tuple =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : Dict ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Tuple =model(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =[input_ids, input_mask]
lowerCamelCase__ : List[Any] =model(lowerCamelCase )
lowerCamelCase__ : Any =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
lowerCamelCase__ : int =False
lowerCamelCase__ : Any =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
lowerCamelCase__ : Dict =False
lowerCamelCase__ : Optional[int] =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : Tuple =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
def snake_case ( self : Tuple, lowerCamelCase : List[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[Any], lowerCamelCase : Tuple, lowerCamelCase : Dict, )-> Optional[Any]:
lowerCamelCase__ : List[str] =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : str ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
lowerCamelCase__ : Tuple =[input_ids, input_mask]
lowerCamelCase__ : Any =model(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model) )
lowerCamelCase__ : List[Any] =False
lowerCamelCase__ : Dict =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : int =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model) )
lowerCamelCase__ : Union[str, Any] =False
lowerCamelCase__ : Optional[Any] =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model) )
def snake_case ( self : str, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : List[Any], lowerCamelCase : Dict, lowerCamelCase : List[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : List[Any], )-> List[Any]:
lowerCamelCase__ : List[str] =TFFunnelForPreTraining(config=lowerCamelCase )
lowerCamelCase__ : List[Any] ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length) )
def snake_case ( self : str, lowerCamelCase : Tuple, lowerCamelCase : str, lowerCamelCase : List[Any], lowerCamelCase : List[Any], lowerCamelCase : str, lowerCamelCase : Tuple, lowerCamelCase : int, )-> List[Any]:
lowerCamelCase__ : Union[str, Any] =TFFunnelForMaskedLM(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : List[Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self : Optional[int], lowerCamelCase : Tuple, lowerCamelCase : Any, lowerCamelCase : List[str], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[int], lowerCamelCase : Dict, )-> Union[str, Any]:
lowerCamelCase__ : Optional[Any] =self.num_labels
lowerCamelCase__ : Tuple =TFFunnelForSequenceClassification(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : List[str] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def snake_case ( self : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : Optional[Any], lowerCamelCase : int, lowerCamelCase : Tuple, )-> int:
lowerCamelCase__ : int =self.num_choices
lowerCamelCase__ : List[Any] =TFFunnelForMultipleChoice(config=lowerCamelCase )
lowerCamelCase__ : int =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Union[str, Any] =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Optional[Any] =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Union[str, Any] ={
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def snake_case ( self : str, lowerCamelCase : Dict, lowerCamelCase : Optional[Any], lowerCamelCase : Any, lowerCamelCase : str, lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Dict, )-> Optional[int]:
lowerCamelCase__ : Optional[Any] =self.num_labels
lowerCamelCase__ : Optional[Any] =TFFunnelForTokenClassification(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def snake_case ( self : Optional[int], lowerCamelCase : Dict, lowerCamelCase : str, lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[int], )-> Tuple:
lowerCamelCase__ : Tuple =TFFunnelForQuestionAnswering(config=lowerCamelCase )
lowerCamelCase__ : Union[str, Any] ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Optional[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 snake_case ( self : int )-> List[str]:
lowerCamelCase__ : List[Any] =self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : Tuple =config_and_inputs
lowerCamelCase__ : str ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
_a = (
{
'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
'fill-mask': TFFunnelForMaskedLM,
'question-answering': TFFunnelForQuestionAnswering,
'text-classification': TFFunnelForSequenceClassification,
'token-classification': TFFunnelForTokenClassification,
'zero-shot': TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
_a = False
_a = False
def snake_case ( self : str )-> Tuple:
lowerCamelCase__ : Any =TFFunnelModelTester(self )
lowerCamelCase__ : Any =ConfigTester(self, config_class=lowerCamelCase )
def snake_case ( self : List[str] )-> Tuple:
self.config_tester.run_common_tests()
def snake_case ( self : str )-> List[Any]:
lowerCamelCase__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def snake_case ( self : str )-> Dict:
lowerCamelCase__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCamelCase )
def snake_case ( self : int )-> List[Any]:
lowerCamelCase__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase )
def snake_case ( self : Dict )-> Any:
lowerCamelCase__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
def snake_case ( self : Tuple )-> Optional[Any]:
lowerCamelCase__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
_a = False
_a = False
def snake_case ( self : int )-> Tuple:
lowerCamelCase__ : Union[str, Any] =TFFunnelModelTester(self, base=lowerCamelCase )
lowerCamelCase__ : Tuple =ConfigTester(self, config_class=lowerCamelCase )
def snake_case ( self : Any )-> Any:
self.config_tester.run_common_tests()
def snake_case ( self : Optional[Any] )-> Optional[Any]:
lowerCamelCase__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*lowerCamelCase )
def snake_case ( self : Union[str, Any] )-> int:
lowerCamelCase__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )
def snake_case ( self : List[str] )-> Optional[int]:
lowerCamelCase__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase )
| 272 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCamelCase : int = {"tokenization_byt5": ["ByT5Tokenizer"]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_lowerCamelCase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 50 ) -> int:
"""simple docstring"""
UpperCamelCase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 28 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : Union[str, Any] ) -> Dict:
A_ = 1
A_ = 2
while i * i <= n:
A_ = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def _UpperCAmelCase ( ) -> Optional[int]:
A_ = 1
A_ = 1
while True:
i += 1
t_num += i
if count_divisors(_UpperCamelCase ) > 5_00:
break
return t_num
if __name__ == "__main__":
print(solution())
| 18 | '''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__snake_case : Optional[Any] = logging.get_logger(__name__)
__snake_case : Tuple = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
__snake_case : Optional[Any] = {
'vocab_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
},
'merges_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
},
}
__snake_case : Tuple = {'allegro/herbert-base-cased': 514}
__snake_case : List[str] = {}
class __UpperCAmelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowercase : Any = VOCAB_FILES_NAMES
__lowercase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__lowercase : Dict = PRETRAINED_INIT_CONFIGURATION
__lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : Optional[int] = HerbertTokenizer
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE="</s>" , **_SCREAMING_SNAKE_CASE , ) -> int:
super().__init__(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
A_ = [self.cls_token_id]
A_ = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 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 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
A_ = [self.sep_token_id]
A_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
A_ = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE )
return tuple(_SCREAMING_SNAKE_CASE )
| 18 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : Tuple = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
lowerCamelCase : Dict = hf_hub_download(
repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
lowerCamelCase : str = VideoClassificationPipeline(model=UpperCamelCase__ , image_processor=UpperCamelCase__ , top_k=2 )
lowerCamelCase : Dict = [
example_video_filepath,
"https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
]
return video_classifier, examples
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> int:
for example in examples:
lowerCamelCase : Optional[int] = video_classifier(UpperCamelCase__ )
self.assertEqual(
UpperCamelCase__ , [
{"score": ANY(UpperCamelCase__ ), "label": ANY(UpperCamelCase__ )},
{"score": ANY(UpperCamelCase__ ), "label": ANY(UpperCamelCase__ )},
] , )
@require_torch
def _lowercase ( self ) -> Dict:
lowerCamelCase : int = "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
lowerCamelCase : Any = VideoMAEFeatureExtractor(
size={"shortest_edge": 10} , crop_size={"height": 10, "width": 10} )
lowerCamelCase : Union[str, Any] = pipeline(
"video-classification" , model=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , frame_sampling_rate=4 )
lowerCamelCase : Optional[Any] = hf_hub_download(repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
lowerCamelCase : Union[str, Any] = video_classifier(UpperCamelCase__ , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase__ , decimals=4 ) , [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}] , )
lowerCamelCase : List[str] = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(UpperCamelCase__ , decimals=4 ) , [
[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
] , )
@require_tf
def _lowercase ( self ) -> int:
pass
| 48 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__ : Optional[int] = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r'\s*\(\s*"(\S[^"]+)"')
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False ) -> int:
with open(_SCREAMING_SNAKE_CASE ,"r" ,encoding="utf-8" ) as f:
lowerCamelCase : List[Any] = f.read()
lowerCamelCase : str = content.split("\n" )
lowerCamelCase : int = []
lowerCamelCase : List[Any] = 0
while line_idx < len(_SCREAMING_SNAKE_CASE ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
lowerCamelCase : Optional[int] = len(re.search(r"^(\s*)\S" ,lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(" " * indent + "(" ):
new_lines.append(lines[line_idx] )
line_idx += 1
lowerCamelCase : Optional[int] = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
lowerCamelCase : List[str] = line_idx
while not lines[line_idx].startswith(" " * indent + ")" ):
line_idx += 1
blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
lowerCamelCase : Union[str, Any] = sorted(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE : _re_identifier.search(_SCREAMING_SNAKE_CASE ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8" ) as f:
f.write("\n".join(_SCREAMING_SNAKE_CASE ) )
elif "\n".join(_SCREAMING_SNAKE_CASE ) != content:
return True
def A ( _SCREAMING_SNAKE_CASE = False ) -> List[str]:
lowerCamelCase : str = [os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) for f in os.listdir(_SCREAMING_SNAKE_CASE ) if f.endswith(".py" )]
lowerCamelCase : Union[str, Any] = [sort_auto_mapping(_SCREAMING_SNAKE_CASE ,overwrite=_SCREAMING_SNAKE_CASE ) for fname in fnames]
if not overwrite and any(_SCREAMING_SNAKE_CASE ):
lowerCamelCase : str = [f for f, d in zip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) if d]
raise ValueError(
f'''The following files have auto mappings that need sorting: {", ".join(_SCREAMING_SNAKE_CASE )}. Run `make style` to fix'''
" this." )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
SCREAMING_SNAKE_CASE__ : List[str] = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 48 | 1 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
a = NewType('''DataClass''', Any)
a = NewType('''DataClassType''', Any)
def _snake_case ( _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_snake_case , _snake_case ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''' )
def _snake_case ( _snake_case : list ) -> Callable[[str], Any]:
'''simple docstring'''
_A = {str(_snake_case ): choice for choice in choices}
return lambda _snake_case : str_to_choice.get(_snake_case , _snake_case )
def _snake_case ( *,
_snake_case : Union[str, List[str]] = None , _snake_case : str = None , _snake_case : Any = dataclasses.MISSING , _snake_case : Callable[[], Any] = dataclasses.MISSING , _snake_case : dict = None , **_snake_case : Tuple , ) -> dataclasses.Field:
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_A = {}
if aliases is not None:
_A = aliases
if help is not None:
_A = help
return dataclasses.field(metadata=_snake_case , default=_snake_case , default_factory=_snake_case , **_snake_case )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Iterable[DataClassType]
def __init__( self : Tuple , _UpperCAmelCase : Union[DataClassType, Iterable[DataClassType]] , **_UpperCAmelCase : Optional[Any] ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_A = ArgumentDefaultsHelpFormatter
super().__init__(**_UpperCAmelCase )
if dataclasses.is_dataclass(_UpperCAmelCase ):
_A = [dataclass_types]
_A = list(_UpperCAmelCase )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(_UpperCAmelCase )
@staticmethod
def lowerCAmelCase_ ( _UpperCAmelCase : ArgumentParser , _UpperCAmelCase : dataclasses.Field ):
_A = F'''--{field.name}'''
_A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , _UpperCAmelCase ):
raise RuntimeError(
'Unresolved type detected, which should have been done with the help of '
'`typing.get_type_hints` method by default' )
_A = kwargs.pop('aliases' , [] )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_A = [aliases]
_A = getattr(field.type , '__origin__' , field.type )
if origin_type is Union or (hasattr(_UpperCAmelCase , 'UnionType' ) and isinstance(_UpperCAmelCase , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(_UpperCAmelCase ) not in field.type.__args__
):
raise ValueError(
'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
' the argument parser only supports one type per argument.'
