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def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str ) -> Dict:
return number | (1 << position)
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Any , UpperCAmelCase_ : int ) -> str:
return number & ~(1 << position)
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[Any] ) -> Optional[Any]:
return number ^ (1 << position)
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : str ) -> str:
return ((number >> position) & 1) == 1
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : List[Any] ) -> Optional[Any]:
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 443 | import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A , num_return_sequences=2 , return_tensors=_A )
self.assertEqual(
_A , [
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
] , )
_UpperCamelCase = text_generator.model.config.eos_token_id
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=_A , num_return_sequences=2 , batch_size=2 , return_tensors=_A , )
self.assertEqual(
_A , [
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
] , )
@require_tf
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=_A )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def UpperCamelCase_ ( self : int , _A : str , _A : Union[str, Any] , _A : Any ):
_UpperCamelCase = TextGenerationPipeline(model=_A , tokenizer=_A )
return text_generator, ["This is a test", "Another test"]
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = '''Hello I believe in'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_UpperCamelCase = text_generator(_A )
self.assertEqual(
_A , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_UpperCamelCase = text_generator(_A , stop_sequence=''' fe''' )
self.assertEqual(_A , [{'''generated_text''': '''Hello I believe in fe'''}] )
def UpperCamelCase_ ( self : Any , _A : List[Any] , _A : Union[str, Any] ):
_UpperCamelCase = text_generator.model
_UpperCamelCase = text_generator.tokenizer
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = pipeline(task='''text-generation''' , model=_A , tokenizer=_A , return_full_text=_A )
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_UpperCamelCase = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_text=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_tensors=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_text=_A , return_tensors=_A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_UpperCamelCase = text_generator('''''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_UpperCamelCase = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_UpperCamelCase = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
_UpperCamelCase = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_A ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
# Classic `model_kwargs`
_UpperCamelCase = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def UpperCamelCase_ ( self : Union[str, Any] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=_A , top_p=0.5 )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = '''Hello world'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_UpperCamelCase = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_UpperCamelCase = logging.get_logger('''transformers.generation.utils''' )
_UpperCamelCase = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 , max_new_tokens=1 )
self.assertIn(_A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_new_tokens=1 )
self.assertNotIn(_A , cl.out )
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 )
self.assertNotIn(_A , cl.out )
| 10 | 0 |
'''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 lowerCAmelCase__ ( __lowercase , __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = StableUnCLIPPipeline
lowerCAmelCase__ = TEXT_TO_IMAGE_PARAMS
lowerCAmelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
lowerCAmelCase__ = False
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 32
__SCREAMING_SNAKE_CASE = embedder_hidden_size
# prior components
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = DDPMScheduler(
variance_type="""fixed_small_log""" , prediction_type="""sample""" , num_train_timesteps=1_000 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule="""squaredcos_cap_v2""" , )
# regular denoising components
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = StableUnCLIPImageNormalizer(embedding_dim=_A )
__SCREAMING_SNAKE_CASE = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="""projection""" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_schedule="""scaled_linear""" , beta_start=0.00085 , beta_end=0.012 , prediction_type="""v_prediction""" , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = AutoencoderKL()
__SCREAMING_SNAKE_CASE = {
# 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 : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict=0 ) -> int:
"""simple docstring"""
if str(_A ).startswith("""mps""" ):
__SCREAMING_SNAKE_CASE = torch.manual_seed(_A )
else:
__SCREAMING_SNAKE_CASE = torch.Generator(device=_A ).manual_seed(_A )
__SCREAMING_SNAKE_CASE = {
"""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[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch_device == """cpu"""
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def UpperCAmelCase__ ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy""" )
__SCREAMING_SNAKE_CASE = StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# 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()
__SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe("""anime turle""" , generator=_A , output_type="""np""" )
__SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__SCREAMING_SNAKE_CASE = StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" , torch_dtype=torch.floataa )
__SCREAMING_SNAKE_CASE = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__SCREAMING_SNAKE_CASE = pipe(
"""anime turtle""" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="""np""" , )
__SCREAMING_SNAKE_CASE = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 627 | def _snake_case ( __snake_case = 100 ):
_UpperCamelCase = (n * (n + 1) // 2) ** 2
_UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 10 | 0 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 473 | import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
def constraint_to_multiple_of(__snake_case , __snake_case , __snake_case=0 , __snake_case=None ):
_UpperCamelCase = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCamelCase = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCamelCase = math.ceil(val / multiple ) * multiple
return x
_UpperCamelCase = (output_size, output_size) if isinstance(__snake_case , __snake_case ) else output_size
_UpperCamelCase , _UpperCamelCase = get_image_size(__snake_case )
_UpperCamelCase , _UpperCamelCase = output_size
# determine new height and width
_UpperCamelCase = output_height / input_height
_UpperCamelCase = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCamelCase = scale_width
else:
# fit height
_UpperCamelCase = scale_height
_UpperCamelCase = constraint_to_multiple_of(scale_height * input_height , multiple=__snake_case )
_UpperCamelCase = constraint_to_multiple_of(scale_width * input_width , multiple=__snake_case )
return (new_height, new_width)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = ["pixel_values"]
def __init__( self : List[Any] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = False , _A : int = 1 , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : List[str] , ):
super().__init__(**_A )
_UpperCamelCase = size if size is not None else {'''height''': 384, '''width''': 384}
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = do_resize
_UpperCamelCase = size
_UpperCamelCase = keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of
_UpperCamelCase = resample
_UpperCamelCase = do_rescale
_UpperCamelCase = rescale_factor
_UpperCamelCase = do_normalize
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase_ ( self : List[str] , _A : np.ndarray , _A : Dict[str, int] , _A : bool = False , _A : int = 1 , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
_UpperCamelCase = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_UpperCamelCase = get_resize_output_image_size(
_A , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : str , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return rescale(_A , scale=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : int , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : Optional[int] , _A : ImageInput , _A : bool = None , _A : int = None , _A : bool = None , _A : int = None , _A : PILImageResampling = None , _A : bool = None , _A : float = None , _A : bool = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(_A ) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
_UpperCamelCase = [to_channel_dimension_format(_A , _A ) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
def UpperCamelCase_ ( self : Any , _A : Any , _A : List[Tuple] = None ):
_UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_A ):
_UpperCamelCase = target_sizes.numpy()
_UpperCamelCase = []
for idx in range(len(_A ) ):
_UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_A )
_UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
_UpperCamelCase = logits.argmax(dim=1 )
_UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 10 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_UpperCAmelCase = logging.get_logger(__name__)
def UpperCamelCase ( __lowercase : List[str] ,__lowercase : Dict ,__lowercase : List[str] ,__lowercase : List[str] ):
'''simple docstring'''
def constraint_to_multiple_of(__lowercase : Union[str, Any] ,__lowercase : Optional[int] ,__lowercase : Any=0 ,__lowercase : Union[str, Any]=None ):
A_ : Union[str, Any] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
A_ : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
A_ : Union[str, Any] = math.ceil(val / multiple ) * multiple
return x
A_ : List[Any] = (output_size, output_size) if isinstance(__snake_case ,__snake_case ) else output_size
A_ , A_ : Tuple = get_image_size(__snake_case )
A_ , A_ : Dict = output_size
# determine new height and width
A_ : Tuple = output_height / input_height
A_ : List[Any] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
A_ : str = scale_width
else:
# fit height
A_ : Optional[int] = scale_height
A_ : List[str] = constraint_to_multiple_of(scale_height * input_height ,multiple=__snake_case )
A_ : List[str] = constraint_to_multiple_of(scale_width * input_width ,multiple=__snake_case )
return (new_height, new_width)
class UpperCAmelCase ( __lowercase ):
'''simple docstring'''
lowerCamelCase_ = ['''pixel_values''']
def __init__( self , lowercase = True , lowercase = None , lowercase = PILImageResampling.BILINEAR , lowercase = False , lowercase = 1 , lowercase = True , lowercase = 1 / 2_5_5 , lowercase = True , lowercase = None , lowercase = None , **lowercase , ):
"""simple docstring"""
super().__init__(**_A )
A_ : Any = size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
A_ : Dict = get_size_dict(_A )
A_ : Union[str, Any] = do_resize
A_ : Optional[int] = size
A_ : Optional[Any] = keep_aspect_ratio
A_ : Union[str, Any] = ensure_multiple_of
A_ : List[Any] = resample
A_ : Optional[int] = do_rescale
A_ : str = rescale_factor
A_ : int = do_normalize
A_ : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A_ : Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase = False , lowercase = 1 , lowercase = PILImageResampling.BICUBIC , lowercase = None , **lowercase , ):
"""simple docstring"""
A_ : List[str] = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''' )
A_ : Any = get_resize_output_image_size(
_A , output_size=(size['height'], size['width']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase = None , **lowercase , ):
"""simple docstring"""
return rescale(_A , scale=_A , data_format=_A , **_A )
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , lowercase = None , **lowercase , ):
"""simple docstring"""
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def lowerCAmelCase_ ( self , lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = ChannelDimension.FIRST , **lowercase , ):
"""simple docstring"""
A_ : str = do_resize if do_resize is not None else self.do_resize
A_ : Union[str, Any] = size if size is not None else self.size
A_ : Optional[int] = get_size_dict(_A )
A_ : int = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
A_ : Dict = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
A_ : Optional[int] = resample if resample is not None else self.resample
A_ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
A_ : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
A_ : Optional[Any] = image_mean if image_mean is not None else self.image_mean
A_ : str = image_std if image_std is not None else self.image_std
A_ : Dict = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
A_ : Optional[Any] = [to_numpy_array(_A ) for image in images]
if do_resize:
A_ : Dict = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
A_ : Dict = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
A_ : Dict = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
A_ : Any = [to_channel_dimension_format(_A , _A ) for image in images]
A_ : str = {'pixel_values': images}
return BatchFeature(data=_A , tensor_type=_A )
def lowerCAmelCase_ ( self , lowercase , lowercase = None ):
"""simple docstring"""
A_ : int = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(_A ):
A_ : List[Any] = target_sizes.numpy()
A_ : str = []
for idx in range(len(_A ) ):
A_ : int = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=_A )
A_ : List[str] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
A_ : List[Any] = logits.argmax(dim=1 )
A_ : Tuple = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 558 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
_UpperCamelCase = self.diffusers_dir
shutil.copy(
os.path.join(_A , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def UpperCamelCase_ ( self : str , _A : List[str] , _A : Optional[Any] , _A : List[str] , _A : Optional[int]=None ):
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
_UpperCamelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
_UpperCamelCase = black.format_str(_A , mode=_A )
_UpperCamelCase = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(_A , '''w''' , newline='''\n''' ) as f:
f.write(_A )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_A ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_A )
with open(_A , '''r''' ) as f:
self.assertTrue(f.read() , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , _A , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , _A ) , )
# Copy consistency with a really long name
_UpperCamelCase = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , F"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , _A , _A ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , _A , overwrite_result=re.sub('''DDPM''' , '''Test''' , _A ) , )
| 10 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class UpperCAmelCase_ ( __lowercase ):
UpperCamelCase ="sew"
def __init__( self , UpperCamelCase_=32 , UpperCamelCase_=7_68 , UpperCamelCase_=12 , UpperCamelCase_=12 , UpperCamelCase_=30_72 , UpperCamelCase_=2 , UpperCamelCase_="gelu" , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.0 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.0_2 , UpperCamelCase_=1E-5 , UpperCamelCase_="group" , UpperCamelCase_="gelu" , UpperCamelCase_=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) , UpperCamelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , UpperCamelCase_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , UpperCamelCase_=False , UpperCamelCase_=1_28 , UpperCamelCase_=16 , UpperCamelCase_=True , UpperCamelCase_=0.0_5 , UpperCamelCase_=10 , UpperCamelCase_=2 , UpperCamelCase_=0.0 , UpperCamelCase_=10 , UpperCamelCase_=0 , UpperCamelCase_="mean" , UpperCamelCase_=False , UpperCamelCase_=False , UpperCamelCase_=2_56 , UpperCamelCase_=0 , UpperCamelCase_=1 , UpperCamelCase_=2 , **UpperCamelCase_ , ) -> Any:
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
__lowercase : List[Any] = hidden_size
__lowercase : Dict = feat_extract_norm
__lowercase : Dict = feat_extract_activation
__lowercase : Optional[int] = list(_A )
__lowercase : Optional[int] = list(_A )
__lowercase : Any = list(_A )
__lowercase : Union[str, Any] = conv_bias
__lowercase : int = num_conv_pos_embeddings
__lowercase : List[Any] = num_conv_pos_embedding_groups
__lowercase : Union[str, Any] = len(self.conv_dim )
__lowercase : Optional[int] = num_hidden_layers
__lowercase : Optional[Any] = intermediate_size
__lowercase : Optional[int] = squeeze_factor
__lowercase : Union[str, Any] = hidden_act
__lowercase : Optional[int] = num_attention_heads
__lowercase : Optional[int] = hidden_dropout
__lowercase : List[str] = attention_dropout
__lowercase : List[str] = activation_dropout
__lowercase : Union[str, Any] = feat_proj_dropout
__lowercase : str = final_dropout
__lowercase : List[str] = layerdrop
__lowercase : Dict = layer_norm_eps
__lowercase : Optional[Any] = initializer_range
__lowercase : Any = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
F"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
F"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowercase : str = apply_spec_augment
__lowercase : Optional[int] = mask_time_prob
__lowercase : str = mask_time_length
__lowercase : Dict = mask_time_min_masks
__lowercase : List[Any] = mask_feature_prob
__lowercase : str = mask_feature_length
__lowercase : List[Any] = mask_feature_min_masks
# ctc loss
__lowercase : str = ctc_loss_reduction
__lowercase : int = ctc_zero_infinity
# sequence classification
__lowercase : List[str] = use_weighted_layer_sum
__lowercase : Dict = classifier_proj_size
@property
def _lowerCamelCase ( self ) -> Dict:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 76 | import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCAmelCase = True
from torch.cuda.amp import autocast
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
UpperCAmelCase = field(
default=0.1, metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
}, )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}, )
UpperCAmelCase = field(
default=0.0_5, metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
}, )
UpperCAmelCase = field(default=0.0, metadata={"help": "The LayerDrop probability."} )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
UpperCAmelCase = field(
default="train+validation", metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The number of processes to use for the preprocessing."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
}, )
UpperCAmelCase = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"], metadata={"help": "A list of characters to remove from the transcripts."}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = True
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
def __call__( self : Union[str, Any] , _A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
_UpperCamelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
_UpperCamelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
_UpperCamelCase = self.processor.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
_UpperCamelCase = self.processor.pad(
labels=_A , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
_UpperCamelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
_UpperCamelCase = labels
return batch
class lowerCAmelCase_ ( __lowercase ):
def UpperCamelCase_ ( self : Dict , _A : nn.Module , _A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
_UpperCamelCase = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
_UpperCamelCase = self.compute_loss(_A , _A )
else:
_UpperCamelCase = self.compute_loss(_A , _A )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
_UpperCamelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
_UpperCamelCase = loss.sum() / (inputs['''labels'''] >= 0).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 = 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()
return loss.detach()
def _snake_case ( ):
# 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 = 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.
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
_UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCamelCase = 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()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
_UpperCamelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
_UpperCamelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
_UpperCamelCase = f"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(__snake_case ):
_UpperCamelCase = re.sub(__snake_case , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
_UpperCamelCase = train_dataset.map(__snake_case , remove_columns=['''sentence'''] )
_UpperCamelCase = eval_dataset.map(__snake_case , remove_columns=['''sentence'''] )
def extract_all_chars(__snake_case ):
_UpperCamelCase = ''' '''.join(batch['''text'''] )
_UpperCamelCase = list(set(__snake_case ) )
return {"vocab": [vocab], "all_text": [all_text]}
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=train_dataset.column_names , )
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=eval_dataset.column_names , )
_UpperCamelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
_UpperCamelCase = {v: k for k, v in enumerate(__snake_case )}
_UpperCamelCase = vocab_dict[''' ''']
del vocab_dict[" "]
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = len(__snake_case )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(__snake_case , __snake_case )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=__snake_case , return_attention_mask=__snake_case )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=__snake_case , tokenizer=__snake_case )
_UpperCamelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
_UpperCamelCase = min(len(__snake_case ) , data_args.max_train_samples )
_UpperCamelCase = train_dataset.select(range(__snake_case ) )
if data_args.max_val_samples is not None:
_UpperCamelCase = eval_dataset.select(range(data_args.max_val_samples ) )
_UpperCamelCase = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(__snake_case ):
_UpperCamelCase , _UpperCamelCase = torchaudio.load(batch['''path'''] )
_UpperCamelCase = resampler(__snake_case ).squeeze().numpy()
_UpperCamelCase = 16000
_UpperCamelCase = batch['''text''']
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(__snake_case ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
_UpperCamelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(__snake_case )
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
# Metric
_UpperCamelCase = datasets.load_metric('''wer''' )
def compute_metrics(__snake_case ):
_UpperCamelCase = pred.predictions
_UpperCamelCase = np.argmax(__snake_case , axis=-1 )
_UpperCamelCase = processor.tokenizer.pad_token_id
_UpperCamelCase = processor.batch_decode(__snake_case )
# we do not want to group tokens when computing the metrics
_UpperCamelCase = processor.batch_decode(pred.label_ids , group_tokens=__snake_case )
_UpperCamelCase = wer_metric.compute(predictions=__snake_case , references=__snake_case )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
_UpperCamelCase = DataCollatorCTCWithPadding(processor=__snake_case , padding=__snake_case )
# Initialize our Trainer
_UpperCamelCase = CTCTrainer(
model=__snake_case , data_collator=__snake_case , args=__snake_case , compute_metrics=__snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
_UpperCamelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
_UpperCamelCase = model_args.model_name_or_path
else:
_UpperCamelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
_UpperCamelCase = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
_UpperCamelCase = train_result.metrics
_UpperCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case )
)
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''train''' , __snake_case )
trainer.save_metrics('''train''' , __snake_case )
trainer.save_state()
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(__snake_case )
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''eval''' , __snake_case )
trainer.save_metrics('''eval''' , __snake_case )
return results
if __name__ == "__main__":
main()
| 10 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class a_ :
def __init__( self : Any , __lowerCAmelCase : int , __lowerCAmelCase : int=1_2 , __lowerCAmelCase : int=7 , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=9_9 , __lowerCAmelCase : str=3_2 , __lowerCAmelCase : int=3_2 , __lowerCAmelCase : Optional[Any]=2 , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : int=3_7 , __lowerCAmelCase : List[Any]=0.1 , __lowerCAmelCase : str=0.1 , __lowerCAmelCase : Any=5_1_2 , __lowerCAmelCase : int=0.02 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=None , ):
__snake_case = parent
__snake_case = batch_size
__snake_case = seq_length
__snake_case = is_training
__snake_case = use_input_mask
__snake_case = use_labels
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = projection_dim
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = dropout
__snake_case = attention_dropout
__snake_case = max_position_embeddings
__snake_case = initializer_range
__snake_case = scope
__snake_case = bos_token_id
def lowercase__ ( self : List[str] ):
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case = None
if self.use_input_mask:
__snake_case = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
__snake_case = input_mask.numpy()
__snake_case , __snake_case = input_mask.shape
__snake_case = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_A ):
__snake_case = 1
__snake_case = 0
__snake_case = self.get_config()
return config, input_ids, tf.convert_to_tensor(_A )
def lowercase__ ( self : str ):
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def lowercase__ ( self : List[str] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any] ):
__snake_case = TFBlipTextModel(config=_A )
__snake_case = model(_A , attention_mask=_A , training=_A )
__snake_case = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowercase__ ( self : Tuple ):
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class a_ ( __lowercase , unittest.TestCase ):
lowercase_ : Dict = (TFBlipTextModel,) if is_tf_available() else ()
lowercase_ : Any = False
lowercase_ : List[Any] = False
lowercase_ : List[Any] = False
def lowercase__ ( self : Dict ):
__snake_case = BlipTextModelTester(self )
__snake_case = ConfigTester(self , config_class=_A , hidden_size=3_7 )
def lowercase__ ( self : Dict ):
self.config_tester.run_common_tests()
def lowercase__ ( self : int ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def lowercase__ ( self : List[Any] ):
pass
def lowercase__ ( self : Tuple ):
pass
@unittest.skip(reason='Blip does not use inputs_embeds' )
def lowercase__ ( self : Dict ):
pass
@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING' )
def lowercase__ ( self : Dict ):
pass
@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING' )
def lowercase__ ( self : List[str] ):
pass
@slow
def lowercase__ ( self : Optional[int] ):
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case = TFBlipTextModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def lowercase__ ( self : int , __lowerCAmelCase : Optional[int]=True ):
super().test_pt_tf_model_equivalence(allow_missing_keys=_A )
| 356 | import math
class lowerCAmelCase_ :
def __init__( self : Tuple , _A : int=0 ): # a graph with Node 0,1,...,N-1
_UpperCamelCase = n
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # adjacency matrix for weight
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase_ ( self : Dict , _A : str , _A : List[str] , _A : Optional[Any] ):
_UpperCamelCase = w
def UpperCamelCase_ ( self : Optional[int] ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCamelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase_ ( self : List[str] , _A : Optional[int] , _A : Optional[int] ):
return self.dp[u][v]
if __name__ == "__main__":
_lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 10 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def snake_case_ ( __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : Union[str, Any] , __snake_case : List[str]) -> Any:
# Load configuration defined in the metadata file
with open(__snake_case) as metadata_file:
lowerCAmelCase_ = json.load(__snake_case)
lowerCAmelCase_ = LukeConfig(use_entity_aware_attention=__snake_case , **metadata['''model_config'''])
# Load in the weights from the checkpoint_path
lowerCAmelCase_ = torch.load(__snake_case , map_location='''cpu''')['''module''']
# Load the entity vocab file
lowerCAmelCase_ = load_original_entity_vocab(__snake_case)
# add an entry for [MASK2]
lowerCAmelCase_ = max(entity_vocab.values()) + 1
config.entity_vocab_size += 1
lowerCAmelCase_ = XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''])
# Add special tokens to the token vocabulary for downstream tasks
lowerCAmelCase_ = AddedToken('''<ent>''' , lstrip=__snake_case , rstrip=__snake_case)
lowerCAmelCase_ = AddedToken('''<ent2>''' , lstrip=__snake_case , rstrip=__snake_case)
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]})
config.vocab_size += 2
print(F'''Saving tokenizer to {pytorch_dump_folder_path}''')
tokenizer.save_pretrained(__snake_case)
with open(os.path.join(__snake_case , '''tokenizer_config.json''') , '''r''') as f:
lowerCAmelCase_ = json.load(__snake_case)
lowerCAmelCase_ = '''MLukeTokenizer'''
with open(os.path.join(__snake_case , '''tokenizer_config.json''') , '''w''') as f:
json.dump(__snake_case , __snake_case)
with open(os.path.join(__snake_case , MLukeTokenizer.vocab_files_names['''entity_vocab_file''']) , '''w''') as f:
json.dump(__snake_case , __snake_case)
lowerCAmelCase_ = MLukeTokenizer.from_pretrained(__snake_case)
# Initialize the embeddings of the special tokens
lowerCAmelCase_ = tokenizer.convert_tokens_to_ids(['''@'''])[0]
lowerCAmelCase_ = tokenizer.convert_tokens_to_ids(['''#'''])[0]
lowerCAmelCase_ = state_dict['''embeddings.word_embeddings.weight''']
lowerCAmelCase_ = word_emb[ent_init_index].unsqueeze(0)
lowerCAmelCase_ = word_emb[enta_init_index].unsqueeze(0)
lowerCAmelCase_ = torch.cat([word_emb, ent_emb, enta_emb])
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
lowerCAmelCase_ = state_dict[bias_name]
lowerCAmelCase_ = decoder_bias[ent_init_index].unsqueeze(0)
lowerCAmelCase_ = decoder_bias[enta_init_index].unsqueeze(0)
lowerCAmelCase_ = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias])
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers):
for matrix_name in ["query.weight", "query.bias"]:
lowerCAmelCase_ = F'''encoder.layer.{layer_index}.attention.self.'''
lowerCAmelCase_ = state_dict[prefix + matrix_name]
lowerCAmelCase_ = state_dict[prefix + matrix_name]
lowerCAmelCase_ = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
lowerCAmelCase_ = state_dict['''entity_embeddings.entity_embeddings.weight''']
lowerCAmelCase_ = entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0)
lowerCAmelCase_ = torch.cat([entity_emb, entity_mask_emb])
# add [MASK2] for 'entity_predictions.bias'
lowerCAmelCase_ = state_dict['''entity_predictions.bias''']
lowerCAmelCase_ = entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0)
lowerCAmelCase_ = torch.cat([entity_prediction_bias, entity_mask_bias])
lowerCAmelCase_ = LukeForMaskedLM(config=__snake_case).eval()
state_dict.pop('''entity_predictions.decoder.weight''')
state_dict.pop('''lm_head.decoder.weight''')
state_dict.pop('''lm_head.decoder.bias''')
lowerCAmelCase_ = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''') or key.startswith('''entity_predictions''')):
lowerCAmelCase_ = state_dict[key]
else:
lowerCAmelCase_ = state_dict[key]
lowerCAmelCase_ ,lowerCAmelCase_ = model.load_state_dict(__snake_case , strict=__snake_case)
if set(__snake_case) != {"luke.embeddings.position_ids"}:
raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}''')
if set(__snake_case) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F'''Unexpected missing_keys: {missing_keys}''')
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
lowerCAmelCase_ = MLukeTokenizer.from_pretrained(__snake_case , task='''entity_classification''')
lowerCAmelCase_ = '''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
lowerCAmelCase_ = (0, 9)
lowerCAmelCase_ = tokenizer(__snake_case , entity_spans=[span] , return_tensors='''pt''')
lowerCAmelCase_ = model(**__snake_case)
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
lowerCAmelCase_ = torch.Size((1, 33, 768))
lowerCAmelCase_ = torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]])
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''')
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , __snake_case , atol=1E-4):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
lowerCAmelCase_ = torch.Size((1, 1, 768))
lowerCAmelCase_ = torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]])
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
F''' {expected_shape}''')
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , __snake_case , atol=1E-4):
raise ValueError
# Verify masked word/entity prediction
lowerCAmelCase_ = MLukeTokenizer.from_pretrained(__snake_case)
lowerCAmelCase_ = '''Tokyo is the capital of <mask>.'''
lowerCAmelCase_ = (24, 30)
lowerCAmelCase_ = tokenizer(__snake_case , entity_spans=[span] , return_tensors='''pt''')
lowerCAmelCase_ = model(**__snake_case)
lowerCAmelCase_ = encoding['''input_ids'''][0].tolist()
lowerCAmelCase_ = input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''))
lowerCAmelCase_ = outputs.logits[0][mask_position_id].argmax(dim=-1)
assert "Japan" == tokenizer.decode(__snake_case)
lowerCAmelCase_ = outputs.entity_logits[0][0].argmax().item()
lowerCAmelCase_ = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''')][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(__snake_case))
model.save_pretrained(__snake_case)
def snake_case_ ( __snake_case : List[str]) -> Dict:
lowerCAmelCase_ = ['''[MASK]''', '''[PAD]''', '''[UNK]''']
lowerCAmelCase_ = [json.loads(__snake_case) for line in open(__snake_case)]
lowerCAmelCase_ = {}
for entry in data:
lowerCAmelCase_ = entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
lowerCAmelCase_ = entity_id
break
lowerCAmelCase_ = F'''{language}:{entity_name}'''
lowerCAmelCase_ = entity_id
return new_mapping
if __name__ == "__main__":
A_ : Union[str, Any] =argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
A_ : Optional[Any] =parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 274 | import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = list_field(
default=[], metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
}, )
UpperCAmelCase = list_field(
default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
UpperCAmelCase = list_field(
default=[8, 32, 128, 512], metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Use FP16 to accelerate inference."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Benchmark training of model"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Verbose memory tracing"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
}, )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Trace memory line by line"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save result to a CSV file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save all print statements in a log file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Whether to print environment information"} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
}, )
UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv."}, )
UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv."}, )
UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv for training."}, )
UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv for training."}, )
UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving environment information."}, )
UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""", metadata={"help": "Log filename used if print statements are saved in log."}, )
UpperCAmelCase = field(default=3, metadata={"help": "Times an experiment will be run."} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
}, )
def UpperCamelCase_ ( self : Union[str, Any] ):
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , _A , )
def UpperCamelCase_ ( self : str ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase_ ( self : List[Any] ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase_ ( self : Optional[int] ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 10 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class UpperCamelCase_ :
def __init__( self :Union[str, Any] , __A :List[Any] , __A :List[Any]=2 , __A :Tuple=True , __A :Any=False , __A :Dict=10 , __A :Optional[int]=3 , __A :Union[str, Any]=32 * 8 , __A :Optional[Any]=32 * 8 , __A :List[str]=4 , __A :Dict=64 , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = is_training
SCREAMING_SNAKE_CASE__ = use_auxiliary_loss
SCREAMING_SNAKE_CASE__ = num_queries
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = min_size
SCREAMING_SNAKE_CASE__ = max_size
SCREAMING_SNAKE_CASE__ = num_labels
SCREAMING_SNAKE_CASE__ = hidden_dim
SCREAMING_SNAKE_CASE__ = hidden_dim
def _snake_case ( self :List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_A )
SCREAMING_SNAKE_CASE__ = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_A )
SCREAMING_SNAKE_CASE__ = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_A ) > 0.5
).float()
SCREAMING_SNAKE_CASE__ = (torch.rand((self.batch_size, self.num_labels) , device=_A ) > 0.5).long()
SCREAMING_SNAKE_CASE__ = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def _snake_case ( self :int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
SCREAMING_SNAKE_CASE__ = self.num_queries
SCREAMING_SNAKE_CASE__ = self.num_labels
SCREAMING_SNAKE_CASE__ = [1, 1, 1, 1]
SCREAMING_SNAKE_CASE__ = self.num_channels
SCREAMING_SNAKE_CASE__ = 64
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = self.hidden_dim
SCREAMING_SNAKE_CASE__ = self.hidden_dim
SCREAMING_SNAKE_CASE__ = self.hidden_dim
return config
def _snake_case ( self :List[str] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def _snake_case ( self :List[str] , __A :Optional[int] , __A :Union[str, Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = output.encoder_hidden_states
SCREAMING_SNAKE_CASE__ = output.pixel_decoder_hidden_states
SCREAMING_SNAKE_CASE__ = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_A ) , config.decoder_layers )
def _snake_case ( self :Union[str, Any] , __A :List[Any] , __A :Dict , __A :int , __A :List[Any]=False ) -> int:
"""simple docstring"""
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = MaskaFormerModel(config=_A )
model.to(_A )
model.eval()
SCREAMING_SNAKE_CASE__ = model(pixel_values=_A , pixel_mask=_A )
SCREAMING_SNAKE_CASE__ = model(_A , output_hidden_states=_A )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_A , _A )
def _snake_case ( self :Union[str, Any] , __A :Optional[Any] , __A :Any , __A :Any , __A :Any , __A :Tuple ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = MaskaFormerForUniversalSegmentation(config=_A )
model.to(_A )
model.eval()
def comm_check_on_output(__A :List[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(pixel_values=_A , pixel_mask=_A )
SCREAMING_SNAKE_CASE__ = model(_A )
comm_check_on_output(_A )
SCREAMING_SNAKE_CASE__ = model(
pixel_values=_A , pixel_mask=_A , mask_labels=_A , class_labels=_A )
comm_check_on_output(_A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class UpperCamelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
lowerCamelCase_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
lowerCamelCase_ = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def _snake_case ( self :Union[str, Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = MaskaFormerModelTester(self )
SCREAMING_SNAKE_CASE__ = ConfigTester(self , config_class=_A , has_text_modality=_A )
def _snake_case ( self :Optional[Any] ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self :List[str] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_A , **_A , output_hidden_states=_A )
def _snake_case ( self :Tuple ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_A )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def _snake_case ( self :Dict ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def _snake_case ( self :str ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def _snake_case ( self :Union[str, Any] ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def _snake_case ( self :List[str] ) -> Any:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`""" )
def _snake_case ( self :Tuple ) -> int:
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _snake_case ( self :Union[str, Any] ) -> Tuple:
"""simple docstring"""
pass
def _snake_case ( self :Optional[int] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ = model_class(_A )
SCREAMING_SNAKE_CASE__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _A )
@slow
def _snake_case ( self :int ) -> List[str]:
"""simple docstring"""
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
SCREAMING_SNAKE_CASE__ = MaskaFormerModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def _snake_case ( self :Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (self.model_tester.min_size,) * 2
SCREAMING_SNAKE_CASE__ = {
"""pixel_values""": torch.randn((2, 3, *size) , device=_A ),
"""mask_labels""": torch.randn((2, 10, *size) , device=_A ),
"""class_labels""": torch.zeros(2 , 10 , device=_A ).long(),
}
SCREAMING_SNAKE_CASE__ = self.model_tester.get_config()
SCREAMING_SNAKE_CASE__ = MaskaFormerForUniversalSegmentation(_A ).to(_A )
SCREAMING_SNAKE_CASE__ = model(**_A )
self.assertTrue(outputs.loss is not None )
def _snake_case ( self :Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(_A , **_A , output_hidden_states=_A )
def _snake_case ( self :List[str] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ = model_class(_A ).to(_A )
SCREAMING_SNAKE_CASE__ = model(**_A , output_attentions=_A )
self.assertTrue(outputs.attentions is not None )
def _snake_case ( self :Optional[Any] ) -> Dict:
"""simple docstring"""
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ = self.all_model_classes[1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ = model_class(_A )
model.to(_A )
model.train()
SCREAMING_SNAKE_CASE__ = model(_A , mask_labels=_A , class_labels=_A ).loss
loss.backward()
def _snake_case ( self :Tuple ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.all_model_classes[1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = model_class(_A ).to(_A )
model.train()
SCREAMING_SNAKE_CASE__ = model(_A , mask_labels=_A , class_labels=_A )
SCREAMING_SNAKE_CASE__ = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
SCREAMING_SNAKE_CASE__ = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
SCREAMING_SNAKE_CASE__ = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
SCREAMING_SNAKE_CASE__ = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class UpperCamelCase_ ( unittest.TestCase ):
@cached_property
def _snake_case ( self :Any ) -> Any:
"""simple docstring"""
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def _snake_case ( self :Union[str, Any] ) -> Any:
"""simple docstring"""
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def _snake_case ( self :Optional[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(_A )
SCREAMING_SNAKE_CASE__ = self.default_image_processor
SCREAMING_SNAKE_CASE__ = prepare_img()
SCREAMING_SNAKE_CASE__ = image_processor(_A , return_tensors="""pt""" ).to(_A )
SCREAMING_SNAKE_CASE__ = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_A , (1, 3, 384, 384) )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(**_A )
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]] ).to(_A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _A , atol=_A ) )
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]] ).to(_A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _A , atol=_A ) )
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]] ).to(_A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _A , atol=_A ) )
def _snake_case ( self :List[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_A ).eval()
SCREAMING_SNAKE_CASE__ = self.default_image_processor
SCREAMING_SNAKE_CASE__ = prepare_img()
SCREAMING_SNAKE_CASE__ = image_processor(_A , return_tensors="""pt""" ).to(_A )
SCREAMING_SNAKE_CASE__ = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_A , (1, 3, 384, 384) )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(**_A )
# masks_queries_logits
SCREAMING_SNAKE_CASE__ = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
SCREAMING_SNAKE_CASE__ = [
[-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
[-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
[-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
]
SCREAMING_SNAKE_CASE__ = torch.tensor(_A ).to(_A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _A , atol=_A ) )
# class_queries_logits
SCREAMING_SNAKE_CASE__ = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
SCREAMING_SNAKE_CASE__ = torch.tensor(
[
[1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
[0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
[0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
] ).to(_A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _A , atol=_A ) )
def _snake_case ( self :str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(_A ).eval()
SCREAMING_SNAKE_CASE__ = self.default_image_processor
SCREAMING_SNAKE_CASE__ = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE__ = inputs["""pixel_values"""].to(_A )
SCREAMING_SNAKE_CASE__ = [el.to(_A ) for el in inputs["""mask_labels"""]]
SCREAMING_SNAKE_CASE__ = [el.to(_A ) for el in inputs["""class_labels"""]]
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(**_A )
self.assertTrue(outputs.loss is not None ) | 6 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , __snake_case ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__snake_case ).base_version ) >= version.parse(__snake_case ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
_UpperCamelCase = None
if isinstance(__snake_case , __snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__snake_case ),)
_UpperCamelCase = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__snake_case , __snake_case ):
values += (getattr(__snake_case , __snake_case ),)
_UpperCamelCase = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_UpperCamelCase = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , __snake_case , stacklevel=__snake_case )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__snake_case ) == 0:
return
elif len(__snake_case ) == 1:
return values[0]
return values
| 10 | 0 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowercase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = CpmAntTokenizer
UpperCAmelCase_ : Optional[int] = False
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
super().setUp()
lowerCAmelCase = [
'''<d>''',
'''</d>''',
'''<s>''',
'''</s>''',
'''</_>''',
'''<unk>''',
'''<pad>''',
'''</n>''',
'''我''',
'''是''',
'''C''',
'''P''',
'''M''',
'''A''',
'''n''',
'''t''',
]
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
@tooslow
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
lowerCAmelCase = CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''' )
lowerCAmelCase = '''今天天气真好！'''
lowerCAmelCase = ['''今天''', '''天气''', '''真''', '''好''', '''！''']
lowerCAmelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
lowerCAmelCase = '''今天天气真好！'''
lowerCAmelCase = [tokenizer.bos_token] + tokens
lowerCAmelCase = [6, 9802, 14962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_A ) , _A )
lowerCAmelCase = tokenizer.decode(_A )
self.assertEqual(_A , _A )
| 312 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
return (preds == labels).mean()
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
UpperCAmelCase = field(metadata={"help": "Should contain the data files for the task."} )
UpperCAmelCase = field(
default=128, metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _snake_case ( ):
# 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 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__snake_case , p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __snake_case , __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def _snake_case ( __snake_case ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 10 | 0 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : str = "T5Config"
class UpperCAmelCase_ ( __lowercase ):
__SCREAMING_SNAKE_CASE : Tuple = 'mt5'
__SCREAMING_SNAKE_CASE : int = MTaConfig
class UpperCAmelCase_ ( __lowercase ):
__SCREAMING_SNAKE_CASE : List[Any] = 'mt5'
__SCREAMING_SNAKE_CASE : Tuple = MTaConfig
class UpperCAmelCase_ ( __lowercase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 'mt5'
__SCREAMING_SNAKE_CASE : str = MTaConfig
| 289 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "trocr"
UpperCAmelCase = ["past_key_values"]
UpperCAmelCase = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : List[str] , _A : Optional[Any]=5_0265 , _A : Optional[Any]=1024 , _A : Optional[Any]=12 , _A : Any=16 , _A : Any=4096 , _A : Optional[Any]="gelu" , _A : Union[str, Any]=512 , _A : Dict=0.1 , _A : List[str]=0.0 , _A : Optional[Any]=0.0 , _A : Union[str, Any]=2 , _A : Any=0.02 , _A : List[str]=0.0 , _A : List[str]=True , _A : str=False , _A : List[str]=True , _A : Optional[Any]=True , _A : Optional[int]=1 , _A : int=0 , _A : Any=2 , **_A : Optional[int] , ):
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , decoder_start_token_id=_A , **_A , )
| 10 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowercase : Optional[int] =logging.getLogger(__name__)
def lowerCAmelCase_ ( _lowercase : Optional[int] , _lowercase : Union[str, Any]) -> str:
"""simple docstring"""
return (preds == labels).mean()
@dataclass
class snake_case__ :
"""simple docstring"""
__lowerCAmelCase :Any = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
__lowerCAmelCase :Any = field(
default=__lowercase , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
__lowerCAmelCase :int = field(
default=__lowercase , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
__lowerCAmelCase :Any = field(
default=__lowercase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
@dataclass
class snake_case__ :
"""simple docstring"""
__lowerCAmelCase :str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
__lowerCAmelCase :str = field(metadata={"help": "Should contain the data files for the task."} )
__lowerCAmelCase :List[Any] = 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."
)
} , )
__lowerCAmelCase :List[Any] = field(
default=__lowercase , metadata={"help": "Overwrite the cached training and evaluation sets"} )
def lowerCAmelCase_ ( ) -> Optional[int]:
"""simple docstring"""
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
a__ : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
a__ , a__ , a__ : Optional[int] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
""" --overwrite_output_dir to overcome.""")