F''' Problem encountered in field \'{field.name}\'.''' )
if type(_UpperCAmelCase ) not in field.type.__args__:
# filter `str` in Union
_A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_A = getattr(field.type , '__origin__' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_A = (
field.type.__args__[0] if isinstance(_UpperCAmelCase , field.type.__args__[1] ) else field.type.__args__[1]
)
_A = getattr(field.type , '__origin__' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_A = {}
if origin_type is Literal or (isinstance(field.type , _UpperCAmelCase ) and issubclass(field.type , _UpperCAmelCase )):
if origin_type is Literal:
_A = field.type.__args__
else:
_A = [x.value for x in field.type]
_A = make_choice_type_function(kwargs['choices'] )
if field.default is not dataclasses.MISSING:
_A = field.default
else:
_A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_A = copy(_UpperCAmelCase )
# Hack because type=bool in argparse does not behave as we want.
_A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_A = default
# This tells argparse we accept 0 or 1 value after --field_name
_A = '?'
# This is the value that will get picked if we do --field_name (without value)
_A = True
elif isclass(_UpperCAmelCase ) and issubclass(_UpperCAmelCase , _UpperCAmelCase ):
_A = field.type.__args__[0]
_A = '+'
if field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
elif field.default is dataclasses.MISSING:
_A = True
else:
_A = field.type
if field.default is not dataclasses.MISSING:
_A = field.default
elif field.default_factory is not dataclasses.MISSING:
_A = field.default_factory()
else:
_A = True
parser.add_argument(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_A = False
parser.add_argument(F'''--no_{field.name}''' , action='store_false' , dest=field.name , **_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : DataClassType ):
if hasattr(_UpperCAmelCase , '_argument_group_name' ):
_A = self.add_argument_group(dtype._argument_group_name )
else:
_A = self
try:
_A = get_type_hints(_UpperCAmelCase )
except NameError:
raise RuntimeError(
F'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
'removing line of `from __future__ import annotations` which opts in Postponed '
'Evaluation of Annotations (PEP 563)' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_UpperCAmelCase ):
_A = '.'.join(map(_UpperCAmelCase , sys.version_info[:3] ) )
raise RuntimeError(
F'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
'line of `from __future__ import annotations` which opts in union types as '
'`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
'support Python versions that lower than 3.10, you need to use '
'`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
'`X | None`.' ) from ex
raise
for field in dataclasses.fields(_UpperCAmelCase ):
if not field.init:
continue
_A = type_hints[field.name]
self._parse_dataclass_field(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : Any=False , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : int=None , _UpperCAmelCase : List[Any]=None , ):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_A = []
if args_filename:
args_files.append(Path(_UpperCAmelCase ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('.args' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_A = ArgumentParser()
args_file_parser.add_argument(_UpperCAmelCase , type=_UpperCAmelCase , action='append' )
# Use only remaining args for further parsing (remove the args_file_flag)
_A , _A = args_file_parser.parse_known_args(args=_UpperCAmelCase )
_A = vars(_UpperCAmelCase ).get(args_file_flag.lstrip('-' ) , _UpperCAmelCase )
if cmd_args_file_paths:
args_files.extend([Path(_UpperCAmelCase ) for p in cmd_args_file_paths] )
_A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_A = file_args + args if args is not None else file_args + sys.argv[1:]
_A , _A = self.parse_known_args(args=_UpperCAmelCase )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(_UpperCAmelCase ) if f.init}
_A = {k: v for k, v in vars(_UpperCAmelCase ).items() if k in keys}
for k in keys:
delattr(_UpperCAmelCase , _UpperCAmelCase )
_A = dtype(**_UpperCAmelCase )
outputs.append(_UpperCAmelCase )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(_UpperCAmelCase )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' )
return (*outputs,)
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : Dict[str, Any] , _UpperCAmelCase : bool = False ):
_A = set(args.keys() )
_A = []
for dtype in self.dataclass_types:
_A = {f.name for f in dataclasses.fields(_UpperCAmelCase ) if f.init}
_A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_A = dtype(**_UpperCAmelCase )
outputs.append(_UpperCAmelCase )
if not allow_extra_keys and unused_keys:
raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(_UpperCAmelCase )}''' )
return tuple(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : bool = False ):
with open(Path(_UpperCAmelCase ) , encoding='utf-8' ) as open_json_file:
_A = json.loads(open_json_file.read() )
_A = self.parse_dict(_UpperCAmelCase , allow_extra_keys=_UpperCAmelCase )
return tuple(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : bool = False ):
_A = self.parse_dict(yaml.safe_load(Path(_UpperCAmelCase ).read_text() ) , allow_extra_keys=_UpperCAmelCase )
return tuple(_UpperCAmelCase )
| 351 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
_A = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _snake_case ( _snake_case : str ) -> dict[str, str]:
'''simple docstring'''
_A = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
_A = remove_duplicates(key.upper() )
_A = len(_snake_case )
# First fill cipher with key characters
_A = {alphabet[i]: char for i, char in enumerate(_snake_case )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_snake_case ) , 26 ):
_A = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
_A = alphabet[i - offset]
_A = char
return cipher_alphabet
def _snake_case ( _snake_case : str , _snake_case : dict[str, str] ) -> str:
'''simple docstring'''
return "".join(cipher_map.get(_snake_case , _snake_case ) for ch in message.upper() )
def _snake_case ( _snake_case : str , _snake_case : dict[str, str] ) -> str:
'''simple docstring'''
_A = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_snake_case , _snake_case ) for ch in message.upper() )
def _snake_case ( ) -> None:
'''simple docstring'''
_A = input('Enter message to encode or decode: ' ).strip()
_A = input('Enter keyword: ' ).strip()
_A = input('Encipher or decipher? E/D:' ).strip()[0].lower()
try:
_A = {'e': encipher, 'd': decipher}[option]
except KeyError:
raise KeyError('invalid input option' )
_A = create_cipher_map(_snake_case )
print(func(_snake_case , _snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 271 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowerCamelCase__ = {
'vocab_file': {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
),
}
}
lowerCamelCase__ = {
'junnyu/roformer_chinese_small': 1_536,
'junnyu/roformer_chinese_base': 1_536,
'junnyu/roformer_chinese_char_small': 512,
'junnyu/roformer_chinese_char_base': 512,
'junnyu/roformer_small_discriminator': 128,
'junnyu/roformer_small_generator': 128,
}
lowerCamelCase__ = {
'junnyu/roformer_chinese_small': {'do_lower_case': True},
'junnyu/roformer_chinese_base': {'do_lower_case': True},
'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
'junnyu/roformer_small_discriminator': {'do_lower_case': True},
'junnyu/roformer_small_generator': {'do_lower_case': True},
}
class lowerCAmelCase__ ( UpperCAmelCase__ ):
lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
lowerCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : Optional[int] = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase : Optional[int] = RoFormerTokenizer
def __init__( self : str , lowerCamelCase__ : Tuple=None , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Union[str, Any]=True , lowerCamelCase__ : List[str]="[UNK]" , lowerCamelCase__ : Tuple="[SEP]" , lowerCamelCase__ : int="[PAD]" , lowerCamelCase__ : int="[CLS]" , lowerCamelCase__ : Any="[MASK]" , lowerCamelCase__ : Any=True , lowerCamelCase__ : int=None , **lowerCamelCase__ : Tuple , ) ->List[str]:
'''simple docstring'''
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , tokenize_chinese_chars=lowerCamelCase__ , strip_accents=lowerCamelCase__ , **lowerCamelCase__ , )
_UpperCAmelCase : Dict = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
pre_tok_state.get("lowercase" , lowerCamelCase__ ) != do_lower_case
or pre_tok_state.get("strip_accents" , lowerCamelCase__ ) != strip_accents
):
_UpperCAmelCase : str = getattr(lowerCamelCase__ , pre_tok_state.pop("type" ) )
_UpperCAmelCase : Tuple = do_lower_case
_UpperCAmelCase : List[str] = strip_accents
_UpperCAmelCase : Dict = pre_tok_class(**lowerCamelCase__ )
_UpperCAmelCase : int = do_lower_case
def __getstate__( self : str ) ->List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] = self.__dict__.copy()
_UpperCAmelCase : List[Any] = BertPreTokenizer()
return state
def __setstate__( self : Union[str, Any] , lowerCamelCase__ : Any ) ->int:
'''simple docstring'''
_UpperCAmelCase : Dict = d
_UpperCAmelCase : Union[str, Any] = self.__dict__["_tokenizer"].get_vocab()
_UpperCAmelCase : Dict = PreTokenizer.custom(JiebaPreTokenizer(lowerCamelCase__ ) )
def lowerCAmelCase__ ( self : List[str] , lowerCamelCase__ : List[str] , lowerCamelCase__ : int=None ) ->str:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = [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 : List[Any] , lowerCamelCase__ : List[int] , lowerCamelCase__ : Optional[List[int]] = None ) ->List[int]:
'''simple docstring'''
_UpperCAmelCase : 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 ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase__ ( self : Optional[int] , lowerCamelCase__ : str , lowerCamelCase__ : Optional[str] = None ) ->Tuple[str]:
'''simple docstring'''
_UpperCAmelCase : Any = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
def lowerCAmelCase__ ( self : int , lowerCamelCase__ : Any , lowerCamelCase__ : Dict=None , lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : List[str]=False , **lowerCamelCase__ : Optional[Any] , ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = BertPreTokenizer()
return super().save_pretrained(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ )
| 234 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
lowerCamelCase__ = None
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
lowerCamelCase__ = {
'vocab_file': {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
},
'tokenizer_file': {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
},
}
lowerCamelCase__ = {
'google/fnet-base': 512,
'google/fnet-large': 512,
}
lowerCamelCase__ = '▁'
class lowerCAmelCase__ ( UpperCAmelCase__ ):
lowerCAmelCase : Dict = VOCAB_FILES_NAMES
lowerCAmelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : Optional[int] = ["input_ids", "token_type_ids"]
lowerCAmelCase : Optional[Any] = FNetTokenizer
def __init__( self : Dict , lowerCamelCase__ : int=None , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Any=False , lowerCamelCase__ : List[str]=True , lowerCamelCase__ : Dict=True , lowerCamelCase__ : str="<unk>" , lowerCamelCase__ : List[str]="[SEP]" , lowerCamelCase__ : Union[str, Any]="<pad>" , lowerCamelCase__ : Optional[Any]="[CLS]" , lowerCamelCase__ : Any="[MASK]" , **lowerCamelCase__ : Any , ) ->List[Any]:
'''simple docstring'''
_UpperCAmelCase : Dict = (
AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ , normalized=lowerCamelCase__ )
if isinstance(lowerCamelCase__ , lowerCamelCase__ )
else mask_token
)
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , remove_space=lowerCamelCase__ , keep_accents=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , **lowerCamelCase__ , )
_UpperCAmelCase : Optional[Any] = do_lower_case
_UpperCAmelCase : Tuple = remove_space
_UpperCAmelCase : List[Any] = keep_accents
_UpperCAmelCase : Tuple = vocab_file
_UpperCAmelCase : str = False if not self.vocab_file else True
def lowerCAmelCase__ ( self : str , lowerCamelCase__ : List[int] , lowerCamelCase__ : Optional[List[int]] = None ) ->List[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = [self.sep_token_id]
_UpperCAmelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCAmelCase__ ( self : int , lowerCamelCase__ : List[int] , lowerCamelCase__ : Optional[List[int]] = None ) ->List[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = [self.sep_token_id]