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , __snake_case)
# Set seed
set_seed(training_args.seed)
try:
a__ : Any = processors[data_args.task_name]()
a__ : Dict = processor.get_labels()
a__ : Union[str, Any] = len(__snake_case)
except KeyError:
raise ValueError("""Task not found: %s""" % (data_args.task_name))
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
a__ : Optional[Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
a__ : Union[str, Any] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
a__ : int = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a__ : int = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_lowercase : List[Any]) -> Dict:
a__ : Tuple = np.argmax(p.predictions , axis=1)
return {"acc": simple_accuracy(__snake_case , p.label_ids)}
# Data collator
a__ : str = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8) if training_args.fpaa else None
# Initialize our Trainer
a__ : Optional[int] = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
a__ : Union[str, Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""")
a__ : str = trainer.evaluate()
a__ : List[Any] = os.path.join(training_args.output_dir , """eval_results.txt""")
if trainer.is_world_master():
with open(__snake_case , """w""") as writer:
logger.info("""***** Eval results *****""")
for key, value in result.items():
logger.info(""" %s = %s""" , __snake_case , __snake_case)
writer.write("""%s = %s\n""" % (key, value))
results.update(__snake_case)
return results
def lowerCAmelCase_ ( _lowercase : Optional[int]) -> Any:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 136 | import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , _A : Optional[Any] , _A : Any=13 , _A : Union[str, Any]=7 , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[Any]=True , _A : Optional[int]=False , _A : Any=False , _A : int=False , _A : Optional[Any]=2 , _A : Any=99 , _A : str=0 , _A : Union[str, Any]=32 , _A : List[Any]=5 , _A : Tuple=4 , _A : List[str]=0.1 , _A : Union[str, Any]=0.1 , _A : int=512 , _A : Union[str, Any]=12 , _A : List[str]=2 , _A : int=0.02 , _A : Optional[Any]=3 , _A : Any=4 , _A : Optional[int]="last" , _A : Any=None , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_lengths
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = gelu_activation
_UpperCamelCase = sinusoidal_embeddings
_UpperCamelCase = causal
_UpperCamelCase = asm
_UpperCamelCase = n_langs
_UpperCamelCase = vocab_size
_UpperCamelCase = n_special
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = summary_type
_UpperCamelCase = use_proj
_UpperCamelCase = scope
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase = None
if self.use_input_lengths:
_UpperCamelCase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase = ids_tensor([self.batch_size] , 2 ).float()
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase_ ( self : str ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def UpperCamelCase_ ( self : str , _A : Union[str, Any] , _A : Optional[Any] , _A : str , _A : Tuple , _A : List[str] , _A : List[Any] , _A : Any , _A : str , _A : Optional[int] , ):
_UpperCamelCase = FlaubertModel(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , lengths=_A , langs=_A )
_UpperCamelCase = model(_A , langs=_A )
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[Any] , _A : str , _A : Optional[int] , _A : Optional[Any] , _A : List[str] , _A : int , _A : str , _A : List[Any] , _A : Any , ):
_UpperCamelCase = FlaubertWithLMHeadModel(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[str] , _A : List[str] , _A : Optional[Any] , _A : Union[str, Any] , _A : str , _A : List[str] , _A : Tuple , _A : Optional[int] , _A : Dict , ):
_UpperCamelCase = FlaubertForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Tuple , _A : str , _A : Tuple , _A : Tuple , _A : Union[str, Any] , _A : List[str] , _A : int , _A : str , _A : Dict , _A : List[Any] , ):
_UpperCamelCase = FlaubertForQuestionAnswering(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase_ ( self : List[Any] , _A : Union[str, Any] , _A : Tuple , _A : str , _A : int , _A : int , _A : Optional[int] , _A : Optional[int] , _A : int , _A : List[str] , ):
_UpperCamelCase = FlaubertForSequenceClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase_ ( self : Optional[int] , _A : List[str] , _A : Optional[Any] , _A : str , _A : Union[str, Any] , _A : List[Any] , _A : int , _A : List[Any] , _A : str , _A : List[str] , ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = FlaubertForTokenClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Tuple , _A : Dict , _A : str , _A : Optional[Any] , _A : List[str] , _A : Any , _A : Optional[int] , _A : Optional[Any] , _A : List[Any] , _A : List[str] , ):
_UpperCamelCase = self.num_choices
_UpperCamelCase = FlaubertForMultipleChoice(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": FlaubertModel,
"fill-mask": FlaubertWithLMHeadModel,
"question-answering": FlaubertForQuestionAnsweringSimple,
"text-classification": FlaubertForSequenceClassification,
"token-classification": FlaubertForTokenClassification,
"zero-shot": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Dict , _A : Dict , _A : Tuple , _A : int , _A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase_ ( self : str , _A : Any , _A : List[str] , _A : Optional[int]=False ):
_UpperCamelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = FlaubertModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , emb_dim=37 )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_A )
@slow
def UpperCamelCase_ ( self : str ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = FlaubertModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@slow
@require_torch_gpu
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_UpperCamelCase = True
_UpperCamelCase = model_class(config=_A )
_UpperCamelCase = self._prepare_for_class(_A , _A )
_UpperCamelCase = torch.jit.trace(
_A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_A , os.path.join(_A , '''traced_model.pt''' ) )
_UpperCamelCase = torch.jit.load(os.path.join(_A , '''traced_model.pt''' ) , map_location=_A )
loaded(inputs_dict['''input_ids'''].to(_A ) , inputs_dict['''attention_mask'''].to(_A ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
_UpperCamelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
_UpperCamelCase = model(_A )[0]
_UpperCamelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _A )
_UpperCamelCase = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _A , atol=1e-4 ) )
| 10 | 0 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}
class lowercase_ ( __lowercase ):
__lowerCamelCase = "efficientnet"
def __init__( self , __A = 3 , __A = 600 , __A = 2.0 , __A = 3.1 , __A = 8 , __A = [3, 3, 5, 3, 5, 5, 3] , __A = [32, 16, 24, 40, 80, 112, 192] , __A = [16, 24, 40, 80, 112, 192, 320] , __A = [] , __A = [1, 2, 2, 2, 1, 2, 1] , __A = [1, 2, 2, 3, 3, 4, 1] , __A = [1, 6, 6, 6, 6, 6, 6] , __A = 0.25 , __A = "swish" , __A = 2_560 , __A = "mean" , __A = 0.02 , __A = 0.001 , __A = 0.99 , __A = 0.5 , __A = 0.2 , **__A , ) -> int:
super().__init__(**_A )
SCREAMING_SNAKE_CASE_ : Any =num_channels
SCREAMING_SNAKE_CASE_ : Tuple =image_size
SCREAMING_SNAKE_CASE_ : Dict =width_coefficient
SCREAMING_SNAKE_CASE_ : int =depth_coefficient
SCREAMING_SNAKE_CASE_ : List[str] =depth_divisor
SCREAMING_SNAKE_CASE_ : Optional[int] =kernel_sizes
SCREAMING_SNAKE_CASE_ : str =in_channels
SCREAMING_SNAKE_CASE_ : Optional[int] =out_channels
SCREAMING_SNAKE_CASE_ : str =depthwise_padding
SCREAMING_SNAKE_CASE_ : str =strides
SCREAMING_SNAKE_CASE_ : Any =num_block_repeats
SCREAMING_SNAKE_CASE_ : Dict =expand_ratios
SCREAMING_SNAKE_CASE_ : Optional[Any] =squeeze_expansion_ratio
SCREAMING_SNAKE_CASE_ : Optional[int] =hidden_act
SCREAMING_SNAKE_CASE_ : List[str] =hidden_dim
SCREAMING_SNAKE_CASE_ : List[str] =pooling_type
SCREAMING_SNAKE_CASE_ : Dict =initializer_range
SCREAMING_SNAKE_CASE_ : Dict =batch_norm_eps
SCREAMING_SNAKE_CASE_ : Optional[int] =batch_norm_momentum
SCREAMING_SNAKE_CASE_ : List[Any] =dropout_rate
SCREAMING_SNAKE_CASE_ : List[Any] =drop_connect_rate
SCREAMING_SNAKE_CASE_ : Dict =sum(_A ) * 4
class lowercase_ ( __lowercase ):
__lowerCamelCase = version.parse("1.11" )
@property
def _snake_case ( self ) -> str:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _snake_case ( self ) -> List[str]:
return 1e-5
| 443 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ :
def __init__( self : Any , _A : int , _A : int=12 , _A : int=7 , _A : Tuple=True , _A : Optional[int]=True , _A : Union[str, Any]=True , _A : str=99 , _A : str=32 , _A : int=32 , _A : Optional[Any]=2 , _A : Dict=4 , _A : int=37 , _A : List[Any]=0.1 , _A : str=0.1 , _A : Any=512 , _A : int=0.02 , _A : Optional[Any]=0 , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = projection_dim
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = bos_token_id
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = None
if self.use_input_mask:
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
_UpperCamelCase = input_mask.numpy()
_UpperCamelCase , _UpperCamelCase = input_mask.shape
_UpperCamelCase = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_A ):
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = self.get_config()
return config, input_ids, tf.convert_to_tensor(_A )
def UpperCamelCase_ ( self : str ):
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : str , _A : Optional[Any] ):
_UpperCamelCase = TFBlipTextModel(config=_A )
_UpperCamelCase = model(_A , attention_mask=_A , training=_A )
_UpperCamelCase = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = (TFBlipTextModel,) if is_tf_available() else ()
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = BlipTextModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , hidden_size=37 )
def UpperCamelCase_ ( self : Dict ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCamelCase_ ( self : List[Any] ):
pass
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : List[str] ):
pass
@slow
def UpperCamelCase_ ( self : Optional[int] ):
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFBlipTextModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def UpperCamelCase_ ( self : int , _A : Optional[int]=True ):
super().test_pt_tf_model_equivalence(allow_missing_keys=_A )
| 10 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class lowerCAmelCase__ ( __lowercase ):
"""simple docstring"""
def __init__( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self.test()
def UpperCAmelCase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = False
while not completed:
if counter == 1:
self.reset()
__SCREAMING_SNAKE_CASE = self.advance()
if not self.does_advance(_A ):
raise Exception(
"""Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.update(_A )
counter += 1
if counter > 10_000:
raise Exception("""update() does not fulfill the constraint.""" )
if self.remaining() != 0:
raise Exception("""Custom Constraint is not defined correctly.""" )
@abstractmethod
def UpperCAmelCase__ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : int ) -> List[Any]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int ) -> Dict:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def UpperCAmelCase__ ( self : Optional[int] , __SCREAMING_SNAKE_CASE : str=False ) -> str:
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class lowerCAmelCase__ ( __lowercase ):
"""simple docstring"""
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : List[int] ) -> int:
"""simple docstring"""
super(_A , self ).__init__()
if not isinstance(_A , _A ) or len(_A ) == 0:
raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.' )
if any((not isinstance(_A , _A ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )
__SCREAMING_SNAKE_CASE = token_ids
__SCREAMING_SNAKE_CASE = len(self.token_ids )
__SCREAMING_SNAKE_CASE = -1 # the index of the currently fulfilled step
__SCREAMING_SNAKE_CASE = False
def UpperCAmelCase__ ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def UpperCAmelCase__ ( self : int , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(_A )}' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def UpperCAmelCase__ ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(_A )}' )
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
if self.does_advance(_A ):
self.fulfilled_idx += 1
__SCREAMING_SNAKE_CASE = True
if self.fulfilled_idx == (self.seqlen - 1):
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = completed
else:
# failed to make progress.
__SCREAMING_SNAKE_CASE = True
self.reset()
return stepped, completed, reset
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = 0
def UpperCAmelCase__ ( self : str ) -> Any:
"""simple docstring"""
return self.seqlen - (self.fulfilled_idx + 1)
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : Optional[int]=False ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = PhrasalConstraint(self.token_ids )
if stateful:
__SCREAMING_SNAKE_CASE = self.seqlen
__SCREAMING_SNAKE_CASE = self.fulfilled_idx
__SCREAMING_SNAKE_CASE = self.completed
return new_constraint
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Any , __SCREAMING_SNAKE_CASE : List[List[int]] , __SCREAMING_SNAKE_CASE : Optional[int]=True ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = max([len(_A ) for one in nested_token_ids] )
__SCREAMING_SNAKE_CASE = {}
for token_ids in nested_token_ids:
__SCREAMING_SNAKE_CASE = root
for tidx, token_id in enumerate(_A ):
if token_id not in level:
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = level[token_id]
if no_subsets and self.has_subsets(_A , _A ):
raise ValueError(
"""Each list in `nested_token_ids` can\'t be a complete subset of another list, but is"""
f' {nested_token_ids}.' )
__SCREAMING_SNAKE_CASE = root
def UpperCAmelCase__ ( self : str , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.trie
for current_token in current_seq:
__SCREAMING_SNAKE_CASE = start[current_token]
__SCREAMING_SNAKE_CASE = list(start.keys() )
return next_tokens
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.next_tokens(_A )
return len(_A ) == 0
def UpperCAmelCase__ ( self : int , __SCREAMING_SNAKE_CASE : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = list(root.values() )
if len(_A ) == 0:
return 1
else:
return sum([self.count_leaves(_A ) for nn in next_nodes] )
def UpperCAmelCase__ ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.count_leaves(_A )
return len(_A ) != leaf_count
class lowerCAmelCase__ ( __lowercase ):
"""simple docstring"""
def __init__( self : List[Any] , __SCREAMING_SNAKE_CASE : List[List[int]] ) -> Optional[Any]:
"""simple docstring"""
super(_A , self ).__init__()
if not isinstance(_A , _A ) or len(_A ) == 0:
raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
if any(not isinstance(_A , _A ) for token_ids in nested_token_ids ):
raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
if any(
any((not isinstance(_A , _A ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )
__SCREAMING_SNAKE_CASE = DisjunctiveTrie(_A )
__SCREAMING_SNAKE_CASE = nested_token_ids
__SCREAMING_SNAKE_CASE = self.trie.max_height
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = False
def UpperCAmelCase__ ( self : List[Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.trie.next_tokens(self.current_seq )
if len(_A ) == 0:
return None
else:
return token_list
def UpperCAmelCase__ ( self : Optional[int] , __SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(_A )}' )
__SCREAMING_SNAKE_CASE = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def UpperCAmelCase__ ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : int ) -> Any:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(_A )}' )
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
if self.does_advance(_A ):
self.current_seq.append(_A )
__SCREAMING_SNAKE_CASE = True
else:
__SCREAMING_SNAKE_CASE = True
self.reset()
__SCREAMING_SNAKE_CASE = self.trie.reached_leaf(self.current_seq )
__SCREAMING_SNAKE_CASE = completed
return stepped, completed, reset
def UpperCAmelCase__ ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def UpperCAmelCase__ ( self : List[str] , __SCREAMING_SNAKE_CASE : int=False ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = DisjunctiveConstraint(self.token_ids )
if stateful:
__SCREAMING_SNAKE_CASE = self.seqlen
__SCREAMING_SNAKE_CASE = self.current_seq
__SCREAMING_SNAKE_CASE = self.completed
return new_constraint
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Tuple , __SCREAMING_SNAKE_CASE : List[Constraint] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = constraints
# max # of steps required to fulfill a given constraint
__SCREAMING_SNAKE_CASE = max([c.seqlen for c in constraints] )
__SCREAMING_SNAKE_CASE = len(_A )
__SCREAMING_SNAKE_CASE = False
self.init_state()
def UpperCAmelCase__ ( self : List[str] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = [constraint.copy(stateful=_A ) for constraint in self.constraints]
def UpperCAmelCase__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def UpperCAmelCase__ ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
__SCREAMING_SNAKE_CASE = constraint.advance()
if isinstance(_A , _A ):
token_list.append(_A )
elif isinstance(_A , _A ):
token_list.extend(_A )
else:
__SCREAMING_SNAKE_CASE = self.inprogress_constraint.advance()
if isinstance(_A , _A ):
token_list.append(_A )
elif isinstance(_A , _A ):
token_list.extend(_A )
if len(_A ) == 0:
return None
else:
return token_list
def UpperCAmelCase__ ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] ) -> Union[str, Any]:
"""simple docstring"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.add(_A )
# the entire list of constraints are fulfilled
if self.completed:
break
def UpperCAmelCase__ ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = False, False
if self.completed:
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.inprogress_constraint.update(_A )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_A ) )
__SCREAMING_SNAKE_CASE = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
__SCREAMING_SNAKE_CASE = None
if len(self.pending_constraints ) == 0:
# we're done!
__SCREAMING_SNAKE_CASE = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(_A ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = pending_constraint.update(_A )
if not stepped:
raise Exception(
"""`constraint.update(token_id)` is not yielding incremental progress, """
"""even though `constraint.does_advance(token_id)` is true.""" )
if complete:
self.complete_constraints.append(_A )
__SCREAMING_SNAKE_CASE = None
if not complete and stepped:
__SCREAMING_SNAKE_CASE = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
__SCREAMING_SNAKE_CASE = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
__SCREAMING_SNAKE_CASE = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def UpperCAmelCase__ ( self : str , __SCREAMING_SNAKE_CASE : Optional[Any]=True ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
__SCREAMING_SNAKE_CASE = [
constraint.copy(stateful=_A ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
__SCREAMING_SNAKE_CASE = self.inprogress_constraint.copy(stateful=_A )
__SCREAMING_SNAKE_CASE = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 627 | from __future__ import annotations
_lowerCAmelCase = [True] * 1_000_001
_lowerCAmelCase = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_lowerCAmelCase = False
i += 1
def _snake_case ( __snake_case ):
return seive[n]
def _snake_case ( __snake_case ):
return any(digit in '''02468''' for digit in str(__snake_case ) )
def _snake_case ( __snake_case = 1000000 ):
_UpperCamelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(__snake_case ) and not contains_an_even_digit(__snake_case ):
_UpperCamelCase = str(__snake_case )
_UpperCamelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(__snake_case ) )]
if all(is_prime(__snake_case ) for i in list_nums ):
result.append(__snake_case )
return result
def _snake_case ( ):
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'{len(find_circular_primes()) = }')
| 10 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCAmelCase_ (_SCREAMING_SNAKE_CASE :Optional[Any] ) -> Dict:
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
UpperCamelCase = [num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def lowerCAmelCase_ (_SCREAMING_SNAKE_CASE :str ) -> Any:
if not isinstance(__snake_case , __snake_case ):
raise ValueError("n must be an integer" )
if n <= 0:
raise ValueError("n must be >= 0" )
a_ : Any = []
for num in range(len(__snake_case ) ):
a_ : List[str] = 0
while 2 * i * i <= odd_composites[num]:
a_ : Optional[int] = odd_composites[num] - 2 * i * i
if is_prime(__snake_case ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(__snake_case ) == n:
return list_nums
return []
def lowerCAmelCase_ () -> Dict:
return compute_nums(1 )[0]
if __name__ == "__main__":
print(F'{solution() = }')
| 473 | import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = DebertaVaTokenizer
UpperCAmelCase = DebertaVaTokenizerFast
UpperCAmelCase = True
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCamelCase = DebertaVaTokenizer(_A , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict , _A : Union[str, Any] ):
_UpperCamelCase = '''this is a test'''
_UpperCamelCase = '''this is a test'''
return input_text, output_text
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(_A ) , 3_0001 )
def UpperCamelCase_ ( self : List[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def UpperCamelCase_ ( self : List[str] ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Optional[Any] ):
pass
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[Any] ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : int ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Tuple ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = tokenizer.encode(_A )
_UpperCamelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''This is a test'''
_UpperCamelCase = [13, 1, 4398, 25, 21, 1289]
_UpperCamelCase = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = DebertaVaTokenizer(_A , keep_accents=_A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
_UpperCamelCase = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = DebertaVaTokenizer(_A )
_UpperCamelCase = tokenizer.encode('''sequence builders''' )
_UpperCamelCase = tokenizer.encode('''multi-sequence build''' )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_A , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 10 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCAmelCase = (720, 1280) # Height, Width
_UpperCAmelCase = (0.4, 0.6) # if height or width lower than this scale, drop it.
_UpperCAmelCase = 1 / 100
_UpperCAmelCase = """"""
_UpperCAmelCase = """"""
_UpperCAmelCase = """"""
_UpperCAmelCase = 250
def UpperCamelCase ( ):
'''simple docstring'''
A_ , A_ : List[str] = get_dataset(__snake_case ,__snake_case )
for index in range(__snake_case ):
A_ : List[Any] = random.sample(range(len(__snake_case ) ) ,4 )
A_ , A_ , A_ : Dict = update_image_and_anno(
__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,filter_scale=__snake_case ,)
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
A_ : str = random_chars(32 )
A_ : Tuple = path.split(os.sep )[-1].rsplit('.' ,1 )[0]
A_ : Any = f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
cva.imwrite(f'''{file_root}.jpg''' ,__snake_case ,[cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}''' )
A_ : Tuple = []
for anno in new_annos:
A_ : int = anno[3] - anno[1]
A_ : List[Any] = anno[4] - anno[2]
A_ : Tuple = anno[1] + width / 2
A_ : Optional[int] = anno[2] + height / 2
A_ : Optional[int] = f'''{anno[0]} {x_center} {y_center} {width} {height}'''
annos_list.append(__snake_case )
with open(f'''{file_root}.txt''' ,'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def UpperCamelCase ( __lowercase : Optional[int] ,__lowercase : Tuple ):
'''simple docstring'''
A_ : Optional[int] = []
A_ : List[str] = []
for label_file in glob.glob(os.path.join(__snake_case ,'*.txt' ) ):
A_ : Optional[Any] = label_file.split(os.sep )[-1].rsplit('.' ,1 )[0]
with open(__snake_case ) as in_file:
A_ : Optional[Any] = in_file.readlines()
A_ : Dict = os.path.join(__snake_case ,f'''{label_name}.jpg''' )
A_ : List[Any] = []
for obj_list in obj_lists:
A_ : Optional[Any] = obj_list.rstrip('\n' ).split(' ' )
A_ : List[Any] = float(obj[1] ) - float(obj[3] ) / 2
A_ : List[str] = float(obj[2] ) - float(obj[4] ) / 2
A_ : Tuple = float(obj[1] ) + float(obj[3] ) / 2
A_ : List[str] = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__snake_case )
labels.append(__snake_case )
return img_paths, labels
def UpperCamelCase ( __lowercase : Optional[int] ,__lowercase : Union[str, Any] ,__lowercase : Any ,__lowercase : List[Any] ,__lowercase : int ,__lowercase : Union[str, Any] = 0.0 ,):
'''simple docstring'''
A_ : Union[str, Any] = np.zeros([output_size[0], output_size[1], 3] ,dtype=np.uinta )
A_ : List[str] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
A_ : Optional[int] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
A_ : Optional[int] = int(scale_x * output_size[1] )
A_ : Optional[Any] = int(scale_y * output_size[0] )
A_ : List[str] = []
A_ : Union[str, Any] = []
for i, index in enumerate(__snake_case ):
A_ : Optional[Any] = all_img_list[index]
path_list.append(__snake_case )
A_ : List[Any] = all_annos[index]
A_ : Union[str, Any] = cva.imread(__snake_case )
if i == 0: # top-left
A_ : Any = cva.resize(__snake_case ,(divid_point_x, divid_point_y) )
A_ : Dict = img
for bbox in img_annos:
A_ : Optional[Any] = bbox[1] * scale_x
A_ : List[str] = bbox[2] * scale_y
A_ : Any = bbox[3] * scale_x
A_ : List[str] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
A_ : List[Any] = cva.resize(__snake_case ,(output_size[1] - divid_point_x, divid_point_y) )
A_ : Any = img
for bbox in img_annos:
A_ : Optional[int] = scale_x + bbox[1] * (1 - scale_x)
A_ : Union[str, Any] = bbox[2] * scale_y
A_ : str = scale_x + bbox[3] * (1 - scale_x)
A_ : List[Any] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
A_ : Any = cva.resize(__snake_case ,(divid_point_x, output_size[0] - divid_point_y) )
A_ : int = img
for bbox in img_annos:
A_ : Union[str, Any] = bbox[1] * scale_x
A_ : str = scale_y + bbox[2] * (1 - scale_y)
A_ : Union[str, Any] = bbox[3] * scale_x
A_ : Union[str, Any] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
A_ : Any = cva.resize(
__snake_case ,(output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
A_ : Any = img
for bbox in img_annos:
A_ : Optional[int] = scale_x + bbox[1] * (1 - scale_x)
A_ : Optional[int] = scale_y + bbox[2] * (1 - scale_y)
A_ : Optional[int] = scale_x + bbox[3] * (1 - scale_x)
A_ : List[Any] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
A_ : List[str] = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def UpperCamelCase ( __lowercase : Any ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
A_ : Any = ascii_lowercase + digits
return "".join(random.choice(__snake_case ) for _ in range(__snake_case ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 558 | import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[vertex]
def UpperCamelCase_ ( self : Optional[Any] , _A : List[str] , _A : Union[str, Any] ):
_UpperCamelCase = pos
def UpperCamelCase_ ( self : Any , _A : List[str] , _A : int , _A : Optional[Any] , _A : Union[str, Any] ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCamelCase = 2 * start + 1
else:
_UpperCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCamelCase , _UpperCamelCase = heap[smallest_child], positions[smallest_child]
_UpperCamelCase , _UpperCamelCase = (
heap[start],
positions[start],
)
_UpperCamelCase , _UpperCamelCase = temp, tempa
_UpperCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _A )
self.top_to_bottom(_A , _A , _A , _A )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : Optional[Any] , _A : int , _A : Optional[int] ):
_UpperCamelCase = position[index]
while index != 0:
_UpperCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
_UpperCamelCase = heap[parent]
_UpperCamelCase = position[parent]
self.set_position(position[parent] , _A )
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , _A )
break
_UpperCamelCase = parent
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , 0 )
def UpperCamelCase_ ( self : int , _A : Tuple , _A : int ):
_UpperCamelCase = len(_A ) // 2 - 1
for i in range(_A , -1 , -1 ):
self.top_to_bottom(_A , _A , len(_A ) , _A )
def UpperCamelCase_ ( self : Any , _A : int , _A : List[str] ):
_UpperCamelCase = positions[0]
_UpperCamelCase = sys.maxsize
self.top_to_bottom(_A , 0 , len(_A ) , _A )
return temp
def _snake_case ( __snake_case ):
_UpperCamelCase = Heap()
_UpperCamelCase = [0] * len(__snake_case )
_UpperCamelCase = [-1] * len(__snake_case ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCamelCase = []
for vertex in range(len(__snake_case ) ):
distance_tv.append(sys.maxsize )
positions.append(__snake_case )
heap.node_position.append(__snake_case )
_UpperCamelCase = []
_UpperCamelCase = 1
_UpperCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCamelCase = 0
_UpperCamelCase = distance
heap.heapify(__snake_case , __snake_case )
for _ in range(1 , len(__snake_case ) ):
_UpperCamelCase = heap.delete_minimum(__snake_case , __snake_case )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(__snake_case )]
):
_UpperCamelCase = distance
heap.bottom_to_top(
__snake_case , heap.get_position(__snake_case ) , __snake_case , __snake_case )
_UpperCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
_lowerCAmelCase = int(input("Enter number of edges: ").strip())
_lowerCAmelCase = defaultdict(list)
for _ in range(edges_number):
_lowerCAmelCase = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 10 | 0 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ = logging.get_logger(__name__)
# TODO: upload to AWS
a_ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
),
}
class UpperCAmelCase_ ( __lowercase ):
UpperCamelCase ="retribert"
def __init__( self , UpperCamelCase_=3_05_22 , UpperCamelCase_=7_68 , UpperCamelCase_=8 , UpperCamelCase_=12 , UpperCamelCase_=30_72 , UpperCamelCase_="gelu" , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=5_12 , UpperCamelCase_=2 , UpperCamelCase_=0.0_2 , UpperCamelCase_=1E-12 , UpperCamelCase_=True , UpperCamelCase_=1_28 , UpperCamelCase_=0 , **UpperCamelCase_ , ) -> Tuple:
super().__init__(pad_token_id=_A , **_A )
__lowercase : Optional[Any] = vocab_size
__lowercase : Tuple = hidden_size
__lowercase : List[str] = num_hidden_layers
__lowercase : Any = num_attention_heads
__lowercase : Dict = hidden_act
__lowercase : Tuple = intermediate_size
__lowercase : Optional[int] = hidden_dropout_prob
__lowercase : Tuple = attention_probs_dropout_prob
__lowercase : Tuple = max_position_embeddings
__lowercase : Union[str, Any] = type_vocab_size
__lowercase : Optional[int] = initializer_range
__lowercase : Any = layer_norm_eps
__lowercase : List[Any] = share_encoders
__lowercase : Tuple = projection_dim
| 76 | import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
@staticmethod
def UpperCamelCase_ ( _A : Any ):
_UpperCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : str , *_A : int , **_A : List[Any] ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCamelCase = kwargs.pop('''main_process_only''' , _A )
_UpperCamelCase = kwargs.pop('''in_order''' , _A )
if self.isEnabledFor(_A ):
if self._should_log(_A ):
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
elif in_order:
_UpperCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
state.wait_for_everyone()
def _snake_case ( __snake_case , __snake_case = None ):
if log_level is None:
_UpperCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , __snake_case )
_UpperCamelCase = logging.getLogger(__snake_case )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__snake_case , {} )
| 10 | 0 |
'''simple docstring'''
from __future__ import annotations
_lowercase = []
def lowerCamelCase__ ( a , a , a ):
for i in range(len(__snake_case ) ):
if board[row][i] == 1:
return False
for i in range(len(__snake_case ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(__snake_case , -1 , -1 ) , range(__snake_case , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(__snake_case , -1 , -1 ) , range(__snake_case , len(__snake_case ) ) ):
if board[i][j] == 1:
return False
return True
def lowerCamelCase__ ( a , a ):
if row >= len(__snake_case ):
solution.append(__snake_case )
printboard(__snake_case )
print()
return True
for i in range(len(__snake_case ) ):
if is_safe(__snake_case , __snake_case , __snake_case ):
__snake_case = 1
solve(__snake_case , row + 1 )
__snake_case = 0
return False
def lowerCamelCase__ ( a ):
for i in range(len(__snake_case ) ):
for j in range(len(__snake_case ) ):
if board[i][j] == 1:
print('Q' , end=' ' )
else:
print('.' , end=' ' )
print()
# n=int(input("The no. of queens"))
_lowercase = 8
_lowercase = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("""The total no. of solutions are :""", len(solution))
| 356 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = "▁"
_lowerCAmelCase = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = BertGenerationTokenizer
UpperCAmelCase = False
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[str] ):
super().setUp()
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''<s>'''
_UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(_A ) , 1002 )
def UpperCamelCase_ ( self : Dict ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [285, 46, 10, 170, 382] , )
_UpperCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
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(_A )
self.assertListEqual(
_A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def UpperCamelCase_ ( self : Union[str, Any] ):
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''Hello World!'''