_UpperCAmelCase : Optional[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 ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase__ ( self : Dict , lowerCamelCase__ : str , lowerCamelCase__ : Optional[str] = None ) ->Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : Union[str, Any] = os.path.join(
lowerCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase__ ):
copyfile(self.vocab_file , lowerCamelCase__ )
return (out_vocab_file,)
| 234 | 1 |
from __future__ import annotations
import unittest
from transformers import 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 numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class lowercase_ :
def __init__( self , lowercase_ , lowercase_=13 , lowercase_=7 , lowercase_=True , lowercase_=True , lowercase_=True , lowercase_=True , lowercase_=99 , lowercase_=32 , lowercase_=2 , lowercase_=4 , lowercase_=37 , lowercase_="gelu" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=512 , lowercase_=16 , lowercase_=2 , lowercase_=0.02 , lowercase_=3 , lowercase_=4 , lowercase_=None , lowercase_=0 , ):
_snake_case : Tuple = parent
_snake_case : List[Any] = batch_size
_snake_case : int = seq_length
_snake_case : Union[str, Any] = is_training
_snake_case : int = use_input_mask
_snake_case : Optional[int] = use_token_type_ids
_snake_case : int = use_labels
_snake_case : Optional[Any] = vocab_size
_snake_case : int = hidden_size
_snake_case : int = num_hidden_layers
_snake_case : Optional[Any] = num_attention_heads
_snake_case : Dict = intermediate_size
_snake_case : int = hidden_act
_snake_case : Tuple = hidden_dropout_prob
_snake_case : Union[str, Any] = attention_probs_dropout_prob
_snake_case : int = max_position_embeddings
_snake_case : str = type_vocab_size
_snake_case : Any = type_sequence_label_size
_snake_case : str = initializer_range
_snake_case : int = num_labels
_snake_case : Optional[int] = num_choices
_snake_case : str = scope
_snake_case : Optional[int] = projection_dim
def UpperCamelCase ( self ):
_snake_case : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case : Tuple = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
_snake_case : str = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case : Optional[int] = None
if self.use_token_type_ids:
_snake_case : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case : Tuple = None
_snake_case : Dict = None
_snake_case : Optional[int] = None
if self.use_labels:
_snake_case : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case : Dict = ids_tensor([self.batch_size] , self.num_choices )
_snake_case : str = BertConfig(
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=lowercase_ , initializer_range=self.initializer_range , )
_snake_case : Tuple = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
_snake_case : Any = TFDPRContextEncoder(config=lowercase_ )
_snake_case : Tuple = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ )
_snake_case : int = model(lowercase_ , token_type_ids=lowercase_ )
_snake_case : Optional[Any] = model(lowercase_ )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
_snake_case : Any = TFDPRQuestionEncoder(config=lowercase_ )
_snake_case : Union[str, Any] = model(lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ )
_snake_case : Optional[int] = model(lowercase_ , token_type_ids=lowercase_ )
_snake_case : List[str] = model(lowercase_ )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
_snake_case : str = TFDPRReader(config=lowercase_ )
_snake_case : Dict = model(lowercase_ , attention_mask=lowercase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def UpperCamelCase ( self ):
_snake_case : str = self.prepare_config_and_inputs()
(
(
_snake_case
) ,(
_snake_case
) ,(
_snake_case
) ,(
_snake_case
) ,(
_snake_case
) ,(
_snake_case
) ,(
_snake_case
) ,
) : Dict = config_and_inputs
_snake_case : List[str] = {"input_ids": input_ids}
return config, inputs_dict
@require_tf
class lowercase_ ( __snake_case , __snake_case , unittest.TestCase ):
_lowerCamelCase = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
_lowerCamelCase = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def UpperCamelCase ( self ):
_snake_case : Dict = TFDPRModelTester(self )
_snake_case : List[str] = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def UpperCamelCase ( self ):
self.config_tester.run_common_tests()
def UpperCamelCase ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*lowercase_ )
def UpperCamelCase ( self ):
_snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*lowercase_ )
def UpperCamelCase ( self ):
_snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*lowercase_ )
@slow
def UpperCamelCase ( self ):
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : List[str] = TFDPRContextEncoder.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : Optional[Any] = TFDPRContextEncoder.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : Tuple = TFDPRQuestionEncoder.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case : str = TFDPRReader.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_tf
class lowercase_ ( unittest.TestCase ):
@slow
def UpperCamelCase ( self ):
_snake_case : Any = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base" )
_snake_case : List[str] = tf.constant(
[[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]] ) # [CLS] hello, is my dog cute? [SEP]
_snake_case : Any = model(lowercase_ )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
_snake_case : Optional[int] = tf.constant(
[
[
0.03_236_253,
0.12_753_335,
0.16_818_509,
0.00_279_786,
0.3_896_933,
0.24_264_945,
0.2_178_971,
-0.02_335_227,
-0.08_481_959,
-0.14_324_117,
]
] )
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4 ) ) | 284 | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case : str = transformerDimSize
_snake_case : Union[str, Any] = imageDimSize
super().__init__(**lowercase_ )
class lowercase_ ( __snake_case ):
_lowerCamelCase = MCLIPConfig
def __init__( self , lowercase_ , *lowercase_ , **lowercase_ ):
super().__init__(lowercase_ , *lowercase_ , **lowercase_ )
_snake_case : List[Any] = XLMRobertaModel(lowercase_ )
_snake_case : int = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def UpperCamelCase ( self , lowercase_ , lowercase_ ):
_snake_case : Tuple = self.transformer(input_ids=lowercase_ , attention_mask=lowercase_ )[0]
_snake_case : Tuple = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(lowercase_ ), embs | 284 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Optional[int] = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = "van"
def __init__( self , UpperCamelCase=224 , UpperCamelCase=3 , UpperCamelCase=[7, 3, 3, 3] , UpperCamelCase=[4, 2, 2, 2] , UpperCamelCase=[64, 128, 320, 512] , UpperCamelCase=[3, 3, 12, 3] , UpperCamelCase=[8, 8, 4, 4] , UpperCamelCase="gelu" , UpperCamelCase=0.02 , UpperCamelCase=1e-6 , UpperCamelCase=1e-2 , UpperCamelCase=0.0 , UpperCamelCase=0.0 , **UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__A )
lowerCamelCase_ = image_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = patch_sizes
lowerCamelCase_ = strides
lowerCamelCase_ = hidden_sizes
lowerCamelCase_ = depths
lowerCamelCase_ = mlp_ratios
lowerCamelCase_ = hidden_act
lowerCamelCase_ = initializer_range
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = layer_scale_init_value
lowerCamelCase_ = drop_path_rate
lowerCamelCase_ = dropout_rate
| 55 |
def lowercase_( SCREAMING_SNAKE_CASE_ = 4000000 ):
'''simple docstring'''
lowerCamelCase : Any = [0, 1]
lowerCamelCase : Union[str, Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
lowerCamelCase : Union[str, Any] = 0
for j in range(len(SCREAMING_SNAKE_CASE_ ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 283 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num_examples=42 , dataset_name='''my_dataset''' )} ),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num_examples=42 )} ),
SplitDict({'''train''': SplitInfo()} ),
] , )
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = split_dict._to_yaml_list()
assert len(_A ) == len(_A )
lowerCAmelCase_ = SplitDict._from_yaml_list(_A )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
lowerCAmelCase_ = None
# the split name of split_dict takes over the name of the split info object
lowerCAmelCase_ = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
'''split_info''' , [SplitInfo(), SplitInfo(dataset_name=_A ), SplitInfo(dataset_name='''my_dataset''' )] )
def __UpperCamelCase ( _A ):
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
lowerCAmelCase_ = asdict(SplitDict({'''train''': split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 365 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [int(_A ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(_A ) == 4 and all(0 <= int(_A ) <= 254 for octet in octets )
if __name__ == "__main__":
_A = input().strip()
_A = '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
print(f"{ip} is a {valid_or_invalid} IP v4 address.")
| 167 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__ ( A__ , A__ , A__ , A__ , A__ ) -> List[Any]:
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
snake_case__ : Union[str, Any] = TapasConfig.from_json_file(A__ )
# set absolute/relative position embeddings parameter
snake_case__ : Optional[int] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
snake_case__ : Dict = TapasForQuestionAnswering(config=A__ )
elif task == "WTQ":
# run_task_main.py hparams
snake_case__ : Optional[int] = 4
snake_case__ : Tuple = True
# hparam_utils.py hparams
snake_case__ : Tuple = 0.6_6_4_6_9_4
snake_case__ : List[Any] = 0.2_0_7_9_5_1
snake_case__ : Dict = 0.1_2_1_1_9_4
snake_case__ : Optional[Any] = True
snake_case__ : Union[str, Any] = True
snake_case__ : List[Any] = False
snake_case__ : str = 0.0_3_5_2_5_1_3
snake_case__ : List[Any] = TapasForQuestionAnswering(config=A__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
snake_case__ : Optional[int] = 4
snake_case__ : Tuple = False
# hparam_utils.py hparams
snake_case__ : Union[str, Any] = 3_6.4_5_1_9
snake_case__ : List[str] = 0.9_0_3_4_2_1
snake_case__ : int = 2_2_2.0_8_8
snake_case__ : Tuple = True
snake_case__ : Union[str, Any] = True
snake_case__ : str = True
snake_case__ : Any = 0.7_6_3_1_4_1
snake_case__ : Any = TapasForQuestionAnswering(config=A__ )
elif task == "TABFACT":
snake_case__ : Dict = TapasForSequenceClassification(config=A__ )
elif task == "MLM":
snake_case__ : Dict = TapasForMaskedLM(config=A__ )
elif task == "INTERMEDIATE_PRETRAINING":
snake_case__ : Union[str, Any] = TapasModel(config=A__ )
else:
raise ValueError(F"""Task {task} not supported.""" )
print(F"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(A__ , A__ , A__ )
# Save pytorch-model (weights and configuration)
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(A__ )
# Save tokenizer files
print(F"""Save tokenizer files to {pytorch_dump_path}""" )
snake_case__ : int = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' , model_max_length=512 )
tokenizer.save_pretrained(A__ )
print('Used relative position embeddings:' , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
lowerCAmelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowerCAmelCase__ : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 143 | import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
lowerCAmelCase__ : Union[str, Any] = '''http://www.mocksite.com/file1.txt'''
lowerCAmelCase__ : Optional[Any] = '''"text": ["foo", "foo"]'''
lowerCAmelCase__ : List[str] = '''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''
class __snake_case :
__lowerCamelCase = 200
__lowerCamelCase = {"""Content-Length""": """100"""}
__lowerCamelCase = {}
def __a ( self , **__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
return [bytes(__UpperCamelCase , 'utf-8' )]
def UpperCamelCase__ ( *A__ , **A__ ) -> Optional[Any]:
return MockResponse()
@pytest.mark.parametrize('urls_type' , [str, list, dict] )
def UpperCamelCase__ ( A__ , A__ , A__ ) -> Any:
import requests
monkeypatch.setattr(A__ , 'request' , A__ )
snake_case__ : Any = URL
if issubclass(A__ , A__ ):
snake_case__ : Optional[Any] = url
elif issubclass(A__ , A__ ):
snake_case__ : Dict = [url]
elif issubclass(A__ , A__ ):
snake_case__ : Any = {'train': url}
snake_case__ : Union[str, Any] = 'dummy'
snake_case__ : List[str] = 'downloads'
snake_case__ : int = tmp_path
snake_case__ : Tuple = DownloadConfig(
cache_dir=os.path.join(A__ , A__ ) , use_etag=A__ , )
snake_case__ : Any = DownloadManager(dataset_name=A__ , download_config=A__ )
snake_case__ : Any = dl_manager.download(A__ )
snake_case__ : Dict = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(A__ , A__ ):
snake_case__ : int = [downloaded_paths]
snake_case__ : Any = [urls]
elif isinstance(A__ , A__ ):
assert "train" in downloaded_paths.keys()
snake_case__ : Union[str, Any] = downloaded_paths.values()
snake_case__ : Any = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(A__ , A__ ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
snake_case__ : int = Path(A__ )
snake_case__ : Optional[int] = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
snake_case__ : Optional[Any] = downloaded_path.read_text()
assert content == CONTENT
snake_case__ : int = downloaded_path.with_suffix('.json' )
assert metadata_downloaded_path.exists()
snake_case__ : int = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('paths_type' , [str, list, dict] )
def UpperCamelCase__ ( A__ , A__ , A__ ) -> Any:
snake_case__ : Tuple = str(A__ )
if issubclass(A__ , A__ ):
snake_case__ : Dict = filename
elif issubclass(A__ , A__ ):
snake_case__ : Any = [filename]
elif issubclass(A__ , A__ ):
snake_case__ : Dict = {'train': filename}
snake_case__ : Union[str, Any] = 'dummy'
snake_case__ : List[Any] = xz_file.parent
snake_case__ : Dict = 'extracted'
snake_case__ : List[Any] = DownloadConfig(
cache_dir=A__ , use_etag=A__ , )
snake_case__ : Optional[int] = DownloadManager(dataset_name=A__ , download_config=A__ )
snake_case__ : Optional[Any] = dl_manager.extract(A__ )
snake_case__ : Union[str, Any] = paths
for extracted_paths in [extracted_paths]:
if isinstance(A__ , A__ ):
snake_case__ : str = [extracted_paths]
snake_case__ : Dict = [paths]
elif isinstance(A__ , A__ ):
assert "train" in extracted_paths.keys()
snake_case__ : Any = extracted_paths.values()
snake_case__ : Dict = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(A__ , A__ ):
assert extracted_path == dl_manager.extracted_paths[input_path]
snake_case__ : Optional[int] = Path(A__ )
snake_case__ : Any = extracted_path.parts
assert parts[-1] == hash_url_to_filename(A__ , etag=A__ )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
snake_case__ : Dict = extracted_path.read_text()
snake_case__ : Union[str, Any] = text_file.read_text()
assert extracted_file_content == expected_file_content
def UpperCamelCase__ ( A__ , A__ ) -> Union[str, Any]:
assert path.endswith('.jsonl' )
for num_items, line in enumerate(A__ , start=1 ):
snake_case__ : Optional[int] = json.loads(line.decode('utf-8' ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize('archive_jsonl' , ['tar_jsonl_path', 'zip_jsonl_path'] )
def UpperCamelCase__ ( A__ , A__ ) -> Optional[Any]:
snake_case__ : Tuple = request.getfixturevalue(A__ )
snake_case__ : Optional[int] = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(A__ ) , start=1 ):
_test_jsonl(A__ , A__ )
assert num_jsonl == 2
@pytest.mark.parametrize('archive_nested_jsonl' , ['tar_nested_jsonl_path', 'zip_nested_jsonl_path'] )
def UpperCamelCase__ ( A__ , A__ ) -> int:
snake_case__ : List[Any] = request.getfixturevalue(A__ )
snake_case__ : str = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(A__ ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(A__ ) , start=1 ):
_test_jsonl(A__ , A__ )
assert num_tar == 1
assert num_jsonl == 2
def UpperCamelCase__ ( A__ ) -> Union[str, Any]:
snake_case__ : Dict = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(A__ ) , start=1 ):
assert os.path.basename(A__ ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 143 | 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
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def lowercase__ ( __snake_case : Accelerator , __snake_case : int = 16 , __snake_case : str = "bert-base-cased" ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = AutoTokenizer.from_pretrained(__snake_case )
UpperCAmelCase_ : Union[str, Any] = load_dataset('glue' , 'mrpc' )
def tokenize_function(__snake_case : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_ : Dict = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__snake_case , max_length=__snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase_ : str = datasets.map(
__snake_case , batched=__snake_case , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=__snake_case )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase_ : Tuple = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(__snake_case : 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(__snake_case , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(__snake_case , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
UpperCAmelCase_ : Any = DataLoader(
tokenized_datasets['train'] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
UpperCAmelCase_ : Optional[Any] = DataLoader(
tokenized_datasets['validation'] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
return train_dataloader, eval_dataloader
def lowercase__ ( __snake_case : int , __snake_case : str ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase_ : List[Any] = config['lr']
UpperCAmelCase_ : Dict = int(config['num_epochs'] )
UpperCAmelCase_ : List[Any] = int(config['seed'] )
UpperCAmelCase_ : List[Any] = int(config['batch_size'] )
UpperCAmelCase_ : Optional[Any] = args.model_name_or_path
set_seed(__snake_case )
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = get_dataloaders(__snake_case , __snake_case , __snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase_ : Dict = AutoModelForSequenceClassification.from_pretrained(__snake_case , return_dict=__snake_case )
# Instantiate optimizer
UpperCAmelCase_ : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase_ : List[str] = optimizer_cls(params=model.parameters() , lr=__snake_case )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase_ : str = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
UpperCAmelCase_ : Dict = 1
UpperCAmelCase_ : Union[str, Any] = (len(__snake_case ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCAmelCase_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=0 , num_training_steps=__snake_case , )
else:
UpperCAmelCase_ : Union[str, Any] = DummyScheduler(__snake_case , total_num_steps=__snake_case , 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.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = accelerator.prepare(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase_ : Any = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase_ : Union[str, Any] = 0
# Now we train the model
UpperCAmelCase_ : str = evaluate.load('glue' , 'mrpc' )
UpperCAmelCase_ : Optional[Any] = 0
UpperCAmelCase_ : List[Any] = {}
for epoch in range(__snake_case , __snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
UpperCAmelCase_ : Union[str, Any] = model(**__snake_case )
UpperCAmelCase_ : int = outputs.loss
UpperCAmelCase_ : Any = loss / gradient_accumulation_steps
accelerator.backward(__snake_case )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
UpperCAmelCase_ : Optional[int] = 0
for step, batch in enumerate(__snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase_ : Any = model(**__snake_case )
UpperCAmelCase_ : Optional[Any] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__snake_case ) - 1:
UpperCAmelCase_ : Tuple = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase_ : List[Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
UpperCAmelCase_ : Union[str, Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"epoch {epoch}:" , __snake_case )
UpperCAmelCase_ : Optional[Any] = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
UpperCAmelCase_ : str = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(__snake_case , __snake_case )
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=__snake_case , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=__snake_case , )
parser.add_argument(
'--output_dir' , type=__snake_case , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=__snake_case , default=__snake_case , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=__snake_case , default=3 , help='Number of train epochs.' , )
UpperCAmelCase_ : Dict = parser.parse_args()
UpperCAmelCase_ : str = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main()
| 145 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : Any = (DDPMParallelScheduler,)
def __UpperCAmelCase ( self , **_UpperCamelCase ) -> Any:
UpperCAmelCase_ : List[str] = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.00_01,
'beta_end': 0.02,
'beta_schedule': 'linear',
'variance_type': 'fixed_small',
'clip_sample': True,
}
config.update(**_UpperCamelCase )
return config
def __UpperCAmelCase ( self ) -> Any:
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> int:
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_UpperCamelCase , beta_end=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Any:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Optional[Any]:
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Optional[int]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
self.check_over_configs(thresholding=_UpperCamelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_UpperCamelCase , prediction_type=_UpperCamelCase , sample_max_value=_UpperCamelCase , )
def __UpperCAmelCase ( self ) -> Any:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase_ : Dict = self.scheduler_classes[0]
UpperCAmelCase_ : int = self.get_scheduler_config()
UpperCAmelCase_ : Union[str, Any] = scheduler_class(**_UpperCamelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.02 ) ) < 1E-5
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase_ : List[Any] = self.scheduler_classes[0]
UpperCAmelCase_ : Any = self.get_scheduler_config()
UpperCAmelCase_ : Union[str, Any] = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Optional[Any] = len(_UpperCamelCase )
UpperCAmelCase_ : Any = self.dummy_model()
UpperCAmelCase_ : List[Any] = self.dummy_sample_deter
UpperCAmelCase_ : Union[str, Any] = self.dummy_sample_deter + 0.1
UpperCAmelCase_ : str = self.dummy_sample_deter - 0.1
UpperCAmelCase_ : Tuple = samplea.shape[0]
UpperCAmelCase_ : Tuple = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCAmelCase_ : str = torch.arange(_UpperCamelCase )[0:3, None].repeat(1 , _UpperCamelCase )
UpperCAmelCase_ : Any = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCAmelCase_ : Dict = scheduler.batch_step_no_noise(_UpperCamelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCAmelCase_ : List[Any] = torch.sum(torch.abs(_UpperCamelCase ) )
UpperCAmelCase_ : Any = torch.mean(torch.abs(_UpperCamelCase ) )
assert abs(result_sum.item() - 11_53.18_33 ) < 1E-2
assert abs(result_mean.item() - 0.50_05 ) < 1E-3
def __UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCAmelCase_ : List[str] = self.scheduler_classes[0]
UpperCAmelCase_ : List[Any] = self.get_scheduler_config()
UpperCAmelCase_ : Any = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = len(_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = self.dummy_model()
UpperCAmelCase_ : Optional[int] = self.dummy_sample_deter
UpperCAmelCase_ : Union[str, Any] = torch.manual_seed(0 )
for t in reversed(range(_UpperCamelCase ) ):
# 1. predict noise residual
UpperCAmelCase_ : Union[str, Any] = model(_UpperCamelCase , _UpperCamelCase )
# 2. predict previous mean of sample x_t-1
UpperCAmelCase_ : str = scheduler.step(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , generator=_UpperCamelCase ).prev_sample
UpperCAmelCase_ : str = pred_prev_sample
UpperCAmelCase_ : Optional[int] = torch.sum(torch.abs(_UpperCamelCase ) )
UpperCAmelCase_ : Optional[int] = torch.mean(torch.abs(_UpperCamelCase ) )
assert abs(result_sum.item() - 2_58.96_06 ) < 1E-2
assert abs(result_mean.item() - 0.33_72 ) < 1E-3
def __UpperCAmelCase ( self ) -> int:
UpperCAmelCase_ : List[Any] = self.scheduler_classes[0]
UpperCAmelCase_ : Optional[int] = self.get_scheduler_config(prediction_type='v_prediction' )
UpperCAmelCase_ : Tuple = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Tuple = len(_UpperCamelCase )
UpperCAmelCase_ : Tuple = self.dummy_model()
UpperCAmelCase_ : List[str] = self.dummy_sample_deter
UpperCAmelCase_ : Any = torch.manual_seed(0 )
for t in reversed(range(_UpperCamelCase ) ):