_UpperCamelCase = [1_8536, 2260, 101]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@slow
def UpperCamelCase_ ( self : int ):
_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 = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@require_torch
@slow
def UpperCamelCase_ ( self : Dict ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
_UpperCamelCase = list(self.big_tokenizer.get_vocab().keys() )[:10]
_UpperCamelCase = ''' '''.join(_A )
_UpperCamelCase = self.big_tokenizer.encode_plus(_A , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = BertGenerationConfig()
_UpperCamelCase = BertGenerationEncoder(_A )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_A )
model(**_A )
@slow
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_A , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 10 | 0 |
'''simple docstring'''
def snake_case_ ( __snake_case : Optional[int] , __snake_case : int , __snake_case : Optional[Any] , __snake_case : List[str]) -> Union[str, Any]:
# Return True if there is node that has not iterated.
lowerCAmelCase_ = [False] * len(__snake_case)
lowerCAmelCase_ = []
queue.append(__snake_case)
lowerCAmelCase_ = True
while queue:
lowerCAmelCase_ = queue.pop(0)
for ind in range(len(graph[u])):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(__snake_case)
lowerCAmelCase_ = True
lowerCAmelCase_ = u
return visited[t]
def snake_case_ ( __snake_case : str , __snake_case : Any , __snake_case : Union[str, Any]) -> str:
# This array is filled by BFS and to store path
lowerCAmelCase_ = [-1] * (len(__snake_case))
lowerCAmelCase_ = 0
while bfs(__snake_case , __snake_case , __snake_case , __snake_case):
lowerCAmelCase_ = float('''Inf''')
lowerCAmelCase_ = sink
while s != source:
# Find the minimum value in select path
lowerCAmelCase_ = min(__snake_case , graph[parent[s]][s])
lowerCAmelCase_ = parent[s]
max_flow += path_flow
lowerCAmelCase_ = sink
while v != source:
lowerCAmelCase_ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowerCAmelCase_ = parent[v]
return max_flow
A_ : Optional[int] =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
A_ , A_ : Tuple =0, 5
print(ford_fulkerson(graph, source, sink))
| 274 | 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 lowerCAmelCase_ ( __lowercase, __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = StableUnCLIPPipeline
UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
UpperCAmelCase = False
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = 32
_UpperCamelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
_UpperCamelCase = DDPMScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1000 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule='''squaredcos_cap_v2''' , )
# regular denoising components
torch.manual_seed(0 )
_UpperCamelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
_UpperCamelCase = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
_UpperCamelCase = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_0085 , beta_end=0.012 , prediction_type='''v_prediction''' , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL()
_UpperCamelCase = {
# 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 : Dict , _A : Tuple , _A : Dict=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''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 ):
_UpperCamelCase = torch_device == '''cpu'''
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''' )
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# 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()
_UpperCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCamelCase = pipe('''anime turle''' , generator=_A , output_type='''np''' )
_UpperCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
def UpperCamelCase_ ( self : Optional[Any] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = pipe(
'''anime turtle''' , prior_num_inference_steps=2 , num_inference_steps=2 , output_type='''np''' , )
_UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 10 | 0 |
_lowerCamelCase = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o': 'ABBAB',
'p': 'ABBBA',
'q': 'ABBBB',
'r': 'BAAAA',
's': 'BAAAB',
't': 'BAABA',
'u': 'BAABB',
'v': 'BBBAB',
'w': 'BABAA',
'x': 'BABAB',
'y': 'BABBA',
'z': 'BABBB',
' ': ' ',
}
_lowerCamelCase = {value: key for key, value in encode_dict.items()}
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Dict ):
SCREAMING_SNAKE_CASE__ = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Tuple ):
if set(__snake_case ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only \'A\', \'B\' and spaces""" )
SCREAMING_SNAKE_CASE__ = """"""
for word in coded.split():
while len(__snake_case ) != 0:
decoded += decode_dict[word[:5]]
SCREAMING_SNAKE_CASE__ = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod() | 6 | from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( __snake_case , __snake_case ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def _snake_case ( __snake_case , __snake_case ):
if dataset.ndim != value_array.ndim:
_UpperCamelCase = (
'''Wrong input data\'s dimensions... '''
f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(__snake_case )
try:
if dataset.shape[1] != value_array.shape[1]:
_UpperCamelCase = (
'''Wrong input data\'s shape... '''
f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(__snake_case )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''' )
if dataset.dtype != value_array.dtype:
_UpperCamelCase = (
'''Input data have different datatype... '''
f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(__snake_case )
_UpperCamelCase = []
for value in value_array:
_UpperCamelCase = euclidean(__snake_case , dataset[0] )
_UpperCamelCase = dataset[0].tolist()
for dataset_value in dataset[1:]:
_UpperCamelCase = euclidean(__snake_case , __snake_case )
if dist > temp_dist:
_UpperCamelCase = temp_dist
_UpperCamelCase = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _snake_case ( __snake_case , __snake_case ):
return np.dot(__snake_case , __snake_case ) / (norm(__snake_case ) * norm(__snake_case ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> int:
lowerCAmelCase , lowerCAmelCase = coefficient_matrix.shape
lowerCAmelCase , lowerCAmelCase = constant_matrix.shape
if rowsa != colsa:
lowerCAmelCase = f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(__snake_case )
if colsa != 1:
lowerCAmelCase = f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(__snake_case )
if rowsa != rowsa:
lowerCAmelCase = (
'''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
f"received {rowsa}x{colsa} and {rowsa}x{colsa}"
)
raise ValueError(__snake_case )
if len(__snake_case ) != rowsa:
lowerCAmelCase = (
'''Number of initial values must be equal to number of rows in coefficient '''
f"matrix but received {len(__snake_case )} and {rowsa}"
)
raise ValueError(__snake_case )
if iterations <= 0:
raise ValueError('''Iterations must be at least 1''' )
lowerCAmelCase = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
lowerCAmelCase , lowerCAmelCase = table.shape
strictly_diagonally_dominant(__snake_case )
# Iterates the whole matrix for given number of times
for _ in range(__snake_case ):
lowerCAmelCase = []
for row in range(__snake_case ):
lowerCAmelCase = 0
for col in range(__snake_case ):
if col == row:
lowerCAmelCase = table[row][col]
elif col == cols - 1:
lowerCAmelCase = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
lowerCAmelCase = (temp + val) / denom
new_val.append(__snake_case )
lowerCAmelCase = new_val
return [float(__snake_case ) for i in new_val]
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> List[str]:
lowerCAmelCase , lowerCAmelCase = table.shape
lowerCAmelCase = True
for i in range(0 , __snake_case ):
lowerCAmelCase = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('''Coefficient matrix is not strictly diagonally dominant''' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 312 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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 lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = ShapEPipeline
UpperCAmelCase = ["prompt"]
UpperCAmelCase = ["prompt"]
UpperCAmelCase = [
"num_images_per_prompt",
"num_inference_steps",
"generator",
"latents",
"guidance_scale",
"frame_size",
"output_type",
"return_dict",
]
UpperCAmelCase = False
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return 32
@property
def UpperCamelCase_ ( self : int ):
return 32
@property
def UpperCamelCase_ ( self : List[str] ):
return self.time_input_dim * 4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 8
@property
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCamelCase_ ( self : List[Any] ):
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(_A )
@property
def UpperCamelCase_ ( self : int ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''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''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
_UpperCamelCase = PriorTransformer(**_A )
return model
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
_UpperCamelCase = {
'''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,
),
}
_UpperCamelCase = ShapERenderer(**_A )
return model
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.dummy_prior
_UpperCamelCase = self.dummy_text_encoder
_UpperCamelCase = self.dummy_tokenizer
_UpperCamelCase = self.dummy_renderer
_UpperCamelCase = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1024 , prediction_type='''sample''' , use_karras_sigmas=_A , clip_sample=_A , clip_sample_range=1.0 , )
_UpperCamelCase = {
'''prior''': prior,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase_ ( self : Tuple , _A : Tuple , _A : Optional[int]=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''prompt''': '''horse''',
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = '''cpu'''
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = pipe(**self.get_dummy_inputs(_A ) )
_UpperCamelCase = output.images[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_UpperCamelCase = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase_ ( self : Any ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = torch_device == '''cpu'''
_UpperCamelCase = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_A , relax_max_difference=_A , )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**_A )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = 1
_UpperCamelCase = 2
_UpperCamelCase = self.get_dummy_inputs(_A )
for key in inputs.keys():
if key in self.batch_params:
_UpperCamelCase = batch_size * [inputs[key]]
_UpperCamelCase = pipe(**_A , num_images_per_prompt=_A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : str ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_np_out.npy''' )
_UpperCamelCase = ShapEPipeline.from_pretrained('''openai/shap-e''' )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(0 )
_UpperCamelCase = pipe(
'''a shark''' , generator=_A , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_A , _A )
| 10 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , A : Optional[Any] , A : Any=1_3 , A : Union[str, Any]=7 , A : List[str]=True , A : List[str]=True , A : List[str]=True , A : List[str]=True , A : List[Any]=True , A : Optional[int]=False , A : Any=False , A : int=False , A : Optional[Any]=2 , A : Any=9_9 , A : str=0 , A : Union[str, Any]=3_2 , A : List[Any]=5 , A : Tuple=4 , A : List[str]=0.1 , A : Union[str, Any]=0.1 , A : int=5_1_2 , A : Union[str, Any]=1_2 , A : List[str]=2 , A : int=0.02 , A : Optional[Any]=3 , A : Any=4 , A : Optional[int]="last" , A : Any=None , A : Dict=None , ):
_UpperCAmelCase : Union[str, Any] = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : str = seq_length
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : Tuple = use_input_lengths
_UpperCAmelCase : Optional[int] = use_token_type_ids
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Optional[Any] = gelu_activation
_UpperCAmelCase : List[Any] = sinusoidal_embeddings
_UpperCAmelCase : Dict = causal
_UpperCAmelCase : int = asm
_UpperCAmelCase : List[str] = n_langs
_UpperCAmelCase : Optional[int] = vocab_size
_UpperCAmelCase : str = n_special
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : List[Any] = num_hidden_layers
_UpperCAmelCase : Optional[int] = num_attention_heads
_UpperCAmelCase : Optional[int] = hidden_dropout_prob
_UpperCAmelCase : str = attention_probs_dropout_prob
_UpperCAmelCase : int = max_position_embeddings
_UpperCAmelCase : List[str] = type_vocab_size
_UpperCAmelCase : int = type_sequence_label_size
_UpperCAmelCase : Optional[int] = initializer_range
_UpperCAmelCase : Any = num_labels
_UpperCAmelCase : List[str] = num_choices
_UpperCAmelCase : Union[str, Any] = summary_type
_UpperCAmelCase : Dict = use_proj
_UpperCAmelCase : Tuple = scope
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : str = None
if self.use_input_lengths:
_UpperCAmelCase : List[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCAmelCase : List[str] = None
if self.use_token_type_ids:
_UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCAmelCase : List[str] = None
_UpperCAmelCase : Optional[Any] = None
_UpperCAmelCase : Any = None
if self.use_labels:
_UpperCAmelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , 2 ).float()
_UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : str = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def snake_case_ ( self : str ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def snake_case_ ( self : str , A : Union[str, Any] , A : Optional[Any] , A : str , A : Tuple , A : List[str] , A : List[Any] , A : Any , A : str , A : Optional[int] , ):
_UpperCAmelCase : List[Any] = FlaubertModel(config=_A )
model.to(_A )
model.eval()
_UpperCAmelCase : Optional[Any] = model(_A , lengths=_A , langs=_A )
_UpperCAmelCase : Dict = model(_A , langs=_A )
_UpperCAmelCase : List[str] = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self : Tuple , A : List[Any] , A : str , A : Optional[int] , A : Optional[Any] , A : List[str] , A : int , A : str , A : List[Any] , A : Any , ):
_UpperCAmelCase : Any = FlaubertWithLMHeadModel(_A )
model.to(_A )
model.eval()
_UpperCAmelCase : List[str] = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ ( self : Tuple , A : List[str] , A : List[str] , A : Optional[Any] , A : Union[str, Any] , A : str , A : List[str] , A : Tuple , A : Optional[int] , A : Dict , ):
_UpperCAmelCase : Optional[Any] = FlaubertForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
_UpperCAmelCase : List[str] = model(_A )
_UpperCAmelCase : str = model(_A , start_positions=_A , end_positions=_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case_ ( self : Tuple , A : str , A : Tuple , A : Tuple , A : Union[str, Any] , A : List[str] , A : int , A : str , A : Dict , A : List[Any] , ):
_UpperCAmelCase : Any = FlaubertForQuestionAnswering(_A )
model.to(_A )
model.eval()
_UpperCAmelCase : List[str] = model(_A )
_UpperCAmelCase : Union[str, Any] = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
_UpperCAmelCase : Optional[int] = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((_UpperCAmelCase ) , ) : Any = result_with_labels.to_tuple()
_UpperCAmelCase : Any = model(_A , start_positions=_A , end_positions=_A )
((_UpperCAmelCase ) , ) : Union[str, Any] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def snake_case_ ( self : List[Any] , A : Union[str, Any] , A : Tuple , A : str , A : int , A : int , A : Optional[int] , A : Optional[int] , A : int , A : List[str] , ):
_UpperCAmelCase : str = FlaubertForSequenceClassification(_A )
model.to(_A )
model.eval()
_UpperCAmelCase : Tuple = model(_A )
_UpperCAmelCase : Dict = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def snake_case_ ( self : Optional[int] , A : List[str] , A : Optional[Any] , A : str , A : Union[str, Any] , A : List[Any] , A : int , A : List[Any] , A : str , A : List[str] , ):
_UpperCAmelCase : Optional[int] = self.num_labels
_UpperCAmelCase : Union[str, Any] = FlaubertForTokenClassification(_A )
model.to(_A )
model.eval()
_UpperCAmelCase : int = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case_ ( self : Tuple , A : Dict , A : str , A : Optional[Any] , A : List[str] , A : Any , A : Optional[int] , A : Optional[Any] , A : List[Any] , A : List[str] , ):
_UpperCAmelCase : str = self.num_choices
_UpperCAmelCase : Optional[Any] = FlaubertForMultipleChoice(config=_A )
model.to(_A )
model.eval()
_UpperCAmelCase : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : str = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : str = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case_ ( self : Tuple ):
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : int = config_and_inputs
_UpperCAmelCase : List[Any] = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"lengths": input_lengths,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : List[str] = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
__SCREAMING_SNAKE_CASE : Tuple = (
{
'feature-extraction': FlaubertModel,
'fill-mask': FlaubertWithLMHeadModel,
'question-answering': FlaubertForQuestionAnsweringSimple,
'text-classification': FlaubertForSequenceClassification,
'token-classification': FlaubertForTokenClassification,
'zero-shot': FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def snake_case_ ( self : Union[str, Any] , A : Dict , A : Dict , A : Tuple , A : int , A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def snake_case_ ( self : str , A : Any , A : List[str] , A : Optional[int]=False ):
_UpperCAmelCase : Optional[Any] = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_UpperCAmelCase : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
_UpperCAmelCase : Optional[int] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def snake_case_ ( self : str ):
_UpperCAmelCase : str = FlaubertModelTester(self )
_UpperCAmelCase : Optional[Any] = ConfigTester(self , config_class=_A , emb_dim=3_7 )
def snake_case_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def snake_case_ ( self : str ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_A )
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_A )
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_A )
def snake_case_ ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_A )
def snake_case_ ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_A )
def snake_case_ ( self : Any ):
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_A )
def snake_case_ ( self : Optional[int] ):
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_A )
@slow
def snake_case_ ( self : str ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Tuple = FlaubertModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@slow
@require_torch_gpu
def snake_case_ ( self : List[Any] ):
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_UpperCAmelCase : List[str] = True
_UpperCAmelCase : Dict = model_class(config=_A )
_UpperCAmelCase : List[Any] = self._prepare_for_class(_A , _A )
_UpperCAmelCase : Optional[int] = torch.jit.trace(
_A , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_A , os.path.join(_A , "traced_model.pt" ) )
_UpperCAmelCase : Union[str, Any] = torch.jit.load(os.path.join(_A , "traced_model.pt" ) , map_location=_A )
loaded(inputs_dict["input_ids"].to(_A ) , inputs_dict["attention_mask"].to(_A ) )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
@slow
def snake_case_ ( self : int ):
_UpperCAmelCase : List[Any] = FlaubertModel.from_pretrained("flaubert/flaubert_base_cased" )
_UpperCAmelCase : Optional[int] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
with torch.no_grad():
_UpperCAmelCase : Union[str, Any] = model(_A )[0]
_UpperCAmelCase : List[str] = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , _A )
_UpperCAmelCase : List[Any] = torch.tensor(
[[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _A , atol=1e-4 ) )
| 289 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCAmelCase = HfApi()
_lowerCAmelCase = {}
# fmt: off
_lowerCAmelCase = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
_lowerCAmelCase = api.list_models(filter="diffusers")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
_lowerCAmelCase = "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]
print(f'Started running {mod.modelId}!!!')
if mod.modelId.startswith("CompVis"):
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
else:
_lowerCAmelCase = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
_lowerCAmelCase = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
_lowerCAmelCase = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
_lowerCAmelCase = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
)
print(f'{mod.modelId} has passed successfully!!!')
| 10 | 0 |
from __future__ import annotations
_lowercase : Tuple =8.988E9 # units = N * m^s * C^-2
def lowerCAmelCase_ ( _lowercase : str , _lowercase : Any , _lowercase : Tuple , _lowercase : Optional[Any]) -> Tuple:
"""simple docstring"""
a__ : str = abs(chargea * chargea)
if (force, chargea, chargea, distance).count(0) != 1:
raise ValueError("""One and only one argument must be 0""")
if distance < 0:
raise ValueError("""Distance cannot be negative""")
if force == 0:
a__ : Union[str, Any] = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
a__ : int = abs(__snake_case) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
a__ : Tuple = abs(__snake_case) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
a__ : Dict = (COULOMBS_CONSTANT * charge_product / abs(__snake_case)) ** 0.5
return {"distance": distance}
raise ValueError("""Exactly one argument must be 0""")
if __name__ == "__main__":
import doctest
doctest.testmod()
| 136 | from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( __snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += [key]
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
def _snake_case ( *__snake_case ):
def decorator(__snake_case ):
_UpperCamelCase = getattr(__snake_case , '''handle_key''' , [] )
handle += keys
setattr(__snake_case , '''handle_key''' , __snake_case )
return func
return decorator
class lowerCAmelCase_ ( __lowercase ):
def __new__( cls : Optional[Any] , _A : Optional[Any] , _A : Optional[int] , _A : Union[str, Any] ):
_UpperCamelCase = super().__new__(cls , _A , _A , _A )
if not hasattr(_A , '''key_handler''' ):
setattr(_A , '''key_handler''' , {} )
setattr(_A , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
_UpperCamelCase = getattr(_A , '''handle_key''' , [] )
for key in handled_keys:
_UpperCamelCase = value
return new_cls
@staticmethod
def UpperCamelCase_ ( cls : str ):
_UpperCamelCase = get_character()
if char != KEYMAP["undefined"]:
_UpperCamelCase = ord(_A )
_UpperCamelCase = cls.key_handler.get(_A )
if handler:
_UpperCamelCase = char
return handler(cls )
else:
return None
def _snake_case ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 10 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def SCREAMING_SNAKE_CASE_ ( ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ : Optional[int] =[randint(-1_0_0_0 , 1_0_0_0 ) for i in range(1_0 )]
SCREAMING_SNAKE_CASE_ : str =randint(-5_0_0_0 , 5_0_0_0 )
return (arr, r)
_lowercase = make_dataset()
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str] ) -> Tuple:
for triplet in permutations(__snake_case , 3 ):
if sum(__snake_case ) == target:
return tuple(sorted(__snake_case ) )
return (0, 0, 0)
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int , UpperCAmelCase_ : Any ) -> List[str]:
arr.sort()
SCREAMING_SNAKE_CASE_ : Union[str, Any] =len(__snake_case )
for i in range(n - 1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict =i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
SCREAMING_SNAKE_CASE_ : Dict ='''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] ='''
triplet_sum1(*dataset)
'''
SCREAMING_SNAKE_CASE_ : Tuple ='''
triplet_sum2(*dataset)
'''
SCREAMING_SNAKE_CASE_ : List[str] =repeat(setup=__snake_case , stmt=__snake_case , repeat=5 , number=1_0_0_0_0 )
SCREAMING_SNAKE_CASE_ : int =repeat(setup=__snake_case , stmt=__snake_case , repeat=5 , number=1_0_0_0_0 )
return (min(__snake_case ), min(__snake_case ))
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowercase = solution_times()
print(F"The time for naive implementation is {times[0]}.")
print(F"The time for optimized implementation is {times[1]}.")
| 443 | import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class lowerCAmelCase_ ( unittest.TestCase ):
UpperCAmelCase = MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A , num_return_sequences=2 , return_tensors=_A )
self.assertEqual(
_A , [
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
] , )
_UpperCamelCase = text_generator.model.config.eos_token_id
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=_A , num_return_sequences=2 , batch_size=2 , return_tensors=_A , )
self.assertEqual(
_A , [
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
[
{'''generated_token_ids''': ANY(_A )},
{'''generated_token_ids''': ANY(_A )},
],
] , )
@require_tf
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_UpperCamelCase = text_generator('''This is a test''' , do_sample=_A )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_UpperCamelCase = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=_A )
self.assertEqual(
_A , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def UpperCamelCase_ ( self : int , _A : str , _A : Union[str, Any] , _A : Any ):
_UpperCamelCase = TextGenerationPipeline(model=_A , tokenizer=_A )
return text_generator, ["This is a test", "Another test"]
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = '''Hello I believe in'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_UpperCamelCase = text_generator(_A )
self.assertEqual(
_A , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_UpperCamelCase = text_generator(_A , stop_sequence=''' fe''' )
self.assertEqual(_A , [{'''generated_text''': '''Hello I believe in fe'''}] )
def UpperCamelCase_ ( self : Any , _A : List[Any] , _A : Union[str, Any] ):
_UpperCamelCase = text_generator.model
_UpperCamelCase = text_generator.tokenizer
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = pipeline(task='''text-generation''' , model=_A , tokenizer=_A , return_full_text=_A )
_UpperCamelCase = text_generator('''This is a test''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_UpperCamelCase = text_generator('''This is a test''' , return_full_text=_A )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_UpperCamelCase = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_UpperCamelCase = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=_A )
self.assertEqual(
_A , [
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
[{'''generated_text''': ANY(_A )}, {'''generated_text''': ANY(_A )}],
] , )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_text=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_full_text=_A , return_tensors=_A )
with self.assertRaises(_A ):
_UpperCamelCase = text_generator('''test''' , return_text=_A , return_tensors=_A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_UpperCamelCase = text_generator('''''' )
self.assertEqual(_A , [{'''generated_text''': ANY(_A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_UpperCamelCase = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_UpperCamelCase = ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 500 , max_new_tokens=20 )
_UpperCamelCase = text_generator('''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_A ):
text_generator(
'''This is a test''' * 500 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
# Classic `model_kwargs`
_UpperCamelCase = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_UpperCamelCase = pipe('''This is a test''' )
self.assertEqual(
_A , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def UpperCamelCase_ ( self : Union[str, Any] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def UpperCamelCase_ ( self : Optional[int] ):
import torch
_UpperCamelCase = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=_A , top_p=0.5 )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = '''Hello world'''
_UpperCamelCase = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_UpperCamelCase = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_UpperCamelCase = logging.get_logger('''transformers.generation.utils''' )
_UpperCamelCase = '''Both `max_new_tokens`''' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 , max_new_tokens=1 )
self.assertIn(_A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_new_tokens=1 )
self.assertNotIn(_A , cl.out )
with CaptureLogger(_A ) as cl:
_UpperCamelCase = text_generator(_A , max_length=10 )
self.assertNotIn(_A , cl.out )
| 10 | 0 |
'''simple docstring'''
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument("""--model_ckpt""" , type=__snake_case , default="""microsoft/unixcoder-base-nine""" )
parser.add_argument("""--num_epochs""" , type=__snake_case , default=5 )
parser.add_argument("""--batch_size""" , type=__snake_case , default=6 )
parser.add_argument("""--gradient_accumulation_steps""" , type=__snake_case , default=1 )
parser.add_argument("""--freeze""" , type=__snake_case , default=__snake_case )
parser.add_argument("""--learning_rate""" , type=__snake_case , default=5E-4 )
parser.add_argument("""--seed""" , type=__snake_case , default=0 )
parser.add_argument("""--lr_scheduler_type""" , type=__snake_case , default="""cosine""" )
parser.add_argument("""--num_warmup_steps""" , type=__snake_case , default=10 )
parser.add_argument("""--weight_decay""" , type=__snake_case , default=0.01 )
parser.add_argument("""--output_dir""" , type=__snake_case , default="""./results""" )
return parser.parse_args()
UpperCAmelCase : int = load('accuracy')
def a__ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = eval_pred
__SCREAMING_SNAKE_CASE = np.argmax(__snake_case , axis=1 )
return metric.compute(predictions=__snake_case , references=__snake_case )
class lowerCAmelCase__ ( __lowercase ):
"""simple docstring"""
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE = trainer
def UpperCAmelCase__ ( self : int , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Tuple ) -> Dict:
"""simple docstring"""
if control.should_evaluate:
__SCREAMING_SNAKE_CASE = deepcopy(_A )
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix="""train""" )
return control_copy
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = get_args()
set_seed(args.seed )
__SCREAMING_SNAKE_CASE = load_dataset("""codeparrot/codecomplex""" , split="""train""" )
__SCREAMING_SNAKE_CASE = dataset.train_test_split(test_size=0.2 )
__SCREAMING_SNAKE_CASE = train_test["""test"""].train_test_split(test_size=0.5 )
__SCREAMING_SNAKE_CASE = DatasetDict(
{
"""train""": train_test["""train"""],
"""test""": test_validation["""train"""],
"""valid""": test_validation["""test"""],
} )
print("""Loading tokenizer and model""" )
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(args.model_ckpt )
__SCREAMING_SNAKE_CASE = tokenizer.eos_token
__SCREAMING_SNAKE_CASE = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt , num_labels=7 )
__SCREAMING_SNAKE_CASE = model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = ClassLabel(num_classes=7 , names=list(set(train_test_validation["""train"""]["""complexity"""] ) ) )
def tokenize(a__ ):
__SCREAMING_SNAKE_CASE = tokenizer(example["""src"""] , truncation=__snake_case , max_length=10_24 )
__SCREAMING_SNAKE_CASE = labels.straint(example["""complexity"""] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
__SCREAMING_SNAKE_CASE = train_test_validation.map(
__snake_case , batched=__snake_case , remove_columns=train_test_validation["""train"""].column_names , )
__SCREAMING_SNAKE_CASE = DataCollatorWithPadding(tokenizer=__snake_case )
__SCREAMING_SNAKE_CASE = TrainingArguments(
output_dir=args.output_dir , learning_rate=args.learning_rate , lr_scheduler_type=args.lr_scheduler_type , evaluation_strategy="""epoch""" , save_strategy="""epoch""" , logging_strategy="""epoch""" , per_device_train_batch_size=args.batch_size , per_device_eval_batch_size=args.batch_size , num_train_epochs=args.num_epochs , gradient_accumulation_steps=args.gradient_accumulation_steps , weight_decay=0.01 , metric_for_best_model="""accuracy""" , run_name="""complexity-java""" , report_to="""wandb""" , )
__SCREAMING_SNAKE_CASE = Trainer(
model=__snake_case , args=__snake_case , train_dataset=tokenized_datasets["""train"""] , eval_dataset=tokenized_datasets["""valid"""] , tokenizer=__snake_case , data_collator=__snake_case , compute_metrics=__snake_case , )
print("""Training...""" )
trainer.add_callback(CustomCallback(__snake_case ) )
trainer.train()
if __name__ == "__main__":
main()
| 627 | def _snake_case ( __snake_case = 100 ):
_UpperCamelCase = (n * (n + 1) // 2) ** 2
_UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 10 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
'RagModel',
'RagPreTrainedModel',
'RagSequenceForGeneration',
'RagTokenForGeneration',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
'TFRagModel',
'TFRagPreTrainedModel',
'TFRagSequenceForGeneration',
'TFRagTokenForGeneration',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 473 | import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
def constraint_to_multiple_of(__snake_case , __snake_case , __snake_case=0 , __snake_case=None ):
_UpperCamelCase = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCamelCase = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCamelCase = math.ceil(val / multiple ) * multiple
return x
_UpperCamelCase = (output_size, output_size) if isinstance(__snake_case , __snake_case ) else output_size
_UpperCamelCase , _UpperCamelCase = get_image_size(__snake_case )
_UpperCamelCase , _UpperCamelCase = output_size
# determine new height and width
_UpperCamelCase = output_height / input_height
_UpperCamelCase = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCamelCase = scale_width
else:
# fit height
_UpperCamelCase = scale_height
_UpperCamelCase = constraint_to_multiple_of(scale_height * input_height , multiple=__snake_case )
_UpperCamelCase = constraint_to_multiple_of(scale_width * input_width , multiple=__snake_case )
return (new_height, new_width)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = ["pixel_values"]
def __init__( self : List[Any] , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = False , _A : int = 1 , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : List[str] , ):
super().__init__(**_A )
_UpperCamelCase = size if size is not None else {'''height''': 384, '''width''': 384}
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = do_resize
_UpperCamelCase = size
_UpperCamelCase = keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of
_UpperCamelCase = resample
_UpperCamelCase = do_rescale
_UpperCamelCase = rescale_factor
_UpperCamelCase = do_normalize
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase_ ( self : List[str] , _A : np.ndarray , _A : Dict[str, int] , _A : bool = False , _A : int = 1 , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
_UpperCamelCase = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_UpperCamelCase = get_resize_output_image_size(
_A , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=_A , multiple=_A , )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : str , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return rescale(_A , scale=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : int , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def UpperCamelCase_ ( self : Optional[int] , _A : ImageInput , _A : bool = None , _A : int = None , _A : bool = None , _A : int = None , _A : PILImageResampling = None , _A : bool = None , _A : float = None , _A : bool = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : ChannelDimension = ChannelDimension.FIRST , **_A : str , ):
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(_A )
_UpperCamelCase = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCamelCase = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(_A ) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
_UpperCamelCase = [to_channel_dimension_format(_A , _A ) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
def UpperCamelCase_ ( self : Any , _A : Any , _A : List[Tuple] = None ):
_UpperCamelCase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_A ) != len(_A ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_A ):
_UpperCamelCase = target_sizes.numpy()
_UpperCamelCase = []
for idx in range(len(_A ) ):
_UpperCamelCase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_A )
_UpperCamelCase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_A )
else:
_UpperCamelCase = logits.argmax(dim=1 )
_UpperCamelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 10 | 0 |
import os
def UpperCamelCase ( __lowercase : Any = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(__snake_case ) ,__snake_case ) ) as input_file:
A_ : List[str] = [
[int(__snake_case ) for element in line.split(',' )]
for line in input_file.readlines()
]
A_ : List[str] = len(__snake_case )
A_ : Optional[Any] = len(matrix[0] )
A_ : Dict = [[-1 for _ in range(__snake_case )] for _ in range(__snake_case )]
for i in range(__snake_case ):
A_ : int = matrix[i][0]
for j in range(1 ,__snake_case ):
for i in range(__snake_case ):
A_ : Optional[Any] = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 ,__snake_case ):
A_ : Optional[int] = min(
minimal_path_sums[i][j] ,minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 ,-1 ,-1 ):
A_ : Tuple = min(
minimal_path_sums[i][j] ,minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 558 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , '''schedulers/''' ) )
_UpperCamelCase = self.diffusers_dir
shutil.copy(
os.path.join(_A , '''src/diffusers/schedulers/scheduling_ddpm.py''' ) , os.path.join(self.diffusers_dir , '''schedulers/scheduling_ddpm.py''' ) , )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = '''src/diffusers'''
shutil.rmtree(self.diffusers_dir )
def UpperCamelCase_ ( self : str , _A : List[str] , _A : Optional[Any] , _A : List[str] , _A : Optional[int]=None ):
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
_UpperCamelCase = comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
_UpperCamelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
_UpperCamelCase = black.format_str(_A , mode=_A )
_UpperCamelCase = os.path.join(self.diffusers_dir , '''new_code.py''' )
with open(_A , '''w''' , newline='''\n''' ) as f:
f.write(_A )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_A ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_A )
with open(_A , '''r''' ) as f:
self.assertTrue(f.read() , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput''' )
self.assertEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
# Base copy consistency
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , REFERENCE_CODE + '''\n''' , )
# With no empty line at the end
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' , '''DDPMSchedulerOutput''' , _A , )
# Copy consistency with rename
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , re.sub('''DDPM''' , '''Test''' , _A ) , )
# Copy consistency with a really long name
_UpperCamelCase = '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
self.check_copy_consistency(
F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , F"""{long_class_name}SchedulerOutput""" , re.sub('''Bert''' , _A , _A ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' , '''TestSchedulerOutput''' , _A , overwrite_result=re.sub('''DDPM''' , '''Test''' , _A ) , )
| 10 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__snake_case ) )
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# Base Case
if index == len(__snake_case ):
return True
# Recursive Step
for i in range(__snake_case ):
if valid_coloring(graph[index] , __snake_case , __snake_case ):
# Color current vertex
__lowercase : List[str] = i
# Validate coloring
if util_color(__snake_case , __snake_case , __snake_case , index + 1 ):
return True
# Backtrack
__lowercase : Optional[Any] = -1
return False
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
__lowercase : Optional[Any] = [-1] * len(__snake_case )
if util_color(__snake_case , __snake_case , __snake_case , 0 ):
return colored_vertices
return []
| 76 | import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCAmelCase = True
from torch.cuda.amp import autocast
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."} )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
UpperCAmelCase = field(
default=0.1, metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
}, )
UpperCAmelCase = field(
default=0.1, metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}, )
UpperCAmelCase = field(
default=0.0_5, metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
}, )
UpperCAmelCase = field(default=0.0, metadata={"help": "The LayerDrop probability."} )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
UpperCAmelCase = field(
default="train+validation", metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "The number of processes to use for the preprocessing."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
}, )
UpperCAmelCase = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"], metadata={"help": "A list of characters to remove from the transcripts."}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = True
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
def __call__( self : Union[str, Any] , _A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
_UpperCamelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
_UpperCamelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
_UpperCamelCase = self.processor.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
_UpperCamelCase = self.processor.pad(
labels=_A , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
_UpperCamelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
_UpperCamelCase = labels
return batch
class lowerCAmelCase_ ( __lowercase ):
def UpperCamelCase_ ( self : Dict , _A : nn.Module , _A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
_UpperCamelCase = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
_UpperCamelCase = self.compute_loss(_A , _A )
else:
_UpperCamelCase = self.compute_loss(_A , _A )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
_UpperCamelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
_UpperCamelCase = loss.sum() / (inputs['''labels'''] >= 0).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 = 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()
return loss.detach()
def _snake_case ( ):
# 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 = 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.
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
_UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCamelCase = 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()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
_UpperCamelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
_UpperCamelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
_UpperCamelCase = f"""[{"".join(data_args.chars_to_ignore )}]"""
def remove_special_characters(__snake_case ):
_UpperCamelCase = re.sub(__snake_case , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
_UpperCamelCase = train_dataset.map(__snake_case , remove_columns=['''sentence'''] )
_UpperCamelCase = eval_dataset.map(__snake_case , remove_columns=['''sentence'''] )
def extract_all_chars(__snake_case ):
_UpperCamelCase = ''' '''.join(batch['''text'''] )
_UpperCamelCase = list(set(__snake_case ) )
return {"vocab": [vocab], "all_text": [all_text]}
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=train_dataset.column_names , )
_UpperCamelCase = train_dataset.map(
__snake_case , batched=__snake_case , batch_size=-1 , keep_in_memory=__snake_case , remove_columns=eval_dataset.column_names , )
_UpperCamelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
_UpperCamelCase = {v: k for k, v in enumerate(__snake_case )}
_UpperCamelCase = vocab_dict[''' ''']
del vocab_dict[" "]
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = len(__snake_case )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(__snake_case , __snake_case )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=__snake_case , return_attention_mask=__snake_case )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=__snake_case , tokenizer=__snake_case )
_UpperCamelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
_UpperCamelCase = min(len(__snake_case ) , data_args.max_train_samples )
_UpperCamelCase = train_dataset.select(range(__snake_case ) )
if data_args.max_val_samples is not None:
_UpperCamelCase = eval_dataset.select(range(data_args.max_val_samples ) )
_UpperCamelCase = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(__snake_case ):
_UpperCamelCase , _UpperCamelCase = torchaudio.load(batch['''path'''] )
_UpperCamelCase = resampler(__snake_case ).squeeze().numpy()
_UpperCamelCase = 16000
_UpperCamelCase = batch['''text''']
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(__snake_case ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
_UpperCamelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(__snake_case )
return batch
_UpperCamelCase = train_dataset.map(
__snake_case , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
_UpperCamelCase = eval_dataset.map(
__snake_case , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__snake_case , num_proc=data_args.preprocessing_num_workers , )
# Metric
_UpperCamelCase = datasets.load_metric('''wer''' )
def compute_metrics(__snake_case ):
_UpperCamelCase = pred.predictions
_UpperCamelCase = np.argmax(__snake_case , axis=-1 )
_UpperCamelCase = processor.tokenizer.pad_token_id
_UpperCamelCase = processor.batch_decode(__snake_case )
# we do not want to group tokens when computing the metrics
_UpperCamelCase = processor.batch_decode(pred.label_ids , group_tokens=__snake_case )
_UpperCamelCase = wer_metric.compute(predictions=__snake_case , references=__snake_case )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
_UpperCamelCase = DataCollatorCTCWithPadding(processor=__snake_case , padding=__snake_case )
# Initialize our Trainer
_UpperCamelCase = CTCTrainer(
model=__snake_case , data_collator=__snake_case , args=__snake_case , compute_metrics=__snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
_UpperCamelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
_UpperCamelCase = model_args.model_name_or_path
else:
_UpperCamelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
_UpperCamelCase = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
_UpperCamelCase = train_result.metrics
_UpperCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case )
)
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''train''' , __snake_case )
trainer.save_metrics('''train''' , __snake_case )
trainer.save_state()
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(__snake_case )
_UpperCamelCase = min(__snake_case , len(__snake_case ) )
trainer.log_metrics('''eval''' , __snake_case )
trainer.save_metrics('''eval''' , __snake_case )
return results
if __name__ == "__main__":
main()
| 10 | 0 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def lowerCamelCase__ ( a ):
__snake_case = np.inf
def set_batch_size(a ) -> None:
nonlocal batch_size
if isinstance(__snake_case , __snake_case ):
__snake_case = min(__snake_case , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(__snake_case , __snake_case ):
__snake_case = min(__snake_case , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(__snake_case , __snake_case ) and feature.dtype == "binary":
__snake_case = min(__snake_case , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(__snake_case , __snake_case )
return None if batch_size is np.inf else batch_size
class a_ ( __lowercase ):
def __init__( self : List[str] , __lowerCAmelCase : NestedDataStructureLike[PathLike] , __lowerCAmelCase : Optional[NamedSplit] = None , __lowerCAmelCase : Optional[Features] = None , __lowerCAmelCase : str = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , __lowerCAmelCase : Optional[int] = None , **__lowerCAmelCase : Dict , ):
super().__init__(
_A , split=_A , features=_A , cache_dir=_A , keep_in_memory=_A , streaming=_A , num_proc=_A , **_A , )
__snake_case = path_or_paths if isinstance(_A , _A ) else {self.split: path_or_paths}
__snake_case = _PACKAGED_DATASETS_MODULES['parquet'][1]
__snake_case = Parquet(
cache_dir=_A , data_files=_A , features=_A , hash=_A , **_A , )
def lowercase__ ( self : List[str] ):
# Build iterable dataset
if self.streaming:
__snake_case = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
self.builder.download_and_prepare(
download_config=_A , download_mode=_A , verification_mode=_A , base_path=_A , num_proc=self.num_proc , )
__snake_case = self.builder.as_dataset(
split=self.split , verification_mode=_A , in_memory=self.keep_in_memory )
return dataset
class a_ :
def __init__( self : Optional[Any] , __lowerCAmelCase : Dataset , __lowerCAmelCase : Union[PathLike, BinaryIO] , __lowerCAmelCase : Optional[int] = None , **__lowerCAmelCase : List[str] , ):
__snake_case = dataset
__snake_case = path_or_buf
__snake_case = batch_size or get_writer_batch_size(dataset.features )
__snake_case = parquet_writer_kwargs
def lowercase__ ( self : int ):
__snake_case = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , 'wb+' ) as buffer:
__snake_case = self._write(file_obj=_A , batch_size=_A , **self.parquet_writer_kwargs )
else:
__snake_case = self._write(file_obj=self.path_or_buf , batch_size=_A , **self.parquet_writer_kwargs )
return written
def lowercase__ ( self : Tuple , __lowerCAmelCase : BinaryIO , __lowerCAmelCase : int , **__lowerCAmelCase : Union[str, Any] ):
__snake_case = 0
__snake_case = parquet_writer_kwargs.pop('path_or_buf' , _A )
__snake_case = self.dataset.features.arrow_schema
__snake_case = pq.ParquetWriter(_A , schema=_A , **_A )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , _A ) , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating parquet from Arrow format' , ):
__snake_case = query_table(
table=self.dataset._data , key=slice(_A , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(_A )
written += batch.nbytes
writer.close()
return written
| 356 | import math
class lowerCAmelCase_ :
def __init__( self : Tuple , _A : int=0 ): # a graph with Node 0,1,...,N-1
_UpperCamelCase = n
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # adjacency matrix for weight
_UpperCamelCase = [
[math.inf for j in range(0 , _A )] for i in range(0 , _A )
] # dp[i][j] stores minimum distance from i to j
def UpperCamelCase_ ( self : Dict , _A : str , _A : List[str] , _A : Optional[Any] ):
_UpperCamelCase = w
def UpperCamelCase_ ( self : Optional[int] ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
_UpperCamelCase = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCamelCase_ ( self : List[str] , _A : Optional[int] , _A : Optional[int] ):
return self.dp[u][v]
if __name__ == "__main__":
_lowerCAmelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 10 | 0 |
'''simple docstring'''
def snake_case_ ( __snake_case : Tuple = 100) -> Union[str, Any]:
lowerCAmelCase_ = (n * (n + 1) // 2) ** 2
lowerCAmelCase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 274 | import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
def _snake_case ( __snake_case=None , __snake_case=None ):
return field(default_factory=lambda: default , metadata=__snake_case )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = list_field(
default=[], metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
}, )
UpperCAmelCase = list_field(
default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
UpperCAmelCase = list_field(
default=[8, 32, 128, 512], metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Use FP16 to accelerate inference."} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Benchmark training of model"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Verbose memory tracing"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}, )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
}, )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Trace memory line by line"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save result to a CSV file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Save all print statements in a log file"} )
UpperCAmelCase = field(default=__lowercase, metadata={"help": "Whether to print environment information"} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
}, )
UpperCAmelCase = field(
default=F"""inference_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv."}, )
UpperCAmelCase = field(
default=F"""inference_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv."}, )
UpperCAmelCase = field(
default=F"""train_time_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving time results to csv for training."}, )
UpperCAmelCase = field(
default=F"""train_memory_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving memory results to csv for training."}, )
UpperCAmelCase = field(
default=F"""env_info_{round(time() )}.csv""", metadata={"help": "CSV filename used if saving environment information."}, )
UpperCAmelCase = field(
default=F"""log_{round(time() )}.csv""", metadata={"help": "Log filename used if print statements are saved in log."}, )
UpperCAmelCase = field(default=3, metadata={"help": "Times an experiment will be run."} )
UpperCAmelCase = field(
default=__lowercase, metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
}, )
def UpperCamelCase_ ( self : Union[str, Any] ):
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , _A , )
def UpperCamelCase_ ( self : str ):
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase_ ( self : List[Any] ):
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCamelCase_ ( self : Optional[int] ):
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 10 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCamelCase_ :
lowerCamelCase_ = 42
lowerCamelCase_ = None
lowerCamelCase_ = None
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE__ = Node(1 )
SCREAMING_SNAKE_CASE__ = Node(2 )
SCREAMING_SNAKE_CASE__ = Node(3 )
SCREAMING_SNAKE_CASE__ = Node(4 )
SCREAMING_SNAKE_CASE__ = Node(5 )
return tree
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: List[Any] ):
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: str ):
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: List[str] ):
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: List[str] ):
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Optional[int] ):
SCREAMING_SNAKE_CASE__ = []
if root is None:
return output
SCREAMING_SNAKE_CASE__ = deque([root] )
while process_queue:
SCREAMING_SNAKE_CASE__ = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Any , UpperCamelCase__: Optional[Any] ):
SCREAMING_SNAKE_CASE__ = []
def populate_output(UpperCamelCase__: Optional[int] , UpperCamelCase__: Optional[Any] ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(__snake_case , __snake_case )
return output
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Union[str, Any] , UpperCamelCase__: List[Any] ):
SCREAMING_SNAKE_CASE__ = []
def populate_output(UpperCamelCase__: Optional[int] , UpperCamelCase__: Tuple ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(__snake_case , __snake_case )
return output
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: str ):
if root is None:
return []
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = height(__snake_case )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(__snake_case , __snake_case ) )
SCREAMING_SNAKE_CASE__ = 1
else:
output.append(get_nodes_from_right_to_left(__snake_case , __snake_case ) )
SCREAMING_SNAKE_CASE__ = 0
return output
def SCREAMING_SNAKE_CASE__ ( ): # Main function for testing.