# 1. predict noise residual
UpperCAmelCase_ : Optional[Any] = model(_UpperCamelCase , _UpperCamelCase )
# 2. predict previous mean of sample x_t-1
UpperCAmelCase_ : Any = scheduler.step(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , generator=_UpperCamelCase ).prev_sample
UpperCAmelCase_ : List[str] = pred_prev_sample
UpperCAmelCase_ : Optional[int] = torch.sum(torch.abs(_UpperCamelCase ) )
UpperCAmelCase_ : Any = torch.mean(torch.abs(_UpperCamelCase ) )
assert abs(result_sum.item() - 2_02.02_96 ) < 1E-2
assert abs(result_mean.item() - 0.26_31 ) < 1E-3
def __UpperCAmelCase ( self ) -> List[Any]:
UpperCAmelCase_ : Any = self.scheduler_classes[0]
UpperCAmelCase_ : Optional[Any] = self.get_scheduler_config()
UpperCAmelCase_ : str = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=_UpperCamelCase )
UpperCAmelCase_ : Tuple = scheduler.timesteps
for i, timestep in enumerate(_UpperCamelCase ):
if i == len(_UpperCamelCase ) - 1:
UpperCAmelCase_ : List[str] = -1
else:
UpperCAmelCase_ : int = timesteps[i + 1]
UpperCAmelCase_ : int = scheduler.previous_timestep(_UpperCamelCase )
UpperCAmelCase_ : Optional[Any] = prev_t.item()
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
def __UpperCAmelCase ( self ) -> List[Any]:
UpperCAmelCase_ : str = self.scheduler_classes[0]
UpperCAmelCase_ : Tuple = self.get_scheduler_config()
UpperCAmelCase_ : Union[str, Any] = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(_UpperCamelCase , msg='`custom_timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Tuple:
UpperCAmelCase_ : Any = self.scheduler_classes[0]
UpperCAmelCase_ : List[str] = self.get_scheduler_config()
UpperCAmelCase_ : List[str] = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Tuple = [1_0_0, 8_7, 5_0, 1, 0]
UpperCAmelCase_ : Optional[Any] = len(_UpperCamelCase )
with self.assertRaises(_UpperCamelCase , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=_UpperCamelCase , timesteps=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase_ : List[str] = self.scheduler_classes[0]
UpperCAmelCase_ : Any = self.get_scheduler_config()
UpperCAmelCase_ : Optional[Any] = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Dict = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_UpperCamelCase , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=_UpperCamelCase )
| 145 | 1 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :list ):
_enforce_args(snake_case_ , snake_case_ )
if n == 0:
return 0
__UpperCAmelCase = float('''-inf''' )
for i in range(1 , n + 1 ):
__UpperCAmelCase = max(
snake_case_ , prices[i - 1] + naive_cut_rod_recursive(n - i , snake_case_ ) )
return max_revue
def lowercase__ ( snake_case_ :int , snake_case_ :list ):
_enforce_args(snake_case_ , snake_case_ )
__UpperCAmelCase = [float('''-inf''' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(snake_case_ , snake_case_ , snake_case_ )
def lowercase__ ( snake_case_ :int , snake_case_ :list , snake_case_ :list ):
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
__UpperCAmelCase = float('''-inf''' )
for i in range(1 , n + 1 ):
__UpperCAmelCase = max(
snake_case_ , prices[i - 1] + _top_down_cut_rod_recursive(n - i , snake_case_ , snake_case_ ) , )
__UpperCAmelCase = max_revenue
return max_rev[n]
def lowercase__ ( snake_case_ :int , snake_case_ :list ):
_enforce_args(snake_case_ , snake_case_ )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
__UpperCAmelCase = [float('''-inf''' ) for _ in range(n + 1 )]
__UpperCAmelCase = 0
for i in range(1 , n + 1 ):
__UpperCAmelCase = max_rev[i]
for j in range(1 , i + 1 ):
__UpperCAmelCase = max(snake_case_ , prices[j - 1] + max_rev[i - j] )
__UpperCAmelCase = max_revenue_i
return max_rev[n]
def lowercase__ ( snake_case_ :int , snake_case_ :list ):
if n < 0:
__UpperCAmelCase = F'''n must be greater than or equal to 0. Got n = {n}'''
raise ValueError(snake_case_ )
if n > len(snake_case_ ):
__UpperCAmelCase = (
'''Each integral piece of rod must have a corresponding price. '''
F'''Got n = {n} but length of prices = {len(snake_case_ )}'''
)
raise ValueError(snake_case_ )
def lowercase__ ( ):
__UpperCAmelCase = [6, 10, 12, 15, 20, 23]
__UpperCAmelCase = len(snake_case_ )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
__UpperCAmelCase = 36
__UpperCAmelCase = top_down_cut_rod(snake_case_ , snake_case_ )
__UpperCAmelCase = bottom_up_cut_rod(snake_case_ , snake_case_ )
__UpperCAmelCase = naive_cut_rod_recursive(snake_case_ , snake_case_ )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 332 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
def lowercase__ ( snake_case_ :Union[tf.Tensor, np.ndarray] ):
if isinstance(snake_case_ , np.ndarray ):
return list(tensor.shape )
__UpperCAmelCase = tf.shape(snake_case_ )
if tensor.shape == tf.TensorShape(snake_case_ ):
return dynamic
__UpperCAmelCase = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(snake_case_ )]
def lowercase__ ( snake_case_ :tf.Tensor , snake_case_ :Optional[int] = None , snake_case_ :Optional[str] = None ):
return tf.nn.softmax(logits=logits + 1E-9 , axis=snake_case_ , name=snake_case_ )
def lowercase__ ( snake_case_ :int , snake_case_ :Union[str, Any] , snake_case_ :str , snake_case_ :Union[str, Any]=1E-5 , snake_case_ :List[str]=-1 ):
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case_ , snake_case_ ):
raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )
# Get mean and variance on the axis to be normalized
__UpperCAmelCase , __UpperCAmelCase = tf.nn.moments(snake_case_ , axes=[axis] , keepdims=snake_case_ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
__UpperCAmelCase = [1] * inputs.shape.rank
__UpperCAmelCase = shape_list(snake_case_ )[axis]
__UpperCAmelCase = tf.reshape(snake_case_ , snake_case_ )
__UpperCAmelCase = tf.reshape(snake_case_ , snake_case_ )
# Compute layer normalization using the batch_normalization
# function.
__UpperCAmelCase = tf.nn.batch_normalization(
snake_case_ , snake_case_ , snake_case_ , offset=snake_case_ , scale=snake_case_ , variance_epsilon=snake_case_ , )
return outputs
def lowercase__ ( snake_case_ :Optional[int] , snake_case_ :List[str]=0 , snake_case_ :Optional[Any]=-1 ):
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
__UpperCAmelCase = tf.shape(snake_case_ )
__UpperCAmelCase = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__UpperCAmelCase = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(snake_case_ , snake_case_ )
def lowercase__ ( snake_case_ :tf.Tensor ):
if not isinstance(snake_case_ , tf.Tensor ):
__UpperCAmelCase = tf.convert_to_tensor(snake_case_ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__UpperCAmelCase = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__UpperCAmelCase = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
__UpperCAmelCase = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowercase__ ( snake_case_ :tf.Tensor , snake_case_ :int , snake_case_ :str = "input_ids" ):
tf.debugging.assert_less(
snake_case_ , tf.cast(snake_case_ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case_ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowercase__ ( snake_case_ :List[Any] , snake_case_ :List[Any] , snake_case_ :List[str] ):
__UpperCAmelCase = 64_512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
__UpperCAmelCase = [x for x in data if len(snake_case_ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'''The following attributes cannot be saved to HDF5 file because '''
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
__UpperCAmelCase = np.asarray(snake_case_ )
__UpperCAmelCase = 1
__UpperCAmelCase = np.array_split(snake_case_ , snake_case_ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
__UpperCAmelCase = np.array_split(snake_case_ , snake_case_ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(snake_case_ ):
__UpperCAmelCase = chunk_data
else:
__UpperCAmelCase = data
def lowercase__ ( snake_case_ :str , snake_case_ :List[str] ):
if name in group.attrs:
__UpperCAmelCase = [n.decode('''utf8''' ) if hasattr(snake_case_ , '''decode''' ) else n for n in group.attrs[name]]
else:
__UpperCAmelCase = []
__UpperCAmelCase = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(snake_case_ , '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def lowercase__ ( snake_case_ :Tuple ):
def _expand_single_ad_tensor(snake_case_ :Optional[int] ):
if isinstance(snake_case_ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(snake_case_ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , snake_case_ )
| 332 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A = logging.get_logger(__name__)
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> None:
warnings.warn(
"""The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use MobileViTImageProcessor instead.""" , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 357 |
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
lowerCamelCase = None
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = None
lowerCamelCase = None
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = True
lowerCamelCase = None
lowerCamelCase = 1
lowerCamelCase = None
lowerCamelCase = False
lowerCamelCase = None
lowerCamelCase = None
def _lowerCAmelCase ( self ) -> "DownloadConfig":
return self.__class__(**{k: copy.deepcopy(__UpperCAmelCase ) for k, v in self.__dict__.items()} )
| 341 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : Any , __snake_case : int ) -> None:
UpperCAmelCase : List[Any] = value
UpperCAmelCase : Node | None = None
UpperCAmelCase : Node | None = None
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : str , __snake_case : Node ) -> None:
UpperCAmelCase : Optional[int] = tree
def A ( self : Union[str, Any] , __snake_case : Node | None ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : List[Any] ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 23 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ) -> Optional[int]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
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 snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ) -> Dict:
UpperCAmelCase : Dict = tmp_path / '''cache'''
UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase : str = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read()
_check_json_dataset(_lowerCAmelCase , _lowerCAmelCase )
@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 snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : int ) -> Optional[int]:
UpperCAmelCase : Any = tmp_path / '''cache'''
UpperCAmelCase : Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : Any = features.copy() if features else default_expected_features
UpperCAmelCase : List[Any] = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Dict = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_json_dataset(_lowerCAmelCase , _lowerCAmelCase )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> Tuple:
UpperCAmelCase : Optional[Any] = tmp_path / '''cache'''
UpperCAmelCase : Optional[int] = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
UpperCAmelCase : int = features.copy() if features else default_expected_features
UpperCAmelCase : Any = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Tuple = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> Union[str, Any]:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
UpperCAmelCase : Tuple = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
UpperCAmelCase : List[str] = features.copy()
UpperCAmelCase : Union[str, Any] = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Tuple = tmp_path / '''cache'''
UpperCAmelCase : List[str] = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> Optional[Any]:
UpperCAmelCase : Any = tmp_path / '''cache'''
UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : List[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , split=_lowerCAmelCase ).read()
_check_json_dataset(_lowerCAmelCase , _lowerCAmelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def snake_case_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> Dict:
if issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : str = jsonl_path
elif issubclass(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase : Dict = [jsonl_path]
UpperCAmelCase : int = tmp_path / '''cache'''
UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : Optional[int] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_json_dataset(_lowerCAmelCase , _lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict=("train",) ) -> Union[str, Any]:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
for split in splits:
UpperCAmelCase : List[str] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ) -> Any:
UpperCAmelCase : Optional[Any] = tmp_path / '''cache'''
UpperCAmelCase : List[str] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase : Optional[int] = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read()
_check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase )
@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 snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> int:
UpperCAmelCase : Dict = tmp_path / '''cache'''
UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : Optional[int] = features.copy() if features else default_expected_features
UpperCAmelCase : Union[str, Any] = (
Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase : Tuple = JsonDatasetReader({'''train''': jsonl_path} , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict ) -> Union[str, Any]:
if split:
UpperCAmelCase : Optional[int] = {split: jsonl_path}
else:
UpperCAmelCase : Any = '''train'''
UpperCAmelCase : Any = {'''train''': jsonl_path, '''test''': jsonl_path}
UpperCAmelCase : Tuple = tmp_path / '''cache'''
UpperCAmelCase : int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
UpperCAmelCase : Optional[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read()
_check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def snake_case_ ( _lowerCAmelCase : List[str] ) -> str:
return json.load(_lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : Dict ) -> str:
return [json.loads(_lowerCAmelCase ) for line in buffer]
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def A ( self : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : Optional[int] ) -> Dict:
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case ).write()
buffer.seek(0 )
UpperCAmelCase : Union[str, Any] = load_json_function(__snake_case )
assert isinstance(__snake_case , __snake_case )
assert isinstance(exported_content[0] , __snake_case )
assert len(__snake_case ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def A ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Optional[Any] ) -> List[Any]:
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case ).write()
buffer.seek(0 )
UpperCAmelCase : Union[str, Any] = load_json(__snake_case )
assert isinstance(__snake_case , __snake_case )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(__snake_case ) == 10
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def A ( self : str , __snake_case : str , __snake_case : str , __snake_case : int ) -> Any:
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , num_proc=2 ).write()
buffer.seek(0 )
UpperCAmelCase : Any = load_json_function(__snake_case )
assert isinstance(__snake_case , __snake_case )
assert isinstance(exported_content[0] , __snake_case )
assert len(__snake_case ) == 10
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def A ( self : Any , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[str] ) -> Any:
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case , num_proc=2 ).write()
buffer.seek(0 )
UpperCAmelCase : List[str] = load_json(__snake_case )
assert isinstance(__snake_case , __snake_case )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(__snake_case ) == 10
def A ( self : List[Any] , __snake_case : str ) -> Dict:
with pytest.raises(__snake_case ):
with io.BytesIO() as buffer:
JsonDatasetWriter(__snake_case , __snake_case , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def A ( self : Optional[int] , __snake_case : Any , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> Union[str, Any]:
UpperCAmelCase : List[str] = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}"""
UpperCAmelCase : List[Any] = str(shared_datadir / F"""test_file.json.{extension}""" )
JsonDatasetWriter(__snake_case , __snake_case , compression=__snake_case ).write()
with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f:
UpperCAmelCase : str = f.read()
with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f:
UpperCAmelCase : Optional[int] = f.read()
assert exported_content == original_content
| 23 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 359 |
"""simple docstring"""
def __A ( a_ :int) -> Union[str, Any]:
__a : int = []
__a : Dict = []
__a : str = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
__a : Tuple = len(a_) if (len(a_) > 7) else 7
# Print table header for output
print(
'''Symbol'''.center(8) , '''Stack'''.center(a_) , '''Postfix'''.center(a_) , sep=''' | ''' , )
print('''-''' * (print_width * 3 + 7))
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(a_) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(a_) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop()) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(a_) == 0:
stack.append(a_) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(a_) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop()) # pop stack & add to Postfix
stack.append(a_) # push x to stack
print(
x.center(8) , (''''''.join(a_)).ljust(a_) , (''''''.join(a_)).ljust(a_) , sep=''' | ''' , ) # Output in tabular format
while len(a_) > 0: # while stack is not empty
post_fix.append(stack.pop()) # pop stack & add to Postfix
print(
''' '''.center(8) , (''''''.join(a_)).ljust(a_) , (''''''.join(a_)).ljust(a_) , sep=''' | ''' , ) # Output in tabular format
return "".join(a_) # return Postfix as str
def __A ( a_ :int) -> List[Any]:
__a : Dict = list(infix[::-1]) # reverse the infix equation
for i in range(len(a_)):
if infix[i] == "(":
__a : Union[str, Any] = ''')''' # change "(" to ")"
elif infix[i] == ")":
__a : List[str] = '''(''' # change ")" to "("
return (infix_2_postfix(''''''.join(a_)))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
A = input('''\nEnter an Infix Equation = ''') # Input an Infix equation
A = ''''''.join(Infix.split()) # Remove spaces from the input
print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''') | 188 | 0 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def lowerCamelCase__ ( __lowerCamelCase : Dict , __lowerCamelCase : int ):
__UpperCAmelCase : List[Any] = checkpoint
__UpperCAmelCase : Tuple = {}
__UpperCAmelCase : Dict = vae_state_dict["""encoder.conv_in.weight"""]
__UpperCAmelCase : Optional[Any] = vae_state_dict["""encoder.conv_in.bias"""]
__UpperCAmelCase : Dict = vae_state_dict["""encoder.conv_out.weight"""]
__UpperCAmelCase : Union[str, Any] = vae_state_dict["""encoder.conv_out.bias"""]
__UpperCAmelCase : List[Any] = vae_state_dict["""encoder.norm_out.weight"""]
__UpperCAmelCase : Tuple = vae_state_dict["""encoder.norm_out.bias"""]
__UpperCAmelCase : Dict = vae_state_dict["""decoder.conv_in.weight"""]
__UpperCAmelCase : Tuple = vae_state_dict["""decoder.conv_in.bias"""]
__UpperCAmelCase : Optional[int] = vae_state_dict["""decoder.conv_out.weight"""]
__UpperCAmelCase : Optional[int] = vae_state_dict["""decoder.conv_out.bias"""]
__UpperCAmelCase : Optional[Any] = vae_state_dict["""decoder.norm_out.weight"""]
__UpperCAmelCase : Union[str, Any] = vae_state_dict["""decoder.norm_out.bias"""]
__UpperCAmelCase : Optional[int] = vae_state_dict["""quant_conv.weight"""]
__UpperCAmelCase : int = vae_state_dict["""quant_conv.bias"""]
__UpperCAmelCase : Union[str, Any] = vae_state_dict["""post_quant_conv.weight"""]
__UpperCAmelCase : Any = vae_state_dict["""post_quant_conv.bias"""]
# Retrieves the keys for the encoder down blocks only
__UpperCAmelCase : int = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """encoder.down""" in layer} )
__UpperCAmelCase : Optional[Any] = {
layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__lowerCamelCase )
}
# Retrieves the keys for the decoder up blocks only
__UpperCAmelCase : Dict = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """decoder.up""" in layer} )
__UpperCAmelCase : Optional[int] = {
layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__lowerCamelCase )
}
for i in range(__lowerCamelCase ):
__UpperCAmelCase : List[Any] = [key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]
if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
__UpperCAmelCase : Optional[Any] = vae_state_dict.pop(
f"""encoder.down.{i}.downsample.conv.weight""" )
__UpperCAmelCase : int = vae_state_dict.pop(
f"""encoder.down.{i}.downsample.conv.bias""" )
__UpperCAmelCase : Optional[int] = renew_vae_resnet_paths(__lowerCamelCase )
__UpperCAmelCase : Optional[Any] = {"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase )
__UpperCAmelCase : Tuple = [key for key in vae_state_dict if """encoder.mid.block""" in key]
__UpperCAmelCase : Optional[Any] = 2
for i in range(1 , num_mid_res_blocks + 1 ):
__UpperCAmelCase : Dict = [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]
__UpperCAmelCase : Tuple = renew_vae_resnet_paths(__lowerCamelCase )
__UpperCAmelCase : Tuple = {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase )
__UpperCAmelCase : List[Any] = [key for key in vae_state_dict if """encoder.mid.attn""" in key]
__UpperCAmelCase : str = renew_vae_attention_paths(__lowerCamelCase )
__UpperCAmelCase : List[str] = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase )
conv_attn_to_linear(__lowerCamelCase )
for i in range(__lowerCamelCase ):
__UpperCAmelCase : Optional[Any] = num_up_blocks - 1 - i
__UpperCAmelCase : Union[str, Any] = [
key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
]
if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
__UpperCAmelCase : int = vae_state_dict[
f"""decoder.up.{block_id}.upsample.conv.weight"""
]
__UpperCAmelCase : Dict = vae_state_dict[
f"""decoder.up.{block_id}.upsample.conv.bias"""
]
__UpperCAmelCase : Dict = renew_vae_resnet_paths(__lowerCamelCase )
__UpperCAmelCase : Optional[Any] = {"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase )
__UpperCAmelCase : Tuple = [key for key in vae_state_dict if """decoder.mid.block""" in key]
__UpperCAmelCase : Union[str, Any] = 2
for i in range(1 , num_mid_res_blocks + 1 ):
__UpperCAmelCase : Dict = [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]
__UpperCAmelCase : List[Any] = renew_vae_resnet_paths(__lowerCamelCase )
__UpperCAmelCase : int = {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase )
__UpperCAmelCase : Dict = [key for key in vae_state_dict if """decoder.mid.attn""" in key]
__UpperCAmelCase : List[Any] = renew_vae_attention_paths(__lowerCamelCase )
__UpperCAmelCase : List[str] = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase )
conv_attn_to_linear(__lowerCamelCase )
return new_checkpoint
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : str , ):
# Only support V1
__UpperCAmelCase : Optional[int] = requests.get(
""" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml""" )
__UpperCAmelCase : Optional[int] = io.BytesIO(r.content )
__UpperCAmelCase : Dict = OmegaConf.load(__lowerCamelCase )
__UpperCAmelCase : str = 512
__UpperCAmelCase : Any = """cuda""" if torch.cuda.is_available() else """cpu"""
if checkpoint_path.endswith("""safetensors""" ):
from safetensors import safe_open
__UpperCAmelCase : List[Any] = {}
with safe_open(__lowerCamelCase , framework="""pt""" , device="""cpu""" ) as f:
for key in f.keys():
__UpperCAmelCase : str = f.get_tensor(__lowerCamelCase )
else:
__UpperCAmelCase : Optional[int] = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )["""state_dict"""]
# Convert the VAE model.
__UpperCAmelCase : Optional[int] = create_vae_diffusers_config(__lowerCamelCase , image_size=__lowerCamelCase )
__UpperCAmelCase : Union[str, Any] = custom_convert_ldm_vae_checkpoint(__lowerCamelCase , __lowerCamelCase )
__UpperCAmelCase : Optional[int] = AutoencoderKL(**__lowerCamelCase )
vae.load_state_dict(__lowerCamelCase )
vae.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
a : List[Any] = argparse.ArgumentParser()
parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
a : Optional[int] = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 114 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a : int = logging.getLogger(__name__)
@dataclass
class a :
"""simple docstring"""
a : Optional[int] = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
a : bool = field(
default=lowercase__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
a : bool = field(
default=lowercase__ , 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.'