SCREAMING_SNAKE_CASE__ = make_tree()
print(f'''In-order Traversal: {inorder(__snake_case )}''' )
print(f'''Pre-order Traversal: {preorder(__snake_case )}''' )
print(f'''Post-order Traversal: {postorder(__snake_case )}''' , """\n""" )
print(f'''Height of Tree: {height(__snake_case )}''' , """\n""" )
print("""Complete Level Order Traversal: """ )
print(level_order(__snake_case ) , """\n""" )
print("""Level-wise order Traversal: """ )
for level in range(1 , height(__snake_case ) + 1 ):
print(f'''Level {level}:''' , get_nodes_from_left_to_right(__snake_case , level=__snake_case ) )
print("""\nZigZag order Traversal: """ )
print(zigzag(__snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 6 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , __snake_case ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__snake_case ).base_version ) >= version.parse(__snake_case ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
_UpperCamelCase = None
if isinstance(__snake_case , __snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__snake_case ),)
_UpperCamelCase = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__snake_case , __snake_case ):
values += (getattr(__snake_case , __snake_case ),)
_UpperCamelCase = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_UpperCamelCase = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , __snake_case , stacklevel=__snake_case )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__snake_case ) == 0:
return
elif len(__snake_case ) == 1:
return values[0]
return values
| 10 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : Union[str, Any] = {
'''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
'''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
'''kssteven/ibert-roberta-large-mnli''': (
'''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
),
}
class lowercase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase_ : Dict = """ibert"""
def __init__( self , __SCREAMING_SNAKE_CASE=30522 , __SCREAMING_SNAKE_CASE=768 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=12 , __SCREAMING_SNAKE_CASE=3072 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=512 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=0.0_2 , __SCREAMING_SNAKE_CASE=1e-12 , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE=0 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE="absolute" , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE="none" , **__SCREAMING_SNAKE_CASE , ) ->Any:
super().__init__(pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , **_A )
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = quant_mode
lowerCAmelCase = force_dequant
class lowercase_ ( __lowercase ):
"""simple docstring"""
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
if self.task == "multiple-choice":
lowerCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 312 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_lowerCAmelCase = logging.getLogger(__name__)
def _snake_case ( __snake_case , __snake_case ):
return (preds == labels).mean()
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained config name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, )
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
UpperCAmelCase = field(metadata={"help": "Should contain the data files for the task."} )
UpperCAmelCase = field(
default=128, metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
}, )
UpperCAmelCase = field(
default=__lowercase, metadata={"help": "Overwrite the cached training and evaluation sets"} )
def _snake_case ( ):
# 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 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__snake_case , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__snake_case , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__snake_case , p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , data_collator=__snake_case , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __snake_case , __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def _snake_case ( __snake_case ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 10 | 0 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class UpperCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self : str ):
_UpperCAmelCase : Tuple = torch.nn.Linear(1_0 , 1_0 )
_UpperCAmelCase : List[str] = torch.optim.SGD(model.parameters() , 0.1 )
_UpperCAmelCase : Any = Accelerator()
_UpperCAmelCase : Any = accelerator.prepare(_A )
try:
pickle.loads(pickle.dumps(_A ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 289 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "trocr"
UpperCAmelCase = ["past_key_values"]
UpperCAmelCase = {
"num_attention_heads": "decoder_attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "decoder_layers",
}
def __init__( self : List[str] , _A : Optional[Any]=5_0265 , _A : Optional[Any]=1024 , _A : Optional[Any]=12 , _A : Any=16 , _A : Any=4096 , _A : Optional[Any]="gelu" , _A : Union[str, Any]=512 , _A : Dict=0.1 , _A : List[str]=0.0 , _A : Optional[Any]=0.0 , _A : Union[str, Any]=2 , _A : Any=0.02 , _A : List[str]=0.0 , _A : List[str]=True , _A : str=False , _A : List[str]=True , _A : Optional[Any]=True , _A : Optional[int]=1 , _A : int=0 , _A : Any=2 , **_A : Optional[int] , ):
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , decoder_start_token_id=_A , **_A , )
| 10 | 0 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
_lowercase : Any =[
{"dataset": "wikipedia", "config_name": "20220301.de"},
{"dataset": "wikipedia", "config_name": "20220301.en"},
{"dataset": "wikipedia", "config_name": "20220301.fr"},
{"dataset": "wikipedia", "config_name": "20220301.frr"},
{"dataset": "wikipedia", "config_name": "20220301.it"},
{"dataset": "wikipedia", "config_name": "20220301.simple"},
{"dataset": "snli", "config_name": "plain_text"},
{"dataset": "eli5", "config_name": "LFQA_reddit"},
{"dataset": "wiki40b", "config_name": "en"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
{"dataset": "natural_questions", "config_name": "default"},
]
def lowerCAmelCase_ ( _lowercase : int=True) -> Optional[Any]:
"""simple docstring"""
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__lowercase ) )
class snake_case__ (__lowercase ):
"""simple docstring"""
__lowerCAmelCase :Dict = None
__lowerCAmelCase :Dict = None
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase ) -> Optional[Any]:
"""simple docstring"""
with TemporaryDirectory() as tmp_dir:
a__ : List[str] = dataset_module_factory(_A , cache_dir=_A )
a__ : int = import_main_class(dataset_module.module_path , dataset=_A )
a__ : List[str] = builder_cls(
cache_dir=_A , config_name=_A , hash=dataset_module.hash , )
a__ : Any = """/""".join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=_A ).replace(os.sep , """/""" ),
config.DATASET_INFO_FILENAME,
] )
a__ : Dict = cached_path(_A , cache_dir=_A )
self.assertTrue(os.path.exists(_A ) )
@pytest.mark.integration
def lowerCAmelCase_ ( _lowercase : int) -> Union[str, Any]:
"""simple docstring"""
a__ : List[Any] = tmp_path_factory.mktemp("""test_hf_gcp""") / """test_wikipedia_simple"""
a__ : Any = dataset_module_factory("""wikipedia""" , cache_dir=__snake_case)
a__ : int = import_main_class(dataset_module.module_path)
a__ : List[Any] = builder_cls(
cache_dir=__snake_case , config_name="""20220301.frr""" , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
a__ : str = None
builder_instance.download_and_prepare()
a__ : int = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def lowerCAmelCase_ ( _lowercase : Union[str, Any]) -> Optional[int]:
"""simple docstring"""
a__ : Union[str, Any] = dataset_module_factory("""wikipedia""" , cache_dir=__snake_case)
a__ : Tuple = import_main_class(dataset_module.module_path , dataset=__snake_case)
a__ : int = builder_cls(
cache_dir=__snake_case , config_name="""20220301.frr""" , hash=dataset_module.hash , )
a__ : List[Any] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(__snake_case , __snake_case)
assert "train" in ds
assert isinstance(ds["""train"""] , __snake_case)
assert next(iter(ds["""train"""]))
| 136 | import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ ( __lowercase ):
def __init__( self : Union[str, Any] , _A : Optional[Any] , _A : Any=13 , _A : Union[str, Any]=7 , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[str]=True , _A : List[Any]=True , _A : Optional[int]=False , _A : Any=False , _A : int=False , _A : Optional[Any]=2 , _A : Any=99 , _A : str=0 , _A : Union[str, Any]=32 , _A : List[Any]=5 , _A : Tuple=4 , _A : List[str]=0.1 , _A : Union[str, Any]=0.1 , _A : int=512 , _A : Union[str, Any]=12 , _A : List[str]=2 , _A : int=0.02 , _A : Optional[Any]=3 , _A : Any=4 , _A : Optional[int]="last" , _A : Any=None , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_lengths
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = gelu_activation
_UpperCamelCase = sinusoidal_embeddings
_UpperCamelCase = causal
_UpperCamelCase = asm
_UpperCamelCase = n_langs
_UpperCamelCase = vocab_size
_UpperCamelCase = n_special
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = summary_type
_UpperCamelCase = use_proj
_UpperCamelCase = scope
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase = None
if self.use_input_lengths:
_UpperCamelCase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase = ids_tensor([self.batch_size] , 2 ).float()
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase_ ( self : str ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def UpperCamelCase_ ( self : str , _A : Union[str, Any] , _A : Optional[Any] , _A : str , _A : Tuple , _A : List[str] , _A : List[Any] , _A : Any , _A : str , _A : Optional[int] , ):
_UpperCamelCase = FlaubertModel(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , lengths=_A , langs=_A )
_UpperCamelCase = model(_A , langs=_A )
_UpperCamelCase = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[Any] , _A : str , _A : Optional[int] , _A : Optional[Any] , _A : List[str] , _A : int , _A : str , _A : List[Any] , _A : Any , ):
_UpperCamelCase = FlaubertWithLMHeadModel(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : Tuple , _A : List[str] , _A : List[str] , _A : Optional[Any] , _A : Union[str, Any] , _A : str , _A : List[str] , _A : Tuple , _A : Optional[int] , _A : Dict , ):
_UpperCamelCase = FlaubertForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Tuple , _A : str , _A : Tuple , _A : Tuple , _A : Union[str, Any] , _A : List[str] , _A : int , _A : str , _A : Dict , _A : List[Any] , ):
_UpperCamelCase = FlaubertForQuestionAnswering(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
_UpperCamelCase = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
_UpperCamelCase = model(_A , start_positions=_A , end_positions=_A )
((_UpperCamelCase) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase_ ( self : List[Any] , _A : Union[str, Any] , _A : Tuple , _A : str , _A : int , _A : int , _A : Optional[int] , _A : Optional[int] , _A : int , _A : List[str] , ):
_UpperCamelCase = FlaubertForSequenceClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A )
_UpperCamelCase = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase_ ( self : Optional[int] , _A : List[str] , _A : Optional[Any] , _A : str , _A : Union[str, Any] , _A : List[Any] , _A : int , _A : List[Any] , _A : str , _A : List[str] , ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = FlaubertForTokenClassification(_A )
model.to(_A )
model.eval()
_UpperCamelCase = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Tuple , _A : Dict , _A : str , _A : Optional[Any] , _A : List[str] , _A : Any , _A : Optional[int] , _A : Optional[Any] , _A : List[Any] , _A : List[str] , ):
_UpperCamelCase = self.num_choices
_UpperCamelCase = FlaubertForMultipleChoice(config=_A )
model.to(_A )
model.eval()
_UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCamelCase = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''lengths''': input_lengths,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": FlaubertModel,
"fill-mask": FlaubertWithLMHeadModel,
"question-answering": FlaubertForQuestionAnsweringSimple,
"text-classification": FlaubertForSequenceClassification,
"token-classification": FlaubertForTokenClassification,
"zero-shot": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Dict , _A : Dict , _A : Tuple , _A : int , _A : Any ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase_ ( self : str , _A : Any , _A : List[str] , _A : Optional[int]=False ):
_UpperCamelCase = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
_UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = FlaubertModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , emb_dim=37 )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_A )
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_A )
@slow
def UpperCamelCase_ ( self : str ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = FlaubertModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@slow
@require_torch_gpu
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_UpperCamelCase = True
_UpperCamelCase = model_class(config=_A )
_UpperCamelCase = self._prepare_for_class(_A , _A )
_UpperCamelCase = torch.jit.trace(
_A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_A , os.path.join(_A , '''traced_model.pt''' ) )
_UpperCamelCase = torch.jit.load(os.path.join(_A , '''traced_model.pt''' ) , map_location=_A )
loaded(inputs_dict['''input_ids'''].to(_A ) , inputs_dict['''attention_mask'''].to(_A ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased''' )
_UpperCamelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
with torch.no_grad():
_UpperCamelCase = model(_A )[0]
_UpperCamelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _A )
_UpperCamelCase = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _A , atol=1e-4 ) )
| 10 | 0 |
def SCREAMING_SNAKE_CASE_ ( ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ : Dict =0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(__snake_case )[-1_0:]
if __name__ == "__main__":
print(solution())
| 443 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ :
def __init__( self : Any , _A : int , _A : int=12 , _A : int=7 , _A : Tuple=True , _A : Optional[int]=True , _A : Union[str, Any]=True , _A : str=99 , _A : str=32 , _A : int=32 , _A : Optional[Any]=2 , _A : Dict=4 , _A : int=37 , _A : List[Any]=0.1 , _A : str=0.1 , _A : Any=512 , _A : int=0.02 , _A : Optional[Any]=0 , _A : Dict=None , ):
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = projection_dim
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = bos_token_id
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase = None
if self.use_input_mask:
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
_UpperCamelCase = input_mask.numpy()
_UpperCamelCase , _UpperCamelCase = input_mask.shape
_UpperCamelCase = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_A ):
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = self.get_config()
return config, input_ids, tf.convert_to_tensor(_A )
def UpperCamelCase_ ( self : str ):
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : str , _A : Optional[Any] ):
_UpperCamelCase = TFBlipTextModel(config=_A )
_UpperCamelCase = model(_A , attention_mask=_A , training=_A )
_UpperCamelCase = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = (TFBlipTextModel,) if is_tf_available() else ()
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = BlipTextModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_A , hidden_size=37 )
def UpperCamelCase_ ( self : Dict ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCamelCase_ ( self : List[Any] ):
pass
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def UpperCamelCase_ ( self : List[str] ):
pass
@slow
def UpperCamelCase_ ( self : Optional[int] ):
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFBlipTextModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def UpperCamelCase_ ( self : int , _A : Optional[int]=True ):
super().test_pt_tf_model_equivalence(allow_missing_keys=_A )
| 10 | 0 |
'''simple docstring'''
def a__ ( a__ = 10**12 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(f"""{solution() = }""")
| 627 | from __future__ import annotations
_lowerCAmelCase = [True] * 1_000_001
_lowerCAmelCase = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
_lowerCAmelCase = False
i += 1
def _snake_case ( __snake_case ):
return seive[n]
def _snake_case ( __snake_case ):
return any(digit in '''02468''' for digit in str(__snake_case ) )
def _snake_case ( __snake_case = 1000000 ):
_UpperCamelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(__snake_case ) and not contains_an_even_digit(__snake_case ):
_UpperCamelCase = str(__snake_case )
_UpperCamelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(__snake_case ) )]
if all(is_prime(__snake_case ) for i in list_nums ):
result.append(__snake_case )
return result
def _snake_case ( ):
return len(find_circular_primes() )
if __name__ == "__main__":
print(f'{len(find_circular_primes()) = }')
| 10 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = MODEL_FOR_CAUSAL_LM_MAPPING
lowerCAmelCase__ : Union[str, Any] = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def A ( self ) -> Union[str, Any]:
a_ : Tuple = pipeline(task="text-generation" , model="sshleifer/tiny-ctrl" , framework="pt" )
# Using `do_sample=False` to force deterministic output
a_ : Union[str, Any] = text_generator("This is a test" , do_sample=_A )
self.assertEqual(
_A , [
{
"generated_text": (
"This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
" oscope. FiliFili@@"
)
}
] , )
a_ : List[Any] = text_generator(["This is a test", "This is a second test"] )
self.assertEqual(
_A , [
[
{
"generated_text": (
"This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
" oscope. FiliFili@@"
)
}
],
[
{
"generated_text": (
"This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"
" oscope. oscope. FiliFili@@"
)
}
],
] , )
a_ : str = text_generator("This is a test" , do_sample=_A , num_return_sequences=2 , return_tensors=_A )
self.assertEqual(
_A , [
{"generated_token_ids": ANY(_A )},
{"generated_token_ids": ANY(_A )},
] , )
a_ : Any = text_generator.model.config.eos_token_id
a_ : str = "<pad>"
a_ : str = text_generator(
["This is a test", "This is a second test"] , do_sample=_A , num_return_sequences=2 , batch_size=2 , return_tensors=_A , )
self.assertEqual(
_A , [
[
{"generated_token_ids": ANY(_A )},
{"generated_token_ids": ANY(_A )},
],
[
{"generated_token_ids": ANY(_A )},
{"generated_token_ids": ANY(_A )},
],
] , )
@require_tf
def A ( self ) -> Union[str, Any]:
a_ : str = pipeline(task="text-generation" , model="sshleifer/tiny-ctrl" , framework="tf" )
# Using `do_sample=False` to force deterministic output
a_ : int = text_generator("This is a test" , do_sample=_A )
self.assertEqual(
_A , [
{
"generated_text": (
"This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
" please,"
)
}
] , )
a_ : str = text_generator(["This is a test", "This is a second test"] , do_sample=_A )
self.assertEqual(
_A , [
[
{
"generated_text": (
"This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
" please,"
)
}
],
[
{
"generated_text": (
"This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"
" Cannes 閲閲Cannes Cannes Cannes 攵 please,"
)
}
],
] , )
def A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
a_ : Any = TextGenerationPipeline(model=_A , tokenizer=_A )
return text_generator, ["This is a test", "Another test"]
def A ( self ) -> List[str]:
a_ : str = "Hello I believe in"
a_ : List[str] = pipeline("text-generation" , model="hf-internal-testing/tiny-random-gpt2" )
a_ : Optional[Any] = text_generator(_A )
self.assertEqual(
_A , [{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}] , )
a_ : Optional[Any] = text_generator(_A , stop_sequence=" fe" )
self.assertEqual(_A , [{"generated_text": "Hello I believe in fe"}] )
def A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
a_ : int = text_generator.model
a_ : Optional[int] = text_generator.tokenizer
a_ : str = text_generator("This is a test" )
self.assertEqual(_A , [{"generated_text": ANY(_A )}] )
self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) )
a_ : Tuple = text_generator("This is a test" , return_full_text=_A )
self.assertEqual(_A , [{"generated_text": ANY(_A )}] )
self.assertNotIn("This is a test" , outputs[0]["generated_text"] )
a_ : int = pipeline(task="text-generation" , model=_A , tokenizer=_A , return_full_text=_A )
a_ : Union[str, Any] = text_generator("This is a test" )
self.assertEqual(_A , [{"generated_text": ANY(_A )}] )
self.assertNotIn("This is a test" , outputs[0]["generated_text"] )
a_ : Any = text_generator("This is a test" , return_full_text=_A )
self.assertEqual(_A , [{"generated_text": ANY(_A )}] )
self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) )
a_ : int = text_generator(["This is great !", "Something else"] , num_return_sequences=2 , do_sample=_A )
self.assertEqual(
_A , [
[{"generated_text": ANY(_A )}, {"generated_text": ANY(_A )}],
[{"generated_text": ANY(_A )}, {"generated_text": ANY(_A )}],
] , )
if text_generator.tokenizer.pad_token is not None:
a_ : Optional[Any] = text_generator(
["This is great !", "Something else"] , num_return_sequences=2 , batch_size=2 , do_sample=_A )
self.assertEqual(
_A , [
[{"generated_text": ANY(_A )}, {"generated_text": ANY(_A )}],
[{"generated_text": ANY(_A )}, {"generated_text": ANY(_A )}],
] , )
with self.assertRaises(_A ):
a_ : List[str] = text_generator("test" , return_full_text=_A , return_text=_A )
with self.assertRaises(_A ):
a_ : Optional[int] = text_generator("test" , return_full_text=_A , return_tensors=_A )
with self.assertRaises(_A ):
a_ : str = text_generator("test" , return_text=_A , return_tensors=_A )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
a_ : str = text_generator("" )
self.assertEqual(_A , [{"generated_text": ANY(_A )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
a_ : Any = text_generator("" )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
a_ : List[str] = ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
if (
tokenizer.model_max_length < 1_0_0_0_0
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator("This is a test" * 5_0_0 , max_new_tokens=2_0 )
a_ : Tuple = text_generator("This is a test" * 5_0_0 , handle_long_generation="hole" , max_new_tokens=2_0 )
# Hole strategy cannot work
with self.assertRaises(_A ):
text_generator(
"This is a test" * 5_0_0 , handle_long_generation="hole" , max_new_tokens=tokenizer.model_max_length + 1_0 , )
@require_torch
@require_accelerate
@require_torch_gpu
def A ( self ) -> int:
import torch
# Classic `model_kwargs`
a_ : str = pipeline(
model="hf-internal-testing/tiny-random-bloom" , model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
a_ : List[Any] = pipe("This is a test" )
self.assertEqual(
_A , [
{
"generated_text": (
"This is a test test test test test test test test test test test test test test test test"
" test"
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
a_ : List[Any] = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
a_ : Optional[int] = pipe("This is a test" )
self.assertEqual(
_A , [
{
"generated_text": (
"This is a test test test test test test test test test test test test test test test test"
" test"
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
a_ : int = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
a_ : Union[str, Any] = pipe("This is a test" )
self.assertEqual(
_A , [
{
"generated_text": (
"This is a test test test test test test test test test test test test test test test test"
" test"
)
}
] , )
@require_torch
@require_torch_gpu
def A ( self ) -> Optional[int]:
import torch
a_ : List[str] = pipeline(model="hf-internal-testing/tiny-random-bloom" , device=0 , torch_dtype=torch.floataa )
pipe("This is a test" )
@require_torch
@require_accelerate
@require_torch_gpu
def A ( self ) -> str:
import torch
a_ : Any = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" , torch_dtype=torch.floataa )
pipe("This is a test" , do_sample=_A , top_p=0.5 )
def A ( self ) -> Dict:
a_ : Optional[int] = "Hello world"
a_ : Dict = pipeline("text-generation" , model="hf-internal-testing/tiny-random-gpt2" )
if text_generator.model.framework == "tf":
a_ : Any = logging.get_logger("transformers.generation.tf_utils" )
else:
a_ : Optional[int] = logging.get_logger("transformers.generation.utils" )
a_ : Tuple = "Both `max_new_tokens`" # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_A ) as cl:
a_ : str = text_generator(_A , max_length=1_0 , max_new_tokens=1 )
self.assertIn(_A , cl.out )
# The user only sets one -> no warning
with CaptureLogger(_A ) as cl:
a_ : Optional[Any] = text_generator(_A , max_new_tokens=1 )
self.assertNotIn(_A , cl.out )
with CaptureLogger(_A ) as cl:
a_ : List[Any] = text_generator(_A , max_length=1_0 )
self.assertNotIn(_A , cl.out )
| 473 | import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = DebertaVaTokenizer
UpperCAmelCase = DebertaVaTokenizerFast
UpperCAmelCase = True
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCamelCase = DebertaVaTokenizer(_A , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict , _A : Union[str, Any] ):
_UpperCamelCase = '''this is a test'''
_UpperCamelCase = '''this is a test'''
return input_text, output_text
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''<pad>'''
_UpperCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(_A ) , 3_0001 )
def UpperCamelCase_ ( self : List[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def UpperCamelCase_ ( self : List[str] ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Dict ):
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase_ ( self : Optional[Any] ):
pass
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[Any] ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : int ):
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Tuple ):
# fmt: off
_UpperCamelCase = ''' \tHeLLo!how \n Are yoU? '''
_UpperCamelCase = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
_UpperCamelCase = DebertaVaTokenizer(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , do_lower_case=_A , split_by_punct=_A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = tokenizer.convert_ids_to_tokens(tokenizer.encode(_A , add_special_tokens=_A ) )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A , add_special_tokens=_A ) )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = self.get_rust_tokenizer()
_UpperCamelCase = tokenizer.encode(_A )
_UpperCamelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''This is a test'''
_UpperCamelCase = [13, 1, 4398, 25, 21, 1289]
_UpperCamelCase = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
_UpperCamelCase = DebertaVaTokenizer(_A , keep_accents=_A )
_UpperCamelCase = DebertaVaTokenizerFast(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
# fmt: off
_UpperCamelCase = '''I was born in 92000, and this is falsé.'''
_UpperCamelCase = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
_UpperCamelCase = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
_UpperCamelCase = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
_UpperCamelCase = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
_UpperCamelCase = rust_tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = DebertaVaTokenizer(_A )
_UpperCamelCase = tokenizer.encode('''sequence builders''' )
_UpperCamelCase = tokenizer.encode('''multi-sequence build''' )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A )
_UpperCamelCase = tokenizer.build_inputs_with_special_tokens(_A , _A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , _A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , _A , )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_A , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 10 | 0 |
from __future__ import annotations
import math
_UpperCAmelCase = """2020.9.26"""
_UpperCAmelCase = """xcodz-dot, cclaus, dhruvmanila"""
def UpperCamelCase ( __lowercase : Union[str, Any] ,__lowercase : Dict ,__lowercase : Union[str, Any] ,__lowercase : Union[str, Any] ,__lowercase : str ):
'''simple docstring'''
if not all(isinstance(__snake_case ,(float, int) ) for val in locals().values() ):
A_ : Optional[int] = f'''Input values must either be float or int: {list(locals().values() )}'''
raise TypeError(__snake_case )
A_ : Optional[int] = ((x * distance) / (z + distance)) * scale
A_ : List[str] = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def UpperCamelCase ( __lowercase : Any ,__lowercase : str ,__lowercase : Optional[Any] ,__lowercase : Optional[int] ,__lowercase : List[Any] ):
'''simple docstring'''
if not isinstance(__snake_case ,__snake_case ):
raise TypeError('Axis must be a str' )
A_ : List[str] = locals()
del input_variables["axis"]
if not all(isinstance(__snake_case ,(float, int) ) for val in input_variables.values() ):
A_ : List[str] = (
'Input values except axis must either be float or int: '
f'''{list(input_variables.values() )}'''
)
raise TypeError(__snake_case )
A_ : Dict = (angle % 3_60) / 4_50 * 1_80 / math.pi
if axis == "z":
A_ : Optional[Any] = x * math.cos(__snake_case ) - y * math.sin(__snake_case )
A_ : Optional[int] = y * math.cos(__snake_case ) + x * math.sin(__snake_case )
A_ : List[str] = z
elif axis == "x":
A_ : Optional[int] = y * math.cos(__snake_case ) - z * math.sin(__snake_case )
A_ : Optional[int] = z * math.cos(__snake_case ) + y * math.sin(__snake_case )
A_ : int = x
elif axis == "y":
A_ : Optional[int] = x * math.cos(__snake_case ) - z * math.sin(__snake_case )
A_ : Optional[Any] = z * math.cos(__snake_case ) + x * math.sin(__snake_case )
A_ : List[str] = y
else:
raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\'' )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")
| 558 | import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[vertex]
def UpperCamelCase_ ( self : Optional[Any] , _A : List[str] , _A : Union[str, Any] ):
_UpperCamelCase = pos
def UpperCamelCase_ ( self : Any , _A : List[str] , _A : int , _A : Optional[Any] , _A : Union[str, Any] ):
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCamelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCamelCase = 2 * start + 1
else:
_UpperCamelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCamelCase , _UpperCamelCase = heap[smallest_child], positions[smallest_child]
_UpperCamelCase , _UpperCamelCase = (
heap[start],
positions[start],
)
_UpperCamelCase , _UpperCamelCase = temp, tempa
_UpperCamelCase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _A )
self.top_to_bottom(_A , _A , _A , _A )
def UpperCamelCase_ ( self : List[str] , _A : Tuple , _A : Optional[Any] , _A : int , _A : Optional[int] ):
_UpperCamelCase = position[index]
while index != 0:
_UpperCamelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
_UpperCamelCase = heap[parent]
_UpperCamelCase = position[parent]
self.set_position(position[parent] , _A )
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , _A )
break
_UpperCamelCase = parent
else:
_UpperCamelCase = val
_UpperCamelCase = temp
self.set_position(_A , 0 )
def UpperCamelCase_ ( self : int , _A : Tuple , _A : int ):
_UpperCamelCase = len(_A ) // 2 - 1
for i in range(_A , -1 , -1 ):
self.top_to_bottom(_A , _A , len(_A ) , _A )
def UpperCamelCase_ ( self : Any , _A : int , _A : List[str] ):
_UpperCamelCase = positions[0]
_UpperCamelCase = sys.maxsize
self.top_to_bottom(_A , 0 , len(_A ) , _A )
return temp
def _snake_case ( __snake_case ):
_UpperCamelCase = Heap()
_UpperCamelCase = [0] * len(__snake_case )
_UpperCamelCase = [-1] * len(__snake_case ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCamelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCamelCase = []
for vertex in range(len(__snake_case ) ):
distance_tv.append(sys.maxsize )
positions.append(__snake_case )
heap.node_position.append(__snake_case )
_UpperCamelCase = []
_UpperCamelCase = 1
_UpperCamelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCamelCase = 0
_UpperCamelCase = distance
heap.heapify(__snake_case , __snake_case )
for _ in range(1 , len(__snake_case ) ):
_UpperCamelCase = heap.delete_minimum(__snake_case , __snake_case )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCamelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(__snake_case )]
):
_UpperCamelCase = distance
heap.bottom_to_top(
__snake_case , heap.get_position(__snake_case ) , __snake_case , __snake_case )
_UpperCamelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
_lowerCAmelCase = int(input("Enter number of edges: ").strip())
_lowerCAmelCase = defaultdict(list)
for _ in range(edges_number):
_lowerCAmelCase = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 10 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __UpperCamelCase ):
if isinstance(__snake_case , np.ndarray ):
return list(tensor.shape )
__lowercase : Optional[Any] = tf.shape(__snake_case )
if tensor.shape == tf.TensorShape(__snake_case ):
return dynamic
__lowercase : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(__snake_case )]
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None ):
return tf.nn.softmax(logits=logits + 1e-9 , axis=__snake_case , name=__snake_case )
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=1e-5 , __UpperCamelCase=-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
__lowercase ,__lowercase : Optional[int] = 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
__lowercase : Any = [1] * inputs.shape.rank
__lowercase : List[str] = shape_list(__snake_case )[axis]
__lowercase : Union[str, Any] = tf.reshape(__snake_case , __snake_case )
__lowercase : Optional[int] = tf.reshape(__snake_case , __snake_case )
# Compute layer normalization using the batch_normalization
# function.
__lowercase : List[Any] = tf.nn.batch_normalization(
__snake_case , __snake_case , __snake_case , offset=__snake_case , scale=__snake_case , variance_epsilon=__snake_case , )
return outputs
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase=0 , __UpperCamelCase=-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
__lowercase : str = tf.shape(__snake_case )
__lowercase : Any = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
__lowercase : Optional[int] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(__snake_case , __snake_case )
def __UpperCAmelCase ( __UpperCamelCase ):
if not isinstance(__snake_case , tf.Tensor ):
__lowercase : Optional[int] = tf.convert_to_tensor(__snake_case ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
__lowercase : Dict = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
__lowercase : Optional[int] = 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))
__lowercase : str = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = "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 __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : List[Any] = 6_45_12
# 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.
__lowercase : Union[str, Any] = [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}""" )
__lowercase : Dict = np.asarray(__snake_case )
__lowercase : Optional[int] = 1
__lowercase : int = 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
__lowercase : Optional[int] = np.array_split(__snake_case , __snake_case )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(__snake_case ):
__lowercase : Tuple = chunk_data
else:
__lowercase : Optional[Any] = data
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
if name in group.attrs:
__lowercase : Any = [n.decode('''utf8''' ) if hasattr(__snake_case , '''decode''' ) else n for n in group.attrs[name]]
else:
__lowercase : Union[str, Any] = []
__lowercase : Dict = 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 __UpperCAmelCase ( __UpperCamelCase ):
def _expand_single_ad_tensor(__UpperCamelCase ):
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 )
| 76 | import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
@staticmethod
def UpperCamelCase_ ( _A : Any ):
_UpperCamelCase = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : str , *_A : int , **_A : List[Any] ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
_UpperCamelCase = kwargs.pop('''main_process_only''' , _A )
_UpperCamelCase = kwargs.pop('''in_order''' , _A )
if self.isEnabledFor(_A ):
if self._should_log(_A ):
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
elif in_order:
_UpperCamelCase = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
_UpperCamelCase , _UpperCamelCase = self.process(_A , _A )
self.logger.log(_A , _A , *_A , **_A )
state.wait_for_everyone()
def _snake_case ( __snake_case , __snake_case = None ):
if log_level is None:
_UpperCamelCase = os.environ.get('''ACCELERATE_LOG_LEVEL''' , __snake_case )
_UpperCamelCase = logging.getLogger(__snake_case )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(__snake_case , {} )
| 10 | 0 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( __lowercase ):
lowercase_ : int = (DDIMParallelScheduler,)
lowercase_ : Union[str, Any] = (('''eta''', 0.0), ('''num_inference_steps''', 50))
def lowercase__ ( self : Dict , **__lowerCAmelCase : Any ):
__snake_case = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'clip_sample': True,
}
config.update(**_A )
return config
def lowercase__ ( self : str , **__lowerCAmelCase : int ):
__snake_case = self.scheduler_classes[0]
__snake_case = self.get_scheduler_config(**_A )
__snake_case = scheduler_class(**_A )
__snake_case , __snake_case = 1_0, 0.0
__snake_case = self.dummy_model()
__snake_case = self.dummy_sample_deter
scheduler.set_timesteps(_A )
for t in scheduler.timesteps:
__snake_case = model(_A , _A )
__snake_case = scheduler.step(_A , _A , _A , _A ).prev_sample
return sample
def lowercase__ ( self : List[str] ):
for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=_A )
def lowercase__ ( self : Union[str, Any] ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=_A )
__snake_case = self.scheduler_classes[0]
__snake_case = self.get_scheduler_config(steps_offset=1 )
__snake_case = scheduler_class(**_A )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1] ) )
def lowercase__ ( self : Union[str, Any] ):
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_A , beta_end=_A )
def lowercase__ ( self : Optional[int] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_A )
def lowercase__ ( self : Tuple ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_A )
def lowercase__ ( self : Union[str, Any] ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_A )
def lowercase__ ( self : Tuple ):
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=_A )
def lowercase__ ( self : List[Any] ):
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=_A )
def lowercase__ ( self : Union[str, Any] ):
self.check_over_configs(thresholding=_A )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=_A , prediction_type=_A , sample_max_value=_A , )
def lowercase__ ( self : str ):
for t in [1, 1_0, 4_9]:
self.check_over_forward(time_step=_A )
def lowercase__ ( self : int ):
for t, num_inference_steps in zip([1, 1_0, 5_0] , [1_0, 5_0, 5_0_0] ):
self.check_over_forward(time_step=_A , num_inference_steps=_A )
def lowercase__ ( self : Dict ):
for t, eta in zip([1, 1_0, 4_9] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=_A , eta=_A )
def lowercase__ ( self : List[Any] ):
__snake_case = self.scheduler_classes[0]
__snake_case = self.get_scheduler_config()
__snake_case = scheduler_class(**_A )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_2_0 , 4_0_0 ) - 0.14771 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_8_0 , 9_6_0 ) - 0.32460 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 , 4_8_6 ) - 0.00979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 , 9_9_8 ) - 0.02 ) ) < 1E-5
def lowercase__ ( self : int ):
__snake_case = self.scheduler_classes[0]
__snake_case = self.get_scheduler_config()
__snake_case = scheduler_class(**_A )
__snake_case , __snake_case = 1_0, 0.0
scheduler.set_timesteps(_A )
__snake_case = self.dummy_model()
__snake_case = self.dummy_sample_deter
__snake_case = self.dummy_sample_deter + 0.1
__snake_case = self.dummy_sample_deter - 0.1
__snake_case = samplea.shape[0]
__snake_case = torch.stack([samplea, samplea, samplea] , dim=0 )
__snake_case = torch.arange(_A )[0:3, None].repeat(1 , _A )
__snake_case = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
__snake_case = scheduler.batch_step_no_noise(_A , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , _A )
__snake_case = torch.sum(torch.abs(_A ) )
__snake_case = torch.mean(torch.abs(_A ) )
assert abs(result_sum.item() - 1147.7904 ) < 1E-2
assert abs(result_mean.item() - 0.4982 ) < 1E-3
def lowercase__ ( self : int ):
__snake_case = self.full_loop()
__snake_case = torch.sum(torch.abs(_A ) )
__snake_case = torch.mean(torch.abs(_A ) )
assert abs(result_sum.item() - 172.0067 ) < 1E-2
assert abs(result_mean.item() - 0.223967 ) < 1E-3
def lowercase__ ( self : str ):
__snake_case = self.full_loop(prediction_type='v_prediction' )
__snake_case = torch.sum(torch.abs(_A ) )
__snake_case = torch.mean(torch.abs(_A ) )
assert abs(result_sum.item() - 52.5302 ) < 1E-2
assert abs(result_mean.item() - 0.0684 ) < 1E-3
def lowercase__ ( self : Any ):
# We specify different beta, so that the first alpha is 0.99
__snake_case = self.full_loop(set_alpha_to_one=_A , beta_start=0.01 )
__snake_case = torch.sum(torch.abs(_A ) )
__snake_case = torch.mean(torch.abs(_A ) )
assert abs(result_sum.item() - 149.8295 ) < 1E-2
assert abs(result_mean.item() - 0.1951 ) < 1E-3
def lowercase__ ( self : List[Any] ):
# We specify different beta, so that the first alpha is 0.99
__snake_case = self.full_loop(set_alpha_to_one=_A , beta_start=0.01 )
__snake_case = torch.sum(torch.abs(_A ) )
__snake_case = torch.mean(torch.abs(_A ) )
assert abs(result_sum.item() - 149.0784 ) < 1E-2
assert abs(result_mean.item() - 0.1941 ) < 1E-3
| 356 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = "▁"
_lowerCAmelCase = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowerCAmelCase_ ( __lowercase, unittest.TestCase ):
UpperCAmelCase = BertGenerationTokenizer
UpperCAmelCase = False
UpperCAmelCase = True
def UpperCamelCase_ ( self : List[str] ):
super().setUp()
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = '''<s>'''
_UpperCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(_A ) , 1002 )
def UpperCamelCase_ ( self : Dict ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def UpperCamelCase_ ( self : int ):
_UpperCamelCase = BertGenerationTokenizer(_A , keep_accents=_A )
_UpperCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [285, 46, 10, 170, 382] , )
_UpperCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
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(_A )
self.assertListEqual(
_A , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCamelCase = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def UpperCamelCase_ ( self : Union[str, Any] ):
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = '''Hello World!'''
_UpperCamelCase = [1_8536, 2260, 101]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@slow
def UpperCamelCase_ ( self : int ):
_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 = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@require_torch
@slow
def UpperCamelCase_ ( self : Dict ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
_UpperCamelCase = list(self.big_tokenizer.get_vocab().keys() )[:10]
_UpperCamelCase = ''' '''.join(_A )
_UpperCamelCase = self.big_tokenizer.encode_plus(_A , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=_A )
_UpperCamelCase = BertGenerationConfig()
_UpperCamelCase = BertGenerationEncoder(_A )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_A )
model(**_A )
@slow
def UpperCamelCase_ ( self : Dict ):
# fmt: off
_UpperCamelCase = {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_A , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 10 | 0 |
'''simple docstring'''
def snake_case_ ( __snake_case : Dict , __snake_case : Optional[int]) -> Tuple:
assert x is not None
assert y is not None
lowerCAmelCase_ = len(__snake_case)
lowerCAmelCase_ = len(__snake_case)
# declaring the array for storing the dp values
lowerCAmelCase_ = [[0] * (n + 1) for _ in range(m + 1)] # noqa: E741
for i in range(1 , m + 1):
for j in range(1 , n + 1):
lowerCAmelCase_ = 1 if x[i - 1] == y[j - 1] else 0
lowerCAmelCase_ = max(l[i - 1][j] , l[i][j - 1] , l[i - 1][j - 1] + match)
lowerCAmelCase_ = ''''''
lowerCAmelCase_ ,lowerCAmelCase_ = m, n
while i > 0 and j > 0:
lowerCAmelCase_ = 1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
lowerCAmelCase_ = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
A_ : int ='''AGGTAB'''
A_ : Optional[int] ='''GXTXAYB'''
A_ : int =4
A_ : Union[str, Any] ='''GTAB'''
A_ , A_ : Optional[Any] =longest_common_subsequence(a, b)
print('''len =''', ln, ''', sub-sequence =''', subseq)
import doctest
doctest.testmod()
| 274 | 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 lowerCAmelCase_ ( __lowercase, __lowercase, __lowercase, unittest.TestCase ):
UpperCAmelCase = StableUnCLIPPipeline
UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
UpperCAmelCase = False
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = 32
_UpperCamelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=_A , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=_A , num_layers=1 , )
torch.manual_seed(0 )
_UpperCamelCase = DDPMScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1000 , clip_sample=_A , clip_sample_range=5.0 , beta_schedule='''squaredcos_cap_v2''' , )
# regular denoising components
torch.manual_seed(0 )
_UpperCamelCase = StableUnCLIPImageNormalizer(embedding_dim=_A )
_UpperCamelCase = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=_A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=_A , layers_per_block=1 , upcast_attention=_A , use_linear_projection=_A , )
torch.manual_seed(0 )
_UpperCamelCase = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.0_0085 , beta_end=0.012 , prediction_type='''v_prediction''' , set_alpha_to_one=_A , steps_offset=1 , )
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL()
_UpperCamelCase = {
# 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 : Dict , _A : Tuple , _A : Dict=0 ):
if str(_A ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(_A )
else:
_UpperCamelCase = torch.Generator(device=_A ).manual_seed(_A )
_UpperCamelCase = {
'''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 ):
_UpperCamelCase = torch_device == '''cpu'''
self._test_attention_slicing_forward_pass(test_max_difference=_A )
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=_A )
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''' )
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
# 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()
_UpperCamelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCamelCase = pipe('''anime turle''' , generator=_A , output_type='''np''' )
_UpperCamelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A )
def UpperCamelCase_ ( self : Optional[Any] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCamelCase = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
_UpperCamelCase = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCamelCase = pipe(
'''anime turtle''' , prior_num_inference_steps=2 , num_inference_steps=2 , output_type='''np''' , )
_UpperCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 10 | 0 |
'''simple docstring'''
import copy
import re
class __A :
'''simple docstring'''
__lowerCamelCase : List[Any] = 'hp'
__lowerCamelCase : str = {}
__lowerCamelCase : Tuple = None
@classmethod
def a__ (cls , A , A ) -> int:
"""simple docstring"""
_a = prefix
_a = defaults
cls.build_naming_info()
@staticmethod
def a__ (A , A ) -> Union[str, Any]:
"""simple docstring"""
if len(A ) == 0:
return ""
_a = None
if any(char.isdigit() for char in word ):
raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number''' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(A ) + 1 ):
_a = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
_a = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(A ):
_a = ''''''
while integer != 0:
_a = chr(ord('''A''' ) + integer % 10 ) + s
integer //= 10
return s
_a = 0
while True:
_a = word + '''#''' + int_to_alphabetic(A )
if sword in info["reverse_short_word"]:
continue
else:
_a = sword
break
_a = short_word
_a = word
return short_word
@staticmethod
def a__ (A , A ) -> Optional[int]:
"""simple docstring"""
_a = param_name.split('''_''' )
_a = [TrialShortNamer.shortname_for_word(A , A ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
_a = ['''''', '''_''']
for separator in separators:
_a = separator.join(A )
if shortname not in info["reverse_short_param"]:
_a = shortname
_a = param_name
return shortname
return param_name
@staticmethod
def a__ (A , A ) -> Optional[Any]:
"""simple docstring"""
_a = TrialShortNamer.shortname_for_key(A , A )
_a = short_name
_a = param_name
@classmethod
def a__ (cls ) -> Dict:
"""simple docstring"""
if cls.NAMING_INFO is not None:
return
_a = {
'''short_word''': {},
'''reverse_short_word''': {},
'''short_param''': {},
'''reverse_short_param''': {},
}
_a = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(A , A )
_a = info
@classmethod
def a__ (cls , A ) -> Tuple:
"""simple docstring"""
cls.build_naming_info()
assert cls.PREFIX is not None
_a = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(f'''You should provide a default value for the param name {k} with value {v}''' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
_a = cls.NAMING_INFO['''short_param'''][k]
if isinstance(A , A ):
_a = 1 if v else 0
_a = '''''' if isinstance(A , (int, float) ) else '''-'''
_a = f'''{key}{sep}{v}'''
name.append(A )
return "_".join(A )
@classmethod
def a__ (cls , A ) -> List[Any]:
"""simple docstring"""
_a = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
_a = []
else:
_a = repr.split('''_''' )
_a = {}
for value in values:
if "-" in value:
_a , _a = value.split('''-''' )
else:
_a = re.sub('''[0-9.]''' , '''''' , A )
_a = float(re.sub('''[^0-9.]''' , '''''' , A ) )
_a = cls.NAMING_INFO['''reverse_short_param'''][p_k]
_a = p_v
for k in cls.DEFAULTS:
if k not in parameters:
_a = cls.DEFAULTS[k]
return parameters
| 11 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowercase_ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class __A :
'''simple docstring'''
def __init__(self , A = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError('''Unsupported Group''' )
_a = primes[group]['''prime''']
_a = primes[group]['''generator''']
_a = int(hexlify(urandom(32 ) ) , base=16 )
def a__ (self ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def a__ (self ) -> str:
"""simple docstring"""
_a = pow(self.generator , self.__private_key , self.prime )
return hex(A )[2:]
def a__ (self , A ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(A , (self.prime - 1) // 2 , self.prime ) == 1
)
def a__ (self , A ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
if not self.is_valid_public_key(A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , self.__private_key , self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def a__ (A , A ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(A , (prime - 1) // 2 , A ) == 1
)
@staticmethod
def a__ (A , A , A = 14 ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
_a = int(A , base=16 )
_a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(A , A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , A , A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 1 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowercase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_ = 10
lowercase_ = 256
def lowerCAmelCase (__A):
"""simple docstring"""
if len(__A) < MIN_NUM_TOKENS:
return None
_a = MinHash(num_perm=__A)
for token in set(__A):
min_hash.update(token.encode())
return min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}
class __A :
'''simple docstring'''
def __init__(self , *,
A = 0.85 , ) -> Optional[int]:
"""simple docstring"""
_a = duplication_jaccard_threshold
_a = NUM_PERM
_a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_a = defaultdict(A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
_a = self._index.query(A )
if code_key in self._index.keys:
print(f'''Duplicate key {code_key}''' )
return
self._index.insert(A , A )
if len(A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A )
def a__ (self ) -> List[List[Dict]]:
"""simple docstring"""
_a = []
for base, duplicates in self._duplicate_clusters.items():
_a = [base] + list(A )
# reformat the cluster to be a list of dict
_a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(A )
return duplicate_clusters
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_duplicate_clusters()
with open(A , '''w''' ) as f:
json.dump(A , A )
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = element
_a = get_min_hash([t for t in NON_ALPHA.split(data['''content''']) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__A , max_queue_size=10_000) , chunksize=100 , ):
if data is not None:
yield data
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = DuplicationIndex(duplication_jaccard_threshold=__A)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A)) , max_queue_size=100)):
di.add(__A , __A)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = get_tokens(__A)
_a = get_tokens(__A)
return len(tokensa & tokensa) / len(tokensa | tokensa)
lowercase_ = None
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for elementa in cluster:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__A , __A) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_a = 1
extremes.append(__A)
return extremes
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
global _shared_dataset
_a = dataset
_a = []
_a = partial(_find_cluster_extremes_shared , jaccard_threshold=__A)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__A , __A , ) , total=len(__A) , ):
extremes_list.append(__A)
return extremes_list
def lowerCAmelCase (__A , __A = 0.85):
"""simple docstring"""
_a = make_duplicate_clusters(__A , __A)
_a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_a = {}
_a = find_extremes(__A , __A , __A)
for extremes in extremes_clusters:
for element in extremes:
_a = element
_a = duplicate_indices - set(extreme_dict.keys())
_a = dataset.filter(lambda __A , __A: idx not in remove_indices , with_indices=__A)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_a = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(__A)}''')
print(F'''Number of duplicate clusters: {len(__A)}''')
print(F'''Files in duplicate cluster: {len(__A)}''')
print(F'''Unique files in duplicate cluster: {len(__A)}''')
print(F'''Filtered dataset size: {len(__A)}''')
return ds_filter, duplicate_clusters
| 11 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
def __init__(self , *A , **A ) -> None:
"""simple docstring"""
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , A , )
super().__init__(*A , **A )
| 11 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = CTRLTokenizer
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Any = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = '''adapt react readapt apt'''
_a = '''adapt react readapt apt'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt react readapt apt'''
_a = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
| 11 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = IFInpaintingSuperResolutionPipeline
__lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
__lowerCamelCase : str = PipelineTesterMixin.required_optional_params - {'latents'}
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def a__ (self , A , A=0 ) -> List[Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = floats_tensor((1, 3, 16, 16) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ (self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def a__ (self ) -> str:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._test_save_load_local()
def a__ (self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 11 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __A ( metaclass=A ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = ['torch', 'transformers', 'onnx']
def __init__(self , *A , **A ) -> Any:
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __A ( metaclass=A ):
'''simple docstring'''
__lowerCamelCase : str = ['torch', 'transformers', 'onnx']
def __init__(self , *A , **A ) -> Any:
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __A ( metaclass=A ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__(self , *A , **A ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> Dict:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __A ( metaclass=A ):
'''simple docstring'''
__lowerCamelCase : str = ['torch', 'transformers', 'onnx']
def __init__(self , *A , **A ) -> str:
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> int:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __A ( metaclass=A ):
'''simple docstring'''
__lowerCamelCase : List[Any] = ['torch', 'transformers', 'onnx']
def __init__(self , *A , **A ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> Any:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
class __A ( metaclass=A ):
'''simple docstring'''
__lowerCamelCase : Dict = ['torch', 'transformers', 'onnx']
def __init__(self , *A , **A ) -> List[str]:
"""simple docstring"""
requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
@classmethod
def a__ (cls , *A , **A ) -> str:
"""simple docstring"""
requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
| 11 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=16 , A=36 , A=6 , A=6 , A=6 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = embedding_size
_a = hidden_size
_a = num_hidden_layers
_a = num_hidden_groups
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ) -> Tuple:
"""simple docstring"""
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def a__ (self , A , A , A , A , A , A , A ) -> int:
"""simple docstring"""
_a = AlbertModel(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , token_type_ids=A )
_a = model(A , token_type_ids=A )
_a = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A ) -> str:
"""simple docstring"""
_a = AlbertForPreTraining(config=A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , token_type_ids=A , labels=A , sentence_order_label=A , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def a__ (self , A , A , A , A , A , A , A ) -> Optional[int]:
"""simple docstring"""
_a = AlbertForMaskedLM(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self , A , A , A , A , A , A , A ) -> Dict:
"""simple docstring"""
_a = AlbertForQuestionAnswering(config=A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , token_type_ids=A , start_positions=A , end_positions=A , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a__ (self , A , A , A , A , A , A , A ) -> Optional[int]:
"""simple docstring"""
_a = self.num_labels
_a = AlbertForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a__ (self , A , A , A , A , A , A , A ) -> str:
"""simple docstring"""
_a = self.num_labels
_a = AlbertForTokenClassification(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = self.num_choices
_a = AlbertForMultipleChoice(config=A )
model.to(A )
model.eval()
_a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = model(
A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Any = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
__lowerCamelCase : str = (
{
'feature-extraction': AlbertModel,
'fill-mask': AlbertForMaskedLM,
'question-answering': AlbertForQuestionAnswering,
'text-classification': AlbertForSequenceClassification,
'token-classification': AlbertForTokenClassification,
'zero-shot': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase : str = True
def a__ (self , A , A , A=False ) -> Optional[Any]:
"""simple docstring"""
_a = super()._prepare_for_class(A , A , return_labels=A )
if return_labels:
if model_class in get_values(A ):
_a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=A )
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
return inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = AlbertModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> int:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Any:
"""simple docstring"""
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = AlbertModel.from_pretrained(A )
self.assertIsNotNone(A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AlbertModel.from_pretrained('''albert-base-v2''' )
_a = torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_a = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_a = model(A , attention_mask=A )[0]
_a = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return len(set(__A)) == len(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowercase_ = logging.get_logger(__name__)
# General docstring
lowercase_ = "RegNetConfig"
# Base docstring
lowercase_ = "facebook/regnet-y-040"
lowercase_ = [1, 1_088, 7, 7]
# Image classification docstring
lowercase_ = "facebook/regnet-y-040"
lowercase_ = "tabby, tabby cat"
lowercase_ = [
"facebook/regnet-y-040",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , A , A = 3 , A = 1 , A = 1 , A = "relu" , **A , ) -> List[Any]:
"""simple docstring"""
super().__init__(**A )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
_a = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
_a = tf.keras.layers.ConvaD(
filters=A , kernel_size=A , strides=A , padding='''VALID''' , groups=A , use_bias=A , name='''convolution''' , )
_a = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='''normalization''' )
_a = ACTaFN[activation] if activation is not None else tf.identity
def a__ (self , A ) -> List[str]:
"""simple docstring"""
_a = self.convolution(self.padding(A ) )
_a = self.normalization(A )
_a = self.activation(A )
return hidden_state
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , A , **A ) -> Tuple:
"""simple docstring"""
super().__init__(**A )
_a = config.num_channels
_a = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def a__ (self , A ) -> Any:
"""simple docstring"""
_a = shape_list(A )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
_a = tf.transpose(A , perm=(0, 2, 3, 1) )
_a = self.embedder(A )
return hidden_state
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , A , A = 2 , **A ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**A )
_a = tf.keras.layers.ConvaD(
filters=A , kernel_size=1 , strides=A , use_bias=A , name='''convolution''' )
_a = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='''normalization''' )
def a__ (self , A , A = False ) -> tf.Tensor:
"""simple docstring"""
return self.normalization(self.convolution(A ) , training=A )
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , A , A , **A ) -> List[str]:
"""simple docstring"""
super().__init__(**A )
_a = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A , name='''pooler''' )
_a = [
tf.keras.layers.ConvaD(filters=A , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=A , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def a__ (self , A ) -> str:
"""simple docstring"""
_a = self.pooler(A )
for layer_module in self.attention:
_a = layer_module(A )
_a = hidden_state * pooled
return hidden_state
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , A , A , A , A = 1 , **A ) -> Any:
"""simple docstring"""
super().__init__(**A )
_a = in_channels != out_channels or stride != 1
_a = max(1 , out_channels // config.groups_width )
_a = (
TFRegNetShortCut(A , stride=A , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
_a = [
TFRegNetConvLayer(A , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
A , stride=A , groups=A , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(A , kernel_size=1 , activation=A , name='''layer.2''' ),
]
_a = ACTaFN[config.hidden_act]
def a__ (self , A ) -> str:
"""simple docstring"""
_a = hidden_state
for layer_module in self.layers:
_a = layer_module(A )
_a = self.shortcut(A )
hidden_state += residual
_a = self.activation(A )
return hidden_state
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , A , A , A , A = 1 , **A ) -> Optional[int]:
"""simple docstring"""
super().__init__(**A )
_a = in_channels != out_channels or stride != 1
_a = max(1 , out_channels // config.groups_width )
_a = (
TFRegNetShortCut(A , stride=A , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
_a = [
TFRegNetConvLayer(A , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
A , stride=A , groups=A , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(A , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(A , kernel_size=1 , activation=A , name='''layer.3''' ),
]
_a = ACTaFN[config.hidden_act]
def a__ (self , A ) -> Any:
"""simple docstring"""
_a = hidden_state
for layer_module in self.layers:
_a = layer_module(A )
_a = self.shortcut(A )
hidden_state += residual
_a = self.activation(A )
return hidden_state
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , A , A , A , A = 2 , A = 2 , **A ) -> str:
"""simple docstring"""
super().__init__(**A )
_a = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
_a = [
# downsampling is done in the first layer with stride of 2
layer(A , A , A , stride=A , name='''layers.0''' ),
*[layer(A , A , A , name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def a__ (self , A ) -> str:
"""simple docstring"""
for layer_module in self.layers:
_a = layer_module(A )
return hidden_state
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self , A , **A ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**A )
_a = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
A , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
_a = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(A , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(A , A , A , depth=A , name=f'''stages.{i+1}''' ) )
def a__ (self , A , A = False , A = True ) -> TFBaseModelOutputWithNoAttention:
"""simple docstring"""
_a = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_a = hidden_states + (hidden_state,)
_a = stage_module(A )
if output_hidden_states:
_a = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=A , hidden_states=A )
@keras_serializable
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
__lowerCamelCase : Any = RegNetConfig
def __init__(self , A , **A ) -> str:
"""simple docstring"""
super().__init__(**A )
_a = config
_a = TFRegNetEmbeddings(A , name='''embedder''' )
_a = TFRegNetEncoder(A , name='''encoder''' )
_a = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A , name='''pooler''' )
@unpack_inputs
def a__ (self , A , A = None , A = None , A = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
_a = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_a = return_dict if return_dict is not None else self.config.use_return_dict
_a = self.embedder(A , training=A )
_a = self.encoder(
A , output_hidden_states=A , return_dict=A , training=A )
_a = encoder_outputs[0]
_a = self.pooler(A )
# Change to NCHW output format have uniformity in the modules
_a = tf.transpose(A , perm=(0, 3, 1, 2) )
_a = tf.transpose(A , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
_a = tuple([tf.transpose(A , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A , pooler_output=A , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Dict = RegNetConfig
__lowerCamelCase : List[str] = 'regnet'
__lowerCamelCase : Optional[int] = 'pixel_values'
@property
def a__ (self ) -> List[str]:
"""simple docstring"""
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa )}
lowercase_ = R"\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"
lowercase_ = R"\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__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 RegNet model outputting raw features without any specific head on top.' , A , )
class __A ( A ):
'''simple docstring'''
def __init__(self , A , *A , **A ) -> List[str]:
"""simple docstring"""
super().__init__(A , *A , **A )
_a = TFRegNetMainLayer(A , name='''regnet''' )
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=A , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def a__ (self , A , A = None , A = None , A=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
"""simple docstring"""
_a = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_a = return_dict if return_dict is not None else self.config.use_return_dict
_a = self.regnet(
pixel_values=A , output_hidden_states=A , return_dict=A , training=A , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , A , )
class __A ( A , A ):
'''simple docstring'''
def __init__(self , A , *A , **A ) -> int:
"""simple docstring"""
super().__init__(A , *A , **A )
_a = config.num_labels
_a = TFRegNetMainLayer(A , name='''regnet''' )
# classification head
_a = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def a__ (self , A = None , A = None , A = None , A = None , A=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
"""simple docstring"""
_a = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_a = return_dict if return_dict is not None else self.config.use_return_dict
_a = self.regnet(
A , output_hidden_states=A , return_dict=A , training=A )
_a = outputs.pooler_output if return_dict else outputs[1]
_a = self.classifier[0](A )
_a = self.classifier[1](A )
_a = None if labels is None else self.hf_compute_loss(labels=A , logits=A )
if not return_dict:
_a = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=A , logits=A , hidden_states=outputs.hidden_states )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) == 0:
return False
_a = len(__A) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __A)
else:
return binary_search(a_list[midpoint + 1 :] , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
lowercase_ = int(input("Enter the number to be found in the list:\n").strip())
lowercase_ = "" if binary_search(sequence, target) else "not "
print(F"""{target} was {not_str}found in {sequence}""")
| 11 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowercase_ = get_logger(__name__)
class __A :
'''simple docstring'''
def __init__(self , A , A=None ) -> List[str]:
"""simple docstring"""
_a = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('''__''' ):
setattr(self , A , getattr(A , A ) )
_a = module._original_module if isinstance(A , _PatchedModuleObj ) else module
class __A :
'''simple docstring'''
__lowerCamelCase : Tuple = []
def __init__(self , A , A , A , A=None ) -> Optional[int]:
"""simple docstring"""
_a = obj
_a = target
_a = new
_a = target.split('''.''' )[0]
_a = {}
_a = attrs or []
def __enter__(self ) -> Any:
"""simple docstring"""
*_a , _a = self.target.split('''.''' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(A ) ):
try:
_a = import_module('''.'''.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
_a = getattr(self.obj , A )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(A , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
_a = obj_attr
# patch at top level
setattr(self.obj , A , _PatchedModuleObj(A , attrs=self.attrs ) )
_a = getattr(self.obj , A )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(A , A , _PatchedModuleObj(getattr(A , A , A ) , attrs=self.attrs ) )
_a = getattr(A , A )
# finally set the target attribute
setattr(A , A , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
_a = getattr(import_module('''.'''.join(A ) ) , A )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , A ) is attr_value:
_a = getattr(self.obj , A )
setattr(self.obj , A , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
_a = globals()['''__builtins__'''][target_attr]
setattr(self.obj , A , self.new )
else:
raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.''' )
def __exit__(self , *A ) -> List[str]:
"""simple docstring"""
for attr in list(self.original ):
setattr(self.obj , A , self.original.pop(A ) )
def a__ (self ) -> str:
"""simple docstring"""
self.__enter__()
self._active_patches.append(self )
def a__ (self ) -> List[Any]:
"""simple docstring"""
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 11 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A ):
_a = self.prefix_sum[i - 1] + array[i]
def a__ (self , A , A ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
lowercase_ = {
"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"
},
}
lowercase_ = {"facebook/blenderbot-3B": 128}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : List[str] = VOCAB_FILES_NAMES
__lowerCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[Any] = ['input_ids', 'attention_mask']
__lowerCamelCase : Dict = BlenderbotTokenizer
def __init__(self , A=None , A=None , A=None , A="replace" , A="<s>" , A="</s>" , A="</s>" , A="<s>" , A="<unk>" , A="<pad>" , A="<mask>" , A=False , A=True , **A , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
A , A , tokenizer_file=A , errors=A , bos_token=A , eos_token=A , sep_token=A , cls_token=A , unk_token=A , pad_token=A , mask_token=A , add_prefix_space=A , trim_offsets=A , **A , )
_a = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , A ) != add_prefix_space:
_a = getattr(A , pre_tok_state.pop('''type''' ) )
_a = add_prefix_space
_a = pre_tok_class(**A )
_a = add_prefix_space
_a = '''post_processor'''
_a = getattr(self.backend_tokenizer , A , A )
if tokenizer_component_instance:
_a = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_a = tuple(state['''sep'''] )
if "cls" in state:
_a = tuple(state['''cls'''] )
_a = False
if state.get('''add_prefix_space''' , A ) != add_prefix_space:
_a = add_prefix_space
_a = True
if state.get('''trim_offsets''' , A ) != trim_offsets:
_a = trim_offsets
_a = True
if changes_to_apply:
_a = getattr(A , state.pop('''type''' ) )
_a = component_class(**A )
setattr(self.backend_tokenizer , A , A )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def a__ (self ) -> str:
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def a__ (self , A ) -> Optional[Any]:
"""simple docstring"""
_a = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else value
_a = value
def a__ (self , *A , **A ) -> BatchEncoding:
"""simple docstring"""
_a = kwargs.get('''is_split_into_words''' , A )
assert self.add_prefix_space or not is_split_into_words, (
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*A , **A )
def a__ (self , *A , **A ) -> BatchEncoding:
"""simple docstring"""
_a = kwargs.get('''is_split_into_words''' , A )
assert self.add_prefix_space or not is_split_into_words, (
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*A , **A )
def a__ (self , A , A = None ) -> Tuple[str]:
"""simple docstring"""
_a = self._tokenizer.model.save(A , name=A )
return tuple(A )
def a__ (self , A , A = None ) -> List[int]:
"""simple docstring"""
_a = [self.sep_token_id]
_a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def a__ (self , A , A = None ) -> Tuple:
"""simple docstring"""
return token_ids_a + [self.eos_token_id]
def a__ (self , A ) -> List[int]:
"""simple docstring"""
_a = []
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(A )
_a = ''' '''.join(A )
_a = self.encode(A )
if len(A ) > self.model_max_length:
_a = 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
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A)
if n > 1:
factors.append(__A)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def lowerCAmelCase (__A):
"""simple docstring"""
if isinstance(__A , collections.abc.Iterable):
return x
return (x, x)
@require_tf
class __A :
'''simple docstring'''
def a__ (self , A , A ) -> Dict:
"""simple docstring"""
pass
def a__ (self ) -> List[str]:
"""simple docstring"""
pass
def a__ (self ) -> int:
"""simple docstring"""
pass
def a__ (self , A , A , A , A , A=None , **A ) -> Any:
"""simple docstring"""
_a = VisionTextDualEncoderConfig.from_vision_text_configs(A , A )
_a = TFVisionTextDualEncoderModel(A )
_a = model(input_ids=A , pixel_values=A , attention_mask=A )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], config.projection_dim) )
def a__ (self , A , A , A , A , A=None , **A ) -> List[Any]:
"""simple docstring"""
_a , _a = self.get_vision_text_model(A , A )
_a = TFVisionTextDualEncoderModel(vision_model=A , text_model=A )
_a = model(input_ids=A , pixel_values=A , attention_mask=A )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], model.config.projection_dim) )
def a__ (self , A , A , A , A , A=None , **A ) -> List[Any]:
"""simple docstring"""
_a , _a = self.get_vision_text_model(A , A )
_a = {'''vision_model''': vision_model, '''text_model''': text_model}
_a = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**A )
_a = model(input_ids=A , pixel_values=A , attention_mask=A )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], model.config.projection_dim) )
def a__ (self , A , A , A , A , A=None , **A ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.get_vision_text_model(A , A )
_a = TFVisionTextDualEncoderModel(vision_model=A , text_model=A )
_a = model(input_ids=A , pixel_values=A , attention_mask=A )
_a = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a = TFVisionTextDualEncoderModel.from_pretrained(A )
_a = model(input_ids=A , pixel_values=A , attention_mask=A )
_a = after_output[0].numpy()
_a = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(A , 1E-5 )
def a__ (self , A , A , A , A , A=None , **A ) -> Tuple:
"""simple docstring"""
_a , _a = self.get_vision_text_model(A , A )
_a = TFVisionTextDualEncoderModel(vision_model=A , text_model=A )
_a = model(
input_ids=A , pixel_values=A , attention_mask=A , output_attentions=A )
_a = output.vision_model_output.attentions
self.assertEqual(len(A ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_a = to_atuple(vision_model.config.image_size )
_a = to_atuple(vision_model.config.patch_size )
_a = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_a = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_a = output.text_model_output.attentions
self.assertEqual(len(A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def a__ (self , A , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = np.abs((a - b) ).max()
self.assertLessEqual(A , A , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**A )
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**A )
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_save_load(**A )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**A )
@slow
def a__ (self ) -> int:
"""simple docstring"""
_a , _a = self.get_pretrained_model_and_inputs()
_a = model_a(**A )
_a = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(A )
_a = TFVisionTextDualEncoderModel.from_pretrained(A )
_a = model_a(**A )
_a = after_outputs[0].numpy()
_a = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(A , 1E-5 )
@require_tf
class __A ( A , unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-vit''' , '''hf-internal-testing/tiny-random-bert''' )
_a = 13
_a = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_a = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_a = random_attention_mask([batch_size, 4] )
_a = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def a__ (self , A , A ) -> str:
"""simple docstring"""
_a = TFViTModel(A , name='''vision_model''' )
_a = TFBertModel(A , name='''text_model''' )
return vision_model, text_model
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = TFViTModelTester(self )
_a = TFBertModelTester(self )
_a = vit_model_tester.prepare_config_and_inputs()
_a = bert_model_tester.prepare_config_and_inputs()
_a , _a , _a = vision_config_and_inputs
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __A ( A , unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> int:
"""simple docstring"""
_a = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''Rocketknight1/tiny-random-deit-tf''' , '''hf-internal-testing/tiny-random-roberta''' )
_a = 13
_a = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_a = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_a = random_attention_mask([batch_size, 4] )
_a = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def a__ (self , A , A , A , A , A=None , **A ) -> List[str]:
"""simple docstring"""
_a , _a = self.get_vision_text_model(A , A )
_a = TFVisionTextDualEncoderModel(vision_model=A , text_model=A )
_a = model(
input_ids=A , pixel_values=A , attention_mask=A , output_attentions=A )
_a = output.vision_model_output.attentions
self.assertEqual(len(A ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_a = to_atuple(vision_model.config.image_size )
_a = to_atuple(vision_model.config.patch_size )
_a = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_a = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_a = output.text_model_output.attentions
self.assertEqual(len(A ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def a__ (self , A , A ) -> Dict:
"""simple docstring"""
_a = TFDeiTModel(A , name='''vision_model''' )
_a = TFRobertaModel(A , name='''text_model''' )
return vision_model, text_model
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = TFDeiTModelTester(self )
_a = TFRobertaModelTester(self )
_a = vit_model_tester.prepare_config_and_inputs()
_a = bert_model_tester.prepare_config_and_inputs()
_a , _a , _a = vision_config_and_inputs
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __A ( A , unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''Rocketknight1/tiny-random-clip-tf''' , '''hf-internal-testing/tiny-random-bert''' )
_a = 13
_a = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_a = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_a = random_attention_mask([batch_size, 4] )
_a = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = TFCLIPVisionModel(A , name='''vision_model''' )
_a = TFBertModel(A , name='''text_model''' )
return vision_model, text_model
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = TFCLIPVisionModelTester(self )
_a = TFBertModelTester(self )
_a = clip_model_tester.prepare_config_and_inputs()
_a = bert_model_tester.prepare_config_and_inputs()
_a , _a = vision_config_and_inputs
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = TFVisionTextDualEncoderModel.from_pretrained(
'''clip-italian/clip-italian''' , logit_scale_init_value=1.0 , from_pt=A )
_a = VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''' )
_a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_a = processor(
text=['''una foto di un gatto''', '''una foto di un cane'''] , images=A , padding=A , return_tensors='''np''' )
_a = model(**A )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_a = np.array([[1.2284727, 0.3104122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , A , atol=1E-3 ) )
| 11 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowercase_ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class __A :
'''simple docstring'''
def __init__(self , A = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError('''Unsupported Group''' )
_a = primes[group]['''prime''']
_a = primes[group]['''generator''']
_a = int(hexlify(urandom(32 ) ) , base=16 )
def a__ (self ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def a__ (self ) -> str:
"""simple docstring"""
_a = pow(self.generator , self.__private_key , self.prime )
return hex(A )[2:]
def a__ (self , A ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(A , (self.prime - 1) // 2 , self.prime ) == 1
)
def a__ (self , A ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
if not self.is_valid_public_key(A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , self.__private_key , self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def a__ (A , A ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(A , (prime - 1) // 2 , A ) == 1
)
@staticmethod
def a__ (A , A , A = 14 ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
_a = int(A , base=16 )
_a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(A , A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , A , A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __A :
'''simple docstring'''
def __init__(self , A ) -> Union[str, Any]:
"""simple docstring"""
_a = str(id_ )
_a = None
_a = None
_a = []
_a = {} # {vertex:distance}
def __lt__(self , A ) -> Dict:
"""simple docstring"""
return self.key < other.key
def __repr__(self ) -> Tuple:
"""simple docstring"""
return self.id
def a__ (self , A ) -> int:
"""simple docstring"""
self.neighbors.append(A )
def a__ (self , A , A ) -> int:
"""simple docstring"""
_a = weight
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
graph[a - 1].add_neighbor(graph[b - 1])
graph[b - 1].add_neighbor(graph[a - 1])
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __A)
graph[b - 1].add_edge(graph[a - 1] , __A)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for u in graph:
_a = math.inf
_a = None
_a = 0
_a = graph[:]
while q:
_a = min(__A)
q.remove(__A)
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_a = u
_a = u.edges[v.id]
for i in range(1 , len(__A)):
a.append((int(graph[i].id) + 1, int(graph[i].pi.id) + 1))
return a
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for u in graph:
_a = math.inf
_a = None
_a = 0
_a = list(__A)
hq.heapify(__A)
while h:
_a = hq.heappop(__A)
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_a = u
_a = u.edges[v.id]
hq.heapify(__A)
for i in range(1 , len(__A)):
yield (int(graph[i].id) + 1, int(graph[i].pi.id) + 1)
def lowerCAmelCase ():
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ = logging.getLogger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if os.path.exists(__A):
if os.path.exists(os.path.join(__A , '''config.json''')) and os.path.isfile(
os.path.join(__A , '''config.json''')):
os.remove(os.path.join(__A , '''config.json'''))
if os.path.exists(os.path.join(__A , '''pytorch_model.bin''')) and os.path.isfile(
os.path.join(__A , '''pytorch_model.bin''')):
os.remove(os.path.join(__A , '''pytorch_model.bin'''))
else:
os.makedirs(__A)
model.save_pretrained(__A)
def lowerCAmelCase (__A , __A=False):
"""simple docstring"""
_a = 2
if unlogit:
_a = torch.pow(__A , __A)
_a = p * torch.log(__A)
_a = 0
return -plogp.sum(dim=-1)
def lowerCAmelCase (__A):
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
for row in range(len(__A)):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
else:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))
def lowerCAmelCase (__A , __A , __A , __A=True , __A=True , __A=None , __A=False):
"""simple docstring"""
_a , _a = model.config.num_hidden_layers, model.config.num_attention_heads
_a = torch.zeros(__A , __A).to(args.device)
_a = torch.zeros(__A , __A).to(args.device)
if head_mask is None:
_a = torch.ones(__A , __A).to(args.device)
head_mask.requires_grad_(requires_grad=__A)
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_a = None
_a = 0.0
_a = 0.0
for step, inputs in enumerate(tqdm(__A , desc='''Iteration''' , disable=args.local_rank not in [-1, 0])):
_a = tuple(t.to(args.device) for t in inputs)
((_a) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_a = model(__A , labels=__A , head_mask=__A)
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_a , _a , _a = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__A):
_a = entropy(attn.detach() , __A)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__A).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_a = 2
_a = torch.pow(torch.pow(__A , __A).sum(-1) , 1 / exponent)
head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
if not args.dont_normalize_global_importance:
_a = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''')
print_ad_tensor(__A)
if compute_importance:
logger.info('''Head importance scores''')
print_ad_tensor(__A)
logger.info('''Head ranked by importance scores''')
_a = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device)
_a = torch.arange(
head_importance.numel() , device=args.device)
_a = head_ranks.view_as(__A)
print_ad_tensor(__A)
return attn_entropy, head_importance, total_loss
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a , _a , _a = compute_heads_importance(__A , __A , __A , compute_entropy=__A)
_a = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , __A , original_score * args.masking_threshold)
_a = torch.ones_like(__A)
_a = max(1 , int(new_head_mask.numel() * args.masking_amount))
_a = original_score
while current_score >= original_score * args.masking_threshold:
_a = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_a = float('''Inf''')
_a = head_importance.view(-1).sort()[1]
if len(__A) <= num_to_mask:
print('''BREAK BY num_to_mask''')
break
# mask heads
_a = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist()))
_a = new_head_mask.view(-1)
_a = 0.0
_a = new_head_mask.view_as(__A)
_a = new_head_mask.clone().detach()
print_ad_tensor(__A)
# Compute metric and head importance again
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , head_mask=__A)
_a = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''')
print_ad_tensor(__A)
np.save(os.path.join(args.output_dir , '''head_mask.npy''') , head_mask.detach().cpu().numpy())
return head_mask
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A)
_a = 1 / loss
_a = datetime.now() - before_time
_a = sum(p.numel() for p in model.parameters())
_a = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
}
for k, v in heads_to_prune.items():
if isinstance(__A , __A):
_a = [
v,
]
assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(__A)
_a = sum(p.numel() for p in model.parameters())
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A , actually_pruned=__A , )
_a = 1 / loss
_a = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , __A , __A , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , __A , __A)
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100)
save_model(__A , args.output_dir)
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=__A , type=__A , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=__A , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''')
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''')
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''')
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''')
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=__A , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=__A , help='''Amount to heads to masking at each masking step.''')