)
} , )
a : Optional[int] = field(
default=lowercase__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
a : Optional[int] = field(
default=lowercase__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
a : Optional[int] = field(
default=lowercase__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
@dataclass
class a :
"""simple docstring"""
a : str = field(
default=lowercase__ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
a : str = field(
default=lowercase__ , metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'} )
a : Optional[str] = field(
default=lowercase__ , metadata={'help': 'Train language if it is different from the evaluation language.'} )
a : Optional[str] = field(
default=lowercase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
a : Optional[str] = field(
default=lowercase__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
a : Optional[str] = field(
default=lowercase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
a : Optional[bool] = field(
default=lowercase__ , metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'} , )
a : bool = field(
default=lowercase__ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
a : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
a : bool = field(
default=lowercase__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
a : bool = field(
default=lowercase__ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def lowerCamelCase__ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__UpperCAmelCase : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCamelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__UpperCAmelCase : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCamelCase )
datasets.utils.logging.set_verbosity(__lowerCamelCase )
transformers.utils.logging.set_verbosity(__lowerCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
__UpperCAmelCase : Dict = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__UpperCAmelCase : Any = 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:
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 )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
__UpperCAmelCase : Tuple = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
__UpperCAmelCase : List[Any] = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase : str = train_dataset.features["""label"""].names
if training_args.do_eval:
__UpperCAmelCase : Any = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase : str = eval_dataset.features["""label"""].names
if training_args.do_predict:
__UpperCAmelCase : Optional[Any] = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase : List[str] = predict_dataset.features["""label"""].names
# Labels
__UpperCAmelCase : Tuple = len(__lowerCamelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__UpperCAmelCase : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCamelCase , idalabel={str(__lowerCamelCase ): label for i, label in enumerate(__lowerCamelCase )} , labelaid={label: i for i, label in enumerate(__lowerCamelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__UpperCAmelCase : Tuple = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , 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 , )
__UpperCAmelCase : Optional[Any] = AutoModelForSequenceClassification.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 , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
__UpperCAmelCase : List[Any] = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__UpperCAmelCase : List[Any] = False
def preprocess_function(__lowerCamelCase : int ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCamelCase , max_length=data_args.max_seq_length , truncation=__lowerCamelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
__UpperCAmelCase : int = min(len(__lowerCamelCase ) , data_args.max_train_samples )
__UpperCAmelCase : Dict = train_dataset.select(range(__lowerCamelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
__UpperCAmelCase : Union[str, Any] = train_dataset.map(
__lowerCamelCase , batched=__lowerCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCamelCase ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
__UpperCAmelCase : Tuple = min(len(__lowerCamelCase ) , data_args.max_eval_samples )
__UpperCAmelCase : List[str] = eval_dataset.select(range(__lowerCamelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
__UpperCAmelCase : Dict = eval_dataset.map(
__lowerCamelCase , batched=__lowerCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
__UpperCAmelCase : Dict = min(len(__lowerCamelCase ) , data_args.max_predict_samples )
__UpperCAmelCase : Tuple = predict_dataset.select(range(__lowerCamelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
__UpperCAmelCase : Any = predict_dataset.map(
__lowerCamelCase , batched=__lowerCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
__UpperCAmelCase : Tuple = evaluate.load("""xnli""" )
# 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(__lowerCamelCase : EvalPrediction ):
__UpperCAmelCase : Optional[Any] = p.predictions[0] if isinstance(p.predictions , __lowerCamelCase ) else p.predictions
__UpperCAmelCase : str = np.argmax(__lowerCamelCase , axis=1 )
return metric.compute(predictions=__lowerCamelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__UpperCAmelCase : Any = default_data_collator
elif training_args.fpaa:
__UpperCAmelCase : Tuple = DataCollatorWithPadding(__lowerCamelCase , pad_to_multiple_of=8 )
else:
__UpperCAmelCase : int = None
# Initialize our Trainer
__UpperCAmelCase : Union[str, Any] = Trainer(
model=__lowerCamelCase , args=__lowerCamelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCamelCase , tokenizer=__lowerCamelCase , data_collator=__lowerCamelCase , )
# Training
if training_args.do_train:
__UpperCAmelCase : List[str] = None
if training_args.resume_from_checkpoint is not None:
__UpperCAmelCase : Optional[int] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__UpperCAmelCase : Union[str, Any] = last_checkpoint
__UpperCAmelCase : Any = trainer.train(resume_from_checkpoint=__lowerCamelCase )
__UpperCAmelCase : Dict = train_result.metrics
__UpperCAmelCase : Optional[Any] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCamelCase )
)
__UpperCAmelCase : Dict = min(__lowerCamelCase , len(__lowerCamelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCamelCase )
trainer.save_metrics("""train""" , __lowerCamelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__UpperCAmelCase : Dict = trainer.evaluate(eval_dataset=__lowerCamelCase )
__UpperCAmelCase : List[str] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCamelCase )
__UpperCAmelCase : Tuple = min(__lowerCamelCase , len(__lowerCamelCase ) )
trainer.log_metrics("""eval""" , __lowerCamelCase )
trainer.save_metrics("""eval""" , __lowerCamelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Optional[Any] = trainer.predict(__lowerCamelCase , metric_key_prefix="""predict""" )
__UpperCAmelCase : int = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCamelCase )
)
__UpperCAmelCase : Optional[int] = min(__lowerCamelCase , len(__lowerCamelCase ) )
trainer.log_metrics("""predict""" , __lowerCamelCase )
trainer.save_metrics("""predict""" , __lowerCamelCase )
__UpperCAmelCase : Optional[int] = np.argmax(__lowerCamelCase , axis=1 )
__UpperCAmelCase : Tuple = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCamelCase ):
__UpperCAmelCase : Tuple = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
if __name__ == "__main__":
main()
| 114 | 1 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
def update_area_of_max_square(__UpperCAmelCase , __UpperCAmelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowerCAmelCase__ : Optional[int] = update_area_of_max_square(__UpperCAmelCase , col + 1 )
lowerCAmelCase__ : Union[str, Any] = update_area_of_max_square(row + 1 , col + 1 )
lowerCAmelCase__ : Any = update_area_of_max_square(row + 1 , __UpperCAmelCase )
if mat[row][col]:
lowerCAmelCase__ : int = 1 + min([right, diagonal, down] )
lowerCAmelCase__ : Tuple = max(largest_square_area[0] , __UpperCAmelCase )
return sub_problem_sol
else:
return 0
lowerCAmelCase__ : Union[str, Any] = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
def update_area_of_max_square_using_dp_array(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowerCAmelCase__ : List[str] = update_area_of_max_square_using_dp_array(__UpperCAmelCase , col + 1 , __UpperCAmelCase )
lowerCAmelCase__ : int = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , __UpperCAmelCase )
lowerCAmelCase__ : str = update_area_of_max_square_using_dp_array(row + 1 , __UpperCAmelCase , __UpperCAmelCase )
if mat[row][col]:
lowerCAmelCase__ : List[str] = 1 + min([right, diagonal, down] )
lowerCAmelCase__ : Optional[int] = max(largest_square_area[0] , __UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
lowerCAmelCase__ : int = [0]
lowerCAmelCase__ : Any = [[-1] * cols for _ in range(__UpperCAmelCase )]
update_area_of_max_square_using_dp_array(0 , 0 , __UpperCAmelCase )
return largest_square_area[0]
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
lowerCAmelCase__ : Optional[int] = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowerCAmelCase__ : Any = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowerCAmelCase__ : List[str] = dp_array[row][col + 1]
lowerCAmelCase__ : Any = dp_array[row + 1][col + 1]
lowerCAmelCase__ : List[str] = dp_array[row + 1][col]
if mat[row][col] == 1:
lowerCAmelCase__ : str = 1 + min(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : str = max(dp_array[row][col] , __UpperCAmelCase )
else:
lowerCAmelCase__ : str = 0
return largest_square_area
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
lowerCAmelCase__ : Any = [0] * (cols + 1)
lowerCAmelCase__ : List[Any] = [0] * (cols + 1)
lowerCAmelCase__ : Optional[Any] = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowerCAmelCase__ : Optional[int] = current_row[col + 1]
lowerCAmelCase__ : List[str] = next_row[col + 1]
lowerCAmelCase__ : str = next_row[col]
if mat[row][col] == 1:
lowerCAmelCase__ : Tuple = 1 + min(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : Dict = max(current_row[col] , __UpperCAmelCase )
else:
lowerCAmelCase__ : Union[str, Any] = 0
lowerCAmelCase__ : Tuple = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 212 |
"""simple docstring"""
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
_A = logging.get_logger(__name__)
_A = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
_A = {
"""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"""
},
}
_A = {"""facebook/blenderbot-3B""": 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowercase_ ( ) -> Tuple:
lowerCAmelCase__ : int = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
lowerCAmelCase__ : Any = bs[:]
lowerCAmelCase__ : Optional[int] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCAmelCase )
cs.append(2**8 + n )
n += 1
lowerCAmelCase__ : Dict = [chr(__UpperCAmelCase ) for n in cs]
return dict(zip(__UpperCAmelCase , __UpperCAmelCase ) )
def lowercase_ ( __UpperCAmelCase ) -> List[Any]:
lowerCAmelCase__ : List[Any] = set()
lowerCAmelCase__ : Union[str, Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCAmelCase__ : Optional[Any] = char
return pairs
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Optional[Any] = VOCAB_FILES_NAMES
_lowerCamelCase :List[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase :Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase :Optional[Any] = ["input_ids", "attention_mask"]
def __init__( self : Any , UpperCamelCase : str , UpperCamelCase : Tuple , UpperCamelCase : Any="replace" , UpperCamelCase : Optional[Any]="<s>" , UpperCamelCase : Union[str, Any]="</s>" , UpperCamelCase : Optional[int]="</s>" , UpperCamelCase : str="<s>" , UpperCamelCase : int="<unk>" , UpperCamelCase : int="<pad>" , UpperCamelCase : Dict="<mask>" , UpperCamelCase : Optional[int]=False , **UpperCamelCase : Optional[Any] , ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else bos_token
lowerCAmelCase__ : int = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else eos_token
lowerCAmelCase__ : Dict = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else sep_token
lowerCAmelCase__ : Union[str, Any] = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else cls_token
lowerCAmelCase__ : int = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else unk_token
lowerCAmelCase__ : Union[str, Any] = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase__ : Union[str, Any] = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else mask_token
super().__init__(
errors=UpperCamelCase , bos_token=UpperCamelCase , eos_token=UpperCamelCase , unk_token=UpperCamelCase , sep_token=UpperCamelCase , cls_token=UpperCamelCase , pad_token=UpperCamelCase , mask_token=UpperCamelCase , add_prefix_space=UpperCamelCase , **UpperCamelCase , )
with open(UpperCamelCase , encoding="""utf-8""" ) as vocab_handle:
lowerCAmelCase__ : Any = json.load(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = {v: k for k, v in self.encoder.items()}
lowerCAmelCase__ : Dict = errors # how to handle errors in decoding
lowerCAmelCase__ : Union[str, Any] = bytes_to_unicode()
lowerCAmelCase__ : List[str] = {v: k for k, v in self.byte_encoder.items()}
with open(UpperCamelCase , encoding="""utf-8""" ) as merges_handle:
lowerCAmelCase__ : Optional[int] = merges_handle.read().split("""\n""" )[1:-1]
lowerCAmelCase__ : Dict = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase__ : Any = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
lowerCAmelCase__ : Union[str, Any] = {}
lowerCAmelCase__ : Dict = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase__ : Tuple = 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 : List[str] ) -> Union[str, Any]:
"""simple docstring"""
return len(self.encoder )
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : str ) -> Union[str, Any]:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
lowerCAmelCase__ : Union[str, Any] = tuple(UpperCamelCase )
lowerCAmelCase__ : List[str] = get_pairs(UpperCamelCase )
if not pairs:
return token
while True:
lowerCAmelCase__ : List[str] = min(UpperCamelCase , key=lambda UpperCamelCase : self.bpe_ranks.get(UpperCamelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase__ , lowerCAmelCase__ : str = bigram
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : List[str] = 0
while i < len(UpperCamelCase ):
try:
lowerCAmelCase__ : Optional[Any] = word.index(UpperCamelCase , UpperCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase__ : List[str] = j
if word[i] == first and i < len(UpperCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase__ : List[Any] = tuple(UpperCamelCase )
lowerCAmelCase__ : Tuple = new_word
if len(UpperCamelCase ) == 1:
break
else:
lowerCAmelCase__ : Any = get_pairs(UpperCamelCase )
lowerCAmelCase__ : Tuple = """ """.join(UpperCamelCase )
lowerCAmelCase__ : Tuple = word
return word
def _lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : Tuple = []
for token in re.findall(self.pat , UpperCamelCase ):
lowerCAmelCase__ : List[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(UpperCamelCase ).split(""" """ ) )
return bpe_tokens
def _lowerCAmelCase ( self : Optional[Any] , UpperCamelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
return self.encoder.get(UpperCamelCase , self.encoder.get(self.unk_token ) )
def _lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
return self.decoder.get(UpperCamelCase )
def _lowerCAmelCase ( self : List[Any] , UpperCamelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = """""".join(UpperCamelCase )
lowerCAmelCase__ : List[str] = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : str , UpperCamelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCAmelCase__ : Union[str, Any] = os.path.join(
UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase__ : int = os.path.join(
UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCamelCase , ensure_ascii=UpperCamelCase ) + """\n""" )
lowerCAmelCase__ : Optional[Any] = 0
with open(UpperCamelCase , """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 UpperCamelCase : 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__ : Dict = token_index
writer.write(""" """.join(UpperCamelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
def _lowerCAmelCase ( self : Dict , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None , UpperCamelCase : bool = False ) -> List[int]:
"""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 _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = [self.sep_token_id]
lowerCAmelCase__ : 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]
def _lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase : Any , UpperCamelCase : Optional[int]=False , **UpperCamelCase : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCAmelCase__ : int = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase ) > 0 and not text[0].isspace()):
lowerCAmelCase__ : Tuple = """ """ + text
return (text, kwargs)
def _lowerCAmelCase ( self : Optional[Any] , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ) -> Any:
"""simple docstring"""
return token_ids_a + [self.eos_token_id]
def _lowerCAmelCase ( self : str , UpperCamelCase : "Conversation" ) -> List[int]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = []
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(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = """ """.join(UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = self.encode(UpperCamelCase )
if len(UpperCamelCase ) > self.model_max_length:
lowerCAmelCase__ : List[str] = 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
| 212 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _A = 4 ):
'''simple docstring'''
snake_case_ = abs(_A ) or 4
return [[1 + x + y * row_size for x in range(_A )] for y in range(_A )]
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return reverse_row(transpose(_A ) )
# OR.. transpose(reverse_column(matrix))
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return reverse_row(reverse_column(_A ) )
# OR.. reverse_column(reverse_row(matrix))
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return reverse_column(transpose(_A ) )
# OR.. transpose(reverse_row(matrix))
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = [list(_A ) for x in zip(*_A )]
return matrix
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = matrix[::-1]
return matrix
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = [x[::-1] for x in matrix]
return matrix
def lowerCamelCase__ ( _A ):
'''simple docstring'''
for i in matrix:
print(*_A )
if __name__ == "__main__":
lowercase__ : Union[str, Any] = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 90 counterclockwise:\n")
print_matrix(rotate_aa(matrix))
lowercase__ : Any = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 180:\n")
print_matrix(rotate_aaa(matrix))
lowercase__ : List[str] = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 270 counterclockwise:\n")
print_matrix(rotate_aaa(matrix))
| 187 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = CodeGenTokenizer
lowerCAmelCase_ = CodeGenTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = {'''add_prefix_space''': True}
lowerCAmelCase_ = False
def snake_case__ ( self : List[Any] ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
snake_case_ = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
snake_case_ = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ = {"unk_token": "<unk>"}
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ = 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(__lowercase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__lowercase ) )
def snake_case__ ( self : Union[str, Any] , **__lowercase : List[str] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def snake_case__ ( self : Optional[Any] , **__lowercase : Union[str, Any] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__lowercase )
def snake_case__ ( self : Optional[int] , __lowercase : List[str] ):
"""simple docstring"""
snake_case_ = "lower newer"
snake_case_ = "lower newer"
return input_text, output_text
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ = "lower newer"
snake_case_ = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ = tokenizer.tokenize(__lowercase , add_prefix_space=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
snake_case_ = tokens + [tokenizer.unk_token]
snake_case_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_rust_tokenizer(add_prefix_space=__lowercase )
snake_case_ = "lower newer"
# Testing tokenization
snake_case_ = tokenizer.tokenize(__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids without special tokens
snake_case_ = tokenizer.encode(__lowercase , add_special_tokens=__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids with special tokens
snake_case_ = self.get_rust_tokenizer(add_prefix_space=__lowercase )
snake_case_ = tokenizer.encode(__lowercase , add_prefix_space=__lowercase )
snake_case_ = rust_tokenizer.encode(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing the unknown token
snake_case_ = tokens + [rust_tokenizer.unk_token]
snake_case_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def snake_case__ ( self : Any , *__lowercase : Union[str, Any] , **__lowercase : Tuple ):
"""simple docstring"""
pass
def snake_case__ ( self : int , __lowercase : str=15 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case_ = self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
# Simple input
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input 1", "This is a simple input 2"]
snake_case_ = ("This is a simple input", "This is a pair")
snake_case_ = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding="max_length" )
# Simple input
self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding="max_length" )
# Simple input
self.assertRaises(
__lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding="max_length" , )
# Pair input
self.assertRaises(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding="max_length" )
# Pair input
self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding="max_length" )
# Pair input
self.assertRaises(
__lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding="max_length" , )
def snake_case__ ( self : str ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input looooooooong", "This is a simple input"]
snake_case_ = ("This is a simple input", "This is a pair")
snake_case_ = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
snake_case_ = tokenizer.pad_token_id
snake_case_ = tokenizer(__lowercase , padding="max_length" , max_length=30 , return_tensors="np" )
snake_case_ = tokenizer(__lowercase , padding=__lowercase , truncate=__lowercase , return_tensors="np" )
snake_case_ = tokenizer(*__lowercase , padding="max_length" , max_length=60 , return_tensors="np" )
snake_case_ = tokenizer(__lowercase , padding=__lowercase , truncate=__lowercase , return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = "$$$"
snake_case_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__lowercase , add_bos_token=__lowercase )
snake_case_ = "This is a simple input"
snake_case_ = ["This is a simple input 1", "This is a simple input 2"]
snake_case_ = tokenizer.bos_token_id
snake_case_ = tokenizer(__lowercase )
snake_case_ = tokenizer(__lowercase )
self.assertEqual(out_s.input_ids[0] , __lowercase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
snake_case_ = tokenizer.decode(out_s.input_ids )
snake_case_ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __lowercase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
snake_case_ = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
snake_case_ = "\nif len_a > len_b: result = a\nelse: result = b"
snake_case_ = tokenizer.encode(__lowercase )
snake_case_ = ["^#", re.escape("<|endoftext|>" ), "^'''", "^\"\"\"", "\n\n\n"]
snake_case_ = tokenizer.decode(__lowercase , truncate_before_pattern=__lowercase )
self.assertEqual(__lowercase , __lowercase )
def snake_case__ ( self : Dict ):
"""simple docstring"""
pass
| 187 | 1 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self ,*,
_SCREAMING_SNAKE_CASE = 4 ,_SCREAMING_SNAKE_CASE = 768 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,) -> Dict:
super().__init__()
_snake_case = nn.Parameter(torch.zeros(_A ) )
# parameters for additional clip time embeddings
_snake_case = nn.Linear(_A ,_A )
_snake_case = nn.Linear(_A ,_A )
# parameters for encoder hidden states
_snake_case = clip_extra_context_tokens
_snake_case = nn.Linear(
_A ,self.clip_extra_context_tokens * cross_attention_dim )
_snake_case = nn.Linear(_A ,_A )
_snake_case = nn.LayerNorm(_A )
def _lowercase ( self ,*, _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> List[Any]:
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
_snake_case = image_embeddings.shape[0]
_snake_case = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
_snake_case = classifier_free_guidance_embeddings.expand(
_A ,-1 )
_snake_case = torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
_snake_case = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
_snake_case = self.embedding_proj(_A )
_snake_case = self.clip_image_embeddings_project_to_time_embeddings(_A )
_snake_case = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
_snake_case = self.clip_extra_context_tokens_proj(_A )
_snake_case = clip_extra_context_tokens.reshape(_A ,-1 ,self.clip_extra_context_tokens )
_snake_case = clip_extra_context_tokens.permute(0 ,2 ,1 )
_snake_case = self.encoder_hidden_states_proj(_A )
_snake_case = self.text_encoder_hidden_states_norm(_A )
_snake_case = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 350 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def __a ( _UpperCamelCase: list[Any] ) -> None:
"""simple docstring"""
create_state_space_tree(_UpperCamelCase , [] , 0 )
def __a ( _UpperCamelCase: list[Any] , _UpperCamelCase: list[Any] , _UpperCamelCase: int ) -> None:
"""simple docstring"""
if index == len(_UpperCamelCase ):
print(_UpperCamelCase )
return
create_state_space_tree(_UpperCamelCase , _UpperCamelCase , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(_UpperCamelCase , _UpperCamelCase , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
UpperCamelCase_ : list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(['''A''', '''B''', '''C'''])
generate_all_subsequences(seq)
| 142 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_50, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_00, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 6_00, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class A ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : Optional[int] )-> Union[str, Any]:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split(),encoding='utf-8',check=lowercase_,)
assert hasattr(self,'env' )
def snake_case__ ( self : Tuple,lowercase_ : Optional[int] )-> Dict:
'''simple docstring'''
A__ = F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
# distributed data settings
A__ = {'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None
# creates estimator
return HuggingFace(
entry_point=self.script,source_dir=self.env.test_path,role=self.env.role,image_uri=self.env.image_uri,base_job_name=lowercase_,instance_count=lowercase_,instance_type=self.instance_type,debugger_hook_config=lowercase_,hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path},metric_definitions=self.env.metric_definitions,distribution=lowercase_,py_version='py36',)
def snake_case__ ( self : Optional[Any],lowercase_ : Tuple )-> Any:
'''simple docstring'''
TrainingJobAnalytics(lowercase_ ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv' )
@parameterized.expand([(2,)] )
def snake_case__ ( self : Dict,lowercase_ : Optional[Any] )-> int:
'''simple docstring'''
A__ = self.create_estimator(lowercase_ )
# run training
estimator.fit()
# result dataframe
A__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
A__ = list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
A__ = list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
A__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds',9_9_9_9_9_9 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F'{estimator.latest_training_job.name}.json','w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss},lowercase_ )
| 7 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
'''simple docstring'''
def __init__( self : int, _lowerCamelCase : List[Any]=None ):
'''simple docstring'''
__A = data
__A = None
def __repr__( self : Union[str, Any] ):
'''simple docstring'''
__A = []
__A = self
while temp:
string_rep.append(f'{temp.data}' )
__A = temp.next
return "->".join(_lowerCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
if not elements_list:
raise Exception('''The Elements List is empty''' )
__A = __A = Node(elements_list[0] )
for i in range(1 , len(__UpperCamelCase ) ):
__A = Node(elements_list[i] )
__A = current.next
return head
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
if head_node is not None and isinstance(__UpperCamelCase , __UpperCamelCase ):
print_reverse(head_node.next )
print(head_node.data )
def lowerCAmelCase ( ):
"""simple docstring"""
from doctest import testmod
testmod()
__A = make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
print('''Linked List:''' )
print(__UpperCamelCase )
print('''Elements in Reverse:''' )
print_reverse(__UpperCamelCase )
if __name__ == "__main__":
main()
| 266 | 0 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Any = '''EncodecFeatureExtractor'''
lowerCamelCase :List[str] = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> Tuple:
super().__init__(lowerCAmelCase_ , lowerCAmelCase_ )
_A = self.feature_extractor
_A = False
def UpperCAmelCase ( self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=True ) -> Dict:
return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase_ , language=lowerCAmelCase_ , no_timestamps=lowerCAmelCase_ )
def __call__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ) -> int:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*lowerCAmelCase_ , **lowerCAmelCase_ )
_A = kwargs.pop("""audio""" , lowerCAmelCase_ )
_A = kwargs.pop("""sampling_rate""" , lowerCAmelCase_ )
_A = kwargs.pop("""text""" , lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
_A = args[0]
_A = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
_A = self.tokenizer(lowerCAmelCase_ , **lowerCAmelCase_ )
if audio is not None:
_A = self.feature_extractor(lowerCAmelCase_ , *lowerCAmelCase_ , sampling_rate=lowerCAmelCase_ , **lowerCAmelCase_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
_A = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
_A = audio_inputs["""padding_mask"""]
return inputs
def UpperCAmelCase ( self , *lowerCAmelCase_ , **lowerCAmelCase_ ) -> List[Any]:
_A = kwargs.pop("""audio""" , lowerCAmelCase_ )
_A = kwargs.pop("""padding_mask""" , lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
_A = args[0]
_A = args[1:]
if audio_values is not None:
return self._decode_audio(lowerCAmelCase_ , padding_mask=lowerCAmelCase_ )
else:
return self.tokenizer.batch_decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
def UpperCAmelCase ( self , *lowerCAmelCase_ , **lowerCAmelCase_ ) -> Union[str, Any]:
return self.tokenizer.decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ) -> List[np.ndarray]:
_A = to_numpy(lowerCAmelCase_ )
_A , _A , _A = audio_values.shape
if padding_mask is None:
return list(lowerCAmelCase_ )
_A = to_numpy(lowerCAmelCase_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
_A = seq_len - padding_mask.shape[-1]
_A = 1 - self.feature_extractor.padding_value
_A = np.pad(lowerCAmelCase_ , ((0, 0), (0, difference)) , """constant""" , constant_values=lowerCAmelCase_ )
_A = audio_values.tolist()
for i in range(lowerCAmelCase_ ):
_A = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
_A = sliced_audio.reshape(lowerCAmelCase_ , -1 )
return audio_values
| 81 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMSNModel',
'ViTMSNForImageClassification',
'ViTMSNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 81 | 1 |
Subsets and Splits
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