parser.add_argument('''--metric_name''' , default='''acc''' , type=__A , help='''Metric to use for head masking.''')
parser.add_argument(
'''--max_seq_length''' , default=128 , type=__A , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=__A , help='''Batch size.''')
parser.add_argument('''--seed''' , type=__A , default=42)
parser.add_argument('''--local_rank''' , type=__A , default=-1 , help='''local_rank for distributed training on gpus''')
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''')
parser.add_argument('''--server_ip''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
parser.add_argument('''--server_port''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
_a = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A)
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_a = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
_a = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
_a = torch.device('''cuda''' , args.local_rank)
_a = 1
torch.distributed.init_process_group(backend='''nccl''') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1)))
_a = GPTaLMHeadModel.from_pretrained(args.model_name_or_path)
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
_a = nn.parallel.DistributedDataParallel(
__A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__A)
elif args.n_gpu > 1:
_a = nn.DataParallel(__A)
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__A)
torch.save(__A , os.path.join(args.output_dir , '''run_args.bin'''))
logger.info('''Training/evaluation parameters %s''' , __A)
# Prepare dataset
_a = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa),
])
_a = (torch.from_numpy(__A),)
_a = TensorDataset(*__A)
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A , batch_size=args.batch_size)
# Compute head entropy and importance score
compute_heads_importance(__A , __A , __A)
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_a = mask_heads(__A , __A , __A)
prune_heads(__A , __A , __A , __A)
if __name__ == "__main__":
main()
| 11 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any = 'bert-generation'
def __init__(self , A=50_358 , A=1_024 , A=24 , A=16 , A=4_096 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=0.02 , A=1E-12 , A=0 , A=2 , A=1 , A="absolute" , A=True , **A , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A )
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = initializer_range
_a = layer_norm_eps
_a = position_embedding_type
_a = use_cache
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 1
for i in range(0 , len(__A)):
total *= numbers[i]
_a = str(__A)
steps += 1
return steps
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''additive_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 0
for i in range(0 , len(__A)):
total += numbers[i]
_a = str(__A)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
for i in range(len(__A) - 1 , 0 , -1):
_a = False
for j in range(__A , 0 , -1):
if unsorted[j] < unsorted[j - 1]:
_a , _a = unsorted[j - 1], unsorted[j]
_a = True
for j in range(__A):
if unsorted[j] > unsorted[j + 1]:
_a , _a = unsorted[j + 1], unsorted[j]
_a = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase_ = input("Enter numbers separated by a comma:\n").strip()
lowercase_ = [int(item) for item in user_input.split(",")]
print(F"""{cocktail_shaker_sort(unsorted) = }""")
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_a = size if size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = DPTImageProcessor if is_vision_available() else None
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPTImageProcessingTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 11 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = 'timm_backbone'
def __init__(self , A=None , A=3 , A=True , A=True , A=None , **A , ) -> Tuple:
"""simple docstring"""
super().__init__(**A )
_a = backbone
_a = num_channels
_a = features_only
_a = use_pretrained_backbone
_a = True
_a = out_indices if out_indices is not None else (-1,)
| 11 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
lowercase_ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __A :
'''simple docstring'''
def __init__(self , A , A=16 , A=13 , A=7 , A=14 , A=10 , A=19 , A=5 , A=4 , A=True , A=16 , A=2 , A=4 , A=4 , A="gelu" , A=0.1 , A=0.1 , A=[1, 2, 3, 4, 5] , A=25 , A=5 , ) -> List[str]:
"""simple docstring"""
_a = d_model
_a = parent
_a = batch_size
_a = prediction_length
_a = context_length
_a = cardinality
_a = num_time_features
_a = lags_sequence
_a = embedding_dimension
_a = is_training
_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 = context_length
_a = prediction_length + label_length
_a = label_length
_a = moving_average
_a = autocorrelation_factor
def a__ (self ) -> Any:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
_a = config.context_length + max(config.lags_sequence )
_a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, _past_length] )
_a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, config.prediction_length] )
_a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_config()
_a = self.prepare_autoformer_inputs_dict(A )
return config, inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModel(config=A ).to(A ).eval()
_a = model(**A )
_a = outputs.encoder_last_hidden_state
_a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_encoder()
encoder.save_pretrained(A )
_a = AutoformerEncoder.from_pretrained(A ).to(A )
_a , _a , _a , _a , _a = model.create_network_inputs(**A )
_a , _a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_a = encoder(inputs_embeds=A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
_a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_decoder()
decoder.save_pretrained(A )
_a = AutoformerDecoder.from_pretrained(A ).to(A )
_a = decoder(
trend=A , inputs_embeds=A , encoder_hidden_states=A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__lowerCamelCase : Tuple = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_a = model_class(A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a , _a = model_class.from_pretrained(A , output_loading_info=A )
self.assertEqual(info['''missing_keys'''] , [] )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*A )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = inspect.signature(getattr(A , '''forward''' ) )
# The main input is the name of the argument after `self`
_a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(A )] , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
_a = getattr(self.model_tester , '''seq_length''' , A )
_a = getattr(self.model_tester , '''decoder_seq_length''' , A )
_a = getattr(self.model_tester , '''encoder_seq_length''' , A )
_a = getattr(self.model_tester , '''d_model''' , A )
_a = getattr(self.model_tester , '''num_attention_heads''' , A )
_a = d_model // num_attention_heads
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_a = len(A )
_a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(A , A )
# decoder attentions
_a = outputs.decoder_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_a = outputs.cross_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
self.assertEqual(out_len + 2 , len(A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCAmelCase (__A="train-batch.pt"):
"""simple docstring"""
_a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__A , repo_type='''dataset''')
_a = torch.load(__A , map_location=__A)
return batch
@require_torch
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch()
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
_a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
_a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
_a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , A )
_a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=A )
_a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , A , rtol=1E-1 ) )
| 11 | 1 |
'''simple docstring'''
import requests
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(__A , json={'''text''': message_body} , headers=__A)
if response.status_code != 200:
_a = (
'''Request to slack returned an error '''
F'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(__A)
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")
| 11 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=False , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> str:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return OpenLlamaConfig(
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=A , initializer_range=self.initializer_range , use_stable_embedding=A , )
def a__ (self , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = OpenLlamaModel(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A )
_a = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Any:
"""simple docstring"""
_a = True
_a = OpenLlamaModel(A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , )
_a = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Dict:
"""simple docstring"""
_a = True
_a = True
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_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(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )['''hidden_states'''][0]
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )['''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(A , A , atol=1E-3 ) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__lowerCamelCase : Any = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__lowerCamelCase : List[Any] = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''single_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''multi_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = ids_tensor([1, 10] , config.vocab_size )
_a = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = OpenLlamaModel(A )
original_model.to(A )
original_model.eval()
_a = original_model(A ).last_hidden_state
_a = original_model(A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = {'''type''': scaling_type, '''factor''': 10.0}
_a = OpenLlamaModel(A )
scaled_model.to(A )
scaled_model.eval()
_a = scaled_model(A ).last_hidden_state
_a = scaled_model(A ).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(A , A , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
| 11 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_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_choices
def a__ (self ) -> str:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = AlbertConfig(
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=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxAlbertModelTester(self )
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''albert-base-v2''' )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Dict:
"""simple docstring"""
_a = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
_a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_a = model(A , attention_mask=A )[0]
_a = (1, 11, 768)
self.assertEqual(output.shape , A )
_a = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a = 0
_a = len(__A) - 2
for i in range(__A , -1 , -2):
# double the value of every second digit
_a = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_a = cc_number[:i] + str(__A) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCAmelCase (__A):
"""simple docstring"""
_a = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''')
return False
if not 13 <= len(__A) <= 16:
print(F'''{error_message} of its length.''')
return False
if not validate_initial_digits(__A):
print(F'''{error_message} of its first two digits.''')
return False
if not luhn_validation(__A):
print(F'''{error_message} it fails the Luhn check.''')
return False
print(F'''{credit_card_number} is a valid credit card number.''')
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 11 | 1 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : BigBirdConfig
__lowerCamelCase : jnp.dtype = jnp.floataa
__lowerCamelCase : bool = True
def a__ (self ) -> Any:
"""simple docstring"""
super().setup()
_a = nn.Dense(5 , dtype=self.dtype )
def __call__(self , *A , **A ) -> Any:
"""simple docstring"""
_a = super().__call__(*A , **A )
_a = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any = FlaxBigBirdForNaturalQuestionsModule
def lowerCAmelCase (__A , __A , __A , __A , __A , __A):
"""simple docstring"""
def cross_entropy(__A , __A , __A=None):
_a = logits.shape[-1]
_a = (labels[..., None] == jnp.arange(__A)[None]).astype('''f4''')
_a = jax.nn.log_softmax(__A , axis=-1)
_a = -jnp.sum(labels * logits , axis=-1)
if reduction is not None:
_a = reduction(__A)
return loss
_a = partial(__A , reduction=jnp.mean)
_a = cross_entropy(__A , __A)
_a = cross_entropy(__A , __A)
_a = cross_entropy(__A , __A)
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : str = "google/bigbird-roberta-base"
__lowerCamelCase : int = 3_000
__lowerCamelCase : int = 10_500
__lowerCamelCase : int = 128
__lowerCamelCase : int = 3
__lowerCamelCase : int = 1
__lowerCamelCase : int = 5
# tx_args
__lowerCamelCase : float = 3E-5
__lowerCamelCase : float = 0.0
__lowerCamelCase : int = 20_000
__lowerCamelCase : float = 0.00_95
__lowerCamelCase : str = "bigbird-roberta-natural-questions"
__lowerCamelCase : str = "training-expt"
__lowerCamelCase : str = "data/nq-training.jsonl"
__lowerCamelCase : str = "data/nq-validation.jsonl"
def a__ (self ) -> List[Any]:
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=A )
_a = os.path.join(self.base_dir , self.save_dir )
_a = self.batch_size_per_device * jax.device_count()
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : int
__lowerCamelCase : int = 4_096 # no dynamic padding on TPUs
def __call__(self , A ) -> Tuple:
"""simple docstring"""
_a = self.collate_fn(A )
_a = jax.tree_util.tree_map(A , A )
return batch
def a__ (self , A ) -> List[str]:
"""simple docstring"""
_a , _a = self.fetch_inputs(features['''input_ids'''] )
_a = {
'''input_ids''': jnp.array(A , dtype=jnp.intaa ),
'''attention_mask''': jnp.array(A , dtype=jnp.intaa ),
'''start_labels''': jnp.array(features['''start_token'''] , dtype=jnp.intaa ),
'''end_labels''': jnp.array(features['''end_token'''] , dtype=jnp.intaa ),
'''pooled_labels''': jnp.array(features['''category'''] , dtype=jnp.intaa ),
}
return batch
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = [self._fetch_inputs(A ) for ids in input_ids]
return zip(*A )
def a__ (self , A ) -> Optional[Any]:
"""simple docstring"""
_a = [1 for _ in range(len(A ) )]
while len(A ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def lowerCAmelCase (__A , __A , __A=None):
"""simple docstring"""
if seed is not None:
_a = dataset.shuffle(seed=__A)
for i in range(len(__A) // batch_size):
_a = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(__A)
@partial(jax.pmap , axis_name='''batch''')
def lowerCAmelCase (__A , __A , **__A):
"""simple docstring"""
def loss_fn(__A):
_a = model_inputs.pop('''start_labels''')
_a = model_inputs.pop('''end_labels''')
_a = model_inputs.pop('''pooled_labels''')
_a = state.apply_fn(**__A , params=__A , dropout_rng=__A , train=__A)
_a , _a , _a = outputs
return state.loss_fn(
__A , __A , __A , __A , __A , __A , )
_a , _a = jax.random.split(__A)
_a = jax.value_and_grad(__A)
_a , _a = grad_fn(state.params)
_a = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''')
_a = jax.lax.pmean(__A , '''batch''')
_a = state.apply_gradients(grads=__A)
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='''batch''')
def lowerCAmelCase (__A , **__A):
"""simple docstring"""
_a = model_inputs.pop('''start_labels''')
_a = model_inputs.pop('''end_labels''')
_a = model_inputs.pop('''pooled_labels''')
_a = state.apply_fn(**__A , params=state.params , train=__A)
_a , _a , _a = outputs
_a = state.loss_fn(__A , __A , __A , __A , __A , __A)
_a = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''')
return metrics
class __A ( train_state.TrainState ):
'''simple docstring'''
__lowerCamelCase : Callable = struct.field(pytree_node=A )
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : Args
__lowerCamelCase : Callable
__lowerCamelCase : Callable
__lowerCamelCase : Callable
__lowerCamelCase : Callable
__lowerCamelCase : wandb
__lowerCamelCase : Callable = None
def a__ (self , A , A , A , A=None ) -> Tuple:
"""simple docstring"""
_a = model.params
_a = TrainState.create(
apply_fn=model.__call__ , params=A , tx=A , loss_fn=A , )
if ckpt_dir is not None:
_a , _a , _a , _a , _a = restore_checkpoint(A , A )
_a = {
'''lr''': args.lr,
'''init_lr''': args.init_lr,
'''warmup_steps''': args.warmup_steps,
'''num_train_steps''': num_train_steps,
'''weight_decay''': args.weight_decay,
}
_a , _a = build_tx(**A )
_a = train_state.TrainState(
step=A , apply_fn=model.__call__ , params=A , tx=A , opt_state=A , )
_a = args
_a = data_collator
_a = lr
_a = params
_a = jax_utils.replicate(A )
return state
def a__ (self , A , A , A ) -> Dict:
"""simple docstring"""
_a = self.args
_a = len(A ) // args.batch_size
_a = jax.random.PRNGKey(0 )
_a = jax.random.split(A , jax.device_count() )
for epoch in range(args.max_epochs ):
_a = jnp.array(0 , dtype=jnp.floataa )
_a = get_batched_dataset(A , args.batch_size , seed=A )
_a = 0
for batch in tqdm(A , total=A , desc=f'''Running EPOCH-{epoch}''' ):
_a = self.data_collator(A )
_a , _a , _a = self.train_step_fn(A , A , **A )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
if i % args.logging_steps == 0:
_a = jax_utils.unreplicate(state.step )
_a = running_loss.item() / i
_a = self.scheduler_fn(state_step - 1 )
_a = self.evaluate(A , A )
_a = {
'''step''': state_step.item(),
'''eval_loss''': eval_loss.item(),
'''tr_loss''': tr_loss,
'''lr''': lr.item(),
}
tqdm.write(str(A ) )
self.logger.log(A , commit=A )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'''-e{epoch}-s{i}''' , state=A )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = get_batched_dataset(A , self.args.batch_size )
_a = len(A ) // self.args.batch_size
_a = jnp.array(0 , dtype=jnp.floataa )
_a = 0
for batch in tqdm(A , total=A , desc='''Evaluating ... ''' ):
_a = self.data_collator(A )
_a = self.val_step_fn(A , **A )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
return running_loss / i
def a__ (self , A , A ) -> Dict:
"""simple docstring"""
_a = jax_utils.unreplicate(A )
print(f'''SAVING CHECKPOINT IN {save_dir}''' , end=''' ... ''' )
self.model_save_fn(A , params=state.params )
with open(os.path.join(A , '''opt_state.msgpack''' ) , '''wb''' ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(A , '''args.joblib''' ) )
joblib.dump(self.data_collator , os.path.join(A , '''data_collator.joblib''' ) )
with open(os.path.join(A , '''training_state.json''' ) , '''w''' ) as f:
json.dump({'''step''': state.step.item()} , A )
print('''DONE''' )
def lowerCAmelCase (__A , __A):
"""simple docstring"""
print(F'''RESTORING CHECKPOINT FROM {save_dir}''' , end=''' ... ''')
with open(os.path.join(__A , '''flax_model.msgpack''') , '''rb''') as f:
_a = from_bytes(state.params , f.read())
with open(os.path.join(__A , '''opt_state.msgpack''') , '''rb''') as f:
_a = from_bytes(state.opt_state , f.read())
_a = joblib.load(os.path.join(__A , '''args.joblib'''))
_a = joblib.load(os.path.join(__A , '''data_collator.joblib'''))
with open(os.path.join(__A , '''training_state.json''') , '''r''') as f:
_a = json.load(__A)
_a = training_state['''step''']
print('''DONE''')
return params, opt_state, step, args, data_collator
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = num_train_steps - warmup_steps
_a = optax.linear_schedule(init_value=__A , end_value=__A , transition_steps=__A)
_a = optax.linear_schedule(init_value=__A , end_value=1e-7 , transition_steps=__A)
_a = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps])
return lr
def lowerCAmelCase (__A , __A , __A , __A , __A):
"""simple docstring"""
def weight_decay_mask(__A):
_a = traverse_util.flatten_dict(__A)
_a = {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
return traverse_util.unflatten_dict(__A)
_a = scheduler_fn(__A , __A , __A , __A)
_a = optax.adamw(learning_rate=__A , weight_decay=__A , mask=__A)
return tx, lr
| 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class __A :
'''simple docstring'''
__lowerCamelCase : List[str] = PegasusConfig
__lowerCamelCase : Any = {}
__lowerCamelCase : int = 'gelu'
def __init__(self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=2 , A=4 , A=37 , A=0.1 , A=0.1 , A=40 , A=2 , A=1 , A=0 , ) -> Union[str, Any]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = eos_token_id
_a = pad_token_id
_a = bos_token_id
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_a = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_a = tf.concat([input_ids, eos_tensor] , axis=1 )
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_a = prepare_pegasus_inputs_dict(A , A , A )
return config, inputs_dict
def a__ (self , A , A ) -> str:
"""simple docstring"""
_a = TFPegasusModel(config=A ).get_decoder()
_a = inputs_dict['''input_ids''']
_a = input_ids[:1, :]
_a = inputs_dict['''attention_mask'''][:1, :]
_a = inputs_dict['''head_mask''']
_a = 1
# first forward pass
_a = model(A , attention_mask=A , head_mask=A , use_cache=A )
_a , _a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_a = ids_tensor((self.batch_size, 3) , config.vocab_size )
_a = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
_a = tf.concat([input_ids, next_tokens] , axis=-1 )
_a = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
_a = model(A , attention_mask=A )[0]
_a = model(A , attention_mask=A , past_key_values=A )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
_a = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
_a = output_from_no_past[:, -3:, random_slice_idx]
_a = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(A , A , rtol=1E-3 )
def lowerCAmelCase (__A , __A , __A , __A=None , __A=None , __A=None , __A=None , __A=None , ):
"""simple docstring"""
if attention_mask is None:
_a = tf.cast(tf.math.not_equal(__A , config.pad_token_id) , tf.inta)
if decoder_attention_mask is None:
_a = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id) , tf.inta),
] , axis=-1 , )
if head_mask is None:
_a = tf.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
_a = tf.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
_a = tf.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
__lowerCamelCase : Union[str, Any] = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase : List[Any] = (
{
'conversational': TFPegasusForConditionalGeneration,
'feature-extraction': TFPegasusModel,
'summarization': TFPegasusForConditionalGeneration,
'text2text-generation': TFPegasusForConditionalGeneration,
'translation': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase : str = True
__lowerCamelCase : Any = False
__lowerCamelCase : Tuple = False
def a__ (self ) -> Dict:
"""simple docstring"""
_a = TFPegasusModelTester(self )
_a = ConfigTester(self , config_class=A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*A )
@require_sentencepiece
@require_tokenizers
@require_tf
class __A ( unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = [
' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
]
__lowerCamelCase : Dict = [
'California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'
' reduce the risk of wildfires.',
'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
__lowerCamelCase : str = 'google/pegasus-xsum'
@cached_property
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def a__ (self , **A ) -> Optional[int]:
"""simple docstring"""
_a = self.translate_src_text(**A )
assert self.expected_text == generated_words
def a__ (self , **A ) -> Optional[int]:
"""simple docstring"""
_a = self.tokenizer(self.src_text , **A , padding=A , return_tensors='''tf''' )
_a = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=A , )
_a = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=A )
return generated_words
@slow
def a__ (self ) -> int:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 11 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase (__A = "laptop"):
"""simple docstring"""
_a = F'''https://www.amazon.in/laptop/s?k={product}'''
_a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
_a = BeautifulSoup(requests.get(__A , headers=__A).text)
# Initialize a Pandas dataframe with the column titles
_a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
_a = item.ha.text
_a = '''https://www.amazon.in/''' + item.ha.a['''href''']
_a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
_a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
_a = '''Not available'''
try:
_a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
_a = ''''''
try:
_a = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
_a = float('''nan''')
except AttributeError:
pass
_a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_a = ''' '''
_a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 11 | 1 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowercase_ = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC",
"KD 6S 9D TH AD",
"KS 8D 4D 9S 4S", # pair
"8C 4S KH JS 4D", # pair
"QH 8H KD JH 8S", # pair
"KC 4H KS 2H 8D", # pair
"KD 4S KC 3H 8S", # pair
"AH 8S AS KC JH", # pair
"3H 4C 4H 3S 2H", # 2 pairs
"5S 5D 2C KH KH", # 2 pairs
"3C KH 5D 5S KH", # 2 pairs
"AS 3C KH AD KH", # 2 pairs
"7C 7S 3S 7H 5S", # 3 of a kind
"7C 7S KH 2H 7H", # 3 of a kind
"AC KH QH AH AS", # 3 of a kind
"2H 4D 3C AS 5S", # straight (low ace)
"3C 5C 4C 2C 6H", # straight
"6S 8S 7S 5H 9H", # straight
"JS QS 9H TS KH", # straight
"QC KH TS JS AH", # straight (high ace)
"8C 9C 5C 3C TC", # flush
"3S 8S 9S 5S KS", # flush
"4C 5C 9C 8C KC", # flush
"JH 8H AH KH QH", # flush
"3D 2H 3H 2C 2D", # full house
"2H 2C 3S 3H 3D", # full house
"KH KC 3S 3H 3D", # full house
"JC 6H JS JD JH", # 4 of a kind
"JC 7H JS JD JH", # 4 of a kind
"JC KH JS JD JH", # 4 of a kind
"2S AS 4S 5S 3S", # straight flush (low ace)
"2D 6D 3D 4D 5D", # straight flush
"5C 6C 3C 7C 4C", # straight flush
"JH 9H TH KH QH", # straight flush
"JH AH TH KH QH", # royal flush (high ace straight flush)
)
lowercase_ = (
("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)
lowercase_ = (
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", True),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", False),
("AS 3S 4S 8S 2S", True),
)
lowercase_ = (
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", False),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", True),
)
lowercase_ = (
("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)
lowercase_ = (
("JH AH TH KH QH", 0),
("JH 9H TH KH QH", 0),
("JC KH JS JD JH", 7),
("KH KC 3S 3H 3D", 6),
("8C 9C 5C 3C TC", 0),
("JS QS 9H TS KH", 0),
("7C 7S KH 2H 7H", 3),
("3C KH 5D 5S KH", 2),
("QH 8H KD JH 8S", 1),
("2D 6D 9D TH 7D", 0),
)
lowercase_ = (
("JH AH TH KH QH", 23),
("JH 9H TH KH QH", 22),
("JC KH JS JD JH", 21),
("KH KC 3S 3H 3D", 20),
("8C 9C 5C 3C TC", 19),
("JS QS 9H TS KH", 18),
("7C 7S KH 2H 7H", 17),
("3C KH 5D 5S KH", 16),
("QH 8H KD JH 8S", 15),
("2D 6D 9D TH 7D", 14),
)
def lowerCAmelCase ():
"""simple docstring"""
_a , _a = randrange(len(__A)), randrange(len(__A))
_a = ['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
_a , _a = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def lowerCAmelCase (__A = 100):
"""simple docstring"""
return (generate_random_hand() for _ in range(__A))
@pytest.mark.parametrize('''hand, expected''' , __A)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
assert PokerHand(__A)._is_flush() == expected
@pytest.mark.parametrize('''hand, expected''' , __A)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
assert PokerHand(__A)._is_straight() == expected
@pytest.mark.parametrize('''hand, expected, card_values''' , __A)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = PokerHand(__A)
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('''hand, expected''' , __A)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
assert PokerHand(__A)._is_same_kind() == expected
@pytest.mark.parametrize('''hand, expected''' , __A)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
assert PokerHand(__A)._hand_type == expected
@pytest.mark.parametrize('''hand, other, expected''' , __A)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
assert PokerHand(__A).compare_with(PokerHand(__A)) == expected
@pytest.mark.parametrize('''hand, other, expected''' , generate_random_hands())
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
assert PokerHand(__A).compare_with(PokerHand(__A)) == expected
def lowerCAmelCase ():
"""simple docstring"""
_a = [PokerHand(__A) for hand in SORTED_HANDS]
_a = poker_hands.copy()
shuffle(__A)
_a = chain(sorted(__A))
for index, hand in enumerate(__A):
assert hand == poker_hands[index]
def lowerCAmelCase ():
"""simple docstring"""
_a = [PokerHand('''2D AC 3H 4H 5S'''), PokerHand('''2S 3H 4H 5S 6C''')]
pokerhands.sort(reverse=__A)
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def lowerCAmelCase ():
"""simple docstring"""
_a = PokerHand('''2C 4S AS 3D 5C''')
_a = True
_a = [5, 4, 3, 2, 14]
for _ in range(10):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def lowerCAmelCase ():
"""simple docstring"""
_a = 0
_a = os.path.abspath(os.path.dirname(__A))
_a = os.path.join(__A , '''poker_hands.txt''')
with open(__A) as file_hand:
for line in file_hand:
_a = line[:14].strip()
_a = line[15:].strip()
_a , _a = PokerHand(__A), PokerHand(__A)
_a = player.compare_with(__A)
if output == "Win":
answer += 1
assert answer == 376
| 11 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if isinstance(__A , torch.Tensor):
return image
elif isinstance(__A , PIL.Image.Image):
_a = [image]
if isinstance(image[0] , PIL.Image.Image):
_a = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
_a = np.concatenate(__A , axis=0)
_a = np.array(__A).astype(np.floataa) / 2_55.0
_a = image.transpose(0 , 3 , 1 , 2)
_a = 2.0 * image - 1.0
_a = torch.from_numpy(__A)
elif isinstance(image[0] , torch.Tensor):
_a = torch.cat(__A , dim=0)
return image
def lowerCAmelCase (__A , __A , __A , __A=0.99_95):
"""simple docstring"""
if not isinstance(__A , np.ndarray):
_a = True
_a = va.device
_a = va.cpu().numpy()
_a = va.cpu().numpy()
_a = np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
if np.abs(__A) > DOT_THRESHOLD:
_a = (1 - t) * va + t * va
else:
_a = np.arccos(__A)
_a = np.sin(__A)
_a = theta_a * t
_a = np.sin(__A)
_a = np.sin(theta_a - theta_t) / sin_theta_a
_a = sin_theta_t / sin_theta_a
_a = sa * va + sa * va
if inputs_are_torch:
_a = torch.from_numpy(__A).to(__A)
return va
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = F.normalize(__A , dim=-1)
_a = F.normalize(__A , dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for param in model.parameters():
_a = value
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
_a = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size['''shortest_edge''']
)
_a = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def a__ (self , A = "auto" ) -> Union[str, Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(A )
def a__ (self ) -> int:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self ) -> str:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = min(int(num_inference_steps * strength ) , A )
_a = max(num_inference_steps - init_timestep , 0 )
_a = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a__ (self , A , A , A , A , A , A=None ) -> List[str]:
"""simple docstring"""
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
_a = image.to(device=A , dtype=A )
if isinstance(A , A ):
_a = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
_a = torch.cat(A , dim=0 )
else:
_a = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 0.18215 * init_latents
_a = init_latents.repeat_interleave(A , dim=0 )
_a = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
_a = self.scheduler.add_noise(A , A , A )
_a = init_latents
return latents
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_a = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_a = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.feature_extractor.preprocess(A )
_a = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a__ (self , A , A , A , A , A , A , A , ) -> Union[str, Any]:
"""simple docstring"""
_a = latents.detach().requires_grad_()
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_a = self.scheduler.alphas_cumprod[timestep]
_a = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_a = torch.sqrt(A )
_a = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
_a = self.scheduler.sigmas[index]
_a = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * sample
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = transforms.Resize(self.feature_extractor_size )(A )
_a = self.normalize(A ).to(latents.dtype )
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
_a = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
_a = latents.detach() + grads * (sigma**2)
_a = noise_pred_original
else:
_a = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> str:
"""simple docstring"""
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
_a = [generator] + [None] * (batch_size - 1)
_a = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_a = [x[0] for x in coca_is_none if x[1]]
_a = ''', '''.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
# get prompt text embeddings for content and style
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_a = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
_a = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
_a = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_a = {}
if accepts_offset:
_a = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_a , _a = self.get_timesteps(A , A , self.device )
_a = timesteps[:1].repeat(A )
# Preprocess image
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = slerp(A , A , A )
if clip_guidance_scale > 0:
_a = self.get_clip_image_embeddings(A , A )
_a = self.get_clip_image_embeddings(A , A )
_a = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_a = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_a = content_text_input.input_ids.shape[-1]
_a = self.tokenizer([''''''] , padding='''max_length''' , max_length=A , return_tensors='''pt''' )
_a = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_a = uncond_embeddings.repeat_interleave(A , dim=0 )
# 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
_a = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_a = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_a = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_a = torch.randn(A , generator=A , device='''cpu''' , dtype=A ).to(
self.device )
else:
_a = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
# check if the scheduler accepts generator
_a = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_a = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_a = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_a , _a = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * latents
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 11 | 1 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self ) -> None:
"""simple docstring"""
_a = {} # Mapping from char to TrieNode
_a = False
def a__ (self , A ) -> None:
"""simple docstring"""
for word in words:
self.insert(A )
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self
for char in word:
if char not in curr.nodes:
_a = TrieNode()
_a = curr.nodes[char]
_a = True
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = self
for char in word:
if char not in curr.nodes:
return False
_a = curr.nodes[char]
return curr.is_leaf
def a__ (self , A ) -> None:
"""simple docstring"""
def _delete(A , A , A ) -> bool:
if index == len(A ):
# If word does not exist
if not curr.is_leaf:
return False
_a = False
return len(curr.nodes ) == 0
_a = word[index]
_a = curr.nodes.get(A )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
_a = _delete(A , A , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , A , 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"""
_a = '''banana bananas bandana band apple all beast'''.split()
_a = 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()
| 11 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = CTRLTokenizer
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Any = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = '''adapt react readapt apt'''
_a = '''adapt react readapt apt'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt react readapt apt'''
_a = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
| 11 | 1 |
'''simple docstring'''
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase (__A = 8):
"""simple docstring"""
_a = ascii_letters + digits + punctuation
return "".join(secrets.choice(__A) for _ in range(__A))
def lowerCAmelCase (__A , __A):
"""simple docstring"""
i -= len(__A)
_a = i // 3
_a = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
_a = (
chars_incl
+ random(__A , quotient + remainder)
+ random(__A , __A)
+ random(__A , __A)
)
_a = list(__A)
shuffle(__A)
return "".join(__A)
# random is a generalised function for letters, characters and numbers
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return "".join(secrets.choice(__A) for _ in range(__A))
def lowerCAmelCase (__A , __A):
"""simple docstring"""
pass # Put your code here...
def lowerCAmelCase (__A , __A):
"""simple docstring"""
pass # Put your code here...
def lowerCAmelCase (__A , __A):
"""simple docstring"""
pass # Put your code here...
def lowerCAmelCase (__A , __A = 8):
"""simple docstring"""
if len(__A) < min_length:
# Your Password must be at least 8 characters long
return False
_a = any(char in ascii_uppercase for char in password)
_a = any(char in ascii_lowercase for char in password)
_a = any(char in digits for char in password)
_a = any(char in punctuation for char in password)
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCAmelCase ():
"""simple docstring"""
_a = int(input('''Please indicate the max length of your password: ''').strip())
_a = input(
'''Please indicate the characters that must be in your password: ''').strip()
print('''Password generated:''' , password_generator(__A))
print(
'''Alternative Password generated:''' , alternative_password_generator(__A , __A) , )
print('''[If you are thinking of using this passsword, You better save it.]''')
if __name__ == "__main__":
main()
| 11 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 | 1 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A ):
_a = self.prefix_sum[i - 1] + array[i]
def a__ (self , A , A ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 1 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
lowercase_ = parser.parse_args()
lowercase_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
lowercase_ = CLIPImageProcessor()
lowercase_ = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
lowercase_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowercase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_ = 10
lowercase_ = 256
def lowerCAmelCase (__A):
"""simple docstring"""
if len(__A) < MIN_NUM_TOKENS:
return None
_a = MinHash(num_perm=__A)
for token in set(__A):
min_hash.update(token.encode())
return min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}
class __A :
'''simple docstring'''
def __init__(self , *,
A = 0.85 , ) -> Optional[int]:
"""simple docstring"""
_a = duplication_jaccard_threshold
_a = NUM_PERM
_a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_a = defaultdict(A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
_a = self._index.query(A )
if code_key in self._index.keys:
print(f'''Duplicate key {code_key}''' )
return
self._index.insert(A , A )
if len(A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A )
def a__ (self ) -> List[List[Dict]]:
"""simple docstring"""
_a = []
for base, duplicates in self._duplicate_clusters.items():
_a = [base] + list(A )
# reformat the cluster to be a list of dict
_a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(A )
return duplicate_clusters
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_duplicate_clusters()
with open(A , '''w''' ) as f:
json.dump(A , A )
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = element
_a = get_min_hash([t for t in NON_ALPHA.split(data['''content''']) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__A , max_queue_size=10_000) , chunksize=100 , ):
if data is not None:
yield data
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = DuplicationIndex(duplication_jaccard_threshold=__A)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A)) , max_queue_size=100)):
di.add(__A , __A)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = get_tokens(__A)
_a = get_tokens(__A)
return len(tokensa & tokensa) / len(tokensa | tokensa)
lowercase_ = None
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for elementa in cluster:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__A , __A) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_a = 1
extremes.append(__A)
return extremes
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
global _shared_dataset
_a = dataset
_a = []
_a = partial(_find_cluster_extremes_shared , jaccard_threshold=__A)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__A , __A , ) , total=len(__A) , ):
extremes_list.append(__A)
return extremes_list
def lowerCAmelCase (__A , __A = 0.85):
"""simple docstring"""
_a = make_duplicate_clusters(__A , __A)
_a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_a = {}
_a = find_extremes(__A , __A , __A)
for extremes in extremes_clusters:
for element in extremes:
_a = element
_a = duplicate_indices - set(extreme_dict.keys())
_a = dataset.filter(lambda __A , __A: idx not in remove_indices , with_indices=__A)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_a = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(__A)}''')
print(F'''Number of duplicate clusters: {len(__A)}''')
print(F'''Files in duplicate cluster: {len(__A)}''')
print(F'''Unique files in duplicate cluster: {len(__A)}''')
print(F'''Filtered dataset size: {len(__A)}''')
return ds_filter, duplicate_clusters
| 11 | 1 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = ['image_processor']
__lowerCamelCase : Dict = 'SamImageProcessor'
def __init__(self , A ) -> List[Any]:
"""simple docstring"""
super().__init__(A )
_a = self.image_processor
_a = -10
_a = self.image_processor.size['''longest_edge''']
def __call__(self , A=None , A=None , A=None , A=None , A = None , **A , ) -> BatchEncoding:
"""simple docstring"""
_a = self.image_processor(
A , return_tensors=A , **A , )
# pop arguments that are not used in the foward but used nevertheless
_a = encoding_image_processor['''original_sizes''']
if hasattr(A , '''numpy''' ): # Checks if Torch or TF tensor
_a = original_sizes.numpy()
_a , _a , _a = self._check_and_preprocess_points(
input_points=A , input_labels=A , input_boxes=A , )
_a = self._normalize_and_convert(
A , A , input_points=A , input_labels=A , input_boxes=A , return_tensors=A , )
return encoding_image_processor
def a__ (self , A , A , A=None , A=None , A=None , A="pt" , ) -> Dict:
"""simple docstring"""
if input_points is not None:
if len(A ) != len(A ):
_a = [
self._normalize_coordinates(self.target_size , A , original_sizes[0] ) for point in input_points
]
else:
_a = [
self._normalize_coordinates(self.target_size , A , A )
for point, original_size in zip(A , A )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
_a , _a = self._pad_points_and_labels(A , A )
_a = np.array(A )
if input_labels is not None:
_a = np.array(A )
if input_boxes is not None:
if len(A ) != len(A ):
_a = [
self._normalize_coordinates(self.target_size , A , original_sizes[0] , is_bounding_box=A )
for box in input_boxes
]
else:
_a = [
self._normalize_coordinates(self.target_size , A , A , is_bounding_box=A )
for box, original_size in zip(A , A )
]
_a = np.array(A )
if input_boxes is not None:
if return_tensors == "pt":
_a = torch.from_numpy(A )
# boxes batch size of 1 by default
_a = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
_a = tf.convert_to_tensor(A )
# boxes batch size of 1 by default
_a = tf.expand_dims(A , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'''input_boxes''': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
_a = torch.from_numpy(A )
# point batch size of 1 by default
_a = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
_a = tf.convert_to_tensor(A )
# point batch size of 1 by default
_a = tf.expand_dims(A , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'''input_points''': input_points} )
if input_labels is not None:
if return_tensors == "pt":
_a = torch.from_numpy(A )
# point batch size of 1 by default
_a = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
_a = tf.convert_to_tensor(A )
# point batch size of 1 by default
_a = tf.expand_dims(A , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'''input_labels''': input_labels} )
return encoding_image_processor
def a__ (self , A , A ) -> Dict:
"""simple docstring"""
_a = max([point.shape[0] for point in input_points] )
_a = []
for i, point in enumerate(A ):
if point.shape[0] != expected_nb_points:
_a = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
_a = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(A )
_a = processed_input_points
return input_points, input_labels
def a__ (self , A , A , A , A=False ) -> np.ndarray:
"""simple docstring"""
_a , _a = original_size
_a , _a = self.image_processor._get_preprocess_shape(A , longest_edge=A )
_a = deepcopy(A ).astype(A )
if is_bounding_box:
_a = coords.reshape(-1 , 2 , 2 )
_a = coords[..., 0] * (new_w / old_w)
_a = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
_a = coords.reshape(-1 , 4 )
return coords
def a__ (self , A=None , A=None , A=None , ) -> Dict:
"""simple docstring"""
if input_points is not None:
if hasattr(A , '''numpy''' ): # Checks for TF or Torch tensor
_a = input_points.numpy().tolist()
if not isinstance(A , A ) or not isinstance(input_points[0] , A ):
raise ValueError('''Input points must be a list of list of floating points.''' )
_a = [np.array(A ) for input_point in input_points]
else:
_a = None
if input_labels is not None:
if hasattr(A , '''numpy''' ):
_a = input_labels.numpy().tolist()
if not isinstance(A , A ) or not isinstance(input_labels[0] , A ):
raise ValueError('''Input labels must be a list of list integers.''' )
_a = [np.array(A ) for label in input_labels]
else:
_a = None
if input_boxes is not None:
if hasattr(A , '''numpy''' ):
_a = input_boxes.numpy().tolist()
if (
not isinstance(A , A )
or not isinstance(input_boxes[0] , A )
or not isinstance(input_boxes[0][0] , A )
):
raise ValueError('''Input boxes must be a list of list of list of floating points.''' )
_a = [np.array(A ).astype(np.floataa ) for box in input_boxes]
else:
_a = None
return input_points, input_labels, input_boxes
@property
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.image_processor.model_input_names
return list(dict.fromkeys(A ) )
def a__ (self , *A , **A ) -> List[str]:
"""simple docstring"""
return self.image_processor.post_process_masks(*A , **A )
| 11 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_a = size if size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = DPTImageProcessor if is_vision_available() else None
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPTImageProcessingTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 11 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = IFInpaintingSuperResolutionPipeline
__lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
__lowerCamelCase : str = PipelineTesterMixin.required_optional_params - {'latents'}
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def a__ (self , A , A=0 ) -> List[Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = floats_tensor((1, 3, 16, 16) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ (self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def a__ (self ) -> str:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._test_save_load_local()
def a__ (self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A = " "):
"""simple docstring"""
_a = []
_a = 0
for index, char in enumerate(__A):
if char == separator:
split_words.append(string[last_index:index])
_a = index + 1
elif index + 1 == len(__A):
split_words.append(string[last_index : index + 1])
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 11 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 | 1 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase_ = {
"facebook/maskformer-swin-base-ade": (
"https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = 'maskformer'
__lowerCamelCase : int = {'hidden_size': 'mask_feature_size'}
__lowerCamelCase : List[str] = ['resnet', 'swin']
__lowerCamelCase : Dict = ['detr']
def __init__(self , A = 256 , A = 256 , A = 0.1 , A = False , A = None , A = None , A = 0.02 , A = 1.0 , A = 1.0 , A = 1.0 , A = 20.0 , A = None , **A , ) -> List[Any]:
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
_a = SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(A , A ):
_a = backbone_config.pop('''model_type''' )
_a = CONFIG_MAPPING[backbone_model_type]
_a = config_class.from_dict(A )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
f'''Supported model types: {','.join(self.backbones_supported )}''' )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
_a = DetrConfig()
else:
# verify that the decoder is supported
_a = (
decoder_config.pop('''model_type''' ) if isinstance(A , A ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f'''Transformer Decoder {decoder_type} not supported, please use one of'''
f''' {','.join(self.decoders_supported )}''' )
if isinstance(A , A ):
_a = CONFIG_MAPPING[decoder_type]
_a = config_class.from_dict(A )
_a = backbone_config
_a = decoder_config
# main feature dimension for the model
_a = fpn_feature_size
_a = mask_feature_size
# initializer
_a = init_std
_a = init_xavier_std
# Hungarian matcher && loss
_a = cross_entropy_weight
_a = dice_weight
_a = mask_weight
_a = use_auxiliary_loss
_a = no_object_weight
_a = output_auxiliary_logits
_a = self.decoder_config.encoder_attention_heads
_a = self.decoder_config.num_hidden_layers
super().__init__(**A )
@classmethod
def a__ (cls , A , A , **A ) -> Optional[Any]:
"""simple docstring"""
return cls(
backbone_config=A , decoder_config=A , **A , )
def a__ (self ) -> Dict[str, any]:
"""simple docstring"""
_a = copy.deepcopy(self.__dict__ )
_a = self.backbone_config.to_dict()
_a = self.decoder_config.to_dict()
_a = self.__class__.model_type
return output
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return len(set(__A)) == len(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowercase_ = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def lowerCAmelCase (__A , __A , __A , __A , __A , __A):
"""simple docstring"""
if got_ver is None or want_ver is None:
raise ValueError(
F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
F''' reinstalling {pkg}.''')
if not ops[op](version.parse(__A) , version.parse(__A)):
raise ImportError(
F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''')
def lowerCAmelCase (__A , __A = None):
"""simple docstring"""
_a = F'''\n{hint}''' if hint is not None else ''''''
# non-versioned check
if re.match(r'''^[\w_\-\d]+$''' , __A):
_a , _a , _a = requirement, None, None
else:
_a = re.findall(r'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)''' , __A)
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
F''' got {requirement}''')
_a , _a = match[0]
_a = want_full.split(''',''') # there could be multiple requirements
_a = {}
for w in want_range:
_a = re.findall(r'''^([\s!=<>]{1,2})(.+)''' , __A)
if not match:
raise ValueError(
'''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
F''' but got {requirement}''')
_a , _a = match[0]
_a = want_ver
if op not in ops:
raise ValueError(F'''{requirement}: need one of {list(ops.keys())}, but got {op}''')
# special case
if pkg == "python":
_a = '''.'''.join([str(__A) for x in sys.version_info[:3]])
for op, want_ver in wanted.items():
_compare_versions(__A , __A , __A , __A , __A , __A)
return
# check if any version is installed
try:
_a = importlib.metadata.version(__A)
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''')
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(__A , __A , __A , __A , __A , __A)
def lowerCAmelCase (__A):
"""simple docstring"""
_a = '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
return require_version(__A , __A)
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) == 0:
return False
_a = len(__A) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __A)
else:
return binary_search(a_list[midpoint + 1 :] , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
lowercase_ = int(input("Enter the number to be found in the list:\n").strip())
lowercase_ = "" if binary_search(sequence, target) else "not "
print(F"""{target} was {not_str}found in {sequence}""")
| 11 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A ):
_a = self.prefix_sum[i - 1] + array[i]
def a__ (self , A , A ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = str(__A)
return len(__A) == 9 and set(__A) == set('''123456789''')
def lowerCAmelCase ():
"""simple docstring"""
for base_num in range(9_999 , 4_999 , -1):
_a = 100_002 * base_num
if is_9_pandigital(__A):
return candidate
for base_num in range(333 , 99 , -1):
_a = 1_002_003 * base_num
if is_9_pandigital(__A):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A)
if n > 1:
factors.append(__A)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
lowercase_ = True
except (ImportError, AttributeError):
lowercase_ = object
def lowerCAmelCase (*__A , **__A):
"""simple docstring"""
pass
lowercase_ = False
lowercase_ = logging.get_logger("transformers-cli/serving")
def lowerCAmelCase (__A):
"""simple docstring"""
_a = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(__A , args.host , args.port , args.workers)
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : dict
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : List[str]
__lowerCamelCase : Optional[List[int]]
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any
class __A ( A ):
'''simple docstring'''
@staticmethod
def a__ (A ) -> Any:
"""simple docstring"""
_a = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=A , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=A , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=A , default=8_888 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=A , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=A , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=A , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=A , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=A , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=A )
def __init__(self , A , A , A , A ) -> List[str]:
"""simple docstring"""
_a = pipeline
_a = host
_a = port
_a = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(f'''Serving model over {host}:{port}''' )
_a = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=A , response_class=A , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=A , response_class=A , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=A , response_class=A , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=A , response_class=A , methods=['''POST'''] , ),
] , timeout=600 , )
def a__ (self ) -> List[str]:
"""simple docstring"""
run(self._app , host=self.host , port=self.port , workers=self.workers )
def a__ (self ) -> List[Any]:
"""simple docstring"""
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def a__ (self , A = Body(A , embed=A ) , A = Body(A , embed=A ) ) -> str:
"""simple docstring"""
try:
_a = self._pipeline.tokenizer.tokenize(A )
if return_ids:
_a = self._pipeline.tokenizer.convert_tokens_to_ids(A )
return ServeTokenizeResult(tokens=A , tokens_ids=A )
else:
return ServeTokenizeResult(tokens=A )
except Exception as e:
raise HTTPException(status_code=500 , detail={'''model''': '''''', '''error''': str(A )} )
def a__ (self , A = Body(A , embed=A ) , A = Body(A , embed=A ) , A = Body(A , embed=A ) , ) -> List[str]:
"""simple docstring"""
try:
_a = self._pipeline.tokenizer.decode(A , A , A )
return ServeDeTokenizeResult(model='''''' , text=A )
except Exception as e:
raise HTTPException(status_code=500 , detail={'''model''': '''''', '''error''': str(A )} )
async def a__ (self , A=Body(A , embed=A ) ) -> List[str]:
"""simple docstring"""
if len(A ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
_a = self._pipeline(A )
return ServeForwardResult(output=A )
except Exception as e:
raise HTTPException(500 , {'''error''': str(A )} )
| 11 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowercase_ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class __A :
'''simple docstring'''
def __init__(self , A = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError('''Unsupported Group''' )
_a = primes[group]['''prime''']
_a = primes[group]['''generator''']
_a = int(hexlify(urandom(32 ) ) , base=16 )
def a__ (self ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def a__ (self ) -> str:
"""simple docstring"""
_a = pow(self.generator , self.__private_key , self.prime )
return hex(A )[2:]
def a__ (self , A ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(A , (self.prime - 1) // 2 , self.prime ) == 1
)
def a__ (self , A ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
if not self.is_valid_public_key(A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , self.__private_key , self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def a__ (A , A ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(A , (prime - 1) // 2 , A ) == 1
)
@staticmethod
def a__ (A , A , A = 14 ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
_a = int(A , base=16 )
_a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(A , A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , A , A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A = 4_000_000):
"""simple docstring"""
_a = []
_a , _a = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__A)
_a , _a = b, a + b
return sum(__A)
if __name__ == "__main__":
print(F"""{solution() = }""")
| 11 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ = logging.getLogger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if os.path.exists(__A):
if os.path.exists(os.path.join(__A , '''config.json''')) and os.path.isfile(
os.path.join(__A , '''config.json''')):
os.remove(os.path.join(__A , '''config.json'''))
if os.path.exists(os.path.join(__A , '''pytorch_model.bin''')) and os.path.isfile(
os.path.join(__A , '''pytorch_model.bin''')):
os.remove(os.path.join(__A , '''pytorch_model.bin'''))
else:
os.makedirs(__A)
model.save_pretrained(__A)
def lowerCAmelCase (__A , __A=False):
"""simple docstring"""
_a = 2
if unlogit:
_a = torch.pow(__A , __A)
_a = p * torch.log(__A)
_a = 0
return -plogp.sum(dim=-1)
def lowerCAmelCase (__A):
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
for row in range(len(__A)):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
else:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))
def lowerCAmelCase (__A , __A , __A , __A=True , __A=True , __A=None , __A=False):
"""simple docstring"""
_a , _a = model.config.num_hidden_layers, model.config.num_attention_heads
_a = torch.zeros(__A , __A).to(args.device)
_a = torch.zeros(__A , __A).to(args.device)
if head_mask is None:
_a = torch.ones(__A , __A).to(args.device)
head_mask.requires_grad_(requires_grad=__A)
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_a = None
_a = 0.0
_a = 0.0
for step, inputs in enumerate(tqdm(__A , desc='''Iteration''' , disable=args.local_rank not in [-1, 0])):
_a = tuple(t.to(args.device) for t in inputs)
((_a) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_a = model(__A , labels=__A , head_mask=__A)
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_a , _a , _a = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__A):
_a = entropy(attn.detach() , __A)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__A).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_a = 2
_a = torch.pow(torch.pow(__A , __A).sum(-1) , 1 / exponent)
head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
if not args.dont_normalize_global_importance:
_a = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''')
print_ad_tensor(__A)
if compute_importance:
logger.info('''Head importance scores''')
print_ad_tensor(__A)
logger.info('''Head ranked by importance scores''')
_a = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device)
_a = torch.arange(
head_importance.numel() , device=args.device)
_a = head_ranks.view_as(__A)
print_ad_tensor(__A)
return attn_entropy, head_importance, total_loss
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a , _a , _a = compute_heads_importance(__A , __A , __A , compute_entropy=__A)
_a = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , __A , original_score * args.masking_threshold)
_a = torch.ones_like(__A)
_a = max(1 , int(new_head_mask.numel() * args.masking_amount))
_a = original_score
while current_score >= original_score * args.masking_threshold:
_a = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_a = float('''Inf''')
_a = head_importance.view(-1).sort()[1]
if len(__A) <= num_to_mask:
print('''BREAK BY num_to_mask''')
break
# mask heads
_a = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist()))
_a = new_head_mask.view(-1)
_a = 0.0
_a = new_head_mask.view_as(__A)
_a = new_head_mask.clone().detach()
print_ad_tensor(__A)
# Compute metric and head importance again
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , head_mask=__A)
_a = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''')
print_ad_tensor(__A)
np.save(os.path.join(args.output_dir , '''head_mask.npy''') , head_mask.detach().cpu().numpy())
return head_mask
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A)
_a = 1 / loss
_a = datetime.now() - before_time
_a = sum(p.numel() for p in model.parameters())
_a = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
}
for k, v in heads_to_prune.items():
if isinstance(__A , __A):
_a = [
v,
]
assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(__A)
_a = sum(p.numel() for p in model.parameters())
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A , actually_pruned=__A , )
_a = 1 / loss
_a = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , __A , __A , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , __A , __A)
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100)
save_model(__A , args.output_dir)
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=__A , type=__A , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=__A , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''')
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''')
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''')
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''')
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=__A , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=__A , help='''Amount to heads to masking at each masking step.''')
parser.add_argument('''--metric_name''' , default='''acc''' , type=__A , help='''Metric to use for head masking.''')
parser.add_argument(
'''--max_seq_length''' , default=128 , type=__A , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=__A , help='''Batch size.''')
parser.add_argument('''--seed''' , type=__A , default=42)
parser.add_argument('''--local_rank''' , type=__A , default=-1 , help='''local_rank for distributed training on gpus''')
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''')
parser.add_argument('''--server_ip''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
parser.add_argument('''--server_port''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
_a = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A)
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_a = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
_a = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
_a = torch.device('''cuda''' , args.local_rank)
_a = 1
torch.distributed.init_process_group(backend='''nccl''') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1)))
_a = GPTaLMHeadModel.from_pretrained(args.model_name_or_path)
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
_a = nn.parallel.DistributedDataParallel(
__A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__A)
elif args.n_gpu > 1:
_a = nn.DataParallel(__A)
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__A)
torch.save(__A , os.path.join(args.output_dir , '''run_args.bin'''))
logger.info('''Training/evaluation parameters %s''' , __A)
# Prepare dataset
_a = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa),
])
_a = (torch.from_numpy(__A),)
_a = TensorDataset(*__A)
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A , batch_size=args.batch_size)
# Compute head entropy and importance score
compute_heads_importance(__A , __A , __A)
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_a = mask_heads(__A , __A , __A)
prune_heads(__A , __A , __A , __A)
if __name__ == "__main__":
main()
| 11 | 1 |
'''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_albert import AlbertTokenizer
else:
lowercase_ = None
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
lowercase_ = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
},
"tokenizer_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
},
}
lowercase_ = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
lowercase_ = "▁"
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
__lowerCamelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[str] = AlbertTokenizer
def __init__(self , A=None , A=None , A=True , A=True , A=False , A="[CLS]" , A="[SEP]" , A="<unk>" , A="[SEP]" , A="<pad>" , A="[CLS]" , A="[MASK]" , **A , ) -> Tuple:
"""simple docstring"""
_a = (
AddedToken(A , lstrip=A , rstrip=A , normalized=A )
if isinstance(A , A )
else mask_token
)
super().__init__(
A , tokenizer_file=A , do_lower_case=A , remove_space=A , keep_accents=A , bos_token=A , eos_token=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , **A , )
_a = do_lower_case
_a = remove_space
_a = keep_accents
_a = vocab_file
_a = False if not self.vocab_file else True
def a__ (self , A , A = None ) -> List[int]:
"""simple docstring"""
_a = [self.sep_token_id]
_a = [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 a__ (self , A , A = None ) -> List[int]:
"""simple docstring"""
_a = [self.sep_token_id]
_a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a__ (self , A , A = 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(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_a = os.path.join(
A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 1
for i in range(0 , len(__A)):
total *= numbers[i]
_a = str(__A)
steps += 1
return steps
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''additive_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 0
for i in range(0 , len(__A)):
total += numbers[i]
_a = str(__A)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import os
import numpy
import onnx
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = a.name
_a = b.name
_a = ''''''
_a = ''''''
_a = a == b
_a = name_a
_a = name_b
return res
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
for i, input_name in enumerate(node_proto.input):
if input_name == name:
node_proto.input.insert(__A , __A)
node_proto.input.pop(i + 1)
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , __A , __A)
_graph_replace_input_with(node_proto.attribute[1].g , __A , __A)
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , __A , __A)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
for n in graph_proto.node:
_node_replace_input_with(__A , __A , __A)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = list(model.graph.initializer)
_a = list(model_without_ext.graph.initializer)
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
_a = inits[i].name
_a = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i])
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , __A , __A)
def lowerCAmelCase (__A):
"""simple docstring"""
_a = os.path.dirname(__A)
_a = os.path.basename(__A)
_a = onnx.load(os.path.join(__A , __A))
_a = list(model.graph.initializer)
_a = set()
_a = {}
_a = []
_a = 0
for i in range(len(__A)):
if i in dup_set:
continue
for j in range(i + 1 , len(__A)):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j]):
dup_set.add(__A)
dup_set.add(__A)
_a = inits[j].data_type
_a = numpy.prod(inits[j].dims)
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print('''unexpected data type: ''' , __A)
total_reduced_size += mem_size
_a = inits[i].name
_a = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(__A)
else:
_a = [name_j]
ind_to_replace.append((j, i))
print('''total reduced size: ''' , total_reduced_size / 1_024 / 1_024 / 1_024 , '''GB''')
_a = sorted(__A)
_remove_dup_initializers_from_model(__A , __A , __A)
_a = '''optimized_''' + model_file_name
_a = os.path.join(__A , __A)
onnx.save(__A , __A)
return new_model
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_a = size if size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = DPTImageProcessor if is_vision_available() else None
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPTImageProcessingTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
class __A :
'''simple docstring'''
def __init__(self , A , A ) -> Optional[Any]:
"""simple docstring"""
_a , _a = text, pattern
_a , _a = len(A ), len(A )
def a__ (self , A ) -> int:
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def a__ (self , A ) -> int:
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def a__ (self ) -> list[int]:
"""simple docstring"""
_a = []
for i in range(self.textLen - self.patLen + 1 ):
_a = self.mismatch_in_text(A )
if mismatch_index == -1:
positions.append(A )
else:
_a = self.match_in_pattern(self.text[mismatch_index] )
_a = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
lowercase_ = "ABAABA"
lowercase_ = "AB"
lowercase_ = BoyerMooreSearch(text, pattern)
lowercase_ = bms.bad_character_heuristic()
if len(positions) == 0:
print("No match found")
else:
print("Pattern found in following positions: ")
print(positions)
| 11 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
lowercase_ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __A :
'''simple docstring'''
def __init__(self , A , A=16 , A=13 , A=7 , A=14 , A=10 , A=19 , A=5 , A=4 , A=True , A=16 , A=2 , A=4 , A=4 , A="gelu" , A=0.1 , A=0.1 , A=[1, 2, 3, 4, 5] , A=25 , A=5 , ) -> List[str]:
"""simple docstring"""
_a = d_model
_a = parent
_a = batch_size
_a = prediction_length
_a = context_length
_a = cardinality
_a = num_time_features
_a = lags_sequence
_a = embedding_dimension
_a = is_training
_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 = context_length
_a = prediction_length + label_length
_a = label_length
_a = moving_average
_a = autocorrelation_factor
def a__ (self ) -> Any:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
_a = config.context_length + max(config.lags_sequence )
_a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, _past_length] )
_a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, config.prediction_length] )
_a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_config()
_a = self.prepare_autoformer_inputs_dict(A )
return config, inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModel(config=A ).to(A ).eval()
_a = model(**A )
_a = outputs.encoder_last_hidden_state
_a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_encoder()
encoder.save_pretrained(A )
_a = AutoformerEncoder.from_pretrained(A ).to(A )
_a , _a , _a , _a , _a = model.create_network_inputs(**A )
_a , _a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_a = encoder(inputs_embeds=A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
_a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_decoder()
decoder.save_pretrained(A )
_a = AutoformerDecoder.from_pretrained(A ).to(A )
_a = decoder(
trend=A , inputs_embeds=A , encoder_hidden_states=A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__lowerCamelCase : Tuple = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_a = model_class(A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a , _a = model_class.from_pretrained(A , output_loading_info=A )
self.assertEqual(info['''missing_keys'''] , [] )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*A )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = inspect.signature(getattr(A , '''forward''' ) )
# The main input is the name of the argument after `self`
_a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(A )] , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
_a = getattr(self.model_tester , '''seq_length''' , A )
_a = getattr(self.model_tester , '''decoder_seq_length''' , A )
_a = getattr(self.model_tester , '''encoder_seq_length''' , A )
_a = getattr(self.model_tester , '''d_model''' , A )
_a = getattr(self.model_tester , '''num_attention_heads''' , A )
_a = d_model // num_attention_heads
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_a = len(A )
_a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(A , A )
# decoder attentions
_a = outputs.decoder_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_a = outputs.cross_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
self.assertEqual(out_len + 2 , len(A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCAmelCase (__A="train-batch.pt"):
"""simple docstring"""
_a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__A , repo_type='''dataset''')
_a = torch.load(__A , map_location=__A)
return batch
@require_torch
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch()
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
_a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
_a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
_a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , A )
_a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=A )
_a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , A , rtol=1E-1 ) )
| 11 | 1 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_a = flax_key_tuple[:-1] + ('''weight''',)
_a = torch.permute(__A , (0, 2, 1))
elif flax_key_tuple[-1] == "kernel" and ".".join(__A):
# linear layer
_a = flax_key_tuple[:-1] + ('''weight''',)
_a = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_a = flax_key_tuple[:-1] + ('''weight''',)
return flax_key_tuple, flax_tensor
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if "metadata" in layer:
_a = layer.split('''metadata''')
_a = ''''''.join(split_layer[0])[:-1]
_a = [tuple(('''metadata''' + split_layer[1]).split('''/'''))]
elif "kvstore" in layer:
_a = layer.split('''kvstore''')
_a = ''''''.join(split_layer[0])[:-1]
_a = [tuple(('''kvstore''' + split_layer[1]).split('''/'''))]
else:
_a = layer.split('''/''')
_a = '''/'''.join(split_layer[:-1])
_a = (split_layer[-1],)
if "kvstore/path" in layer:
_a = F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
elif "kvstore/driver" in layer:
_a = '''file'''
else:
_a = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = rename_keys(__A)
_a = {}
for k, v in current_block.items():
_a = v
_a = new_current_block
torch.save(__A , __A)
def lowerCAmelCase (__A , __A , __A , __A , __A = WEIGHTS_NAME):
"""simple docstring"""
_a = convert_file_size_to_int(__A)
_a = []
_a = {}
_a = 0
_a = 0
os.makedirs(__A , exist_ok=__A)
with gfile.GFile(switch_checkpoint_path + '''/checkpoint''' , '''rb''') as fp:
_a = serialization.msgpack_restore(fp.read())['''optimizer''']['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = {}
for layer in checkpoint_info.keys():
_a , _a , _a = get_key_and_tensorstore_dict(
__A , __A , __A)
if curr_real_layer_name in all_layers:
_a = content
else:
_a = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_a = ts.open(unflatten_dict(all_layers[key])).result().read().result()
_a = torch.tensor(__A)
_a = raw_weights.numel() * dtype_byte_size(raw_weights.dtype)
# use the renaming pattern from the small conversion scripts
_a , _a = rename_base_flax_keys(tuple(key.split('''/''')) , __A)
_a = '''/'''.join(__A)
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_a = os.path.join(
__A , weights_name.replace('''.bin''' , F'''-{len(__A)+1:05d}-of-???.bin'''))
rename_and_save_block(__A , __A)
sharded_state_dicts.append(current_block.keys())
del current_block
_a = {}
_a = 0
_a = raw_weights.to(getattr(__A , __A))
current_block_size += weight_size
total_size += weight_size
# Add the last block
_a = os.path.join(__A , weights_name.replace('''.bin''' , F'''-{len(__A)+1:05d}-of-???.bin'''))
rename_and_save_block(__A , __A)
sharded_state_dicts.append(current_block.keys())
# If we only have one shard, we return it
if len(__A) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_a = {}
_a = {}
for idx, shard in enumerate(__A):
_a = weights_name.replace(
'''.bin''' , F'''-{idx+1:05d}-of-{len(__A):05d}.bin''') # len(sharded_state_dicts):05d}
_a = os.path.join(__A , weights_name.replace('''.bin''' , F'''-{idx+1:05d}-of-???.bin'''))
os.rename(__A , os.path.join(__A , __A))
_a = shard
for key in shard:
_a = shard_file
# Add the metadata
_a = {'''total_size''': total_size}
_a = {'''metadata''': metadata, '''weight_map''': weight_map}
with open(os.path.join(__A , __A) , '''w''' , encoding='''utf-8''') as f:
_a = json.dumps(__A , indent=2 , sort_keys=__A) + '''\n'''
f.write(__A)
return metadata, index
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
type=str,
required=False,
help="Path to the output pytorch model.",
)
lowercase_ = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowerCAmelCase ():
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_a = SwitchTransformersConfig.from_pretrained('''google/switch-base-8''')
config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted''')
_a = SwitchTransformersForConditionalGeneration.from_pretrained(
'''/home/arthur_huggingface_co/transformers/switch_converted''' , device_map='''auto''')
_a = TaTokenizer.from_pretrained('''t5-small''')
_a = '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''
_a = tokenizer(__A , return_tensors='''pt''').input_ids
_a = model.generate(__A , decoder_start_token_id=0)
print(tokenizer.decode(out[0]))
| 11 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=False , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> str:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return OpenLlamaConfig(
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=A , initializer_range=self.initializer_range , use_stable_embedding=A , )
def a__ (self , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = OpenLlamaModel(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A )
_a = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Any:
"""simple docstring"""
_a = True
_a = OpenLlamaModel(A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , )
_a = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Dict:
"""simple docstring"""
_a = True
_a = True
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_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(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )['''hidden_states'''][0]
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )['''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(A , A , atol=1E-3 ) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__lowerCamelCase : Any = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__lowerCamelCase : List[Any] = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''single_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''multi_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = ids_tensor([1, 10] , config.vocab_size )
_a = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = OpenLlamaModel(A )
original_model.to(A )
original_model.eval()
_a = original_model(A ).last_hidden_state
_a = original_model(A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = {'''type''': scaling_type, '''factor''': 10.0}
_a = OpenLlamaModel(A )
scaled_model.to(A )
scaled_model.eval()
_a = scaled_model(A ).last_hidden_state
_a = scaled_model(A ).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(A , A , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
| 11 | 1 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase (__A = "laptop"):
"""simple docstring"""
_a = F'''https://www.amazon.in/laptop/s?k={product}'''
_a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
_a = BeautifulSoup(requests.get(__A , headers=__A).text)
# Initialize a Pandas dataframe with the column titles
_a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
_a = item.ha.text
_a = '''https://www.amazon.in/''' + item.ha.a['''href''']
_a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
_a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
_a = '''Not available'''
try:
_a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
_a = ''''''
try:
_a = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
_a = float('''nan''')
except AttributeError:
pass
_a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_a = ''' '''
_a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_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_choices
def a__ (self ) -> str:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = AlbertConfig(
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=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxAlbertModelTester(self )
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''albert-base-v2''' )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Dict:
"""simple docstring"""
_a = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
_a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_a = model(A , attention_mask=A )[0]
_a = (1, 11, 768)
self.assertEqual(output.shape , A )
_a = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 11 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : int = 'yolos'
def __init__(self , A=768 , A=12 , A=12 , A=3_072 , A="gelu" , A=0.0 , A=0.0 , A=0.02 , A=1E-12 , A=[512, 864] , A=16 , A=3 , A=True , A=100 , A=True , A=False , A=1 , A=5 , A=2 , A=5 , A=2 , A=0.1 , **A , ) -> str:
"""simple docstring"""
super().__init__(**A )
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = initializer_range
_a = layer_norm_eps
_a = image_size
_a = patch_size
_a = num_channels
_a = qkv_bias
_a = num_detection_tokens
_a = use_mid_position_embeddings
_a = auxiliary_loss
# Hungarian matcher
_a = class_cost
_a = bbox_cost
_a = giou_cost
# Loss coefficients
_a = bbox_loss_coefficient
_a = giou_loss_coefficient
_a = eos_coefficient
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str = version.parse('1.11' )
@property
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def a__ (self ) -> float:
"""simple docstring"""
return 1E-4
@property
def a__ (self ) -> int:
"""simple docstring"""
return 12
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a = 0
_a = len(__A) - 2
for i in range(__A , -1 , -2):
# double the value of every second digit
_a = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_a = cc_number[:i] + str(__A) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCAmelCase (__A):
"""simple docstring"""
_a = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''')
return False
if not 13 <= len(__A) <= 16:
print(F'''{error_message} of its length.''')
return False
if not validate_initial_digits(__A):
print(F'''{error_message} of its first two digits.''')
return False
if not luhn_validation(__A):
print(F'''{error_message} it fails the Luhn check.''')
return False
print(F'''{credit_card_number} is a valid credit card number.''')
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 11 | 1 |
'''simple docstring'''
import math
def lowerCAmelCase (__A):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__A) + 1) , 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase (__A = 0.1):
"""simple docstring"""
_a = 3
_a = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1):
primes += is_prime(__A)
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["DeiTFeatureExtractor"]
lowercase_ = ["DeiTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DeiTForImageClassification",
"DeiTForImageClassificationWithTeacher",
"DeiTForMaskedImageModeling",
"DeiTModel",
"DeiTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDeiTForImageClassification",
"TFDeiTForImageClassificationWithTeacher",
"TFDeiTForMaskedImageModeling",
"TFDeiTModel",
"TFDeiTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase (__A = "laptop"):
"""simple docstring"""
_a = F'''https://www.amazon.in/laptop/s?k={product}'''
_a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
_a = BeautifulSoup(requests.get(__A , headers=__A).text)
# Initialize a Pandas dataframe with the column titles
_a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
_a = item.ha.text
_a = '''https://www.amazon.in/''' + item.ha.a['''href''']
_a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
_a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
_a = '''Not available'''
try:
_a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
_a = ''''''
try:
_a = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
_a = float('''nan''')
except AttributeError:
pass
_a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_a = ''' '''
_a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
while a != 0:
_a , _a = b % a, a
return b
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if gcd(__A , __A) != 1:
_a = F'''mod inverse of {a!r} and {m!r} does not exist'''
raise ValueError(__A)
_a , _a , _a = 1, 0, a
_a , _a , _a = 0, 1, m
while va != 0:
_a = ua // va
_a , _a , _a , _a , _a , _a = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 11 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if isinstance(__A , torch.Tensor):
return image
elif isinstance(__A , PIL.Image.Image):
_a = [image]
if isinstance(image[0] , PIL.Image.Image):
_a = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
_a = np.concatenate(__A , axis=0)
_a = np.array(__A).astype(np.floataa) / 2_55.0
_a = image.transpose(0 , 3 , 1 , 2)
_a = 2.0 * image - 1.0
_a = torch.from_numpy(__A)
elif isinstance(image[0] , torch.Tensor):
_a = torch.cat(__A , dim=0)
return image
def lowerCAmelCase (__A , __A , __A , __A=0.99_95):
"""simple docstring"""
if not isinstance(__A , np.ndarray):
_a = True
_a = va.device
_a = va.cpu().numpy()
_a = va.cpu().numpy()
_a = np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
if np.abs(__A) > DOT_THRESHOLD:
_a = (1 - t) * va + t * va
else:
_a = np.arccos(__A)
_a = np.sin(__A)
_a = theta_a * t
_a = np.sin(__A)
_a = np.sin(theta_a - theta_t) / sin_theta_a
_a = sin_theta_t / sin_theta_a
_a = sa * va + sa * va
if inputs_are_torch:
_a = torch.from_numpy(__A).to(__A)
return va
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = F.normalize(__A , dim=-1)
_a = F.normalize(__A , dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for param in model.parameters():
_a = value
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
_a = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size['''shortest_edge''']
)
_a = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def a__ (self , A = "auto" ) -> Union[str, Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(A )
def a__ (self ) -> int:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self ) -> str:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = min(int(num_inference_steps * strength ) , A )
_a = max(num_inference_steps - init_timestep , 0 )
_a = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a__ (self , A , A , A , A , A , A=None ) -> List[str]:
"""simple docstring"""
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
_a = image.to(device=A , dtype=A )
if isinstance(A , A ):
_a = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
_a = torch.cat(A , dim=0 )
else:
_a = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 0.18215 * init_latents
_a = init_latents.repeat_interleave(A , dim=0 )
_a = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
_a = self.scheduler.add_noise(A , A , A )
_a = init_latents
return latents
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_a = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_a = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.feature_extractor.preprocess(A )
_a = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a__ (self , A , A , A , A , A , A , A , ) -> Union[str, Any]:
"""simple docstring"""
_a = latents.detach().requires_grad_()
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_a = self.scheduler.alphas_cumprod[timestep]
_a = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_a = torch.sqrt(A )
_a = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
_a = self.scheduler.sigmas[index]
_a = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * sample
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = transforms.Resize(self.feature_extractor_size )(A )
_a = self.normalize(A ).to(latents.dtype )
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
_a = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
_a = latents.detach() + grads * (sigma**2)
_a = noise_pred_original
else:
_a = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> str:
"""simple docstring"""
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
_a = [generator] + [None] * (batch_size - 1)
_a = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_a = [x[0] for x in coca_is_none if x[1]]
_a = ''', '''.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
# get prompt text embeddings for content and style
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_a = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
_a = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
_a = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_a = {}
if accepts_offset:
_a = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_a , _a = self.get_timesteps(A , A , self.device )
_a = timesteps[:1].repeat(A )
# Preprocess image
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = slerp(A , A , A )
if clip_guidance_scale > 0:
_a = self.get_clip_image_embeddings(A , A )
_a = self.get_clip_image_embeddings(A , A )
_a = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_a = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_a = content_text_input.input_ids.shape[-1]
_a = self.tokenizer([''''''] , padding='''max_length''' , max_length=A , return_tensors='''pt''' )
_a = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_a = uncond_embeddings.repeat_interleave(A , dim=0 )
# 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
_a = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_a = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_a = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_a = torch.randn(A , generator=A , device='''cpu''' , dtype=A ).to(
self.device )
else:
_a = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
# check if the scheduler accepts generator
_a = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_a = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_a = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_a , _a = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * latents
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 1
for i in range(0 , len(__A)):
total *= numbers[i]
_a = str(__A)
steps += 1
return steps
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''additive_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 0
for i in range(0 , len(__A)):
total += numbers[i]
_a = str(__A)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = CTRLTokenizer
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Any = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = '''adapt react readapt apt'''
_a = '''adapt react readapt apt'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt react readapt apt'''
_a = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
| 11 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Union[List[PIL.Image.Image], np.ndarray]
__lowerCamelCase : Optional[List[bool]]
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 11 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 | 1 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
parser.add_argument(
'''-m''' , '''--pretrained_model_name_or_path''' , type=__A , default=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , )
parser.add_argument(
'''-c''' , '''--caption''' , type=__A , default='''robotic cat with wings''' , help='''Text used to generate images.''' , )
parser.add_argument(
'''-n''' , '''--images_num''' , type=__A , default=4 , help='''How much images to generate.''' , )
parser.add_argument(
'''-s''' , '''--seed''' , type=__A , default=42 , help='''Seed for random process.''' , )
parser.add_argument(
'''-ci''' , '''--cuda_id''' , type=__A , default=0 , help='''cuda_id.''' , )
_a = parser.parse_args()
return args
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if not len(__A) == rows * cols:
raise ValueError('''The specified number of rows and columns are not correct.''')
_a , _a = imgs[0].size
_a = Image.new('''RGB''' , size=(cols * w, rows * h))
_a , _a = grid.size
for i, img in enumerate(__A):
grid.paste(__A , box=(i % cols * w, i // cols * h))
return grid
def lowerCAmelCase (__A , __A="robotic cat with wings" , __A=7.5 , __A=50 , __A=1 , __A=42 , ):
"""simple docstring"""
_a = torch.Generator(pipeline.device).manual_seed(__A)
_a = pipeline(
__A , guidance_scale=__A , num_inference_steps=__A , generator=__A , num_images_per_prompt=__A , ).images
_a = int(math.sqrt(__A))
_a = image_grid(__A , rows=_rows , cols=num_images_per_prompt // _rows)
return grid, images
lowercase_ = parse_args()
# Load models and create wrapper for stable diffusion
lowercase_ = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
lowercase_ = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
lowercase_ = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
lowercase_ = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
lowercase_ = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowercase_ = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
lowercase_ = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, "unet", unet)
else:
lowercase_ = unet.to(torch.device("cuda", args.cuda_id))
lowercase_ = pipeline.to(unet.device)
lowercase_ , lowercase_ = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
lowercase_ = os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, "{}.png".format(idx + 1)))
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
lowercase_ = "hf-internal-testing/tiny-random-bert"
lowercase_ = os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
lowercase_ = "9b8c223d42b2188cb49d29af482996f9d0f3e5a6"
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = cached_file(A , A )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(A ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(A , A ) ) )
with open(os.path.join(A , '''refs''' , '''main''' ) ) as f:
_a = f.read()
self.assertEqual(A , os.path.join(A , '''snapshots''' , A , A ) )
self.assertTrue(os.path.isfile(A ) )
# File is cached at the same place the second time.
_a = cached_file(A , A )
self.assertEqual(A , A )
# Using a specific revision to test the full commit hash.
_a = cached_file(A , A , revision='''9b8c223''' )
self.assertEqual(A , os.path.join(A , '''snapshots''' , A , A ) )
def a__ (self ) -> Dict:
"""simple docstring"""
with self.assertRaisesRegex(A , '''is not a valid model identifier''' ):
_a = cached_file('''tiny-random-bert''' , A )
with self.assertRaisesRegex(A , '''is not a valid git identifier''' ):
_a = cached_file(A , A , revision='''aaaa''' )
with self.assertRaisesRegex(A , '''does not appear to have a file named''' ):
_a = cached_file(A , '''conf''' )
def a__ (self ) -> int:
"""simple docstring"""
with self.assertRaisesRegex(A , '''does not appear to have a file named''' ):
_a = cached_file(A , '''conf''' )
with open(os.path.join(A , '''refs''' , '''main''' ) ) as f:
_a = f.read()
self.assertTrue(os.path.isfile(os.path.join(A , '''.no_exist''' , A , '''conf''' ) ) )
_a = cached_file(A , '''conf''' , _raise_exceptions_for_missing_entries=A )
self.assertIsNone(A )
_a = cached_file(A , '''conf''' , local_files_only=A , _raise_exceptions_for_missing_entries=A )
self.assertIsNone(A )
_a = mock.Mock()
_a = 500
_a = {}
_a = HTTPError
_a = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('''requests.Session.request''' , return_value=A ) as mock_head:
_a = cached_file(A , '''conf''' , _raise_exceptions_for_connection_errors=A )
self.assertIsNone(A )
# This check we did call the fake head request
mock_head.assert_called()
def a__ (self ) -> int:
"""simple docstring"""
self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' , A ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , A ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , A ) )
def a__ (self ) -> str:
"""simple docstring"""
self.assertIsNone(get_file_from_repo('''bert-base-cased''' , '''ahah.txt''' ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(A , '''is not a valid model identifier''' ):
get_file_from_repo('''bert-base-case''' , A )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(A , '''is not a valid git identifier''' ):
get_file_from_repo('''bert-base-cased''' , A , revision='''ahaha''' )
_a = get_file_from_repo('''bert-base-cased''' , A )
# The name is the cached name which is not very easy to test, so instead we load the content.
_a = json.loads(open(A , '''r''' ).read() )
self.assertEqual(config['''hidden_size'''] , 768 )
def a__ (self ) -> List[str]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
_a = Path(A ) / '''a.txt'''
filename.touch()
self.assertEqual(get_file_from_repo(A , '''a.txt''' ) , str(A ) )
self.assertIsNone(get_file_from_repo(A , '''b.txt''' ) )
| 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowercase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_ = 10
lowercase_ = 256
def lowerCAmelCase (__A):
"""simple docstring"""
if len(__A) < MIN_NUM_TOKENS:
return None
_a = MinHash(num_perm=__A)
for token in set(__A):
min_hash.update(token.encode())
return min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}
class __A :
'''simple docstring'''
def __init__(self , *,
A = 0.85 , ) -> Optional[int]:
"""simple docstring"""
_a = duplication_jaccard_threshold
_a = NUM_PERM
_a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_a = defaultdict(A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
_a = self._index.query(A )
if code_key in self._index.keys:
print(f'''Duplicate key {code_key}''' )
return
self._index.insert(A , A )
if len(A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A )
def a__ (self ) -> List[List[Dict]]:
"""simple docstring"""
_a = []
for base, duplicates in self._duplicate_clusters.items():
_a = [base] + list(A )
# reformat the cluster to be a list of dict
_a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(A )
return duplicate_clusters
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_duplicate_clusters()
with open(A , '''w''' ) as f:
json.dump(A , A )
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = element
_a = get_min_hash([t for t in NON_ALPHA.split(data['''content''']) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__A , max_queue_size=10_000) , chunksize=100 , ):
if data is not None:
yield data
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = DuplicationIndex(duplication_jaccard_threshold=__A)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A)) , max_queue_size=100)):
di.add(__A , __A)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = get_tokens(__A)
_a = get_tokens(__A)
return len(tokensa & tokensa) / len(tokensa | tokensa)
lowercase_ = None
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for elementa in cluster:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__A , __A) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_a = 1
extremes.append(__A)
return extremes
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
global _shared_dataset
_a = dataset
_a = []
_a = partial(_find_cluster_extremes_shared , jaccard_threshold=__A)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__A , __A , ) , total=len(__A) , ):
extremes_list.append(__A)
return extremes_list
def lowerCAmelCase (__A , __A = 0.85):
"""simple docstring"""
_a = make_duplicate_clusters(__A , __A)
_a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_a = {}
_a = find_extremes(__A , __A , __A)
for extremes in extremes_clusters:
for element in extremes:
_a = element
_a = duplicate_indices - set(extreme_dict.keys())
_a = dataset.filter(lambda __A , __A: idx not in remove_indices , with_indices=__A)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_a = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(__A)}''')
print(F'''Number of duplicate clusters: {len(__A)}''')
print(F'''Files in duplicate cluster: {len(__A)}''')
print(F'''Unique files in duplicate cluster: {len(__A)}''')
print(F'''Filtered dataset size: {len(__A)}''')
return ds_filter, duplicate_clusters
| 11 | 1 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowercase_ = logging.get_logger(__name__)
lowercase_ = 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"),
]
)
lowercase_ = 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"),
]
)
lowercase_ = 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"),
]
)
lowercase_ = 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"),
]
)
lowercase_ = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
lowercase_ = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
lowercase_ = 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"),
]
)
lowercase_ = 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"),
]
)
lowercase_ = 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"),
]
)
lowercase_ = 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"),
]
)
lowercase_ = 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"),
]
)
lowercase_ = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
lowercase_ = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
lowercase_ = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowercase_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Dict = FLAX_MODEL_MAPPING
lowercase_ = auto_class_update(FlaxAutoModel)
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : List[Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : int = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Any = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : str = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Tuple = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : str = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class __A ( _BaseAutoModelClass ):
'''simple docstring'''
__lowerCamelCase : Any = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
lowercase_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)
| 11 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 | 1 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
lowercase_ = get_logger()
lowercase_ = None
class __A ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__(self , A=None , A=None , **A ) -> List[str]:
"""simple docstring"""
super().__init__(features=A )
import jax
from jaxlib.xla_client import Device
if isinstance(A , A ):
raise ValueError(
f'''Expected {device} to be a `str` not {type(A )}, as `jaxlib.xla_extension.Device` '''
'''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
'''the device with `str()` to get its string identifier that will be internally mapped '''
'''to the actual `jaxlib.xla_extension.Device`.''' )
_a = device if isinstance(A , A ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_a = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f'''Device with string identifier {self.device} not listed among the available '''
f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
f'''device: {str(jax.devices()[0] )}.''' )
_a = str(jax.devices()[0] )
_a = jnp_array_kwargs
@staticmethod
def a__ () -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(A ): device for device in jax.devices()}
def a__ (self , A ) -> int:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(A , A ) and column:
if all(
isinstance(A , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(A , axis=0 )
return column
def a__ (self , A ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(A , (str, bytes, type(A )) ):
return value
elif isinstance(A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_a = {}
if isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
_a = {'''dtype''': jnp.intaa}
else:
_a = {'''dtype''': jnp.intaa}
elif isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_a = {'''dtype''': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A , PIL.Image.Image ):
_a = np.asarray(A )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_a = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(A , **{**default_dtype, **self.jnp_array_kwargs} )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(A , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(A , '''__array__''' ) and not isinstance(A , jax.Array ):
_a = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
elif isinstance(A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
return self._tensorize(A )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , A , map_list=A )
def a__ (self , A ) -> Mapping:
"""simple docstring"""
_a = self.numpy_arrow_extractor().extract_row(A )
_a = self.python_features_decoder.decode_row(A )
return self.recursive_tensorize(A )
def a__ (self , A ) -> "jax.Array":
"""simple docstring"""
_a = self.numpy_arrow_extractor().extract_column(A )
_a = self.python_features_decoder.decode_column(A , pa_table.column_names[0] )
_a = self.recursive_tensorize(A )
_a = self._consolidate(A )
return column
def a__ (self , A ) -> Mapping:
"""simple docstring"""
_a = self.numpy_arrow_extractor().extract_batch(A )
_a = self.python_features_decoder.decode_batch(A )
_a = self.recursive_tensorize(A )
for column_name in batch:
_a = self._consolidate(batch[column_name] )
return batch
| 11 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = IFInpaintingSuperResolutionPipeline
__lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
__lowerCamelCase : str = PipelineTesterMixin.required_optional_params - {'latents'}
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def a__ (self , A , A=0 ) -> List[Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = floats_tensor((1, 3, 16, 16) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ (self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def a__ (self ) -> str:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._test_save_load_local()
def a__ (self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
if not nums:
return 0
_a = nums[0]
_a = 0
for num in nums[1:]:
_a , _a = (
max_excluding + num,
max(__A , __A),
)
return max(__A , __A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 | 1 |
'''simple docstring'''
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 __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=2 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , A=0 , ) -> 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 = projection_dim
def a__ (self ) -> int:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = 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=A , initializer_range=self.initializer_range , )
_a = 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 a__ (self , A , A , A , A , A , A , A ) -> int:
"""simple docstring"""
_a = TFDPRContextEncoder(config=A )
_a = model(A , attention_mask=A , token_type_ids=A )
_a = model(A , token_type_ids=A )
_a = model(A )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A ) -> Tuple:
"""simple docstring"""
_a = TFDPRQuestionEncoder(config=A )
_a = model(A , attention_mask=A , token_type_ids=A )
_a = model(A , token_type_ids=A )
_a = model(A )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A ) -> List[str]:
"""simple docstring"""
_a = TFDPRReader(config=A )
_a = model(A , attention_mask=A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids}
return config, inputs_dict
@require_tf
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
__lowerCamelCase : List[str] = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}
__lowerCamelCase : int = False
__lowerCamelCase : Any = False
__lowerCamelCase : Any = False
__lowerCamelCase : List[Any] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = TFDPRModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*A )
def a__ (self ) -> int:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*A )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*A )
@slow
def a__ (self ) -> List[Any]:
"""simple docstring"""
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = TFDPRContextEncoder.from_pretrained(A )
self.assertIsNotNone(A )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = TFDPRContextEncoder.from_pretrained(A )
self.assertIsNotNone(A )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = TFDPRQuestionEncoder.from_pretrained(A )
self.assertIsNotNone(A )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = TFDPRReader.from_pretrained(A )
self.assertIsNotNone(A )
@require_tf
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Any:
"""simple docstring"""
_a = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''' )
_a = 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]
_a = model(A )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
_a = tf.constant(
[
[
0.03236253,
0.12753335,
0.16818509,
0.00279786,
0.3896933,
0.24264945,
0.2178971,
-0.02335227,
-0.08481959,
-0.14324117,
]
] )
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return len(set(__A)) == len(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
lowercase_ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __A :
'''simple docstring'''
def __init__(self , A , A=16 , A=13 , A=7 , A=14 , A=10 , A=19 , A=5 , A=4 , A=True , A=16 , A=2 , A=4 , A=4 , A="gelu" , A=0.1 , A=0.1 , A=[1, 2, 3, 4, 5] , A=25 , A=5 , ) -> List[str]:
"""simple docstring"""
_a = d_model
_a = parent
_a = batch_size
_a = prediction_length
_a = context_length
_a = cardinality
_a = num_time_features
_a = lags_sequence
_a = embedding_dimension
_a = is_training
_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 = context_length
_a = prediction_length + label_length
_a = label_length
_a = moving_average
_a = autocorrelation_factor
def a__ (self ) -> Any:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
_a = config.context_length + max(config.lags_sequence )
_a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, _past_length] )
_a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, config.prediction_length] )
_a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_config()
_a = self.prepare_autoformer_inputs_dict(A )
return config, inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModel(config=A ).to(A ).eval()
_a = model(**A )
_a = outputs.encoder_last_hidden_state
_a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_encoder()
encoder.save_pretrained(A )
_a = AutoformerEncoder.from_pretrained(A ).to(A )
_a , _a , _a , _a , _a = model.create_network_inputs(**A )
_a , _a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_a = encoder(inputs_embeds=A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
_a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_decoder()
decoder.save_pretrained(A )
_a = AutoformerDecoder.from_pretrained(A ).to(A )
_a = decoder(
trend=A , inputs_embeds=A , encoder_hidden_states=A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__lowerCamelCase : Tuple = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_a = model_class(A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a , _a = model_class.from_pretrained(A , output_loading_info=A )
self.assertEqual(info['''missing_keys'''] , [] )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*A )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = inspect.signature(getattr(A , '''forward''' ) )
# The main input is the name of the argument after `self`
_a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(A )] , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
_a = getattr(self.model_tester , '''seq_length''' , A )
_a = getattr(self.model_tester , '''decoder_seq_length''' , A )
_a = getattr(self.model_tester , '''encoder_seq_length''' , A )
_a = getattr(self.model_tester , '''d_model''' , A )
_a = getattr(self.model_tester , '''num_attention_heads''' , A )
_a = d_model // num_attention_heads
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_a = len(A )
_a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(A , A )
# decoder attentions
_a = outputs.decoder_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_a = outputs.cross_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
self.assertEqual(out_len + 2 , len(A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCAmelCase (__A="train-batch.pt"):
"""simple docstring"""
_a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__A , repo_type='''dataset''')
_a = torch.load(__A , map_location=__A)
return batch
@require_torch
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch()
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
_a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
_a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
_a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , A )
_a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=A )
_a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , A , rtol=1E-1 ) )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) == 0:
return False
_a = len(__A) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __A)
else:
return binary_search(a_list[midpoint + 1 :] , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
lowercase_ = int(input("Enter the number to be found in the list:\n").strip())
lowercase_ = "" if binary_search(sequence, target) else "not "
print(F"""{target} was {not_str}found in {sequence}""")
| 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["YolosFeatureExtractor"]
lowercase_ = ["YolosImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
"YolosForObjectDetection",
"YolosModel",
"YolosPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A ):
_a = self.prefix_sum[i - 1] + array[i]
def a__ (self , A , A ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Tuple = 'umt5'
__lowerCamelCase : Optional[int] = ['past_key_values']
def __init__(self , A=250_112 , A=512 , A=64 , A=1_024 , A=8 , A=None , A=6 , A=32 , A=128 , A=0.1 , A=1E-6 , A=1.0 , A="gated-gelu" , A=True , A=True , A="T5Tokenizer" , A=True , A=0 , A=1 , A=0 , **A , ) -> Any:
"""simple docstring"""
super().__init__(
is_encoder_decoder=A , tokenizer_class=A , tie_word_embeddings=A , pad_token_id=A , eos_token_id=A , decoder_start_token_id=A , **A , )
_a = vocab_size
_a = d_model
_a = d_kv
_a = d_ff
_a = num_layers
_a = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_a = num_heads
_a = relative_attention_num_buckets
_a = relative_attention_max_distance
_a = dropout_rate
_a = layer_norm_epsilon
_a = initializer_factor
_a = feed_forward_proj
_a = use_cache
_a = self.feed_forward_proj.split('''-''' )
_a = act_info[-1]
_a = act_info[0] == '''gated'''
if len(A ) > 1 and act_info[0] != "gated" or len(A ) > 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 = '''gelu_new'''
@property
def a__ (self ) -> Any:
"""simple docstring"""
return self.d_model
@property
def a__ (self ) -> Tuple:
"""simple docstring"""
return self.num_heads
@property
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self.num_layers
class __A ( A ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
_a = {
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
_a = '''past_encoder_sequence + sequence'''
_a = {0: '''batch'''}
_a = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
_a = {0: '''batch''', 1: '''decoder_sequence'''}
_a = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(A , direction='''inputs''' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def a__ (self ) -> int:
"""simple docstring"""
return 13
@property
def a__ (self ) -> float:
"""simple docstring"""
return 5E-4
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A)
if n > 1:
factors.append(__A)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowercase_ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class __A :
'''simple docstring'''
def __init__(self , A = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError('''Unsupported Group''' )
_a = primes[group]['''prime''']
_a = primes[group]['''generator''']
_a = int(hexlify(urandom(32 ) ) , base=16 )
def a__ (self ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def a__ (self ) -> str:
"""simple docstring"""
_a = pow(self.generator , self.__private_key , self.prime )
return hex(A )[2:]
def a__ (self , A ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(A , (self.prime - 1) // 2 , self.prime ) == 1
)
def a__ (self , A ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
if not self.is_valid_public_key(A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , self.__private_key , self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def a__ (A , A ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(A , (prime - 1) // 2 , A ) == 1
)
@staticmethod
def a__ (A , A , A = 14 ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
_a = int(A , base=16 )
_a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(A , A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , A , A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''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 __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self , A , A ) -> Optional[int]:
"""simple docstring"""
_a = jnp.ones((batch_size, length) ) / length
return scores
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = None
_a = 20
_a = self._get_uniform_logits(batch_size=2 , length=A )
# tweak scores to not be uniform anymore
_a = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
_a = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
_a = jax.nn.softmax(A , axis=-1 )
_a = FlaxTemperatureLogitsWarper(temperature=0.5 )
_a = FlaxTemperatureLogitsWarper(temperature=1.3 )
_a = jax.nn.softmax(temp_dist_warper_sharper(A , scores.copy() , cur_len=A ) , axis=-1 )
_a = jax.nn.softmax(temp_dist_warper_smoother(A , scores.copy() , cur_len=A ) , 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 a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = None
_a = 10
_a = 2
# create ramp distribution
_a = np.broadcast_to(np.arange(A )[None, :] , (batch_size, vocab_size) ).copy()
_a = ramp_logits[1:, : vocab_size // 2] + vocab_size
_a = FlaxTopKLogitsWarper(3 )
_a = top_k_warp(A , A , cur_len=A )
# 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
_a = 5
_a = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
_a = np.broadcast_to(np.arange(A )[None, :] , (batch_size, length) ).copy()
_a = top_k_warp_safety_check(A , A , cur_len=A )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = None
_a = 10
_a = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
_a = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
_a = FlaxTopPLogitsWarper(0.8 )
_a = np.exp(top_p_warp(A , A , cur_len=A ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
_a = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(A , A , atol=1E-3 ) )
# check edge cases with negative and extreme logits
_a = np.broadcast_to(np.arange(A )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
_a = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
_a = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
_a = top_p_warp(A , A , cur_len=A )
# 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 a__ (self ) -> Tuple:
"""simple docstring"""
_a = 20
_a = 4
_a = 0
_a = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=A )
# check that min length is applied at length 5
_a = ids_tensor((batch_size, 20) , vocab_size=20 )
_a = 5
_a = self._get_uniform_logits(A , A )
_a = min_dist_processor(A , A , cur_len=A )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('''inf''' )] )
# check that min length is not applied anymore at length 15
_a = self._get_uniform_logits(A , A )
_a = 15
_a = min_dist_processor(A , A , cur_len=A )
self.assertFalse(jnp.isinf(A ).any() )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = 20
_a = 4
_a = 0
_a = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A )
# check that all scores are -inf except the bos_token_id score
_a = ids_tensor((batch_size, 1) , vocab_size=20 )
_a = 1
_a = self._get_uniform_logits(A , A )
_a = logits_processor(A , A , cur_len=A )
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
_a = 3
_a = self._get_uniform_logits(A , A )
_a = logits_processor(A , A , cur_len=A )
self.assertFalse(jnp.isinf(A ).any() )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = 20
_a = 4
_a = 0
_a = 5
_a = FlaxForcedEOSTokenLogitsProcessor(max_length=A , eos_token_id=A )
# check that all scores are -inf except the eos_token_id when max_length is reached
_a = ids_tensor((batch_size, 4) , vocab_size=20 )
_a = 4
_a = self._get_uniform_logits(A , A )
_a = logits_processor(A , A , cur_len=A )
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
_a = 3
_a = self._get_uniform_logits(A , A )
_a = logits_processor(A , A , cur_len=A )
self.assertFalse(jnp.isinf(A ).any() )
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = 4
_a = 10
_a = 15
_a = 2
_a = 1
_a = 15
# dummy input_ids and scores
_a = ids_tensor((batch_size, sequence_length) , A )
_a = input_ids.copy()
_a = self._get_uniform_logits(A , A )
_a = scores.copy()
# instantiate all dist processors
_a = FlaxTemperatureLogitsWarper(temperature=0.5 )
_a = FlaxTopKLogitsWarper(3 )
_a = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
_a = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=A )
_a = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A )
_a = FlaxForcedEOSTokenLogitsProcessor(max_length=A , eos_token_id=A )
_a = 10
# no processor list
_a = temp_dist_warp(A , A , cur_len=A )
_a = top_k_warp(A , A , cur_len=A )
_a = top_p_warp(A , A , cur_len=A )
_a = min_dist_proc(A , A , cur_len=A )
_a = bos_dist_proc(A , A , cur_len=A )
_a = eos_dist_proc(A , A , cur_len=A )
# with processor list
_a = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
_a = processor(A , A , cur_len=A )
# scores should be equal
self.assertTrue(jnp.allclose(A , A , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = 4
_a = 10
_a = 15
_a = 2
_a = 1
_a = 15
# dummy input_ids and scores
_a = ids_tensor((batch_size, sequence_length) , A )
_a = input_ids.copy()
_a = self._get_uniform_logits(A , A )
_a = scores.copy()
# instantiate all dist processors
_a = FlaxTemperatureLogitsWarper(temperature=0.5 )
_a = FlaxTopKLogitsWarper(3 )
_a = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
_a = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=A )
_a = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A )
_a = FlaxForcedEOSTokenLogitsProcessor(max_length=A , eos_token_id=A )
_a = 10
# no processor list
def run_no_processor_list(A , A , A ):
_a = temp_dist_warp(A , A , cur_len=A )
_a = top_k_warp(A , A , cur_len=A )
_a = top_p_warp(A , A , cur_len=A )
_a = min_dist_proc(A , A , cur_len=A )
_a = bos_dist_proc(A , A , cur_len=A )
_a = eos_dist_proc(A , A , cur_len=A )
return scores
# with processor list
def run_processor_list(A , A , A ):
_a = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
_a = processor(A , A , cur_len=A )
return scores
_a = jax.jit(A )
_a = jax.jit(A )
_a = jitted_run_no_processor_list(A , A , A )
_a = jitted_run_processor_list(A , A , A )
# scores should be equal
self.assertTrue(jnp.allclose(A , A , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 11 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ = logging.getLogger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if os.path.exists(__A):
if os.path.exists(os.path.join(__A , '''config.json''')) and os.path.isfile(
os.path.join(__A , '''config.json''')):
os.remove(os.path.join(__A , '''config.json'''))
if os.path.exists(os.path.join(__A , '''pytorch_model.bin''')) and os.path.isfile(
os.path.join(__A , '''pytorch_model.bin''')):
os.remove(os.path.join(__A , '''pytorch_model.bin'''))
else:
os.makedirs(__A)
model.save_pretrained(__A)
def lowerCAmelCase (__A , __A=False):
"""simple docstring"""
_a = 2
if unlogit:
_a = torch.pow(__A , __A)
_a = p * torch.log(__A)
_a = 0
return -plogp.sum(dim=-1)
def lowerCAmelCase (__A):
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
for row in range(len(__A)):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
else:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))
def lowerCAmelCase (__A , __A , __A , __A=True , __A=True , __A=None , __A=False):
"""simple docstring"""
_a , _a = model.config.num_hidden_layers, model.config.num_attention_heads
_a = torch.zeros(__A , __A).to(args.device)
_a = torch.zeros(__A , __A).to(args.device)
if head_mask is None:
_a = torch.ones(__A , __A).to(args.device)
head_mask.requires_grad_(requires_grad=__A)
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_a = None
_a = 0.0
_a = 0.0
for step, inputs in enumerate(tqdm(__A , desc='''Iteration''' , disable=args.local_rank not in [-1, 0])):
_a = tuple(t.to(args.device) for t in inputs)
((_a) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_a = model(__A , labels=__A , head_mask=__A)
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_a , _a , _a = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__A):
_a = entropy(attn.detach() , __A)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__A).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_a = 2
_a = torch.pow(torch.pow(__A , __A).sum(-1) , 1 / exponent)
head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
if not args.dont_normalize_global_importance:
_a = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''')
print_ad_tensor(__A)
if compute_importance:
logger.info('''Head importance scores''')
print_ad_tensor(__A)
logger.info('''Head ranked by importance scores''')
_a = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device)
_a = torch.arange(
head_importance.numel() , device=args.device)
_a = head_ranks.view_as(__A)
print_ad_tensor(__A)
return attn_entropy, head_importance, total_loss
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a , _a , _a = compute_heads_importance(__A , __A , __A , compute_entropy=__A)
_a = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , __A , original_score * args.masking_threshold)
_a = torch.ones_like(__A)
_a = max(1 , int(new_head_mask.numel() * args.masking_amount))
_a = original_score
while current_score >= original_score * args.masking_threshold:
_a = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_a = float('''Inf''')
_a = head_importance.view(-1).sort()[1]
if len(__A) <= num_to_mask:
print('''BREAK BY num_to_mask''')
break
# mask heads
_a = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist()))
_a = new_head_mask.view(-1)
_a = 0.0
_a = new_head_mask.view_as(__A)
_a = new_head_mask.clone().detach()
print_ad_tensor(__A)
# Compute metric and head importance again
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , head_mask=__A)
_a = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''')
print_ad_tensor(__A)
np.save(os.path.join(args.output_dir , '''head_mask.npy''') , head_mask.detach().cpu().numpy())
return head_mask
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A)
_a = 1 / loss
_a = datetime.now() - before_time
_a = sum(p.numel() for p in model.parameters())
_a = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
}
for k, v in heads_to_prune.items():
if isinstance(__A , __A):
_a = [
v,
]
assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(__A)
_a = sum(p.numel() for p in model.parameters())
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A , actually_pruned=__A , )
_a = 1 / loss
_a = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , __A , __A , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , __A , __A)
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100)
save_model(__A , args.output_dir)
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=__A , type=__A , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=__A , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''')
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''')
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''')
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''')
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=__A , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=__A , help='''Amount to heads to masking at each masking step.''')
parser.add_argument('''--metric_name''' , default='''acc''' , type=__A , help='''Metric to use for head masking.''')
parser.add_argument(
'''--max_seq_length''' , default=128 , type=__A , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=__A , help='''Batch size.''')
parser.add_argument('''--seed''' , type=__A , default=42)
parser.add_argument('''--local_rank''' , type=__A , default=-1 , help='''local_rank for distributed training on gpus''')
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''')
parser.add_argument('''--server_ip''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
parser.add_argument('''--server_port''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
_a = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A)
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_a = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
_a = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
_a = torch.device('''cuda''' , args.local_rank)
_a = 1
torch.distributed.init_process_group(backend='''nccl''') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1)))
_a = GPTaLMHeadModel.from_pretrained(args.model_name_or_path)
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
_a = nn.parallel.DistributedDataParallel(
__A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__A)
elif args.n_gpu > 1:
_a = nn.DataParallel(__A)
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__A)
torch.save(__A , os.path.join(args.output_dir , '''run_args.bin'''))
logger.info('''Training/evaluation parameters %s''' , __A)
# Prepare dataset
_a = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa),
])
_a = (torch.from_numpy(__A),)
_a = TensorDataset(*__A)
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A , batch_size=args.batch_size)
# Compute head entropy and importance score
compute_heads_importance(__A , __A , __A)
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_a = mask_heads(__A , __A , __A)
prune_heads(__A , __A , __A , __A)
if __name__ == "__main__":
main()
| 11 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowercase_ = random.Random()
if is_torch_available():
import torch
def lowerCAmelCase (__A , __A=1.0 , __A=None , __A=None):
"""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
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=400 , A=2_000 , A=1 , A=0.0 , A=16_000 , A=True , A=True , ) -> Optional[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 = return_attention_mask
_a = do_normalize
def a__ (self ) -> List[str]:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def a__ (self , A=False , A=False ) -> Optional[Any]:
"""simple docstring"""
def _flatten(A ):
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
@require_torch
@require_torchaudio
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : int = ASTFeatureExtractor
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ASTFeatureExtractionTester(self )
def a__ (self ) -> Dict:
"""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 , padding=A , return_tensors='''np''' ).input_values
_a = feat_extract(A , padding=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 = 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 ) )
@require_torch
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
import torch
_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 a__ (self , A ) -> Optional[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]
@require_torch
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = torch.tensor(
[-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
-1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
-1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
-0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869] )
# fmt: on
_a = self._load_datasamples(1 )
_a = ASTFeatureExtractor()
_a = feature_extractor(A , return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape , (1, 1_024, 128) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , A , atol=1E-4 ) )
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 1
for i in range(0 , len(__A)):
total *= numbers[i]
_a = str(__A)
steps += 1
return steps
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''additive_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 0
for i in range(0 , len(__A)):
total += numbers[i]
_a = str(__A)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
_a , _a = array[indexa], array[indexa]
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
if length > 1:
_a = int(length / 2)
for i in range(__A , low + middle):
comp_and_swap(__A , __A , i + middle , __A)
bitonic_merge(__A , __A , __A , __A)
bitonic_merge(__A , low + middle , __A , __A)
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
if length > 1:
_a = int(length / 2)
bitonic_sort(__A , __A , __A , 1)
bitonic_sort(__A , low + middle , __A , 0)
bitonic_merge(__A , __A , __A , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by a comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print("\nSorted array in ascending order is: ", end="")
print(*unsorted, sep=", ")
bitonic_merge(unsorted, 0, len(unsorted), 0)
print("Sorted array in descending order is: ", end="")
print(*unsorted, sep=", ")
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_a = size if size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = DPTImageProcessor if is_vision_available() else None
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPTImageProcessingTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a = 0
_a = len(__A) - 2
for i in range(__A , -1 , -2):
# double the value of every second digit
_a = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_a = cc_number[:i] + str(__A) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCAmelCase (__A):
"""simple docstring"""
_a = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''')
return False
if not 13 <= len(__A) <= 16:
print(F'''{error_message} of its length.''')
return False
if not validate_initial_digits(__A):
print(F'''{error_message} of its first two digits.''')
return False
if not luhn_validation(__A):
print(F'''{error_message} it fails the Luhn check.''')
return False
print(F'''{credit_card_number} is a valid credit card number.''')
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 11 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
lowercase_ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __A :
'''simple docstring'''
def __init__(self , A , A=16 , A=13 , A=7 , A=14 , A=10 , A=19 , A=5 , A=4 , A=True , A=16 , A=2 , A=4 , A=4 , A="gelu" , A=0.1 , A=0.1 , A=[1, 2, 3, 4, 5] , A=25 , A=5 , ) -> List[str]:
"""simple docstring"""
_a = d_model
_a = parent
_a = batch_size
_a = prediction_length
_a = context_length
_a = cardinality
_a = num_time_features
_a = lags_sequence
_a = embedding_dimension
_a = is_training
_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 = context_length
_a = prediction_length + label_length
_a = label_length
_a = moving_average
_a = autocorrelation_factor
def a__ (self ) -> Any:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
_a = config.context_length + max(config.lags_sequence )
_a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, _past_length] )
_a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, config.prediction_length] )
_a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_config()
_a = self.prepare_autoformer_inputs_dict(A )
return config, inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModel(config=A ).to(A ).eval()
_a = model(**A )
_a = outputs.encoder_last_hidden_state
_a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_encoder()
encoder.save_pretrained(A )
_a = AutoformerEncoder.from_pretrained(A ).to(A )
_a , _a , _a , _a , _a = model.create_network_inputs(**A )
_a , _a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_a = encoder(inputs_embeds=A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
_a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_decoder()
decoder.save_pretrained(A )
_a = AutoformerDecoder.from_pretrained(A ).to(A )
_a = decoder(
trend=A , inputs_embeds=A , encoder_hidden_states=A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__lowerCamelCase : Tuple = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_a = model_class(A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a , _a = model_class.from_pretrained(A , output_loading_info=A )
self.assertEqual(info['''missing_keys'''] , [] )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*A )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = inspect.signature(getattr(A , '''forward''' ) )
# The main input is the name of the argument after `self`
_a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(A )] , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
_a = getattr(self.model_tester , '''seq_length''' , A )
_a = getattr(self.model_tester , '''decoder_seq_length''' , A )
_a = getattr(self.model_tester , '''encoder_seq_length''' , A )
_a = getattr(self.model_tester , '''d_model''' , A )
_a = getattr(self.model_tester , '''num_attention_heads''' , A )
_a = d_model // num_attention_heads
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_a = len(A )
_a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(A , A )
# decoder attentions
_a = outputs.decoder_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_a = outputs.cross_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
self.assertEqual(out_len + 2 , len(A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCAmelCase (__A="train-batch.pt"):
"""simple docstring"""
_a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__A , repo_type='''dataset''')
_a = torch.load(__A , map_location=__A)
return batch
@require_torch
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch()
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
_a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
_a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
_a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , A )
_a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=A )
_a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , A , rtol=1E-1 ) )
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A = None , __A = None):
"""simple docstring"""
if start is None:
_a = 0
if end is None:
_a = len(__A) - 1
if start >= end:
return
_a = (start + end) // 2
slowsort(__A , __A , __A)
slowsort(__A , mid + 1 , __A)
if sequence[end] < sequence[mid]:
_a , _a = sequence[mid], sequence[end]
slowsort(__A , __A , end - 1)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 11 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=False , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> str:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return OpenLlamaConfig(
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=A , initializer_range=self.initializer_range , use_stable_embedding=A , )
def a__ (self , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = OpenLlamaModel(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A )
_a = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Any:
"""simple docstring"""
_a = True
_a = OpenLlamaModel(A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , )
_a = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Dict:
"""simple docstring"""
_a = True
_a = True
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_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(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )['''hidden_states'''][0]
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )['''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(A , A , atol=1E-3 ) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__lowerCamelCase : Any = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__lowerCamelCase : List[Any] = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''single_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''multi_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = ids_tensor([1, 10] , config.vocab_size )
_a = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = OpenLlamaModel(A )
original_model.to(A )
original_model.eval()
_a = original_model(A ).last_hidden_state
_a = original_model(A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = {'''type''': scaling_type, '''factor''': 10.0}
_a = OpenLlamaModel(A )
scaled_model.to(A )
scaled_model.eval()
_a = scaled_model(A ).last_hidden_state
_a = scaled_model(A ).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(A , A , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
| 11 | 1 |
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